{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2RosoNrCxVng"
      },
      "source": [
        "# Fine-tuning SpeechT5 for multilingual TTS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uELTb9CcOaCp"
      },
      "source": [
        "This notebook demonstrates how to fine-tune the SpeechT5 model from 🤗 Transformers on the **text-to-speech** task.\n",
        "\n",
        "The unique thing about SpeechT5 is that the model is first pre-trained on a combination of speech-to-text and text-to-speech data, so that it learns a unified space of hidden representations shared by both text and speech. This allows us to fine-tune the same pretrained model on different tasks. Read more about SpeechT5 [in our blog post](https://huggingface.co/blog/speecht5).\n",
        "\n",
        "In this notebook we will start from an existing fine-tuned TTS model that was originally trained on English speech from LibriTTS, and fine-tune it for the Urdu Language.\n",
        "\n",
        "This TTS model will support multiple speakers through x-vector speaker embeddings."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xB8fh-ht4kK"
      },
      "source": [
        "## Install required packages\n",
        "\n",
        "We install Transformers from GitHub since not all the SpeechT5 features we need have been merged into an official release yet.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xJe8TF3atyqL",
        "outputId": "964d222e-94c0-4de8-be56-59d67c3c148d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting datasets\n",
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            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
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            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
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            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
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            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.9->speechbrain)\n",
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            "\u001b[?25hCollecting ruamel.yaml>=0.17.28 (from hyperpyyaml->speechbrain)\n",
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            "  Downloading ruamel.yaml.clib-0.2.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (526 kB)\n",
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            "Installing collected packages: xxhash, ruamel.yaml.clib, requests, pyarrow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, dill, ruamel.yaml, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, nvidia-cusolver-cu12, hyperpyyaml, datasets, speechbrain\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 hyperpyyaml-1.2.2 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 pyarrow-17.0.0 requests-2.32.3 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8 speechbrain-1.0.0 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets soundfile speechbrain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQCuMz3fwuHa",
        "outputId": "d1509a7b-505f-47db-aa74-9fb8341e787a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/huggingface/transformers.git\n",
            "  Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-m7fgjodb\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-m7fgjodb\n",
            "  Resolved https://github.com/huggingface/transformers.git to commit 96a074fa7e2c04b904f72d9e827398d4c5f90f25\n",
            "  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: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.43.0.dev0) (3.15.4)\n",
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            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.43.0.dev0-py3-none-any.whl size=9403605 sha256=c2505d30926e465339b5e53f7c1b7963724dddf757ce1523549bfbc390ce681b\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-b7r1edg2/wheels/e7/9c/5b/e1a9c8007c343041e61cc484433d512ea9274272e3fcbe7c16\n",
            "Successfully built transformers\n",
            "Installing collected packages: transformers\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.42.4\n",
            "    Uninstalling transformers-4.42.4:\n",
            "      Successfully uninstalled transformers-4.42.4\n",
            "Successfully installed transformers-4.43.0.dev0\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfX4YNbs_HKP",
        "outputId": "414c91dc-0e2f-4cc4-a2e3-bc15af8f0bf8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.32.1)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.3.1+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.23.5)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.15.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.13.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.20.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.3.1)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate) (12.5.82)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.7.4)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_Xffd7sRlpk"
      },
      "source": [
        "Do we have a GPU?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-XLi4c9DRnxA",
        "outputId": "6d793249-e445-4b81-c0b7-8d14192bf4c8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mon Jul 22 19:12:49 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   48C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmdgnloAQPQ1"
      },
      "source": [
        "In case no GPU is found, from the menu choose **Runtime > Change runtime type** and set **Hardware accelerator** to **GPU**. Then restart the runtime to activate the GPU."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GB6Ur8DHuUy8"
      },
      "source": [
        "## Load the model\n",
        "\n",
        "We'll start from SpeechT5 that's already been fine-tuned for English TTS, and fine-tune it again but for a new language. For more info about the original checkpoint, you can find its model card on the [Hugging Face Hub](https://huggingface.co/microsoft/speecht5_tts)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365,
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        },
        "id": "W7spxtTGtmba",
        "outputId": "0dc03789-0b00-4a88-b99c-40c395d2b8b2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5cd4197ba6cb43a79e8e48e3281d556a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/433 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/232 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "64917c2ed79841e3a5080ffbe11baba8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "spm_char.model:   0%|          | 0.00/238k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f9735b84f4d3473a8307e736305cf252",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/40.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "992902679da84067b429893c7870b816",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/234 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6c036b53916642e3993b40b66532f305",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/2.06k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f093bd48a659437692d44be6308d0a53",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/585M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech\n",
        "\n",
        "processor = SpeechT5Processor.from_pretrained(\"microsoft/speecht5_tts\")\n",
        "model = SpeechT5ForTextToSpeech.from_pretrained(\"microsoft/speecht5_tts\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UzjxPwP3tmsl"
      },
      "source": [
        "## Load the dataset\n",
        "\n",
        "We'll fine-tune on the Urdu language.\n",
        "\n",
        "You can find [common_voice_urdu1 on the Hugging Face Hub](https://huggingface.co/datasets/m-aliabbas/common_voice_urdu1).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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            "4ee7e82441c74fc9b726d996a49c6908",
            "cc1c35d92b754ff29989ecc023bb9e25"
          ]
        },
        "id": "FjKjCViFtc5M",
        "outputId": "f5b6a991-b15b-4bc7-cc83-430e183d111a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "47168eafb8684feab256de9dd23345d7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/1.15k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9e64b696d550460792e4829c6ebd5342",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/260M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2758d263bbab4816bd2b568e00168b59",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/256M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "01a46b988d1347d7ba1cba3670ebed23",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/114M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8c4bc267343547898e5daf05dfc1b244",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/4112 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "09aa1693c138490db1fcb29f3d049a86",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import load_dataset, Audio\n",
        "\n",
        "dataset = load_dataset(\n",
        "    \"m-aliabbas/common_voice_urdu1\",split=\"train\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0omK1unnU2z0"
      },
      "source": [
        "It's important to set the sampling rate of the audio data to 16 kHz, which is what SpeechT5 expects."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sibMqU6qU-h8"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=16000))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZYhgObQVAzj"
      },
      "source": [
        "Let's quickly check how many examples are in this dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-tFbBI8aU__2",
        "outputId": "d4055db2-7f67-4917-c9a3-dfbbfc4e509e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4112"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mn2qaGfJVn8o"
      },
      "source": [
        "That should be sufficient for fine-tuning. We'll have a closer look at these examples soon."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-5qFwV2ouqkU"
      },
      "source": [
        "## Clean up the text\n",
        "\n",
        "The dataset may contain characters that are not in the SpeechT5 tokenizer vocabulary.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6BtRe6qXTYp"
      },
      "source": [
        "\n",
        "First we'll need to find out what the unsupported tokens are. The `SpeechT5Tokenizer` works with characters as tokens, so let's extract all distinct characters of the dataset. We write a mapping function `extract_all_chars` that concatenates the transcriptions from all examples into one long transcription and then transforms the string into a set of characters. It is important to pass the arguments `batched=True` and `batch_size=-1` to `dataset.map(...)` so that the mapping function has access to all transcriptions at once."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0KmO3JnXBs6Z"
      },
      "outputs": [],
      "source": [
        "tokenizer = processor.tokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "okCE9DjNvXM1"
      },
      "source": [
        "Next, we will define a function to map these characters to valid tokens and then run it on the dataset. No need to handle space, that's already replaced by `▁` in the tokenizer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "3883ac03bf194a0593df865f3fe7f4bc",
            "01e97533b18e4dd48d07a993c8e44b1f",
            "db2c1cf6f56c4aefa44e2da5b1b85a67",
            "10545f9c4b774b8f9bd9ddc7ea140a39",
            "afecd676c8024a89bc20165215fc7f5c",
            "252843c3c20f4c9f9327a6bec256db60",
            "0f526d71ed8f480da0f258d1282e3042",
            "82287f32ca37416b824ac2dcef8adcc1",
            "e376e343655744e0b09f3fe3212c1d41",
            "8306a7f425464783b14fd397b5a4be33",
            "b2612a5852164e98bc46b7242bf4ff4d"
          ]
        },
        "id": "ybqsbUGZvtCa",
        "outputId": "00a96210-2b7d-49ea-e942-bc08518a969d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3883ac03bf194a0593df865f3fe7f4bc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/4112 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def urdu_to_roman_urdu(text):\n",
        "    urdu_to_roman_dict = {\n",
        "        'ا': 'a', 'ب': 'b', 'پ': 'p', 'ت': 't', 'ٹ': 't', 'ث': 's', 'ج': 'j', 'چ': 'ch',\n",
        "        'ح': 'h', 'خ': 'kh', 'د': 'd', 'ڈ': 'd', 'ذ': 'z', 'ر': 'r', 'ڑ': 'r', 'ز': 'z',\n",
        "        'ژ': 'zh', 'س': 's', 'ش': 'sh', 'ص': 's', 'ض': 'z', 'ط': 't', 'ظ': 'z', 'ع': 'a',\n",
        "        'غ': 'gh', 'ف': 'f', 'ق': 'q', 'ک': 'k', 'گ': 'g', 'ل': 'l', 'م': 'm', 'ن': 'n',\n",
        "        'ں': 'n', 'و': 'w', 'ہ': 'h', 'ء': 'a', 'ی': 'y', 'ے': 'e', 'آ': 'a', 'ؤ': 'o',\n",
        "        'ئ': 'y', 'ٔ': '', ' ': ' ', '۔': '.', '،': ',', '؛': ';', '؟': '?','ھ': 'h'\n",
        "    }\n",
        "\n",
        "    roman_text = ''.join(urdu_to_roman_dict.get(char, char) for char in text)\n",
        "    return roman_text\n",
        "\n",
        "def cleanup_text(inputs):\n",
        "    inputs[\"transcription\"] = urdu_to_roman_urdu(inputs[\"transcription\"])\n",
        "    return inputs\n",
        "\n",
        "dataset = dataset.map(cleanup_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-OL3s6YUv9ND"
      },
      "source": [
        "## Speaker embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsmonrEW1IlH"
      },
      "source": [
        "To allow the TTS model to distinguish between multiple speakers, we'll need to create a speaker embedding for each example. The speaker embedding is simply an additional input into the model that captures a particular speaker's voice characteristics.\n",
        "\n",
        "To create the speaker embeddings, we use the [spkrec-xvect-voxceleb](https://huggingface.co/speechbrain/spkrec-xvect-voxceleb) model from SpeechBrain. The following defines a function `create_speaker_embedding()` that takes an input audio waveform and outputs a 512-element vector containing the corresponding speaker embedding."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zPXvVi2T31N8",
        "outputId": "1d943655-b3fc-4e97-d9e0-01872a2d869c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.0.0\n"
          ]
        }
      ],
      "source": [
        "import speechbrain\n",
        "print(speechbrain.__version__)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VzxF2DYP50X8",
        "outputId": "f00dcdd6-9a4c-4543-b6a8-8d5e93c6f8bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/speechbrain/speechbrain.git@develop\n",
            "  Cloning https://github.com/speechbrain/speechbrain.git (to revision develop) to /tmp/pip-req-build-37q6gxx1\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/speechbrain/speechbrain.git /tmp/pip-req-build-37q6gxx1\n",
            "  Resolved https://github.com/speechbrain/speechbrain.git to commit 46e282f46e3047fca30fc914d0cb39ab2684238d\n",
            "  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: hyperpyyaml in /usr/local/lib/python3.10/dist-packages (from speechbrain==1.0.0) (1.2.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from speechbrain==1.0.0) (1.4.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from speechbrain==1.0.0) (1.25.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from speechbrain==1.0.0) (24.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from speechbrain==1.0.0) (1.11.4)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from speechbrain==1.0.0) (0.1.99)\n",
            "Requirement already satisfied: torch>=1.9 in /usr/local/lib/python3.10/dist-packages (from speechbrain==1.0.0) (2.3.1+cu121)\n",
            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (from speechbrain==1.0.0) (2.3.1+cu121)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from speechbrain==1.0.0) (4.66.4)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from speechbrain==1.0.0) (0.23.5)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (3.15.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (1.13.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (2.20.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (12.1.105)\n",
            "Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->speechbrain==1.0.0) (2.3.1)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.9->speechbrain==1.0.0) (12.5.82)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->speechbrain==1.0.0) (6.0.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->speechbrain==1.0.0) (2.32.3)\n",
            "Requirement already satisfied: ruamel.yaml>=0.17.28 in /usr/local/lib/python3.10/dist-packages (from hyperpyyaml->speechbrain==1.0.0) (0.18.6)\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==1.0.0) (0.2.8)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.9->speechbrain==1.0.0) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->speechbrain==1.0.0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->speechbrain==1.0.0) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->speechbrain==1.0.0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->speechbrain==1.0.0) (2024.7.4)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.9->speechbrain==1.0.0) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/speechbrain/speechbrain.git@develop\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "smJs3_VB1Da2",
        "outputId": "b8c498cb-603a-4761-8f16-849cb8d1ca49"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8e8d1b0adcd4d47b061aad1486604cb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "hyperparams.yaml:   0%|          | 0.00/2.04k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "59b8375db47b41709137a8831f06a693",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "embedding_model.ckpt:   0%|          | 0.00/16.9M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "1fff9026fa29474ca2b652a2e0e98235",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "mean_var_norm_emb.ckpt:   0%|          | 0.00/3.20k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "5492a2c2d56844bdabc9aaaab9bdc35f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "classifier.ckpt:   0%|          | 0.00/15.9M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "1c0c9d45f86f4945965a4784109020ff",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "label_encoder.txt:   0%|          | 0.00/129k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import os\n",
        "import torch\n",
        "from speechbrain.inference.speaker import EncoderClassifier\n",
        "\n",
        "spk_model_name = \"speechbrain/spkrec-xvect-voxceleb\"\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "speaker_model = EncoderClassifier.from_hparams(\n",
        "    source=spk_model_name,\n",
        "    run_opts={\"device\": device},\n",
        "    savedir=os.path.join(\"/tmp\", spk_model_name)\n",
        ")\n",
        "\n",
        "def create_speaker_embedding(waveform):\n",
        "    with torch.no_grad():\n",
        "        speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))\n",
        "        speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)\n",
        "        speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()\n",
        "    return speaker_embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UczHmLsuFkC6"
      },
      "source": [
        "Note: the `speechbrain/spkrec-xvect-voxceleb` model was trained on English speech from the VoxCeleb dataset, while our training examples are in Dutch. We're assuming here that this model will still make reasonable speaker embeddings, but this may not be true. First training an X-vector model on the target speech, such as Dutch, might work better."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_a8ouSY-Fnxy"
      },
      "source": [
        "## Preparing the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dTI_2WkH-3_Z"
      },
      "source": [
        "The `prepare_dataset` function takes in a single example and uses the `SpeechT5Processor` object to tokenize the input text and load the target audio into a log-mel spectrogram. It also adds the speaker embeddings as an additional input."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mmu8f555v_Ea"
      },
      "outputs": [],
      "source": [
        "def prepare_dataset(example):\n",
        "    # load the audio data; if necessary, this resamples the audio to 16kHz\n",
        "    audio = example[\"audio\"]\n",
        "\n",
        "    # feature extraction and tokenization\n",
        "    example = processor(\n",
        "        text=example[\"transcription\"],\n",
        "        audio_target=audio[\"array\"],\n",
        "        sampling_rate=audio[\"sampling_rate\"],\n",
        "        return_attention_mask=False,\n",
        "    )\n",
        "\n",
        "    # strip off the batch dimension\n",
        "    example[\"labels\"] = example[\"labels\"][0]\n",
        "\n",
        "    # use SpeechBrain to obtain x-vector\n",
        "    example[\"speaker_embeddings\"] = create_speaker_embedding(audio[\"array\"])\n",
        "\n",
        "    return example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYYxiCoC15WG"
      },
      "source": [
        "Let's verify the processing is correct by looking at a single example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GYiXtnFczVIP"
      },
      "outputs": [],
      "source": [
        "processed_example = prepare_dataset(dataset[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dr_zXRhiGu-G"
      },
      "source": [
        "This should give us:\n",
        "\n",
        "- the tokens for the input text in `input_ids`\n",
        "- the speaker embedding in `speaker_embeddings`\n",
        "- the target spectrogram in `labels`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wkboc3EUzXtp",
        "outputId": "e8c37612-4b23-4f29-b28e-f8b518f8119a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['input_ids', 'labels', 'speaker_embeddings']"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list(processed_example.keys())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvNI_JrE2oA8"
      },
      "source": [
        "The tokens should decode into the original text, with `</s>` to mark the end of the sentence."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "uu8PgZqH07lW",
        "outputId": "9559a4fa-c992-4971-f5be-201d41e77335"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'kash yh wqt kbhy nh gzre.</s>'"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.decode(processed_example[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOdMw-xa2u_8"
      },
      "source": [
        "Speaker embeddings should be a 512-element vector:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuybSBq32AuH",
        "outputId": "de2838f0-c816-48df-d28d-dcd72a800d6f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(512,)"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "processed_example[\"speaker_embeddings\"].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzyoPE2y2zHk"
      },
      "source": [
        "The labels should be a log-mel spectrogram with 80 mel bins."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "dKu2km552GL-",
        "outputId": "6498e8bd-a15f-49c3-996e-f1f9c31c25c2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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YqGKTZsCDd96F/XjugUQfh96XVRIa9D3eA6rSn91FaAJAgA+/zkwFIAM01p3RWGHzvgDqiVkk70CGP3v/fSJ9Qh/luzhXooZrVgDjgEjnoM0/yjhLxnXSsMs1679J7J6g57ihFUwNM1qM3YYUc7aiBRBhWzO9OdC64bOGZqgY4ND3bBrmUBPChsfPZ6KChTlAw6WR6WC5pb3XJ0FwkhmtsYE9xklygBcdnzXGa6MUAKYK4nq+dvv6z0MYeatJ9yXbZ9wIc3CiN9YNK18jwuHX7JhxfWPphqGvkaMXREi/l2D7gx1j67z4sb62ATtPuS7Gsr/muBaTAZWxjd6SOE76+aDnp2aOwsvY4ae035awy5e//GX8lb/yV/DH//gfx5/8k38S/+Af/APc39/jr/21v/Zdn6PO0BsvjLvzAhHCzAVXadsd91AnPLQZD1VjwovFDIujc2FswliN/7HWhHPNuN0OuxipbxYLV2TWRbIM7mufjAAwczGww9hawlYTDmnDwgV3ZUFNjIeq4ON2W3CqE7aq5/KNe7wmkajVPFVsy/DwG2E0xwXQyVJ8YuokJiJQA6gAknSySMbToQoPx/hsItHrtO7SE3Ds1SDBOmccLYSSuOE1CeZUMafa3e7c8Go+xSbh45O5gacNKydslXHCZBu3vYlJXzgp+k0RC/ewgFjHRoTQKulxBsRGYMFTM7Bl3pKV9i5LO04tBF3AYZ4athexFgMeVX941TEghoIjnys+/uO5ud+HL1Axj4XxrlwDGTjKEl4LALjmVTcdO9xd8FvLOLUJDYTNAIRbdc6FmKjifTngk+0GMxdM1JCpIhOwpoIGwrHMqI2ROGHlhswVW1MQ+To94No2hmob9rlNOGOK69UBBBzbHDwAJsGBN/Oo9Pv5fL7F75m+rZuzSLdUZdHN2zbuT8oLuxd9F5gEx7bgth7wUCecW9ZNhARXvIa3zb05TIKrvMWGMHPBxNov9+Dctqt4NifzdswDOmeoVb7whhe2ebnH4bZe7fpXwWFQKNDplvwIBjZJONYlPA9TqjjQZptqxkQF1zjHmPkmP84VQEEMG8irwtgm9bacmm7ox9Y9xQfacOANX8xvNWREm4FECU9CBeHYFpxkwvt2wG29wrEt+IH8Hh+lO7ziEw5UsNqacEMFDMHhAmRuIGxSFcwhYUWK8ejjKgA1vK3XMWcdkDZh3ErCgQoOol6LCQ3vZMH7dsBMNc41UcWbdMRJNjMSFWSfW8ZDnbGJrrELV7zLV1i44JpXfYZU1MMjCbf2r4PFCgXXr9MxgFojtvcWasiap7w0/RnXOAB4WPUdeYdDzN+x5dTUAeCci9R0PTOAII10bVXbEgDCyHIOmn9GDizcVcRk/AxdF30N1YnT94UI1TTqHpHcgNzAucV3PeytRhh1z7n0c0CgvDbY7wnKAZG+zlIh8ArQ/0zPBwD85b/8l/GNb3wDf+fv/B189atfxR/7Y38Mv/RLv/SIhPodm1mitZFOBEk4m3cBQLy4VUhJVGJgAMm8DdQRbEuBSrea8H47hJUOYHC3KTg5lzxspH0RzhcvJGDgwX6AbjH6RPYJ3IRw3HTi+vwAgPOW1U0n1F1uQJ9coytsnOdiccBxIyQACR+02OMc1a5D9l1H5hP2yBhArYTV+pdTxZFnnGvbbQozgLV2wOcbRYNyRVB9/HVDJyMwOflTqrsYDYRYqIWSgqPwSDgZNQZf0JjDYhAHUujx1RhsO48MHizHdkTYGQHx/25REDqAu4xnjmAIwGpuWzarXL1eGp6YoKG5Gz7jJHd9jG2OeRjQvQKnNoUHwa1jBynnljXM0pRsB+g8dsDCJGgkONeMrQma3ODFdMZH8z0mLrjhNUIDEU6Id6pfyy1n/8xJiMk8IXqthhs+4yU/mKtdB2SiAjTgQN2ynS68AlXUk3Br4YU9f8HewbZ3q3srjZGJw9vj3gu3yCfrp2+oAMDSMEnCx+kONzzh3qxg91a4J0cBloe4JmyiACINoGv0fDiIc+7IgTa84YdHFv1TPB69b9od62O/IuHYFqyU4roLb5ip4E064kAbXvIJB6o4xJjqeU4Dp2SVFMBlMtLouOFvHpqipIRPqjHXtD89ROag1cMZHr4YAceBtnjG93bdBsbJxixBlANFLY7zsE4DxzvQoKBVgekUXuOFq3lhdH84YlbATkXfFxDOLe/mTbOQ7rEtmKhiooKTecpuywFv16vwdtyuB3t39Pe1JLQBjJy2rFEHM2QAoFq4ufPGbIM3Z4T+PwEX24j4Gu+f+zrFg0fpcoH6kJPB17/xuj7XKgVJOK4XXmEMa6Vdxr0sozcl+qA/VN3zIXvv+3dov22E05/6qZ/6nsIsjxrp5iFNF5i1JqxVQxtni6Wj4dHEeqi6uR/LFPG90Wvhse+3pysj4lVkewHOLeFUJzxsE9aasDVzuZYEJmDOJbwlRI+ffHeHamintBQhmWYA6WSomQaAsZaEsiWdgNzPL802ustNdLx0s4dvfxfu/I0+lhduOPueDrJ33o5L7dHEbjVhE4JMNVyLiQVzLvHyrQBSnYLb0sdk8NwIgVkg3MDuKWAlf24163066xtkpNrWPTPWf9oIVJ3oqVZEsLXdg+MEVLJ7EyOxEqERI7J5gieinpYONIYBIDx2V8ZN4dF4bS3hoc27w7aUMKHiFZ/wUTqpxfz4bNGd1UKBbrl6HPzeLNjbeoWtZbznK7wv6l17ZIFRQyPCqWQ0IdxhRmmM/8O1ckrepHu85NMuDj8RIiiUiLCKAsiTzd9LwqRb2TwMgG8OB9u8JqrYJAUw+GJ6D0Bd700IJ8k48IpjW5BqQ2mPR0Y3tr7wex88ZKpeCH2PKxjHtuBNOmKmhoWAmSjGO7UVJ0lAfotNcg+pWL8TBNd8xsf8oPc4hHD9PpPd93SxHo+N0a870eN7aoZ8PTtmzISpADZRb8PR1ruTTDjZPTrQ+zjd4YY2vOQNEwGTffdsIeng88hk3qUr5ejwhgOvuxCOzy3fnDzL5X09hDfmJDOOF3MbQHgaPKxx4A0v00OAs2/VF+H5mqhgk4xrPuOVkQQmKgFujtJDiTXmG+OuLlgNcE9csRhoBAw4Gbn9SHPMjz5v+rwtST0ikwHMs3lU3q5X+Mr965hjmwGJ2jTUct7yLmTcbE3icc0WUjJ7G0CGWOicRLNIRkPRf/F1ZPQKw/5/nDrD+h9h4d2ajtg7PTzvYRkAev0C9VwM62X82HFczFuSjArANrEujF9UAhcCbwQqAiofQkSP2//0bJcPNd7QHwR04zrXjGOZcV8WtKQI1rkepam3w7kaxzLbJqkL3NYSziXj9rRgzhVzLhcLmQKGtZrFyuqaqo1xWicQAaVqjPlhe5ylcDOt6q7jpKDIQJJ7PKqdqzRGa4TWOKzuUhKaZ1l4Gi1gIZKLyYEhLmfWuGSd9A5AdsRL6mMYn2H4zL0e/scR3NgL0QpBEoNT6y+YCGrzVFtRr8iFexx4bN3VqvdOTgBltRqIBUii7sXxK2Oqq4MKmBvS+9j2L2uMT/KPOuoXYVTz+oxepki79UXDv2v9IssSIhYFeIX3pNTBG1OEca7Ksp+oYeF9qHDcxL0xHr+0bJbhBCDhjJXUZewL8207YOENE004Abv0U38OVTgAoj+PA2+x6TRhbKTvjbvZExGm8FIQKgQHAqo0VLiV/Ki7aFDQNHk4ArCsF7N0n9ioN7OyRm6LkjH7/GzCuK9KFgc0dZgtZu7v4rllLNy9EMpVqFgIOBBhIkYTwQbBsWXcy4Rv1RcD8dUAE2/4ON2Z5V7jHvoz0TYCNGCfPqv30I/ZRHAyZvKYNbPBAZ17DO1coB6Ck4z37YBjW/CN8moYN/3bbbvCRno/HgJyUu9JJnyjvMIqOTwTt/WAa17xMp0AAPe8WNhlC+/OybKwXvIDGC2Im4kaDrLhQOuOvOsej1ObjIMi4eVzPstMFRUS3qG39RoAcJNWJAhm2rAal2YjBVv3bYnzbpLi+a5NCfqZkpGyG5qNZSNGtmSEzA1MKxYhPBifyMN6GqrU4zSUM0WKehPNbnTgcd4m1MqRISfNvLPuYeBhDWnQLL6RwEkANlIvtXsGhrU7OGMWvujrvU28PBiUBEvxt2u7XTqeO+ERiAnPrp+70p434q9bUqOsedjevSA+8eOz/j0P80vC0y/5B9pnF3wUAQ2kvia6uD6UCbdFGeMtaZilgYLh71yDU1ErfDJewlYTHraM03mCQL0Y3vylL025IeouF5xrQqmMbTX3n5k5jngBmPXe8HBQhO1ck5FoKmJaF41QK6NWRtmSusTcIi+8A1sAYrP1zdNTreLBm7UvWdS54STmIb870rBGEDC6/cj+I3Y+R8t2ffUYMCCClglp8HzXwUIV7harCOl89Q0PEgBFGms/2UGldSMpKVX78AQap8vfR4thuId4seRxKvLoQfGv7TRD+vjHy86i+iez2sXEDY1Zu2hM9NBdcTDZGGdSUnMx8DFamBv4EQHS02ovPQnuRp+oYRHCgSqOpq/wllcsrCTfJgrE1WOnxGq6AIDOkfBFN/ojjESCDYTJQMNEDNfHAGxzte42NNWsME0LBR2CTfYegYOBGGZN1+QBcDU0JYOi4WRZNEyda6XHqNFQhHG7LSgt4TqvQTYHgAfLHnuoE67S9ijl9ECEAyVMlHCUDVUE9zLhbbvGJ/VFEG29vcERX8zvcKD62GMCAhPF/+v4aT82tFijGxCToYpgw95z5OEVzwZSfk8Pc+n3OIDAbT3gth3wrlwHr8FDHrUR7rHPFqwg3NYrnGTC17dXOLUJD1U9weeW8So/oNn1D3SFlt/vsl08RHLDZ8tkUg9QhBBZj7ltB+WzGUAojZUXQZ2LkqC/qwdESdSbJNy2A5ga3vARByq45qI7nn0XAN7LIZ6Pe84aMW4NhFaumEg3Ub23FOTqJZfgJTUQzqvuByf77ju+AkOQWcHRg2nkOPBotl6XmrCuCa2mWHOaaQGJvffEphtUOuAYU/8BgDeyz+0DUsOmXZkhZUYjrQPfwt8VQkey0PXdecwyxIu5ON7w/QKxrolx1yLV99KT4WssbO1zENGMazfuF74Heag+AS0BLRPocWT0g+0zCz4IsE2SsK4ZNTFK1UW1COM4rXiR1WU3WtcPZTIiqLrM7s6ziVwRzucJ6/0MaYQlV9xvMzIpAm8gnEvWc2/6Um9VY3zaIXugJEhJFAlXRsoNzIJMPcXSuSejZ8U3AnfJeexvzK64jAWOqcYBPEYsUcgmlWjmS5CRsPdgPDW+vsnu3HYOWvpGAxKQkZSmqSKlhilXy3xhlKrpw8wS9ytCmHPBnGrEQ92bRNyAltRCAIHMHU2AEqGupY+PzwFyUmofNwDdSvAu21j62FAZvCQ+R/xFd+xi6WK7dDRCD780AprodRnQrJgLdD94PZgbrvOKV/mMhTU1OdKvwWbNZXyrvjBXdM8oAXrGxQ2fkYwU6RwLQDeATTLe1wPu6iG8a5EBNrwL/v/iwA8GQKC/37clNi63ZF+mB9zQGtyGMc0yDQMU8X/Zu+CrMA68YTarN+L6Mcn1XdPN10jZ7Qrfqi9wVw84Gnnc221ZsNa8yw7zNPfMDS+X1QwMtnHQjJIby/Y4iWCTgoqC28Y4mkegCuNtvcZdPQTXJKFhm5ISHduEW+7suUshssvnlezvJ8nB0XDuiKf21pH8aVwez+Lwc12mV68GQJQzVDGZxa5jTdhkiTH3lOeDhWWqaNqsg4OTTJG2W+18i83NyMqhnjHzA+n9jr/j3piTTJhIwcCBNjST0T7RFKGMcXxSADE2b2DFm+mIazoPfBMeUpU794VJ0MyD583nQhPGph9o9mKdUFrCA6khuPAU4Hwz4HXrYo/cMBtQ8YSEtXZ+HgC9rr3vIlBvpxCk0M6rIBbWDeBhYWGysK8Ma6nzxdqkXt5dmJctlOxhDECBQjZAEbw8/a6vWQE6kl3LHbKkhpzLLoz7AVXqOkje3MsxNpIARWGYTe2xwQfr5+OgwAfbZxZ8ALZfNAtLeKgCanH7BJm5IFtopQljM66GE39O62TfVVVNnBglpUgfvS9zD43YeTfTpaiVdzE+CgCiP01IM0VHwpFtBH5Ob7pgmldAhpjgOClkABnjOAQk9WPs30Iaz8s6kZykqfG5J9xfw6YbLcCMARd5fCAnQUoNKemCPyVdGraqi8m6aqiKuW8RRJoRs7u8gQgiaOYJ9AUHdVEythBSrf2l9/BII4aQxWUEQE0G2uwC/v8Czf7ZKNyCIIoXdOTAuOOHtsEiIAElshceHSRClMx7+XyGPjABV2nDTT7jZerZP5PxH6qJRn21vMZvnj+HCvXYjZtbZk0F9M3GQYDH6t3qva2HIOBdhrk0M8X+3+53nI8V3N3sbcJtO+BYF7zOx8hOSCS7tE+9j+4xPLUZ36ovDCR5aEVwaFsQWX0D1r4ZedMsfSe5+r1oaEA9RnGNOuGhaJaJh/XYUmszNRwutGfUa9Q0k8S8MmfRcNAn7RBkyQYO4PF28+wWPe/7fMCJpl2WjvNCOtjQa46bvqcRn2TC23qNhGYehc6TaMJ4V69wrB20OXhcDFh41s01nwMU+jNQD1m/Xw1JZCOQKonXyZ4A8CYdgzB83xa85evwomi4o9h3S3A8HLi95HXnhdvAlkWjYZSYj8aXCUIvNeurAbUxrRaMGRve8HFP1h0AR42UWEESTfMdwWeAAyN0NRLj2DEKesi3cAIyIu28CEeY7ibn6GtpKpngHkPPgo0Qua3JMmoTjS3Wb/V4OPAID7R7qIeQcqSvXi7TCarW7EZUx+zabD0jUF/vmiZSNNOHEuegCAGiAEOaGWduO1XNkozrm/HrQCaOM8MsHpWoJIJM0gn9gyEn+Yl95wPtMws+Gnv6KJkCp+Y4Mze0VLHWhPfnw6PvudfC+RplS+qlOCedOKzu8tN5inCMH7/VFICjNcJ2Uglw1+AAlFzE5mZjEuTcMGeNL+YBxRfjkDgIWU1ILLuMeFIXnQAaD3Qi0rixRVzNJlN4A9A1s5p+H86M5hFEjIjXUOuohudgB9AXx2KA6j0xtxqrNT/PBa8OZyy5YEkXfJmiL+x9PQRQqAfGnFQHxF2GTIj7F9GwE6eqnqPc+SQAMM9FwYp1tTXGSWbUgt0LTOgeDgD28tu/DcENaZNAltaJtQOfpgMvd4vai00G7qpaOxEicuAyPq+BwhFpfxYrD/LcsKl+nO5wOGzhQfC4+ag5s0nCsfRNyjemzRj8d3VRi65mnIrGrD0Uli7CYDr+Coq/Xa7Vss3vFWRw2xH7Tph24k6AEj71HG3nEenaE5NZuTrGumEoSfFtu96Rv79VNdU2VCjRIgXS++DX8nTeQyqYk6baZ7NYtbSByqt/frnD66zpw255r2CcpJN2X/KKl6xk02NbcM2rCv+l7j0CgNt2hWnIANrseH+ODRShq8/n97oBE4dHy9+NA294k44AjjEW+v1Lvk/3aunY9mwQ9xCd2oz7NsOzTBzcfTx/K7JqDlRxTXWnuno0jZO3TT0TkWqaj/go3+FNug9P161cYTXPBtAzhlynZczE6lyMHCJnrr/BbcapzdjIhB7R9TYY6gl0HRL3qmzmATpJB31jyrqf59yU9+fPK1PDnMou5F4bo+QNa9KQPENUIr1OOJeMJsAndK2lICxl+1QmHLcJx/MUXm1xb+HGGnp2HkfE4STWZtdZEhbIIrvQiD104+TZWt5MNIxs7fH1OYlGp7IrMCMAwA68uJFJpKR8YBc6CSOWnL8h/RggPCJdaRWACNqEWAd97RWCruECkBjoKIgwEpn45eh1+W7aZxZ8RAzMwhQNACdEGMQ19kcvCKCkUPcu1MbqLRnJgQDQCLUkbGZxNwcbm4ZZ2DZBWdWyFgaQBC1rRDcyLc1aTwPwiEsI7X6qIWgHL8QWmXNg8ESKUmxudsG4xAhQRMMCSjzqSPcS0dKwSUaoRbCX4BWCOFI2IANoX6dUcTVtqnJqJLJz0pRkCIxkpeen1FAyh5vfN0HX1WDPeUcHc4lbaGQQgJyqSoCnqiz2yjjTY5+esIBdLZCHe2rDugBY4ZIu+CPEOua1v6SjkSaG53xqUeEO9uzZCHdw48fTwKlYeDNCZw+Eugfh2sSdfDO5bQesprXhYkoABou5i7A1UGgeeDkBFzrqYnkS7wYP81yE8FBnnNIUC//Mm1r2zu9AF3RyMHRqU7xnE1csVLr6pWXKNCGA1BuWwEowNE6Dp6CuklX3BEDKd1AZ73VnzY8tvByWlbYkTZkt1NDib+opepFOxjPoL8i4gR2oYvINjzVd1UmSowXqYz8T21jkECT0MJmH0jbJSCJGqKQAaZ7K6ummPHgCuveke6vS7qUe+6ICWSdaMZEST+9lsVIHypl4ySs+4oKZCBMYTEoI30jQWr04X4qQjuqCrDjQZllCtPO0VPJQkhJyT9Ln4pj+3cmxvdzCJgmrKCG0H8cmANlLB3hIpF87B9AZQZqLNBbzbvs8L06cdq9JUzDumhzZ5sipTkoCb7ovPGxdt8OVmM9b1j2gJrSiayiRhMcjSlZ4qOSpELcBgDhmMPIG7BHrVBiKw54C7sKSYYA29YBHWD2AhoS0gq/lj5TBx+/4ZepFHwKAQAmWu4NVeXr08AbwaIPnl/CBWfx0+8yCD6qdcOoIUBpQtoQjFsxTFyjyzBTVybAXGuZufqpVQj2bFgIb0BAFNlKHXPundPCFcDnEndjXeR9Ad6dmk90GgFbTXtfCwi8xCcbkDunzC6KhAYsCQFit+TjWAFL/wP5NgFCLXTQyPzx64ZN/0MZwGd8I4+AxyGNqPSTlQ7Kxuv2g7sra1CVahXA8z5pSXCylmPQapaSwOGMc2T0g6vmIhWSq6gY9pccF3wRhaTQj4Nak3iBx3ROBXRshFOYLCpUBvfs9jf+fpD/1om5I59u0SQALTQGWrREgoEUc37MKri83SHTrvMe8eefuH/89tgWflBf4H/Ia3zy/iFh15rZT1Hww0nW2bIAxhVVj8ytuaMU1n/Gl3DcDb06MdJ7CqD/CwwZ26QnR7/b7uLnwovye6RNMqLgxToV7DI5tiYwG3WiyAgtaLXPBBNwIAbbWmjAzY2INs1QhVFKvS4Lghlsnvtpm2mrFJhpecHXMZhvoi3TCD+TbSGWtwlFXxTkq1b574A0fpTvMcL0Jxhs+BihTj8SqgGdYyy9FINswbvp3gqdZnzzbRRZ8q7yI7JKJ3lt6rd73JkCD4D5SoxNOkvGN+hL3bcE3yku8q9f4H+trvEhnfJTv8bbqMzvwiglVU23bhJPMSFDPWKKGA1YDoad49i6r/kl9gXflZuc5YpLuQWnYzV8P7fi8Uo+mcWJAPVUXmz5LC48dq4bkzjX3ec0NjWkPrIvKJEzGN/MEAtducm/GuSQQVPhwLRnn0xR/cw+nFCPHB5HUHpCDjXVYgwbvuB8TW4Wg7yObbeLU/+5rrg2eft35G+6C8GNHL68XmLO1dBdmHvuFwVPixpkZWgqIBiVnBxFeN4bNmG1i/aEQn/T/JwMuozH23bTPLvjwhzY+TwMJtTBq0jQovtS0sOaTsZ/solWNg9XqrmndmELsBegs5gvyo/flEfHwU5qCJMHmLGXnfDyBmneDMLj4aTwOj4+NiTKga3erifTJFcjYEDr5W9Au7sfQ+6WIGqCbSxnio37uEZwpudEWV+npxeNzcVn1WiU4IUBDJc3WkNSPj5RcYHePGMYm4pVuZoyPKb7TAZ9718g9ROwv2TgOiDgqyF5admAjxrvpvJ/QnxjeRNegmKia4JWNo/1bI6ukX9iJmU0IK7pbOkFwz4vGsofrdNKpcSOQ+/OTPTdJ+ySWEVLtRzStd8jw2EwWf+MzNmiNlPH7nlY7kW6qvpF2MEUR1gmehJFZF9L6RvdScS9z7zc1tEG22+9rbL5Bu0Xsfx+t5QS9n4UYB3Ipd4G7GdmeSRric16bxoW7XORLpei7JoiLqb2yqrJVfeNDWErH9SVJjKePJagDEE9P9ucNIMbtZHwY52vcWlXXkQPShFGJcBL1/nk2zb1971v1RdTN+aTcqLIzGk48YeEJ9+ZZq6a90ez7IAUBocJ6sTS4FHuqsgsn9rnL8eMhuFFSfvcsQTGucX4z5DZBePmct+H6SyyavlqtYq5KKjC2qmvTxFrXS3kdHGKUnn0YhS9NZ0nXFwzWPeCK0I/I/xiOg62zPC5CfgB1T4iDB1+ThneRzCjydTr+Pqa4Bqft4vq+NDmo4XGQpXNLLIYzluXYGVzRGey8Nx4e2vVrODYc7e49+S7bZxZ88Cq2KbgeAwJoNNEQiQggU8Gca4ANz8munhVhE0rmwa1rG1bbEtYyqGP6g/VNkHp4wOuqtMZAMXBT1XIvVcXEHuqEKRZQ9XicnURuC79KhXNHzTYx2jwgXGtkE4XPg3YFusUN+GZrE4zREbS9MGOqbnx/N6lFvyN2gxv1yWohlJw1w8ULXzVjh9+eFqxbVkCQW0xMMgGxUhnJdDwyN2AqSInRWkOxEJdUCvl0ACGtvnEDc8bDebZnQ1qieuUAg57vzqu9GCOfZXjW1GDxWuoxTiBCNiMbXDNwngAgbr1YTFcOAHIDJa0QzNzC83FbDpHSqo+CcCS1KhM33DYJMOGeBJ0ze9d8bO4kmNCwBclxH6aYjQ/hWSG+cS+mZaPhGP3dScCbJHyrvoi4vrrgC15y1QgVMSZoxVNPkW1oqCIYU2036EZa0SOHEwQHrpigOiHJN/sLZdhNBEdpuK/qaj+3CQlaIC9ZYUgvm+CckTWKO3bV4OIhosHroi5/wTUnHChjoQnHtuIsBd+oV/hWu8G7eoPbesBX11cokkLt+CQTJilxzRtU9abY70CXIJ+dRGvX93BCcBkAHJsKft22Gfcyh5iXNyfg7mv0SOhpnGTCu3KNb5drfH66w+fzbfBavlpfYy3JwhW9emtCi5RW1/fwLKKHNuPrmxZVe8fX+Cjf7UKAH2XVOflieh8gClDAd5ZejbkS42VSBddvFy0376HAe8v2cZ6Jp+++9LRdG6OJC1h0zrueiZOrfY6fW8Z9WfDt9UqLg1p6teSKJD1RQKseZ5NVMJ2mVAJkbDVh2zJEYO/qACjcqGzYJwNYlpv4Jj7uu2Le4awGCOXWvauR1m+WkXtqR+AxeCmiYu2QgODpukFOHXlsNJTQGA1YN8TsRxJhvyBaHyY17C6lHfzQR0KVF46A3emq/rSE32HZLkAPP3hzD0h1D8hgdTZXmDNb6BLWOYNHYGYnPbaM+aKCIPrfBXuvhwgijri2bHHoTvZzzoML1jQjt4YXxS10vrgWEOmz2m88aTWMx+7vE/tJ/OhWLo/vKP2yOa/FWxHehWGIYOQrK8pHHWz1NFsd8E0sb1wovE3uAXKgB1HLSdo+dNaCJNqR/0i12QGGSwAiurAEwSvQ/YDaP9QuXzqzcnjSNOuUlTTrp6iy5194iuWG7rk4yYR7mXeS3B7KcOubzf0MdPKhx8Uj/BFhRkHjGhoZ3pgkMgBYejkBV0sFEJZ8oxXAio0qKlXdeJA+qOJZoamso2CWE/xmtPiea4Z4C/AC85ZILwbXeQH9vkZQATwObfYfK/xGHBZ8E73WhoqzFNxLGwCAFli7L1oRdmaK+ikJDe+Nr+HqmwAwC4zHwOHur+YRci9JQsNNPLeKe8k4Nqur0q5w3+bgT/iz8OJn/qyZlLjbVTp7QTUnmyYIbi2FdiR/6jGefvw4TdXl98/GTVKPx4Z1uH4yb85Te4nzMjyMciDT07Bn4Pc0yqOPwM0rGYOqCeiNxfKMmGsgyp+xayjt7kUIaAxwi/Woayqx6Y6oR0hFHik8r77+ECF0m8IjK51cHm1cHy88Du6WduMyuGI+T3deBzyqzvBkc5eEoIfCLwHA5Xp3sfb58b6m7vZC91KIb2qf1pXxexfeHd+bfVv9neD5cEEvstQmaeaaN0sTQmgN2KzmyJRqEBpbE7TqehqWReKxMnbgQXv30RgCyTorxwnoBEyC8RFIkLJa+FtNuNvmnUu7GjHq/WlR8bI1R7pwlFoG+qS4nFSAhQ8E7QAllZbOLoa4+wyRteGllHe+NMv8iGv5i5EeT6Kw7h2YFYK0hPaCkOzF6hV8GYepoHDPUonIjhDmueAwabrmlGosBu9PjFoTauEurgaAnL/iqW0rxb24F0rK4PVwEEJAPQxvm/T8endVciE0f6YRQ7WxSiadPXrGgPA0jYJt8SJnnYPTXDQrx/7spNmFa6RNMjVstmnctwUnzDjxhG+VF/hmefWI2OltJK2OGRBMgnf1Cl9fX+HcMl5PD1EIy8mYrvboWQEHK7rm2QGbMN5u17GZNSG8yOdO2qQtOA0qAtVF0mzQ4CqaqqCZcJIZB1qjJspExeqNqEdloq79cLJ7PknXPPlqeY1vb9d4aDPuyhxjqJ4cxqlqRsprK17ogGOrqio8alm4U+htu8J1u0WykmnfsKJs/+/1B3DbDvjm9hIPVUULAeUIqeDU78FV2vA6PQR/wzd1T4EeC5WFHLdkvKtXuOYV/8fl65io4IZW3MuM23qFt1XTXC/Jpdd81uq9pgTrZFZXHQUQxNhpGF8PG7H0uXuwwnJep2Yx+fBD2/AJbvB2u8LW9J7nQ9cMWSWHRLoLet3WK4xVdv0ex6ybip7aqvNT3wE/10s+xd8cZN42xv9j+0Hc8BkfpV7fyNNzj20JgOIaJ4kUOC+J8OpwxtYYpy2bmq+Cqc4Za6iN8LDlqDfl8gnuwS6m2dEa617hnokRWHhI1Z0WZoCJeVzF18zCQNMyEU9u4uMaT07e7OvL6L3eeRw2gCsBxbzgY/hk3OQNSHhEVAidBGo/dNG3UQ+KGoEfbE9J0uUIgB4XHoGNKD+Oyt5T851AzGX7zIKPXYxrtNyfqK/hSFezVC7/iO4CI2BHUgjvw7ihqYV8Gdd68v+hqLlW82wIh8VWBiTeLF9cHHiMYOCir+HKu7xmhFWwT+Eaz+Po1yd6GybdJbgZkfJwX4qw3cOgX/JqrcDekxOeDZYeqzQQkkgCeGSz4j6txXOLvtuzISWBdbeIHzZk+PicaLR7bOGCxIUVQMP4Di9+jOFTL9N4XnfNDu2pBXis9gnAPByb6SsosNAiZ3WX4TLOIy1Jr+JVbiFulsIN6Div0Nj2NKShNmGs/HSdlCZKBPbshCIJKFAvojCOXMz9XbDmtCsQ5mEaDxN8wwDUqU24Tme85JOlc+pxJ0w4DtkegGdPdJ2Re5P+PrcprHK3dqtQyMU79yNzRbZ09inVAH1puIb3deSoaKbIHGPt6ZnjuBQRK2ApuMPyiIB75CX0Vy7b1jLelSvUxFZOHqi2cTO10MToxfj03C4o581Fvrwo3z0tveAfuofHQ3PJgOWEshOlc++PEzl9voxL4Mgr8nG5s7ongBbbO2GKIoLVeRwGPEbNjb2nQjMFR5Gyy/fBxzzGz7wdDjw26dlcHnrzpILx7Ru1mABb3uyzOlyye2Oha7GQVdh+1K2LtVG63IJc/tFvZNyEab/GDCDA3QSCC4L7B1p4ZiOEM5xz9MLgA+ca19IxbGTH9v1hX8l2Jyswfsf20Edxge/Gm3PRPrPggyrARX+iNHEDhDhIfl0bQpEtgAhrRCli+346MlqGaj2Q7D0PQAc6/v9E5gHRh0ZsHhcATlisjUCi119LwpaNZQ2vf2KCYvZwWyPI2YSxngiFUDPNiqwIOOqYhJUO88j4zEDcQxQS8lTQQpFOBSBQs8cLqVBH2uPEtDgjb0CdAOSeBgug66FIJ215SMazEbYta8ppqlhSwSFtKsNMSRdKG0sh6qGU0fJwS+HRAFk/LUwlk74lCpa0z4BZAIIgkcZ5PXV7sC6cA7JTkPVjLxcH+xxF6/CUbGnCqamhZIdNnoIa08k3DMLEBR+lO/xAusUfxFeD9e+L7ZjV4l6Bc5vwzsq8MxQ0LKwbUxGtwnxXFmRLgfXQSrE032OZgngXmitQXsmGBBY9Z628UxcFgFf5tekyrBHbd5LnWLl5E8bH0z0wG2eCKr62vQlg0kC4HgqZjcRJpoZ35TrUWhNphen7OuNUJhRhvJjOmLngJp9tg1U+TDsQDqmE1+ZNOsY1TjLhq+WlPQMOroVvkg9N68XcGL8kqgFDRanet6t4fue6Fz97kVfMpufiqaVeeZdJInSjqbAnfMz3ca++GX8o1XYknL5HxX2bwXQVAO19u+pEWWgY5k3qlYI9dfvUJnyzvFSdkqqgK5OGCnkXHjGgaOGdb5oOy+enO/X6CGPiop4gGCgiBIA8twnHOmNtOUCC9qsGSHqdjwPheNNMGgPTp6baHvdtMT2Qnsp7apoi62JznmorAJIlBDRxzyvFeDZbo51kPeWKHMcwysb7sC9drKlAAArnoQXICT6hrgk9Y67PD92j8LQ3wLwXuyXO1m66MGpkVnVlGomoYhcewQ7QPb+tr33Ox3CVVKrGGxkFxqT3x09NwUkcOu/7hQAQgVTlywk/vVx/p/bZBR9NsNsMfNArVFvfj7vwhLhFGpkrsYGPB/nD666vcZJE6pGDEEOqLvkdpe7dS5HUGzDqWTi7urnn40OhlZgwNHbJ+qk/NAYJSRAl4d2y93k4GhYGcCJEFzyH7iWAeEot+ng4MAIUdOWGxPrj/I3MDWiMSgIeZpzfay2MMhkvhLvFErd1GXv0fkl/Xo88M7sxof5dv9fxcxvo3YssHSiOYa6QaB+9JoPHZPx+R3J6fKsUonTMDSlfaCo0z7DQeXHDK274bGGIvgnVgS/RNQ8SViRoye/JNEN8w58AmEw2tJDWrqqsXS/mn2dTCKM0zZBBy+ElcRl4zciRqOEB9BAAG9NNORi9JahXi0Urjfp1Nr+vJx7iZCviGC7QzA3bPOxeXNfB3yu38P2YyIQZLOrq7kEAmy1v7rXR7A1P5WUjaGohPM+WAIDMNcbCrfjKFO8Xk+DK6upcpzUAIVg382teTTm0ht7HfOHtuGz14j1aoWGto2ioyJVsHSwo72ILT0gIgpECANUlWXBbDwGKzibK5gBhbRnHOuvzYMT5/bl65pQDpsiCAWOVHHwUJbPOj4DrU/falXylE6yH0FFCw4audeMaNoCFVaSDCyblDSVSD4cbfE81/Rt2IAWAGqkyLLyy/w4EqmHUpK8bEULxr1iq68U6JF4rawQ3ceGxc+jr3u6Yx+PXp/qlYfZo+XtEzxivd+E06XthM2NsBBXj5cYvBRhxQPO4W5/WPrPgI536Qh950AzlbmwJbWU084A4MvURFSGrKkiqydAo8pY7yNinWu4mk7nXyEEO6WeekRETqgFIgjoJ1injwYSxxrzzUpTfEFVrzWq/8H2Ctq6rIaNXxtNA0a10sRpHVLrHgxgWLrK5Xi9EYbh/H2JeAoe6LFrT6RLsJUGeK+ZcMXHFnCoyKX+gNMbtuoTYWwi1rRnb0eLUs7oixDbWAGQjaXQMMY01CAT6zMbicCNIFPSsHHvhI9UMNj6Do4qggJQuvOWeSRTHuofI8Q1fUHMHToysGsNmS7PNVvXXF3EXCEukxeW+mN/iDZ/xhdSQQBGaqBCk4e1mEBo2VKw4yz2qCG5FSY2f1Gt8q77AV7bP4SQZSy1R1Xmipox/kyh3zodOMYpndLsh6lq8yg/4eLrHdTrHpslG6nQ3e41zdGvZvRmV9zVHnOg41is5pBLcgYkKXqVTkGs17LLYZuOxfeMykFq5D2XCi+msAAD7TW28P+/XqJq6ScKcjsqrEE0n/Zq8iWJra8tIaYvwzgLgRTqHt8c3QfcKeFjjc/neNFvOMVbat65Sqloha2QtAdgVl/M6MK6J4s09YMoRucJvnj8XNW+OdcYdHwLYONH0k3qDreUAjg42vn5+GWCjiIppOWh7vx1wNi7NVdrC83TNKyaueJlOAZ7iFUQXjTtJxje3F/hkvcFtWXCqE2YumLnio6nznibWzCH1liDEx7z+j79XPr9ui0r9+z34/PNaUVrWQsOxvoGtSCi1z/GUdA47D6RUjizJVpXMTgxk04satZ785W+iUgBY2Tyhe8NIvCz9YLS516HZOt3XpT24Ce+rG5gXoXQSaMrsaKdV7EDKfs0SyGxfJN/X0PkkDgwcHJE88uySgyDRiIMkW3I9u1IQqcK6D1mNmQL9gPEY6HxK+8yCDw/FkgCoJq7lFq0MoiwAINSr7o0TZPBmBGnIU08NUBC5l4P69XYdGb7ngMFRok1YNEGrWvhOYxYj0r6w9Ic+e9vpbqRhorhHYOQ7DN6OCMsYQUisSBp8rDw9a5xk/ncPTVT3EuzvNyacW3/mki7EUdwvMnhGa4Is1Za79TqqbmZuaFM11Vp+bEXQEE0cPSLAo5irA5XwXgygQfyl8aGmYVzHccjorsQPNcOMMo67QEWIAM22YtmNgxeFA3o2AwDLjNATNfENSa03p86NJe0ZAIgwiaCay3u6QFBMpvzpvBx0bweAgQfS51wRDbFMXHFsnVDYiKzfxazchkS9Ng1DAcPC0yOQ4ZasbyIKNjpB8obPmFCj2qlvZCdoBtDCj2V+3ds2enD8ngFoXY6mCqQMsfNsJt2+WWXTx1wD15FgSFTO1YKUCa7TUqGF6iZ7EElaKLt61VZXL71smr1TcesAFLIDGlU4ZMQ9TTZ52NIyf26bFg98W657SIj0/mbTSonrNc3cCa9PSxFmAS70eIY5gKaquh460o1dLfZjm8GUBwn0zknqz0G03AK63LlzntIQGtJ7TjE2x7bgBOXBjJkx03BfI49krJcVYnNDH0YVX0Bfb+YWSYTuGZFmSqOMzv/wNa+ZgShqqEgh3XuKgYuRPmX7EoAI+6rAoq3Jo2EERAi8f077tfzCS3H5b3jIh2c+rumX18P4v2LG7iCvEN/3c5iR5U6aRhTr5y58Hd/rtV1CSr496az5YPvMgo8699HnzRCoe/V80Bg9E8KzWrwNAAFA51BUaNGw1MxKVuBBJlQgZjqTm9DjOZ17cokljHR6XjMwA0wVzVC0q+b1gBp25x3DLSFYNfaZDGiNs9FjevZCSFaxK8l7kTExRN3JnPYPi46lb+SDl0D/3sEHs6A2wqlknJG7VwdaWK6YSA8A07oQYCnIpr0yLhiZGq7mDTk1LfRXRBekRgF0PM25uy+GjvvYG9LW6pHqNQKguevDCxkKsOYluZQmFhbziEnP/rEaOxHH3YVu7GewclBSgMZ6YwulhQS8CJxrUGgxt4R3TXBv+g2+8O7kuNHwkjcwgINdfiKgoVe5dXe7W+NXachIsM3UF2L3PACIzJHSWHVBREM+L9IZxzbjZTrhmlcszJhRdoXTknFGLsmizdzwYwZIFa0O+zI94GO+xzV71othYgD3jXFLWt5e72ENDss4jokbiiSszUIz5BtOw32ZA0iVxnEPn8v3eEkPIQam7vwUgILR03l1PFSrJ3MbUlqd+NktdPc4uFfgERFXFOqs5jFxr47zTVTeXPt+V5WXcVeV57CwpvQ+1CnGQWuZTFodOW1RL+bayt0nmnASxlmU9zMWKKxg9T7ARNhaRnGVV1FP5EkmsIWdHHidOZvA10j+7am+gBZPfJ11bN/kI25SijBMg97LGArchvEBgK+W1zrf2xYARfvdw2iXlcF3Rg6GfZOc+N7MIORI7/cimMdVN4/ghfB+pxSXQfAsvEZWc0srzEKANkv3fAqirEP3BECVkEeQ4X01Dl5bZJeNMuo37YjvGIwm98ZCuoCYG2W0X9MiBG39IdsDQAiw1LNm+rVa1uu7VlJ41L1InoujuZAlaDCC9RpcgfaYh/3B9pkFHwA8fbq38f+bojlNex2Agm++IwHTNlLnZzyygtEt7rBwoxOIQYeHbkg0/deRZqMQyyrVappYhkutPEipYyc2M25mZBuaXKJrWP99Qx7dBIZn9F0dwM14ChpuUyiqGUq289kMCA5rG3RPmtbAeTjP2KouzC4aBgClWhXIyJPnQT0QO6Lq6AEBeoG54rLl/uwe3cDQ9yBRKYiMF80dKBdjt/P4uMfCL0NWw2ZqGrqbWoAIVLLgB/Vr2NjAX66Ra2Qu2ZCUtxCHp8964bhP6gtc0xkbn2MzOja1fl27wjd51U7ouh+AbmhHWfC2XivJz1zxd3XZxdt9M/XxXluDpseaWmrrGiFeAPG9sBIwW8Z10nRShuA6KcHzhs8Rjhmt87EA3cG8Mq4vcZIJ3PRealMlWydXqmS7aV/UKxzbjCIq1PdQp+irV6fOVDFzNQ+Lzt1RaMzvRz0dKoR3ahPuaYnsDK834lwCL6V+ty0RGlxrjkwNb57V4URf9+Yw7HoXJFoNMZUAlMHRCA+gApoXBvReZ608694l1/XowmA3cX6v+OqegjEc9CKfwyvh/CAdn873KBfAVOfLFOdvQlgMrHiIdaKKyTy68bvNj5OkCNcAWosmOF6095IA/Xet79KrIetr1ImqB97wrl71+kVDfz3Um42Llk1fKBng2AyMqP5Sju94CFw9ELqWe6HK4AgKxd6hIpdDiqvXULE9JozCS3PfP7M1KMLBowSP/83LCgkgXtMFA/C4WBJjTdvFS2Kiap/d5kqi92F7AzV5BIp2npUAMQaQoOudEEzHaTgWo1FLfQ3eywx9avvsgo8xRiaELoZin7PoRu6D5puBu+JXHlxZAkz6tDXNivbkTFB3hTlYubCQQQDNLa4XnBLb1KSpcukmQCkpqiJGNoep5YUe/xDziwqsA2oF0Ce5o9GQ+kU/h/QSzpcTySeD8Fj+2CaKaYLIuIkCkOYeIv2pG6O1CStNmOaisVTLbinVCve1bpGMZxsJgi4C5IuIWyTr1NTFtyYDEsNzHrw4ANTbUKgrvvpa4XUIUh8ncquCLS45PG991DYOC0BTQ56r8jsMMLZqIkSje9TO3TJ2dXS83kKrGoZyl7dvcm6Rf728wg2fcZRziFl5KfsITUF2liDQNyugb3C+Md3VBbfbAcfS5cmPZY5MJP2ObQxWkKuJ1ntZslYD9c2+NMZ1Puy8KAur1+RVPnVOh4UNNgML7vJ+mU54kU4RWritV6HaeaANJ6tkCnQg5dLft1X5Bw91wt22xPXX1uvWaEXbfTrtVlMUD5tIs30mLlGPBtAUW9eLGFNVS0s41Ql36wKx+TpxRbEwmd6fAjwHdLOFt9wrc5U2TEb49ayYzBU/MGumiFchVm9A1+9wj5KLmI0hFPeWvK3XeFe1IrCqfM6hfVEbI1n14ZNkLLzhGj2byDkTnk7s47i2FHVO3KvkHC73ShRhZGrIrEJz13kFCMjmefNqzT63b7dDeKIc2G6SMKFiVNyN19i4PT4Wnt3l7UArGpPNzXkHlhQgpqhIm8wjdCXDvaWM4zrpfLeSDtuWNKyy6i65q2nl6417GWyd9X8liwIPywyUwegB3BOAYe2WndegVwp34IDgV7CBH+clthndwCXsPCHh9b7kVvg5rT6Ve5Ili/Ee+z4hZoSHR3cEOATAMzob+vpHQJsGjHWBtdyLo3sNvuv2mQUfaW3gDftywP6/AiWe+s26F4HQEasMnwm6TK5vtu779Qc6kA77xewfT0stBDCBLGQD8x6ocqZdTpy4RN0rEtkjA1OFbWI4ADBQsPOIVO0fYfgcjortHjyDAxeW/tD/p+4pmlx8bv2JvHaCpuYllQ9n7lkAramMfR28OV7Z1SXVp1R7JsPQOfeagEz+3lB6pLF536xYEjB4Lsz1aSR9dZMSlL8xGCJiwEM5OOgvveixrlQKgoKnNvAiDNiFJeLXb3hk6YxjLkK4H4AA4GEI5U8cSIuRNdYxWyVhQ495hxqqx/3rVVjD7knx4luZGxYpKGnbeZh0M+lAo8uQP14ZunZGw2zCXqrQa+XIhTEBeF+8rkg/hws/MQRJGo5QfoMDKA9z/H/WjwF01/s4NlvLuKsLjm2Oc89cojx6lDUf7mMs0uYu94QWJEm/RgXjtl0F50bDLcB52GRdoHA839aSAQpEtocXtnMJ+0xVs77QN2u9R0a2eihaeO4eKbcI18y24bpX5LKaryt8ek2Xk6h2SwPtNFw26EZ8khxEU1BDtXu9M/Dh6cQOLjz82eyex/RUNk+HA4+FS2T0RP8MlPk8VC+NehMS7bkg/szH8IuHDL3UgNd+uWwqsKfXv8XS57AZMWTzmUyXZa0qfnguKizmng7n3LmRpDeNJ0LoF8ar7SHkx4Zh2PsaAMbDGozgE+5rwQDiC3iEUPQPbbK9qkisa8Ld4OznsIXNNhry69s13WuPRuAKk5vo5yMxTsfo+fCwMg3X8zGq1JWxDQzFq+9gyYfT9q9Lr8p3ap9Z8MGrgDeAXIXYb0oQYYYQxDILP9DfpWfDJ9Cw0bqVG8CjDBkP2H+XqmlreP0P0gJkiHNI7PzmYYs0XycwPRXqifsyq124n8frsjhAEgcDQKDhXS0TR7yX7QkGUISLxn93gw84YZVIgUfONVJuvQuaJ89omwYXyTZzFyWbkmbJZG7mDegXclY6WX8oqe5HFPEjey5btxrixy4eiq92Dw17aeMAHkk6r8etHG7KkUkmiwx02XuBgQ+B73TCom5NV8odx22wHpqQeSEkCI2+2eqGtOINn6KQm7dNnIyqWR+37bAT43rXriMu71a+k1pv8hmLlIiRZ64DtyPh1PIuDfEyBdItXP8XAFZkbE29ChuAh7KgtKQZNLb5zqnio/kIFkGj/s55ZVp3sX9ru8FDm/F+OwQ4mlh5KqPHoDTdqOZU0QrvAMd3akyChYrWGhEVwDqaeJlnoDif4IwprPYmhCXt+zuOk4vleWXgo2jJ9iVREIaLMGbuKbzuQVhY9SxCMXZIsfYNWbkVvQhfhRbiS9KioNxEFQU9NKcbusr1O9H0mtbw1nim1epVYC2rxz1UiRtm6/dI3Mzc4CJuE6mnybN+vDlHpUIJuq7zonOGMHOJOeRjWm3u9xRqjvo5fkwUnrMxaWgGPjYbY1UBVkFJXQQ8tLlW5YecitZ1KZUjxBKVauueLxLCWx5+dlkGW+8DbJiRAwAew47MSSgocOO0uVE67FURsrV1R5LE+qQZPrqf8RjuZuncPzeWDGDsjNPRQ+99bUN/fTMa6ZDOK6nd+wLgIvuGetahr6cOmli1QsaED+Hh2N8Jng+Nf6GTH32Tzmbpj6jLSUBAR5/uxfOHX6jHo3ygTTo3RLuGrAkAsZjuPA2e1ut/MB6HNELdJFJ/I37oYi1PbPK0EtIDq7vNpdHNlScEmyAGkOx6YkDl0QQfXqyw/gVKloKCsyhQRP0F400R66MwDAsoaeG0aaqYc8HVvOli2ZRoWkvq6ceBaAC65HqYmx+5aFlrWwTFPCoA24IyjI0zzAMsSAchO0A2fMfwAgYv744sFQuDRFE6tQYYdKiRsk1JLV4Z+SjmFh0tklikhsYkmJNabB4zB1Qv4yWfcEMrJvgG1LNbDqQVV6+poAF4yRs2YVzzGce24CU/4CSzeQgSXPlxJAGqlW5et6FjrocwmwVdGqNRJwJfpQ0Ll9hsnKcAIDYMTd/VMATggKUGP8QFpUZej/9/5oYZKhDWhPEqPyiB0gi555bBkLgX3wBnEKoUJAMJzi2og4W9JC2W55wMr0/iAKIK40U64YbP8Z2zZca8mM7qzXDOQkOEcGLsIJiNPLmJip1d5xU3JjJ2lVwHpc/3hQs+l+/xJh0xU406LCGlD+dj6H2o6qqmHKveiynNtgXv6hXu6xIgwp/D4kRYLmiN8Em5QWls5FUOjtW55p1nxttlfSagqzKjdqAyccUdLzGPNJTTPVhsIGUMG2auBkp6NtSLpGPtnCKdHyr85p4P9/at9q9f08/rWkoaShE8bErEXXJR4cPGOG8Z65ZRaxdBhFB/jy8NwHHtNCsmSJW+mfoSHkaNux70x9edsRQDbZ2ouvMuu/EsowYTUK8taWBSAw5ZrFpjP6dMqiRNG1uBOel9YwkQ4OWdfG0HdyI+wQzp8OSMe5moUJoZ8QG8EnZcDs8SjXtzMHUZJv8O7bMLPtKwWdD+/3eN0b0Fg5vIC60Fm7h2K9s9CGSeBnFg4tZxdAJ9J7eHiHHiAcFDgBcsk0udjqEPDhxEQQVvhHTW3H9Mw1OzDRImbBQxNQDEUH0TYKjZsgceve/o1VodaPl3ncm96gStfn8+PrYRu6W0TAWHXHRxtmwET10bx4tIwzRjnRuXRM5oQQZjEjRyafYhj80nsLsExxfX9xwPozBC28QXAbqc//ZMJaFbOUCvxKg5lJCphWwzkYJIFUiDZlJt6GPjfXQ+zxg3BSyluJMjm1CQNpVQKnErY1rtwn1TvZZm2S33OPEpXPGf1Bc4tQm3rYuMtcFi1OF53B/A6EyCnfquelF0A7nmFddJLXRPm/Xz+6Z5Ng6Hp9a6fLbXEPEwwFhUL5kV69f7eLqPLBLdZA67kEwisZBIxcRDyQLzaGQDVkzN5Pt79eBtWCV9XA60mex7r0yb7J4BoG2EAte/eIJvY3MODZhs43wzHbFwwYt07joW1iaqeMmnyEjxc3qqrYdUfHze1uuoXOuF+fx+nAtzbhpOWB18YNtVQL4vS9SpaUKh47LuvGFk45IfAQ8AEWorVqritiiZ84Gn4Lg4qMmkzzSRAEM4KJ5fSyjonCDPfnGQCGg9GR+HhBbkWefnePquj0cdhBshhPNGaFk9qxGWqYxts1ovdl/krlFvl78DjzdNt/JxUe5iXIfk4mfU4CjDXuJrkgMPN/7Q1+W2GPF95BUKG/gYwQ71hc73PvjnCKBBrmkkgFfq1nMiOIZh1Nkp1WCWMKrJ9y73DPu1bH/tvJE+nE842j/YPsPgY5wsCMs3cqr9T6IbvzN9Y6L4GuSeECOcImvpY550eSRuysdhJ5B2ANPFx0wLZHRF+ebjoYF5mKD+APzlHtKzaOUAMy0D5QqxoVG10w/hILTRYod+HuJn0ieZXY+c5+CI3IBQD+vs+9Y9S9LHsxCkCloSsFn/QRi1mCeTYDms2LaEcuqZFtKAWjTmei45Qi/u8tzMOjlvWWviVCOAjRu59y0PQA4jGtdxabP0MWnoxQNtDL38hthYhSUzAhr7PjbN2mm+Lo1EXC+tTdiDDR9jmxC+qbtF7NkHD3WKdMtV0q6s+oG2yHBxcSol4tWhki1rCfWmRcjcPX1XFnz19GrYVPb/lgvr0fVZ3MIdj7+pM27SijdTwmYgBIRdlguAnZteh67X+fCsHu/fy3Sywnh7b8Ir1rLqXmrdSalKitQQgfM9dhlSBog83NSYcDASqguBvSvXu/TY66yCYYkaTnat3zx/Du/LAd9er7FWJXKO41GEQ3FzogrmgkUINfm4cXBAdPx4x8fZKAXhVTk7XY7eq9dug8fKORSeKusgbeGiXiEDRR4Wcd5Fnx+acfJQJ/VwEHZ8H8D1UFSwbateZE062ZTp0XdGoDFxxcxlp/zqacEPdYoQY5OEAkR2zbHMAR4m7iKFxzabIJ1nQLUh9VzDSS4Ed1cWnMtQz4jbULtFZQAceIwcFl9LQtU0DBtHA9TXjNGCZ+rqpJ6pQoOMge0/NFTZ5gLAo1O2NuxelTEjj9A3+fi7GjyU7N5GQmjrCtijQUln48wN6bputJOoBzd0nqxTkXbr5/bveVLFhUe8zRJ7R9yvf9dDMb4/+V7yXbbPLvjwm6DhGQX6Gw+0PznTF1a22ECHXHotWOP8ydQoiYCaBNJ8c6YdiKCG4JM48lS3FXVvi/dn3LB8ohIQ3BTAHn5XM20zgtgknhblVr+DLH/e/rtNIHezjRPTsPoFSsYOnQLDvCPsJmF3JXZBnvjTsDARgJws5r3mYWwQhNsqBG57l6+nTpZi7lOPxY6s8+gX9lbGBTG4eV2WQo4RAxDAYp9jaEZoNwT6XX8xB4IwgJ5a5sp+Izl4dH0NRpRPkWnY/BiCBxOo0boihK3myGRwQSzVbJD4/zfp2DfZgUfgrRnZ71hm+/0xN8I3x3FDcutRAKBxaGp4c1LjxD0DY7Z7GbNwRmLsqGUyeh48m+GGz/F9RtP6I/HdvtFpNkYO1c2Re+EbcPw/utT/KMfuGzmgnAv3PgAIq/q+Lni3XWGtWTMkSh+Dbl1zkFv9mhlaqK9SVzRthmgjfdc4OQpC8k4h1Y/3bKhLpdaQUPc6POhAcswcC68SuvBaMfDj3BPPOhnnTW3cyZiV9f21jdwJvmN/PF3WQYOnGSdSDpenwZaW1PtBPjco+EYPRRVnPaMKCSitKviyyyWRXYq2ez08U8ezdLy5V7U2Bomltwt6mj+AyGyU/h3xzSTWcgxrK/UwxrA4yuhFHBZDL74Zr0SDAhDqa874Ooqvs+NabNeJNYqVfI/WxcpiqfHq5HKxD4DAnm3nOkfJkxBsLygu0jlubNi38U++hoqtscMazMUBVBfr3N8jvuv2mQUfGipAT5McHjY1hCiKpzPxWdGnfq7/SlKPRHA6GEhL1YwNFitxjyjtvis9D+hESggRL8yti5p5H3NTEJAFyMPfK+84C5duqgi/MCDBS+ibmgOoeEEEnbswu2aJApado9gmDm+kxeFmDSlRgfnd0c8fE25wEQr6i0N9AxMhbM1JXrowLVNBYkZZtChZPWXtKgtqZRzPMw7ztsvFP2RNVXw4TZBKaOcU4QsXgYsOymBtQO9JuTvm3XEvhLsIB5JYgLEM1INaFa7hMYZ03Ovjpa1p0xTtkLK/8Lb1BYBD5KzNsltoNuMxnNoUG+KxznhbryPNUiu/VgUQmHeWsFvuYwjAPQrnYbNvIFznFWtVF7dvGGH9uRy1LeRbTbah6oC6BXuueff/79IVPkk3mC3boKuU9jRL31Rv6wHnlmMj2oQjS+EdXRko4ah1ktBwRJfpdre71xoBgKvcU33dUs/csHAHQa7GudaEOdXIjvDU1pMRTQHg1grE3dWDklBdxIwaMhOWXHZg525boIXeryMLyNNpvb1DL/Tnz7wTTQs+mu8jrOTP08dQQzGCA607GftTm3ZE2XdFdS7u7Z1jCK7Sis/le1zzigOtuLWCcz+43AYnpgnjoTYTKJvDi3SuGWvJOG0Z59OEeSlYpg3vsWAqU9z/i/mMOamuypKKkUj12bluiGvM3JcZxzJrpWHu8zVS6tnKMQijmZdkywkv8lm5SQw08+BMVHFNWlE5JOJtbs7uPW0MJlUFFigA8ZeTSI1KLS4KLafAohswGI1aSCSkB31WLkXwKNvDPQq+6dva6facrgUDEGCgzR1wBD/M1n8uem5JDUjda0ui6xlNTb37tt6TceEkrCo8kle/tHV3IEm6seVAgqB8EGEzqN2bIdC1bDhfdUE1ghlyw3j5EHmShhlnXPA7Q2TMY2OXiGzn+fC9unn1PicYAcQ6S9qIzsx68HTQgKnDBjc2R6vhkhrCFpFVMVjIkZ5q14KzgM2X/1g6HEogusxSIb3hR2WL3dh2ZB0xyeGrltHh8ToyfRRpiDRgkDyOzQ3ju5vI4Vreu/XF3aVsVV0baWawncc9H8EvILJsA+kkMKEOPDykEum0vhgMD2N89p/WxvlBADx8Y6nOIR42NvM68RDCAmEgLveJ6N4XcnK79EuKkAk5pdgg/f/Pll54gMpjN/Jy5z11US3pfdeclOlAJsIooi7xxqoxM8pL725NaPczGHwW2lCyYbFaM54FMVHGA2sFWS+ktiWXyFZr/rYcUKULlwFmpVulXAcBG6XYwMYwhAtqbRYO0k2vr2AP6JtieD6GAVJLeZDzhqAFOOnZH4DKhXtNGm+hdEo9zFD8uVl/PE3ZPXiuZ+EhAwBY68A1Sd0DtEC5Dgfewss1ys0r8VhTTlfWlOuZDrinJcjFY2Pzjh1oDY9UIwvL2XPcoB6szbw3Hr7S548Id9bKKJx0mZJRulzH9HIOVeo8EZe1L8Im0w7jluy/57/72Pr82lpSA9CtS2HAvYXGyXEwG2tG9KV7OUCy07QBzMvhf5e+3jtJMqqHA2a9d6MvWqw3PUvOl4RLxWgAPa3ffzdjh6wPUrtkwhj+lsHVMN5DLCyjJx7Dvjhc3w0ouVyMPtTi3B1MhYFP0sGJ90eGulmXfXFZCw9RfQ/tewIfP/dzP4d/+S//JX7jN34DV1dX+NN/+k/j7/29v4c/8Af+QBxzOp3wt/7W38I/+2f/DOfzGT/+4z+Of/SP/hG+8IUvfG8de9A78Y1w98DGzyopOXCmkNf2WBWSdFVS28xqYU0JBfpGnBpkgpUu7tcyDLCrB0Ms4TUBqRwv7DhxbgDQs2Kcc7EZjDTQEDsAC8SfwnifpN6KkVApIS/viHr8nWIScTES6eJwF+B1AD2gQPot9+u5FgYCVPk46XdrYwgJzpuVPjeXreuAkCF3qQxk5Qk06SqVAMztqwufWyFUVTgMBLTDBTel9VtsBJArssYLaGRhhgJNf17cX6JwDxqDXMhQezGODHUClq3haJM9G3LBMrMYMqJvkhXMuLfNBcrerlexwALAufXN75rP+DjdRXgF6KTRCjKBKQ3LeH2PzUiGnvGhBFEVYmqJw8LnxmBSzYMqjIfiXoUOGqsQTuuEnBryvGJJBUsuKt09iIuV5sQ//e51XjGR1pCpQrgvSxQqm1PFTVojO8It5buy4A4LvnJ+s9MHARD34EJlt9sh/jaKXu2BUz9HcA9A8R2GhBhbJSvJXqbOxTAX/ghu1ooo034yjtKSlFh9Pa+7c/vm3AyIjBVXXVvlVLs0++v8gN81vcWBPfRUVCnU7yG0QXIPQ9n5POTmIR8IdqBJ9TEM4Jm0+l1dcG+EUweiM1c0I2QG4bMmbJxRtoSyJWyzlkPwQmzfKtdILMALYK4lvA89ZVxwrjnSrgFYWCRhljp4QVrooriKaqYaYacFBS9Ml8Xv2eXhAeC+zrjblt38XWvC7b3OlZxNe2jQaQHM8NmSrkMDiVsqgVYGnwn5XtfAekBkPfYDsVtfRwoAgCGLhTR0vpgVYk4YBzbhMXWPvVycP87Zifucx70J2JWdsGuMnOjY64ZEiB03jgR17sYmVQKdSTmVQxZM3+z8d9tTin6Hq3pNnLSvgGOQWB/G57tt3wM9BPiVX/kV/ORP/iR+7dd+Df/u3/07bNuGv/AX/gLu7+/jmL/5N/8m/s2/+Tf4F//iX+BXfuVX8JWvfAV/6S/9pe+tVwBG+LYboCC5DB4GAxxBnnQrd+B5+IlcRvdSQp3cSn7q59OQ5LAJjrni3jdKLchEu4f96OaGcz31EKk7afw7I98k0Gf4xPpLE0DXrfXLyU8Dcr8AetFNsySqpbs5oNhZ2mMa8tAeWd6jF2McPwzXDg8S+vNm76f0l9J+HHQK93tRZcL9POl+yGExAB79TsOiEeGzcTz8eXiM2AGJaMy+OLnT3PFVdAPU7JJi9VIKbmjDKzrjJZ/whh/wJh3xJt1bXZRTz0Dxsvd0wX0YVqJLXoiPvT83759bwP7sXLp8Me0NVRLVz3bESpjrvuVQzAQQ6ZULdxe9b5xKRFVegJMT/cc1IkYp+F3fh419bCP3hC8eTJSAd87EcF4HLK7bcdkcZF9yTfq5e4n5aQhJeCXXQ97UcxNhIPVyBKnYuSlgbEiR9XLflp3cvoejHHCO75h7jKqM3BQHVim4H32OtODHOMHUQYYAFnpWES6fK6WxZo3UFOqoq3s6WrJ0YNqNC4AoLVDa3lvj/X4EIAduVFfw7Qq09eJ4f0ZaAK6Xchj1O8RAQRSRq/ZTeKcyTRdr5eViMIqE9TXILzJ8FzC+xrAn7Tp8cWrRc/tPTN9GeLw5jB1C56N82iY/7gdj3y84KAEaLvbBR5eVi2OHf3fX/LQ98gPte/J8/NIv/dLu93/6T/8pfvAHfxC//uu/jj/7Z/8s3r17h3/yT/4JfvEXfxF//s//eQDAL/zCL+AP/aE/hF/7tV/Dn/pTf+q7vlbLvB8c3zj8V39wIXuLKMneS9cP37EBEjCI6xB6QYQLPtjcmi4MsKCymuEyZL+ECp5NRMqWqpoERA2VBY0YOOks6IJXfg1FOcSkaZ3SwUXwD7Kh0abX222WLrHO6AXVAC2OZOTLHalqGsAaiYUX9uXjyUEZ9KU/m2jPtiVdpKhvaAAwLUXJpjUhZVVDBRAVO30xD/eigQzJ6NZDeDwuXv4hi+fS7ahxS3eNIDRNZJb9OQJIdL2UKKhEOgkoxsuVB8c3UuJRkQMP7qDIThGhFr9v19B4mU5a4Mx4GyeaMFHFDK8IK3hFmmXyhh8iY+IkE76aXuPUJnySXvQQjRBWyjjVCe/OV6HY6SRMTWcmnIsSOx0wsrmlHzYNA7026fI30xHXaY3KrYCGV1RzQrkDb7drMASvp1N4ORYuoSwKKCBItknvhK/qHJsnYJYuCa6gIOGY5kib9eZeiJm7O97vbSLd8DMpEXSjtEsF1rnHkT7spNjfPH8uBNkiTZkbriblJwX4gES4Yk76DF3V1bkwmm46kF5BwY2ZyLxEMivQ8Jot5s0a05GBvZaKE0+LKCnY+3FuGe/KNY5GCPZznu18L/MZbGmwnu20mox8NU9VE0LKDbIpHPJy8rullkTTdAuhJQ3vvZjOIQ5XZcNNWnFfZ5xKz3Zz8q5n53i/9TMJ4PYinfEinfCKHyJT6rZdRWjMOUSZa6yrQb7NNfpcq3JlmLWwXPWMF7+ZwuaNYKAC6aTvd5t0PaWGXhHdDZ9Kkd3iESHfT8Q9CxjWJPc4uPHpi4RntNia1TNGxmXJvLRglRyY9Fqqx4FeDM44gsIAr31voKb3pARQ61OyPcAydiJ7snVPdximZlgFDvM1tfkaCT3A7msEHm7wxmffIwD5/4nz8e7dOwDARx99BAD49V//dWzbhh/7sR+LY/7gH/yD+L2/9/fiV3/1V58EH+fzGedzFwB6//49AL/poZn1u0Ok6GgzwMiIzEaL1b9XAWGGtAaBgZQ45umfCLt4vQ8gFEw7cjXgY/EBMRU+4bZ/JmQX8nOPaVI0Xpf6gw0NkR5aicnu3xuzOz7UIoYHtep9M2V6En1dfvTYynBil6btudeD6CJ+icfzkhggZ2ULlB9zeeC4GsrTf39UV+FD3x+zaXwYRhAzjtvwOQ0Lx5MtXsB+XrYNqiuc7r/ryqWAKz9qBgijRbl5T6NkamCx4mwW21fL2Eu/26JAYkS7rtNwyQHZvN6QuW1qA9asG9MhbTi3rOqkhNDwmKSDAd3cNTSTuYZ+xyUZcyTMpmHT9818LDYW+h7VQxuMJv7vh9VNw7NhVvgmCYuUIWyh/emWden8msEL55LzcnGdBgqNjCYcuhmZurjaWFjOr1dB8ewgGSfpz8C9GXf1gK2lHQgDOvhwsbXx+fq8iv4JIw1AbdJ4q8nj92wc9YylyHLZrPClCDoPwprzf8bm83dXLuCJsbr8f82C6R6PamuEVkXePyO9zmNPVGT8gEJgbBQjFFswJd4BCkXTfVbL8O77d520/qFGw0+cbzRmpBtvfrytF/5YhPpeJOa5CCDjfd6tV2RAqHtuwiOyswrRM/d8PwCiblnwWAYvCcX9y36t+7QhEMS+AyOo9gSQ4b69H98v8NFaw9/4G38Df+bP/Bn8kT/yRwAAX/3qVzHPM968ebM79gtf+AK++tWvPnmen/u5n8PP/uzPPvpcmPqNhSseWvjM67qkvol7dofYhGGLazWfQOatoKIei21QjhtBRsSx0GN7XtmwgaN0vW/g3mhjZfu616UmCAnqTJq/7Rvg+LAqdS4GDI1PEkg1rl8B8ckkGn9zJOsolqodbwu8vzjuNpPJ5osBmny0e5psXC9E3QAMIaq+wXnFWmeTNyG01TaQSU/OxgXZajIBMfUuOaMgsSBPBYWSVp0cgANtT7wZg3UBszw8X13G/vL4QlK/IPyY/hLHMy5A8EagfJldfy65HgRbeCi8SQ4g60vlv7yeH/B6OuEmK6h2VdAKUqt3U+vXS6pH+IIEn5/usJgaqpISnXfQQrDqLBn/4/TaNnKN77+YzurNKBMe1hm1USjS+uJ/Wqcg50VdHsuCuV9mvJ+u8PFyj5fTCa/zg4Z8eA0gAgAf5fvoz6WctwOCBa5VskWWR0JDnfQ+XcXzvi04twnf3F5gTVqQbW0Zt+uhkxNbB0wO5nRK6L/HMiNTw3lSXsJSuyfC66v4JueVgHWT1+yazTZlJ+KiMYTVW+Rk19rUAr/J550Y27h5OpHVPUWqw6FWvqel+jN2q95DI4vpZ2RuYNvgPN00C+HBfxeXpvfsE8HJpOJ/YL4F0D0+xzaDm8QY3q4LTlvGw3nWMItlL3CqYAPWrZmuh8kQLKngkAqu8xrAx5VWtY/K5RhTlRlqeJxqthRe9bbdr3PPdoPgfMiRCQUoj6SLsh0MwOr43G8zjtuEt3dXUUEbsM1WegidaAi1jF4I34zNyyoCIPvvouvL5QbPojyLoYyD70lKxgTaPHhrm17f+XWxZlhri/QwMKFrRrkXxUBHe+iyBf3L6CTZZsCDVUWbBEhnCm+Ky0JIlp0TxpYt/ZsBFgUX2v8xCkkChO4j6z47eqZdoDIM99rLWnwvAOR/M/j4yZ/8SfyX//Jf8B//43/833oKAMDP/MzP4Mtf/nL8/v79e3zpS1+KLI3Y5EcOwBPxNbFN+HIDjaJwQHe7Wzw/QjPjw6buYYhzD0xmcnQqg9dimCcKCDQ/nHhA4YZCJXnVWPTvjlkTA6IMIrj/0XZWrS2Djqj9eNe9kOE9ugDOUQAPOqG57gsOfcgaaEJRITJS2cyC9jBXq9qvBgZR0w0urmubxjhWPt6E7tV6yiKIjBcE0BydOE40jqHGMC6XzzIGejjn+O9FE3sBA+hIP+cYnunjq9oTrnDqi2voOBCbC7kPdJQ+b4x3dIWFJ2xJs0OOrReN84wZJ7L20ErTUB91L8uoyTJybfqP3oc/x2paCvd1jn6drB+9DsnIdEuR+jturgBUjMu8NhWMJFpMzFN8XZjMwcu57fU+3FXv/CIBAmiFZY1eaIyTRJXUHddjN8ZazGwb+Ai77C371/kQPllqY2ymP/JQp6hbMyqRAuq1cNEtb6PsuoeJJuPGNCFMrqliejCePu3zRmunTFjNv90M0Ix1ap5qLrv/UGfj5+R4/p7ld5kh4nPGx6NYoTbtu3uZOH4Pef+iuixp4AaNG2cdPBaJYce5QbL37FRwJ19L1/qorYuHARhru4UBBDQQdd7KznsN9MxA12xyD8BogPq/IxiJfQHYaXcMXoXwfIan2g7xMLevzSwqcmlE9qh2vnsI/dqR4ts66X1n8A7DHbIM/vmoQeK3MdxrhJUhnUs4tsvPLv/fX69hjB995zu0/03g46d+6qfwb//tv8V/+A//AT/0Qz8Un3/xi1/Euq54+/btzvvxta99DV/84hefPNeyLFiW5dHnLn/tP8JQ1w/BCvvYw/fQgyG9ZjEzwDZXz6KYgOBa0Hg+dx/0zZvP1Df90VXm1vBmpKWC4BcEihRHkSZQA/2uEk+dAIUuWmXpWFQAYt28tW82aR2BDs09L2PtAMkAMnruOQ/vzqjL7+iUFATxkSAT0G6glsDUrMs2bqKENGaNr7rVJAbConCee0kMlWMik05Hd7mip96Fe1SHqqNqGcfQ/uwF5Aw48ja8WARsL9ArDPv8QD9PeFPM7Sv8+A3ZreMOZIy0GtwOB5y2yFCxCpLumSFdGF16erRkiyR8e7s2SW59oAsVTKnimhnf2m5wbhnf2m4AYG/lU898UG2QjCWV4e+Cc9Osk3FzLTWBSFCNUKjjrgq0AHabz9Y0O6Y2xns+4NvrldZfCZ6DbhovknpzdHNMQwpwz065ShuWVPAyn3DgDa/TQxAwAbXKm/FIjnXGJ9vNTuysiW5qqk2ic22tCS3bpkQ9e8VFvZx7cs1rpPhug6drVJodgZ+DmnFzmy17ojRRD8g64TbpGnUyFVEPRYzKr6Ul3JUFDMGr+SFq5kxc8ZrVk/QyPUSYbWzOe1glq6YLCMe6WHVaBT8FA4gxrZBmoMo9Hq658rXzK6wt4W7TujAONlQHg4fQBEG490WEcD5nEAHf5ivk1HDMc4SAJm64ntaoIOvgYM4FEz8GQ2eruaLXb3g5n3GdVedGs7gulF5FORzHNuO+zDjVnnGlVbUp+lmrVdZeGZJJhSKNYPoozGpr7aXRGuKPw0YPV4M2Hp1m0kHXxmGT1474mtDT9Ns8cDsSlFeWVTqdZw0Mtmz93Lhbg5etkHJUxg1eBtCRbOldbG2y67usOsP4JMO+6esoF/1Xhd9k56XRfW+w7nx9HgzuMStTfI/O9Jgu8SntewIfIoKf/umfxr/6V/8K//7f/3v8/t//+3d//+Ef/mFM04Rf/uVfxk/8xE8AAP7rf/2v+G//7b/hR3/0R7+XS4E3Mbc2gmAZlfcAwEMLohMo1pMhNi8s6l6zw/ss9APQpbMDudLeAzBY0Gh7ZB/HCAWydCt4tLwhgFR+mtQaL8ZgWX+gjc6ZuD+zwsOi341LP99ILjVoBqoCFjKlUOlo2/vl5MyLxbk50LAiRIHGHcc1aBpoI4iwuXVHy2W4SUf/DqIGIBJhFnTgQWaZ7ICL94GkE0Htb0L76z0yFi8tn3GsfUzc4rh8Nv7cgPA80BAfT2ho6F6KqBKazjvNC0CtXxcQA3whpvhXMxwQnzl/AnDPRs88UtIkwhPiPAiP8XfCr9j38ah5zP4o3RPCJLjnxcBQi+yX8e+ukFqFQtWyCu+8Ei6+dm4ZDyZWVRrjVKcIgXiWjYf6Rr6Dk22bMM4lB8dh9MyM6bXOf8jcgAacjQcxeoQ8G6gNG2i2Ks6JJQAQQ9A8C0gInrTr3JFMNQTRrtKmBF6rDOthqLgPaEaHpthygA6XFz9LxrHNeLddDc9lv0v5WLyvB7guRpMuo+/PxYm0ZRDx841Y39PHFspl9s/YXF1257EavG2ALa9D2Axwb133Tp2ke4o83fzymt7/nJpm1ISIGJsXmiCVUQfl6Qi1XOzsl5zB6Kh7A6r2K46iYS245HcEwOnDBvT1ebecCyKM7YaMP4OoYI7RgKUwaMXP6SezfVG8f8Mdkn23+TovBGpDJiMNoxH7oO4FsYcB4RlVb7wpTo19IEQEwj0tQQX4Ltv3BD5+8id/Er/4i7+If/2v/zVevnwZPI7Xr1/j6uoKr1+/xl//638dX/7yl/HRRx/h1atX+Omf/mn86I/+6PeU6QIA6VzBBfZDaBbGABCD4ixoR2qPUBcDbfJgGLqLXAiRHeEx+8G13wvD2aJtL6xP3AAK2WqrVFg8D6o1gY4QPZYnl+41bzKgU2+jS8/6pJ/vf1cXomgccIjPkcUdqXQwUQ8CcK+e2ya9r5Swe0Eii0agCqS1u+Zdjl4KQwr1ir0OCsMlqGCsZlaUz62DmKlGFgwBGvOstGOQg4DGHp4iYLNn4Atdkt3MJSCkhp2PEYsFCyTbi+Fqfg290N4AcNAvESvIKCgmBjYiVOQVlv3SqacyXpJMM1d8lO9xzWd8lO9MIKqEyNQ2O1dAY+FebOzr66uezSCMB/FsjhbiXp4VMYIPvaaSndeiG62CQBUlg00dkZ5mOaZjalgj4bypkNS5ZAjUI5NYLVgApptRMaeCmTRdt5kXYG0Zt8MYjLLlY9jEuRcuqw4AS1aPgadvzvzYU3BuGcd1wmWr5rZ3AufoNTrkDQ9t3nFhtsrhGUrcx2LmgiUlbNmE1czLon3vRQN9s2cSvJmPuEobXuUTXqQzPpfvccNnXA9VdTfJWCXhaJouLiZ2rPMuTLdJwifrNb56/wpLKriZzvH3qgS4mBtfPb0KqX0AoUMC6IZ/yCYxb14vsfAwkXnCROcvAQb47H0YNmt/9u7Rc90QB26eURXCZja/xnOMoSIn317+zQG3A5wpVeWgTAWlsvJWKqMWd0lANYN8F3arPApJinoYGnq4G+hGjodPiyuRQjlybsiElW//euaHeZ7d2BHbD6QNtq4vA42AgqhZFnIPDAgk6nURDV4YUi/KeF1qxvGQ3n/xe4CBlg3Kf3FgUEwDKw1ZNMNe4uFjsXtQ9EF9z2QgeOeCUEcdq/s675BGnt13aN8T+PjH//gfAwD+3J/7c7vPf+EXfgF/9a/+VQDA3//7fx/MjJ/4iZ/YiYx9r02SunBCDnbwMkS2hyAetAyDCSAGd4dOh4HbHet/31nc9BjFDQQm1QXp1jC1zvPYn5vUtHQ07iIywMA3sWMDSGGw2se+2Xd5iAFGX72a4cW9mFuEDHnHy+hj5BtvMdJQs411CPW0yppJHC+MgLIdO+Sn7zJ3/H3lZsPUKwsA3UsQ/fSxsvPH556Ka+MY4S1CuBc9lKa3Sibu1jOP3HMR8waDgeTjJbS3LsgtEelhmot+OmkrPrJxcPn5UTAJGBdXdzcvAUBGq11TNc+YpKJNar2/KypV3qq6/3UDHKp8wlMZ+wbyIcXTkTh8WX3Yz+MFwDhLWPX+t57u2jBn1bi4yavJquvqs+e6qD6EFvvqhck2UVVVB2oPZYoNDMCTq1MbTDcm9Up0xdduOXtfISp37gqgQCerukU9pYZKQKm8OyZqurjXoOTI7Dmkohk/w/gxBC+n0y4F1wsK9vo33cPhkvm+2d7VRdVwPcsGCi6XVAJQ6ryimFk9s0jBknuM3DN2KhNOQlrIcQh/Oi9itxyYJSui52fz/EwWhvL5/WBS7F5ywT1HQvKozsrl/Ftbxqk23JcFZ8448aTqr1FJWUHixlbTxYrgjTo1tV6c19e58MDS8GKir71MPSTra59QGCHBxbgwHHYAwjZ6AN0TP3TFM00aRMMeg7HiG5VUQNa0M4jGDJhY931NM69DGIVD/4H+ua7bAkx2n9zLgFyGdYQQNAa/RxJEnSzfp+Jvwzr5SK+qYVfB97tt33PY5Tu1w+GAn//5n8fP//zPfy+nftRaZsvFhuYmB+FzGAgg3FONdeB32hjjpgIEHyAQ20gUatT1QmR/HUmODmGDS31C+djYOR6RknymNCjbkpsRj6Bhi8axMbokbwCnHfEJA1I3QOGbvWBAKfodAXRy+aQNZIzuGXB3fENwF6SSMsI9xUqgMeIhC4U9JgpE3JIG74XYS+bAoyvCXkoIS2QhxUfVXjbqVYzbJB1Z1x5iS+Z+hNe0EtcxMW8SWUaKK/7Z+Z2ZLp4BNYCIPgH7C+qeKXcv+niyqf+B9O9FAFfA1IJYnXDqREEAsSGd27QrLDcWcHtluhk3fMYqTj6drVpoCm+HC3Tt3dq66Fx6X3rWkk6a0a2+s04hmFNVIa6k5Ei3pB2UAOpZuUmrWfoPoX3Rh1BDH5c8lisrS/9QZ2zGxRhTMlezzpsQJqsx8qHmREdPtXX9DN/4XXPjOp13bn3PEsqmRwHswQfQQwuJG87bhLUkXE0KYlyM7SafI+V41PvQ+21KEKaEI2Yc69Kf4cDNcQLn+7Lo5lz2EvBe62YUFEs2oVXgrYVq68t8DoDkqrKuRDumqeoJO/EYACTVSNdsrFlpsym+6phZZtTWeRgKPMyDgkGszC8xVM/1bKwmFLotEzW8mY6YphrAw39K03m+1RS8nCqEVlPX5oh12I0gW3PZ/t/vOcn+99Bq0o81UYXA8kQK7lOGKAEyXS4asLVa16DqwMM5g5ahAlGl1eCYeH98v6Bu8Iz7ULzOBA3zjvuCgw/T+Ohh9riFGCLy/dG0QeIaTQ06zewkO1/fB3ydrBcRBqp9L3rC1vlg+8zWdlEiJzpZ5mIQAxU6uvM/Xs4HurC5HXDEPm1ZKSOYuBjBnTcBHaR6qMQf4A6JWrjFN6+YgGNfBxAVbrURpV4ym0ewIR0g0HBO74ftL0GIDfDmqH2w5oXVwwQgQjfhSiyshLQ0YC2K2rkXqH/oLIkJ/+iP5+BX5764VUTQFy8RIDJ4fnQgJEu4IMfnS6J9poShno30ufKEARPPym89xqKPKWR3Sz0G6+fy+cYGkqxDft4mhGPReiY8WA8VWkq9QjfYu3rAu2LFyahnakQpeD4Hl8EBzF4+nLDVCaeqImPbbjPT6xar0FqNYOgp0CISOgK1MogQG34/h4ZnnFx5nRUw9I2IQm49cw3CJIDhXtrOq+PaH16p1wWl3m7XKC1hSjX0KEQINe05HOO9AQpmfHO8rPjqGhxVeMc18T562KUM3iO39ONcojwPTyWdkvJKVD68Br/Fr9uMi+OS8f7c3RPi8u4AIhvKnz2Andy8k3kdOGh/lNNxrAsadx7NgTd8NB9RWoo+PdQpgMxW1bskRKgCBQNOvhyMglAnFgUg5y3Hhg8Aa8nh1Rg9KK4tM/4e2XCw940V3J1LRqYWAnUvkqYv+7hVqAfori7xLMXOa683mBuaop1H+iy9OrVd2KfL5Vrg3/NN2V7yZjmjj5wfT+n8hEE4/s2BnPRwdP84/j8KVtrXI1141DGhYY0XWH2uYU0Kb8TwM6x/u+b7W5K+rzre8bATEKCOAEvJNUMuD9e3pcIBB59U5oILIL8TCsu1TBZyGXaDAeHB/he8f0B4PA86ogRCuU3RKWKz9b+N3w8Pb7uYjIEye9/8AcZ1HAnGG9MBQJxD9hOd2njR4TbinMN34/6sAN0lQpa+Qe8Y2s1gw8UY7lRRPc7ZoPHCTCoIhnhXDaz1hyEkuxQ42GbMLOYyZrQGtJp2ufp+bNS7sWcSL7VZNRFCCVIZom4Lm1syOhiKpbsumrDS+PI6Ubi7EcmF5MabBQIAjVaRNOgb1LB7Zscyg0kJpkAn453bhA0ZExe8K1d4W65xrqo54V4Bz2L5eL7DQqoc2mtqqIWNBhRKBnRmHLcZp5JxyCXi4wzNYBkl8HkAhS6pDRithnTDd/c+k2CWCs5bkGX1nhSEFNtol6GgmaYQE1JSmu3BZORfpyMSNVxz18aoINzWKxyb1u54qFP3QJQESbrpjenDl40huMpbhAXGzzmIWt2zMAp4OV/B00oBBxcuDNcMvClxk6jzHTI7sGoh2d6Jwhpm+mS7CY7LwiU0X/R3lcpXFdSKydRKfVxc/+W2HIwHs2itGwvD+Ma8kM6VhQo+P931DCBJeLdd4cHAaREehP8o1ECbLQzOVdoRTEX5ITo+Ooe2kiJjhuBePjVC6sCb2QMPJzkbR6kmXE0Kap0bk0hBnfd/rFED4FEZB04NqIzqRdekW6fUSHlksWjau+rrIGALo59M+r9NvapU0Dl643l2666vH6JrQPzdFg5f832dv9i7AoCEQTnwGIHBWwKg2Rpvyxy5AWQepyDi9xegr6c+NP7ZDpiIjlUDZKVYs8P7T/YsLesPDpJ8fbaszbQCfNZ/6+8E8EFVQwGtEnjtAwgMg5n6YEbJeHriB4jNafcAhr+NCqm+GanglE7kHe/EN8tL95xfB9jxSlykDBWAk7ucHeyH0fgdf5F2v3Zw5S5Pk/SGDJiFOooW4JGinm/MI8YRRnchDpsoibslh80LutBIJcg57Qi7ERKxeCNzCwvJyap5ql0rpJmuYYORt0jHoFin6PEYC+n5/T0fI1syo2u9DOMCC8GNIZudl8bvW4YxT9hZGFSG+K1do02y84r5vV7nFdd5DfCx2uebJLxIJ3yU7vE6PRjvIUeGw6hv4DH7r22vgjcxEk+9hoemha6YUg35dNfGqKav4GEYv03mhpSAWvx5EoCkno6UQQBmrlgBFCPCHdIW3AdPM60gbM5BsU3WvSBeTn6iitfpGMfAgICWju9hCL/nOVUc5i02S88yccvam276HSi5oNdYqMzDHGnII1R5cBVOezGpd+lUcmxwKpyVI5SRqGFONbwjY3aPpx8D2KXcemryzC2k9Z3X4EDjUhW1T3BWUMGCwlqccDUA4WMQoQnuYSb36jzUCUUS7ovK4Wcv/kiCanPClXCJBOTeW9HsNAiieOaUK3KqOEzm7RpAgHsiYN41AAPAQZc5h65H25aQEqGmquTilnAltAvTeRsFzc7xbDqgSeYRa02LhArXLi52TiZC6IvmaESiL5Ix3uiGpLdxbXDQMPBIwsgUgJjQIqYNC10QukQDAohQblHUtAGK+ovsDNCdseNUAkcBHt7wkLdrljgocbztGUADANEuko3HoBRuCQbNi8+NAMnHB+gJAd43XyvbxTg/bSc82T674KNJEFmoGgqrugEQm3pbEDMtXclR2piDPIIAD3VQ/x5wATx88G2TjxALIZjAly64p9K3RoIsBsE0Xnm/yV1a+YZ0+46K/YQc+uSnp+FSwHA+DN8dwgPxd/8yyQ7IxYvVNAXskcx61dQ2FBrqBlBIpjRWF27UhfFuEZCzWubbllHR+jgXu69kC2CxF8W9Uz6xHVzQwCy367Sxlov0W5QEeBqYh5RG3tD+5obn4i/vwPOIrCQa3ZfauWxW+sHqgLiYVIG6vbeWkJLgZXqIYmP6WNmq2ab497YdcG4T3pcDiqTwkJQLkaxMFZx0077f5qjj4rH4sECHW0yk7w95FlJjVDS0llByRU4qLT4DOEsnjs7GJdAicghS5FMFwBrpZj1xDQvfWwWbRHyOTdO9HJkbptx5JZGN8sQ7pnOqS9i7qqpfw8W2AN3QtJR9DQnyg/FZmA4Qs+KjAKBN+khTtVDQueQAKaOA2PhMDmnDx8s9rtKG1/mh805YCwS6lPhliq2eS8MoaDDyaAvg4cXWnFCaINiAXWbPQ51xNsCiQm8WUoEDBkuB936T9CJtJjnAcwWnhtnBRy67+6sGODQ8pt/vzwOmoIudB8QVVZuB49IUbO+E64bGJCGpfpk14+DDwTOzoDFBmNBWfszXA3q9L2+DgSdsx4xedmAAFPu518O8usFTouHYDj5g3hYvhEnJQ9ACTFDBQeL9pu1G0G4LsFRXoIdofN2m8Tu2dlbstJ3ifpsaTgQKD0vsQ27QDYab8gr7Pfu1HHTsSKhPv56f2j6z4COdG9IK83AQWhbI1Ad/FycD9bRHGn6wR6l2MADsOB4RdvGUWxtY9mySRppGe2hqRbuXQKApXCPTd3x4QA+3XICFnSvMEe4TC+wQMYoXZgQ/ToSVhM7k3l3frnm52VpIKyI9DsD8u/FDqrHSutfAj3GiUdQycG9U0hdNc/MrEgu20kljbbRi/YVpw7VpABIkUdJ51CqJF731z3bhM8IeNHloiQaQ9sQLP44rKoFtfLnoHIQYDyX38dI03G5NXqUNL9IZ17wGGTRzs8/OATwOvGG2yfOSHwD0ui+37QonUULquU349nat4KSoy31rCRkNnARNWtRzif6TgAloMAVb0YKARLp4iyBE4NAw8HN6xVwAkWILGNjhaoRO3QBd3OsqbWGRjxobW034mrzaVS4F1Av0vhx23pyuq9E3pMQNJL1EfLtY6CJTps6xKXs7VvWquOdhVBLtJFcOjgmRpkl7MTvNIKngSXBnoY+HbcL9OmmIhhuupy28I5kqXi8nzFzwZnpQTRcPrZhqqYNL1/JoINzWg4KLgZwcqrfCOKQNK2U8FA3P+YYddWyo4Tqp8NcDTZiI8PFyH/PkSBPu1i7kWEtCfchd3ycmDXa/z7lgTjUIr6WpJ+bBhMO88m2tPdziJ6pWhNJJ5ylXpNSwTAVX0xZcoeiTefKquHhdihCXp4yftgy04XlxU++p6X1I5S4MNmQmjuHxmB62VkRtKRd+ZIA2LXvRZtuUXXhxEFQcDcDwCozAwTwNLTdgauDcQsZebA4Ti3qcPWtwaCEuaSAn0oHd20DdkPLjJIj3wyMNo6sbupGYMQII84qEN970SHZrpidVNCvx4X01gU/h30aRse9n49q616MATNQ3pNGbsLP2h9+9DVa8+JN56jjqJwv3kz244CHYD2VbHBtBquwqn8aEHAB9qOo5WoXlThsACU8MLgBI338ft+EwByjOTH4E8Hl4U+w2A3z5DBgNkNhUDey4S/NiccLwUoQEvXkMyFIgE4uFBIyl/sStXH7ooZUdaBwtl76uDAPePw9L4uIZ97DZ8MfBcojffU1uUDVVm4dMlhHVVE1xHIcxi8fLy/tG3KW1S5AEE7UIHczD4B+sANpMNSqhHknLrHMTlGQZAOZNYQgKeFfJVqBAQb0CZnk2q+dBvZhYZL34UHzAZxqZKLYx7CXDNXb/Oj1ESEHVRVNY460SHjDtQE0TwkObd9of3i5ThD/E+dj10bwcR+paFw5qgD1ZN0TZoJ4b9xJl4z54OMVF01gaEk8gEpRq1njTqs1zqmGJMwluBlGxFOfpFX5dudRl6SsYt+UQ6bU+fwArFgcNgV2WqfdUa783hmDy1N+QcGc8YMLKe0VbEVIulwte2aZIaZ+NlswTFZlO3LTPFs6r5kVxDtcI9pvo+ZOlrjIrVyRzr0zs4m/10mtm9+aZPBF+M9uw6w7ZEmHAQxrF5uite3r382XUqAieCCQ8BztjbVwjRiPJzvGU88YBSHizCXEf/SA7BwY1bP+Tv6PmrQ/P8uDpdcwQBq2F9i8ByFieZFR4jX1u2E89ZBOeDgcsSWvciE8XDwsBnVM4hry/i/aZBR/lkNFmWLE1oM5amEeGiUSVgILOAAYeWfxuJXvWhMfuadyAAbVoyXQ6SgcdAmXxNrueAF3Zs1BnVwO7zZmtlHPzOe0hm0mfeCBJU1jlC7IraJgc/pFfZwQusr9PuJNOOuodZ8RlATmBeRbO+5di946cGNgYNTcLC6Hv8NTRuAswgRltsxh1gcWcgZwq1pJ3nA9kfZvqcO14jgOwHIGVe7icg5FWCgA4zo84ByG8F/q34VjjwNB4zmHsuPTfg+19AWxcgj0PL/alxgeA4EF8q7wIMTGXyvY02wOvITwGAG/SETesZdKPbca3txstby/GB2g9LTVxw5JLZGcU21QB82wkjf+Xsvc8aaaHplkmVuv/Km84pA0vZ+VQ+CZ0lTZMXPEmH7GwEmJdOt03z2tWMayP8n238q0WjJJm1ZLeBh4L0w3ebwe4SJuHW7yNYY7+Ge+EyYBeJde9TQs/qMcpnR9Jq99tC+7WBWfL9MnJvB1JtUsmrgGOXH0V8waxc2du+NxyxJwqXk8PJp1/NhCgBEonj55bjuyWCP9Y5tDrpF6vs3FTRg/RbT1o2EUYp6qA5a4syicaHqOCWeBNPva6MJbGvBpPJUIXJJ3QaHPALRhBr1nzsE0qu79NEX4SISy5xLu9SUbZulKha/1JpLh2sOCy7iK9ErHPi7E5kHo5n7C0HPVjjucJtTKKZ2YJdeBRKKTKZepyCiGa1XQj33mHva+emeJGagKayJDab0BgIKk218gYN+zR4GRo6HkjhYa5acZg6B5ZX91b6yJpOgC9eKkAJGzebTHvQgc9bhh1Th9MKgEdHCTfb54wTDPg+1F4c8QlK1SwzMNS3r82CzD1Pbd7hnW//m7bZxZ8uJtICOoevCB8xkP3AY1a9/Z9GMC4zEk2NKqHP0acuz7YOXTTkz453PUl9ChDRr9IcOtZr0cGEqyPARRIkapgcN0N1v1o2V+c/jKM82Tnxfs+fG/srPR/dwJhjC4ABigxykILu5DMYBG4PgCZC1HMIqpEo+K9nl+oO67InmvadXJ/vJOuyjgm9sLEwoJO9PIX2h+Z99GBg7WoxDiMxe73CzC4G7Phs/AuWXx9VPG8XFg1JTXj2BacJEd10sUqv95I0ri/cReSETuVz6Cqn5PsS9gDCK+Cez/Ggl0+lTTLBbsYfRQH9Gdi3/Pz+SbcuR7qvdFNtuxSgkFdHZShq+KCDcc2YxIFXkya5ZHQUNmzGw64M+Kq93/s31MeGed01Itw0xiy8EJu7pFp0DDjmIrsVXNjLOLeFWwxCBu61HozI8V/d0+Jj8soeOZhj7MVpWtMUZhuDEP5vwd5XMjNpfRHj9G5aWZQsrzIcS5MXMGi3jD37LSnVojx/SaJdW6c4ApcE8TvNTwzzd5dTZsP8cLLd5cknp/Yu+eKteP9Xd4Dm2dEs58KVmPFtsZaV2pcq8wrizpKlIuCHpLwTsAzU+KL3kd0MDbWLUkUVau7J1iP91BEPxftPSUY1prWw0BqIIwHDT/DWv24/gxive21WiTuN+7bjcbBeNwZkcMzcoN1lKHw7/p9jvfclZwHB4BfC5a+zxeh+e/QPrvgg9XNr5kY6MRMs0i92BiA2MjDggVsYG1T9U3GY1aQCMM4X2S0ImTSvObQkBgeJgRdb4NMfAqk5MsanQkCT7zPFgMUc7MhNUO+UK+BcxeAmMy7PO5x0o/gy64FwAriDQAB2JE1SRB1AZq79ZrJsLuAmo2xJARfRhaLTTrS9bLzPpGN0MmbLgY1CVAY25rRctttIM60ryVpKMf76q67cVG4AAAkCNEwrecj/R7dSwGoOq71i+xlkQRUFnAZ3JcCdT9TH8tmaXMBfieJsd69dBdriHMomIDb0uPrLoCFBny73OBFOuEmq8T6RJ3Id1uV4/HJdoOtPX4t91oZuhl50TJvpzqpFHpNkT5KNGS6kCAZ6S3UWCtBalIeQNFYumt+XE8Jh1Qwp4LPL/ehy+B92VrGJ+1FXP/YZtzVJTbWz033eMknvElHeDE1poYbS7l1ifGvWKbMbARdakqgTdzU22DNVWNdL8NJqhNX3KQzXqQzXudjhDe8PsrWMsAIEauX6YQEDZksueDF4YxSEx5Mqv24zCjMKDa2o6jaqWSsJWElHc+tqj7JsUyPCvFNXHHFK655xeenO7xIJ7zkk3k+yqNsl6MpoepnqoMCaBjCCaenoaT9mXOELs5tigJ0gMmrCw2hnIbVwHFKDbL0VOSwaDd1i8qkoY6cdIyuBl5LaeptcmVTIijvqKoXIjzBBIAIjQVoSTNtWHBMeu6HwwSG4K4spozbRdrcK/Tt9QqnOsX1mBsqUYRYvC4XDQKIut7t1/MAD6LGHgSglfrxSeI9Fw9LEtSDML7oVk07vCkAUAnJ150LTpqQrauV0FLSrLKrEryqFt4nMkFIifWQzmx9xgA8fFxHEDWMt4dWBo9EhH3GsRBo2B+drxdAwv+18DJZOCr0tnb11WwIvQ/fY/vMgo9wfY8b0OXvwM4iHS1Yj8OHGx19M+/AA3rCS1Q8dsM2711YZ4dyrBsDGPDvPTI4hokUfX/CKHk0DtJvy8HMbhwG5Hx5/OPzdfAFYJdBA0F3sRmfw7/ypMiOIWf3QAT50/rRRL0e7u59klPwhKfj8WXo4nfEc7Qh2gm8uidGFxPsxjg0YYbjxxd890zoYtyf6EP8LXgUn/4W+gaohNM1Pt8kAw04Y0Izl727pgFg2hWlwAezPwAMeg1xG9C4u/8dAFTrw2PL43c962Mz0h+3LsSl12VsA53ePTwPdVI3P2Vklxi3+3Xew0w17mUm4DSky44/4AY0RjNX/4eaH++A50AbKlmFXequbO8HMAA49Fo81azz2vp9uoVehjRcn8vV5thq6psTz6FT4t4QALhJEtV2r3kN4LGrait7WXgHWF6191xVEVX74fOCwaLeJgCRCeUer9m0dTI1ZKo7vRRNsZWY1BL/eXpsE3XJ9njmMsjgu+eE6NF7Io3ifRLSFNxIa8bjEgR+3cvrPDnf/R0cyKW79dUvCuw91I00BOt/9hB7eKYv1jvp77aqL6MbgI/WDnnsoTfjE1ZxV3aW7lP3NYAC/2jcT8Kt8eRXYyjQtNTEjnjv+4NEd2Pdjz5jf7x7b7wC+6O98mLv+W7bZxZ8pHNFfrCbZdJCaIvdYzYWrvEPYqBFrW+IoVaPjwFm3asSm34A4z+YBU00gBM75tJnNUwwnSDUs2M26tkxUAtaUneRjehbxqJ2Q560u8XEYorejzFn3X/fgV+Pb3rmzAhKLpnZfo3LUqYeuhjih5IttumuS78O0F+4Zql2xqcg6DmkEtqatNCeEXRH4aGUK5oV8ZJKnbS7Azl7RK0lsS20I0YGFagYnbshJR5PkGBHEbo2AZhbZ477i8PdQwK7B59XIImS0zpGBN4oxkyzfPTviRs+mo94mU+q5QECcMCBN/zg9B5v0hFfzG8t46WECmjNb1VjQjJWaKrtfVvw/zr/IE5twrtyFSGN+7rg1kSYuu6GaNl5A3lEPYVymR4r/5TKxr8RSKrIuSElrePhruGtprCgv3F6EZa9k0xzEBvJvBHKqXioVlSuHJCp4QfmW1ynFZ/L9wYQ1tDFGFVHD6ZU6vdSmYHSOR2b7DetkZsSYZhhYd8k4dQmvJ4e8Dod1dtAglpe4tQmq83Ss3tS2teKAVQw7qFMuFtnnLe+XDoIWYVAxOEByVeqgArW8MHn8tGyXs5oQrhvC5ocrH97jZdTm0KzozTGQ5vxfjvg7XoVMuMAAtgUSchwldSGlAQfWcXk67SiCuN9OeB9ucLaVC/jSIJsmRfbmq1wpL2XuYMnwHRiuOFctXJwbYy1JZxKxv15xsNx6Q7WJKBcgmjqabuy4+00bFtCmTjuwz1pzUz7CiUKK5eHQ+dlLZZVIwRKyhOLTTzJrjglkWU/DlY6bYOnvKl2lHvQ2wTUBs1uWVpfK73opW/Qxo8YvR6+TghDs+HcO2B7BRWTAzDNqIasUgSenem8QQvxaL2q/freJukcEHQgtQNUvgf6PmUZKVKpiz/6HkGI9cs/1/Vc9udqmvEpDpxsP9W6a/1Y3rTfXIH2O0JkbCDTUDWAP6QfRTiBHoPdaKMl3DoJcxdCsWvtvBHRCT1gFJyKzarBXHh9Ul9a4DsPhAx/GK4tJEM4xj4byEI+seMUw+ligro3I/o93IJvsAaC4qjL++QeAiHzjPS+D8d+CNmSFWWy05HzPmTPJXBuwad6PMbnQ0BkARkAEuzJXTs3J9BDOT7mw8scQGw43sNnQgjiGeq+fwFo6ANzzZoXZTuwpnd6Sfdes0K9AaMceUO3+A8omFDRiJFY8MIUTs8tm0Xfr67y4EkrjA4DolLaYiBCdqmr/gz24m+qejmlqtoORlzNpGDE007H8y+8BXnSMzVUz6QBUJ5CFQKDcGxarVWJhAVnUvl5J+B6qMG/G/cg1vf22Dr2cMPIhXBvQfQTg0fEODV6LEcW0s4bAAWPmWpkD2WqYOTQy/jQOF62MaOmDqCoPnF42lk8/V5UIl09H+5luORvKIFWAU9C38wvxbucMEp2r/KB4LxuXl2jpzZGNU9Uow/MfLGUWpYIHzdBz/YABuVMiv5MXB/1c7y/S74KueFHUCl4q8MiycUWqa/RzZQxxrVkWANwuQZ9p9AB9cN25xk+cwEw8boyg8EnQoiU2nFfGPkyDijQr7Ffp4f7eOrv3hnft+Kz/XG+/u28xcOxH/ReuDHGww2MHo9P++4T7TMMPgT5BIAE9UAheAlAwyQ7BdH+PcmX50G4jagoSmvZGLu+kTi3wVjSXjU2PArmborsFPOi0Cjjy4KW+/UceYw8EDChTW33EoIAWJ50xOf8Pt2z4v2ICWTVXYeUMrfkI+4IwxTG56gHGENbOgJG70ed+3eoAbTtJcex2dgSopR0z0PX84TXoOgYoRDaxtioW6/NM4X8HfP/vyRieQqgWVCovc9kKE8ckHozF6MLzpFxUJo/C1sMRxn8iGW61UImWOf8oNafH3l82MHL3J+LJGAyxdkX6YzX6QEv0imsby0gt6IJ4307GOF0sqJyFTd8xkQFB9qQIKoHIhv+4PIVnGTCN/Ir3NYrfG17hXPT+hgFWvcjc0MGgqDnqarjxhwzRwjngUdBJAE8lqng9XLCq/mEm7yGhgeg1mgi1fW45hW/a36HifRayvU44LYecF8WTLmXjweAd9sVqhC+SS9i08lU8bnpqCmXwU9QcLUOfJWZKxp7iqbezwiE/NjzEzyZF+mE35WP+Djd4ZrPkWF0Ww+4M17OmDZ8NW+4mja8mM7h3XmoEw6pYMkFD9MUIm4OBjyccchlp3hamoahvrm9wDWvOPKiKqdRSNCIsKQgrYFxzyvOli7QLOx1qhOOW08hKI1xbgkTkZqapK/h56e7EDJjksiy+cb6ovNAuOF6WXF3WrAZv0M3IduszAuSkiDnarLq2pcp1aiYe8hFQcmivK5SGMzKefL3eEOyiJDuSDwAYCJNSb4aC/6N5FNScnMxTQ9As7CYPT6iobhGTY2mJFqDKlGom/IGUOOuhWHv6MjdkiRo7k13zZMxpLFfIrvxOuzkYms2VWC6J9QZ8Mq2Yus/FYqQDZleEcJLS4OVSXuO4bhOX+iAAOZpGRpdroU0rmF2n4NH3g189/z6Pfr/eMRhd6+xlu7B2qfZkh9qn1nwEfEyJwc2Aw5iZNBhIxlJM9K/2r0jjkbTEA55IsblEwmtb7y7CeLsYp+Yj+IZ/XpxG80/7PVG+gWH//cXxDwhfu/xsgg8vNvvc0SgXudgPL29cB6CiHimA53o3x7Fh8XgP45qnRsTqHe4B+qgLcIRTd2utXSCo1gKcvS1XT4AGFlhbxGoIeHX7y+sVujdAf7oGtn/7IyrEbEDiBiVWzNDqMc9a3Z73aMy9vcJqO/ZEgkNXmEVAE4yA1jBMmE1UFJJ009PMiGhhQLmDesG7l4B95q4JT9umlqZVfUoMvEjnYwx3OW1ShJb6iTQVRf9kQzf9UwWNjfri3QO3sJ4nTqERADPWtH7nk1zYsyaccGv3XelEyQ9HfOpWP9l/5y74dwPP+fI89DxnyLt9aHNweXwlgaQk63qauaKBSq5PjHjjPxobMc13z1RcU4o8fTaROdcXM49YGNNk5WUGDs15Y/4eI3j3IRU84OVvJ2pAaz36x6WKth5gcbvjkqho4eD0DlCITTHDUx4EsSyf5esIB11Ofwdx4AGr8RuzLxPQ1gGCrhK8xBen7fxvWYpu258fSdL2wHDU44ecmNNhvUIjzhmj74WYflhC2CYaNvwmXuD3XA24BHnH9cT6evYo3X6Yn0ePQ3A43uL7tuxoz17ef/jOPWN0/8u+3M99T10UPa9ts8s+GgTw4uIscWTZAXKgYBrIzpWWDU96um4lnIuxlUI2VgDLG2yz8eKtB7fN69AcOlc8tsedqxjgojzUzXxFVjiLmMHXJJ5CSTprOyFhIabfeIFcE0Sl5YH0K1sy87ZzdsqGsfzyW8WupeicBb2yDHxjVm9OAgAtLvWoBrqhdlG4NCRsh3rL695ieTMaNtwTZdkt1iqx0c9Y0fjtoNnywAmrHQ9GTcnLAgaxm1gnVP/evBRRu9R96L1foWlIZ4yJpH1QwXdS+a3bLFQYg2dAAiCXmwsFm7YWsY7XOFME06yGb9jwtEUL73Cq4uQ/eD0PjYqDxd4psZ5sGRnqTjkLerIAFoTQ6uhurR4D5v4oj6GHABEKftT6qbOISXlEpg660QVr/MxwI9qdPQN3VNKAQUfN1m/82rW4mELlUchgVObcFcXPLQZa0so9mKuLWGtqT/aJ0IvgApwHXJXlPVsl00SFt4wU7GKwhM+KS9wWw/4xvoS92WOasBO0kzmlZlZuTieUZRIcE7ZLHSt9hoKsjaES6poRFhXFWG7yhuYGl7kMz6f7/CF6S1e8cmK6xnPx+7uvi3YkDrRNGnY6j4tOOeMRNcAlINzKhPe8yG8LIdUsKQSxebOTRVyvWaOZ964F+FhndBMk8fBh4PPsmm205wrpqyy6pfkT58rkUVj/JE4R2HlgA3hFQA7VeOQZx9CbmNa8vuy4FSnR0TjWll/Tu5iRd/AB0AQ6zNL9wj7OuEbtxlmbbYN1gw5Xoc0dPfkuBFk7TLTMjIbF3SeWJJI95UE8EpI1cJRQ/JCpLKaB7YtewuKHOA45QAIr6y/RpqhZwCNsdMyIV+HR+5hyFMgPCNR1mK8PKuhv1szfexob9yxrfHfiwfkMws+0AR1UrdPyzBkiR6b9+YbxhBu0M8lEKlOOndDYQ8Q/Bz+/2Ey98H2QR4N3dHpAUYAmu6a6mTIEQzE+UeU6Ru2M65tsvvEiweO3rd4YaLPQ4613/vuHqV/z78zoOkxnBsekyyDLK/swYn1e/QIjTs+CSCFQGns9/6l8H73FxwBBnasan8Bx0GjjrZ3XixXAhzGzf82Ahq5ENyJ69T9SxjPfABZT3mYxhZWqFn0bt0e6wIkYJISxdfa4CFwb0CkskonGfrGdGqTfperWsF27Kjr0cvPt51l6c3j/0l6BkdONT53S7sI431ZMNGEipOm9ta6659Lg58NgEQ/SCXPPV01s1roLILNdBv8fm7LAXdlxrHMQax0r4Rno3w3KqcObJrtRAmtp66Kknl1U04h2FUaRzgH6J6FlFzO3TJ9oOMRxzVGGTbYramWbGZTPjUA01VVGaskoC32PK2ooCgw2iThk/IigOixzsH5GD1CT2qe2EZ+alNIeHvF3UwNHgRjEuSk86bWp8Gcj4EIPfJ0hLctVWyNo1p1FKnzdYSG9O4IGyA8h7UxjmXGwjXSiQGdDyNheMf/EAVJ0gQteVkAQmTluTc0Omz/evoq9NpeQ2rnfR0NGPRjg0vnYdoRvPgxuHj/R7DgIf/2+LhHr6Vgf+5dH3z5FFv3aN//4eTUxAy1/ee7/g3ebvXUj/0a1shLQ+1i6jkAcd7NU973T2ufWfBBApRrQj0AdQEkA3UZBsaOG8XI/HsuHoVLFGi56CPS3TEIxUwZfxgNe5BxcbjH51qW7o0YYmdajviJRVMwgCJE6EgSrET7EL7wSUyAXKjHdTEtFUu71KIYvToanpA9YBFPk3WSFGKT1myh4Z7ivP2N45Wi+rAAMW5ipM100voIva8U52dfOFhMnMzGonUZfGLzgBi61jEzhM1qTei97cFYeLLsOK9jIxkdu4g+WBmfgy1O8T3YYt+UOxMaIOM4oI+3bwzqBVA+hy+mXsukwQXDNO4fZFAz5npp9oRNMj4pN5EBoYepFTsbF6Ong6bBld03ji5YRXH8mGJKUN70ktSDkAN8ME5F+SlNCG+WGVdpw3nKcb2HOuFb55u4jlaC9c0WeBCtn/IyZ2SuuEp5JwJ2XxacW8bdtuBUM+5WrZ9y3jKy1U7xMb0Mr3yoJTQkQtRS2SThZC/Ou3KNu7rgbls0i8W4FIsVTlM+B/didElCO2OtCop8HNeStKqqhRuYc2TKMAQvpjNu8tmIuQ2rJKxVPRjO9zmat8Izfj5Zb0K5tgjjVCacai8U+Gn37bwOD8uFmBnXkGtP3KJCrRgA6dLofZETe34CRIp1IsEhb7vxF/MChUcUBhBIAAM3yVR1qy2kbU1Yp4TbVdVsr9Iaz/dDITbn1zA3SALSXNEqoZ1Tz3IZh4b6miU0rKVVy76797lBbG0lCDdEobg2ejYoPNA7fY8nWoTYnRto62+E3X2vYmj5CGDQSNL1cLeWtmGZIWjmCrox5GvXzos7bo58sSd4H4Hdhik8jMmwxoXRNQCVuFdB0BfaZGN4egJUfUr7zIKPdKoh19oWGSqIYnCbSUe+NjA+0DFQFwgx0i69DHwMMAFTJycBcG5TtEcI0J/QsOHHxg/bNB2RD5tc70y3tMf+7rwdvL9uTALqwAMWOom87nEztReCqgQ5y/tOTTfwuFcfwyc8QzHrCUbg7emmo4dhMHx3ZK9H3gKGhamGF5qwI34CMBcnxUuwG0P/eCDkEg2Lh1lk0hR8yRiqceBli+z4ku8euIFY9775x/790duj+g9WY6TNYfVu0oHB1pKWescSQMBrgIxKp0yChbaQKF+oDB4NGbInepG38RoAUAYuBdABiH9uU0rd6EPp9ZlVXjxzRRMGU8Mr20hf5VPc08ITnqpq61yFyf62iW5yvkFeqlnOqWjIpakGRAjS2XgCprWBvf7G2Lz2zFiczUNbLuYVmS+pRHhHgPAuiV3HJeuXVIJkO6eKIgVTmVBTQ21NBbSAR94Ir01SRJ+198ebezpivGye3OSzFlWjjHNLaEm9LYkEFQjhuLH2TIwjJO55Q7L0XbKMn4brrJu8pgQ3tKbaH62RphiTepoBr6YLTEORP5Wd77VxlBTaUFMXM3NOhgy8jPB8CAVHo5lQ2alOERpikZhLXdNknO97cOTnBHSNGN9FX+NHT4J7kccUe3KJ9GEcR4Kmb9xkOQLhURhDLuO6bkYTeFhYxBMBrK/NgdvwPbsV0smjxvO4Hvna57xAO6YbzcOxvi9a3zhc9n4R6f30Nqq4ekR0CPE8AiN+WbtPB1RPYMdPbZ9d8HHcNP960UwN12zgQkHciTgWdRd6hDecbDpK7kI/p4Ke9z3sfdVQn7dwSe3iiTaJpH/UN17frARRzM3BxUVlxQALQ6niuOgIPuxexvVtd7yhbPdAaG0V2Wf5QDfGKP7mL1QhpLMCkrogJtL/l73/C7lt6dKD8GdUzbned+/z/ekkJt1J/JQGA0kQMdKisb1RA7kwoBgUIRdBBEFEY1owtCiBJiTqjY0Yo2mk0QsJiCh6EUUC6k3UJt4oQiso2CrdEpP+znfO3u9ac1aN38UYz6hRtda7zz6J5jv0bxfs/b7vWvNPzaqaNZ7x7xmggMboBxJIEGcVLIfdf80wmsBf0WTK40OllzYF2k4gRXRoNTmYiRuKzqCD9wUQfk45AZoCeb++K3qdfaDsVowt78/zMjpMLyCLA8Y/19Let8tEn02yKCufbmXUjZDrYqDDBdwuDX/d/gWeyoEfKRZb8Z3yPqqhUpjyuqblezqoDDdBr7aJ39wNQqsIeUCC5CkLDRfqmwvn7+wvkdmylxbVWd96IOyhG97VC57KGZYexnyQ3ZOA6PPjGS/NMje6SgTIfnd/8Wf3GJI2qM7NAOmEXjC3BVN6mwyQQcsOeT6Y2gwxGvurx9EwtfdtveFNPdAh+Et4a/EHaXk1Ubw7L8B2M/Iw7+tnrqG/3/ZRnXnGHJOr4uaWIVbu7bVEX1nNlqnAOSXY6OafsHtRvLOcVpjRmVX1ImFlGiRgGmCkoeDoEkX1inQ81RPfhmXBXNt4WUuzirDPlwN7GWDmnddyed6OqNYLGCjpvq6qp2EDBr6ux4azW/HC3sUCy4EAaMF+6uDk5dzwVG29cIwj26nXAKrR1yzYNAXNdkS8mlYZ+xSLw2UBrRhF4+CyQeZ9eVVIC/eHPg6zkzGyB/3vwBOZX4T3pRDvAtHBGLpa36PPWQz4cZFWzCzIfA8gOJmUlWiLoouAW9gU1Epw4fEr8RinUSyEK4Z7NWujpUxBqcMo0KH4teN2eeeeyqS9wzVlFbG02J10tBqDOAlmIKFYzq5/ntDxQNIYNVbUTWF5g8mCzheyacBqaZe7Wjn5FKik2V8IBI9ELNRGRJzWYEawnpY11STxWgE0N1LYEjWHrzGD3A4IUqoXMKFWpmZNbQlADYtBUWADevWYDvY3vUB31yJKS9HkcR7HqyrkMkzt/ajobhLURP+uWXtJ2k3c4gKgK2RHBGdNJkPAfA0ErQMTDctLevniEdYA19ToZusq2J18y4qtDbbKL51KeitXvKlHmMazyfkHniJpQtR4Pip6BE4CwCGpAmrKfrnqFoF71HqLKLrzg5xOSz5NsQwekNN98afXYHlTrRbJjiG8c7DoLkN7P7RETMSpJd674imrT+XEZ659U3PfpYe2a8eOwF3YFKKdw4pj01awuYYf5yXhvUsLnowGmfhPGBeRW1fBkQrLtdITMCuepj2sLsZV4se58OZ1GC9TpePb2xU/sr/Db336VTyVA5+Va1hm3skF175HTA5BU5CLtR2nFw08tQaobd2o800wnxZHQxmc3EVjbh1QKCK+hXO+hUvLAkvpLov17JYOAo+JfRZOLOfoK4CvW61KAfRitoptt/NPVHTpVunWadv32iKwN9dD4n0YmHo7re/HYa6ufnghuVNQbgX16vQJriTQWjFkhymtOY2VQevlZufaIMoMCPhxxUhCQAISiUzMvjCQlUEP9/RYhgVmMVAAGMSINL9qTaBquTYw9rrJApMUxL7Bi5f6V5vVMzKLz4KWgbHvnzIsL0jbnuMzlg/J4Ej6+Dxq2LT7W7zWvrng4+U6TGUn3G8vkcHSL32UXQcAlIfEUJMlweMXAvnx3c2DqQjBXW7pPIHXcUGYoMrp69UjkWXvkM3IdtiTs/WoQQDRQWLll5Yi4boIIerWh8jI6TriERSD4ZTPznHyfkQwZfon+cXgYuU45GDdhPrtZdARJ8JB9dLyuuvgGnGhHBYXWlF4/2w5iMnh7wA2hewdT2+OAB/n1nGWzTeaMh0eRGNpHDjm3es0mLsJKEdGEBgvtq+t8PUWs35FAJVKVLUl+MoBuDTRBvDoAxCw0BiFqYGPC7AB38H7O4IlAo73bcd77FEjBQCeyoFvy/sQpBXqRdoUO4x/oaLjim3QoEMiLuRwhMkiaiKD4JmMqAozyasaeOG536pXNCkp1XdQGGZt+5aCTVtSrYpKZL2Epo5hrTCBeQFJvAqsP81rALHP52KtMWE1hCmBh6U30+2Qghd11ITJbptcy0ZciGbL0NENiPHZxMEas13OZD1i20rHt/cX/Ib9S/yW/S8b4Zwcll6dAre6FkA6jm6Btu/bjlMNXBy94ta2eO7WLUbj9NiTIqaJbt63Mcc1YikiTRrqlXEHAy65YUQGSyy/J4i6lJkzhsyqm7tarrJN8R8tMtIUW7VU3a3aG2CcFwW6CerW457Z4kEgSqK+roLDn/dk7aEm0KNETZdyY0XuUUoDJYMPe0eN0TSVglDbD40RWwbP0iLkNSmBEc/HuLAtHeTtNR42xu6tcXtjf/Q/yb9Baw7dGVT+vHNOyDBbxAtMvmw5wlXCEgLKD82KH0I+hELNVgIjBe0DY+8iTkTTPtjxawN86Gdvxu/c8JN2zvL2sYsWQJMFJDTxBTnm4B/ImOxskiIfvm42WTTZZ0rZiSn0wYJT3ouL42JSqnihtSipvNsmwqBL6Yg04bBeeP8iCNIF6Cj37s/02sJPGE3OOWgp3D4feBZUqwVhtMaAtg5tBf2UIG5jJkq2DoQLpwAR/05LlN9IdwOR9alN+fzBilo0ii5FVPv6fAkE8L7KhyxiL3zSGJRdDN/tmFcWHiR1LjWXQdWO2bQ7gTzB2czt8q4e2FtDh+Bdu6CrTNaOo1ccqCM11/+VolFgCzBQ8tJ3/GVYYCc5IQ6t2KVFim0WyO+9wNxRTKi+eNn5LZnWTy24JvKqS22eHkreEBsckl79RXw7slzYj2vf8KvHW1x7NVcFqAVv8TsAvG97pK0apbmGW+jaNnx+POPL4+JVVBG08NSmWYwwB9Ty561v2HoPy8HbMjImGsgyiji+weMNzj2yP2r1jCK3Luye+WOuCwmXFa0QBChnK3j/skN7Qakd2IG9NjzXI6r+vuiOF08//rI/BSFbtsDQUnPtWwTynr1E8CndPGZZw3ChFQnXCoCwJEGNb4Rr/tq2IGN7quc0lmzZzfFcnb22ngPwisUVHVrw0nYDSM3Soa/HhtbKVC0ZdUghgQeiqhoNu2hYkmydUGuyg3Nl5pNunKy0uzu2XO2c9jyUAvE9jRYF9ZTXs5q7gyCARSgJTOpVYm/hPtx3u27fae317/m+n+lvb1RSQ5awlIYCpUnQpJN8stxksjTkfakcyS1fjAiSLu7Jkput2QpT1IgwqKweElQMfG4eMymyvKaDNwbpx94acTLpgbu5/EMZ/Mj2jQUf/fkSD6IrAlSEYJ4q0hYunJzFgcdCtQBR/fCcTVJD4GAsYBkTPIEbPJSHBhayVu4WkeLApp0Aqr2QKL6A2MmiQRlsVozEiZGevXg8hGXKSFRmnPsx/xmg4+RCt5cSD54lgj6rQrz2hxS1mixV0f3lR36BiKgFD+I9YLZ1LmRRYO+QarVeBvgYVgQSqdESkS0d0Rx4WIwHoPuYP7rpCNhiLZzjOrRkKa+FufZNrLsMPOLlg7/AQ2u+ugB5LsfE3PhUTyev0hA2ucR6le4BpxomefJ/fNGeQgMl0DCzdXNBMlweNydpYqPwu5Qz+t3bKEdfPUvl7XYL6wS1XAsWvcQznDp4MW59w5fnxYM0t8j0eFjCHbDrt0Ga9dI23LplubBKqs2/jLiKBXiMKbe/z258JgRkDDCtUBzAFNjJLJubuzQYL7GVPnNX+LOfbi269WGB4H1btz5SGweAXnsAt2efx5uzql77jh+0Z7zrlkJ77Rv2BObsWaoJ93MfwbXJ5RLPrkZbf/aC0+f9UtuQqDKCjrta/AcDbBm3IWms15TardhzPBXW2hn97N3v6/+OZj8ba8TI+nJ6l8T2vib3NPW5PSpeNwpTukLSAbSRGRKZeQLXwGW4D1xgBo8Qs/+qB9wfppCVY1FI/L3uu+1/4J4g4ztaCbKVRIrOTNIig8Y8/aNCGfXApmD2AaLKDbGfagFIoRIzJuP4+R4EUpQRMqwWgFtoFrqB9LsFyfq1070mWcetkHvro735A+2bCz6eqlGDn2lBEAwIzWUFeuowiQmiDPKjqoR2jfn4aG4xKW2mbh/nL8KoKvqTjOsobBOaCDMQAlNvxQlg+jxJDil1o+0gLRq3IvTLEK4klyGCJyKdLDzJChIkMrQIYF6ofUvuEWC4bljB0BnWau0otUME6LkMc3E3h8DiXvy6pnGouVO27pHuAtx8PHIAK0FO/A3UqtDSUFTQS4FuioZq50vqKy1GzcFYvA0wdw1SXx278GVXXkMxZzkppg0lpunEiAmScW/yoYhv5F8cT0ZY5bvT+7aHaRkArm5SBiwt90TB5/qMrgVPxdJdv11fPMDzZm4Xp2qnhv/lOcjJmB2zlYY3OKagQuOtyIGcI3Xx7X5M1U6/OJ7COvKd/QVPpeFb9WpVWestgMWhFV+cT7i6yf8mI7gxx69kSwswNFoCrqd6hovny+MphGxXBbpZLJQasy9Qc1V41gUGMDFh3iZLx9EtdZaWJNbY4TgwboQC+DwrjtIj7uCzekOvAuAa6a/vzgturaLIkwnFs5grYLPMkffnjqfTeDoKNAJ0n4qlVn8X7zwAeQOrATMIuUh310eJVFvOVS0d29ax1+7pswYQyANy9oKCGmm1fEYW4+P4bWXQpBuAqAGI2Qi+aLFifM5TPXEpJ757ebF4kNLxtBnl/Lu642g1+ENqTbFbKrhd92G96QXXc8Ne9iCl61rwwgyYZlaV091LGbgM8O91qnz/oUVyohPP729Kvwf8nVWgb4LSFHo1AFIOAyuWaTnIKMO96ptC7KGSZM9qEUj7SLgqwtpqCnJ3q2xcj333IM7M4NwT4VdcEwhXTrlZQb9e+kSTIOqF9lRmuaeIxItQpLf4yroZv6T78tjsfoZhX/21UFiOyHTy6/tkU2OPvOau0N1EdwCHtGgCsQQaTZBNYNp4wOTUiRlzzP1bAYzCzWuC2Ubo3zHwtMjd12EiS4ITGP0U2CWnANOsNEn6lz/D8ndOw20ysjQ4VppesNx3R/RGIrQAMwcgypeQc+MR0rJ1lM3IgbSnSpQZo6U3KmtFI+XMwEuTktDWGLqI1l5eYMYUh9WK8zkBtfF7WLwwHxfayCNyHz6Dn9u7BXYefcQd0ApxaMHmJnY206aHyb1DsKnHgxQXWq5iNAjetScbBwxggQIUNCeVGrEVeXy7a8o2mvYAT/UM8NG6mfhVJaVXmhb/VE4PfNWowmpkZy3M8bnx3sxqGc86AAqzfFg199a3wXnSC0ijL8t8A3hYhj3ARDqWhcnq3dH35zJmgTEzfA4L6O24ClljK04pk5UuLGgqOGgRcKCYU393L/7WYC4xHtNh64rAaLe0NRTpCZhrxJuM4M8ObnS0yrDQXAHB5ohx4XxuDiSu2OLvlZBuXE9Q1Oq9BD9McvXRSqYA6qk4S4n07zy2TMEF7B05HGjy3egQvLQd708DM2Zpgj/7vC+s7+waCB7vcBaydNky+4aPKl5XixYT5/Ppl5SRwmsmsTErkAl4ZH0ngx3ff9kX9QNC8WPAp6Tj6yJj8rLX/NwyLL/wZxGB0kQfe2DKZlz2y8jcS+4b8WtFn9LtJ/mZ+vQwqPWV9o0FH9LMfBWDUWxBYPwJVqoNs3me+GMI6iGATJqJ0oQmY2A9nkMx7ilN76yI1LanhTOhUY+P8K9Aci13kSjzZgPs+MlrIKgA5AiZ0k3F0Kkq3Bw4I+5IQeaGmGq7KIGCAktg/CgR3/3F2xBuKXHq5NhwgTCxqvg8CIBMo+6mUS3DhSFFoU/NgNjhsP0oBkx2q5FRfOwzQGP6XhSZA/zeYhao3V++cO3YOOSyMXyRaF2JNfEASGSLVU4CEH7Hr5lqxngktXFhQbbi9kgG0t36hqcysjIY75FbSwLgXbvg++cbFFF8u75M3+eAwkMrvvBU3K4Fn21XvFFz/5y94slLnVNYvC2edZKKfWVrBVNlz27EV78qb8yykNJnmZXx5XnBrW14aZtpwyni7FSbN7J9bvWI1GD73qwJX5xPUbnV/o2gT8twkOABObSgaDdw59aI2s16UESxt1Hbhc90uID+f47P8KVTdzctBhRatXu2gnZUvAD4i+8+w5vdnovU9dny8c7jU4ooLm8PaBds+6Akz5kjtEpxjK/6FO6Xa98iyPh0avGu4hlHFptyPTccKavmdm74HM94sx94qjKq7/pc2jzXyHCiC+7WKy7FXwJYUPBn283XqfXxy/MJpw4Cu3gGB5hPXqX58MKG396ueK4H+l7w2W6xLYPG36xtXx5mKTrPYkGjLxvOUvFy27GXHpwqgIHVN5sBm0JmX+G1CvpZLNg0xX5Jt3gNuua5VwY+ISu2x43Ffk5gk64VMR4bQkiTdOxOCaW1gp0MgKAeo4ZQ8sKanCzM3GMILoRWDMYXBsp5RekhoEqWdpYOies3cXeLjDgY0RmY5X0wKWjLtmSfcRioqHFcqPwVjGydj2jfYPDRJyQ5mbTiIKQvdfnM/6XMC1EXao4Mw2TGUwLo+Gddwk0x3aI7kJkcYumemqjB2dUIplO3tOh03sNiRhmZzgHM1uo4bB2K6faPxo0Mr3wuzYGoD55t6tci1YkHEjvp6P8AJCIOQADg8OM7XHALVpNQxACkS07P4910ZQ+oaa4E0KzytvQ4/tyTGTR/F+OQxjHNf+wfZYDW6QtvBAPBtaGzfz3cAd6JAsvLZ5wIs0GKKJ5LnSwM1JKLqPvhhwa5u2ns9FTRTRo6LD6AMQnUWHuaH8CsIdRw+QxmVylWME36YtUpw/1B8096/vg9WyT8RWs6LA35WAvIRtKYUwCqB9Wu5Gn8jLEOE023H3frW4CYwW+CiDEiOdb1MCvMrVdsvaGVFH9BN4aYK2T3VFLL7KBLo8/PThDE+A/dohZOzm5hjEn8XArf2bUwjVmupDuNs2LEjdAKsrzQe2kBYgHgvexm5RBaOcZ1w9ria9AACyawOawoGuN8qp1Rq6UmN3+v8jMEbbt4dWbp6EUgDrgiAJ3cSFmr4DvqCtBkFaaCl2QD9/PcaFwjSAlrcFIwp70jn6/z9xMlQjo29u0+fx97yQQGNAEQu8ldtmDuC+MhM12CJnCQ/63XiM6M7TrhnvsYjxWHcfzzXv2R7a8KfPzL//K/jJ/+6Z/GH/pDfwg/+7M/CwB4eXnBP/fP/XP4M3/mz+B6veL3/t7fi3/z3/w38aM/+qNf69rlenrRsxSZTMDAeag6rB98amrOKQ2XpnkLUhqbrW7qPrdhQeF3+ec6AbxoFm7lWoAXQK8FjfwcouNlYWQ03KRc/cIpQGq9BwldirtbklJsC+TkyzY6WIqmPHKMhRlMNfM9oh5N6QjSM9ZtImA6C85Soyz2VN1VgP7UwWJyXPQqClw6yt5Rt7FBKZklxQX31t06ZK07WKzVNbleoNrRtUIu3awNL3Wg+Ta0HvatnN69iQRoAUz+lZXedq2BmxMAuaXpiJcbAVgtfsfnMc2dqpmPt9KN+6JbfESB4u12Q4fgS2e9JDNo3oCZlsrqsb9++zKuTVCyixUx42em9ZpWmhdJdiFsThRWxIqn0RKzCvHPthu+s73Hd7YXPJcD3/LYk4vbdH+1vcW79oT/5zAq8K10e/4NwYwKUFjZwHxxPMW9rC/mHvruxaw539otLuLl3ILbYVTitYkyQVadQ2QLXhGykzJ24Clp7cxusaXQUxbFKLLWegk2zrBQiwWifmu/4lLM7cLU/O9e3uPb+wu+PJ5w6yP75f2xo5buQcU94nH+4vltAyyL4H9y61hXwZuyTenKBCNfHE/48rzgem5QF/61KJ5qCyG/lRZstAAChD5VG4dNOk5tEcT67rzgW/sVT6XhTTHLB9lQvzyfIhB3Lw2bW9YaCirM/bZrm+Jm2Hax9b47GHrfWEPH52U/IQK0s2C7NLy9HPhsv+HtNgoivjt35zYxC9iX1wuOYzPr11mAa7VsPcYpOOGW3IDtRbC9ICyR7cksGdIxCoT6nh8uc8Css2IFK++4jrj/e+G4QZVu3/UEFLKiJW3+m9dSgZF3BWACqltNNAGIIIf0vYb7cICZBDC4Z+Ump0TcXn4OWmIi04X9SrTvjIXhhXNyBfucrSVSMNJrV6X3K9q98/Qj2y/8wi/g3/63/238LX/L3zJ9/of/8B/Gf/qf/qf4D/6D/wD/1X/1X+H/+r/+L/xD/9A/9Fd0D4IPOb2y7Ume/THiDw0G7i6INE8KG0fFAUY8RiPm4asGLmvDfl78Y0TxIZCjRCVW+znuayYqnznl78uD5O8/FEXM83r6XiUASckU8g8HOCFmDw617KGk4mdgt6Jvvnxc6Pml4CGCqJxZihGwBQmbL+LVpxsFqoBUsCqdGxpB+gk8zmxaFwiXjixfrWOUxj7CDhLwuLsPEBoaUxDPpM2uwicDDxYtm4CAH1+kxz+m4wIjKyDHJ2SBT/8/gycBAyB0nbwWq8G4AKsPYuRY3ynv8bZcB5fGYi4qy7VyTMdatC3zdABIWjafB1MMEMeV57f4feb9OJe/8z2nvmL0VYEpjZOuxTm2gsRhOjJBnHZ8K8byWf3vWuZxya1pmf7NY7XMA3TMexrXfFTEY6Tza6wBjedcrzGsDbPLjQAkW1vIx5JdR+RUyaRjNiYtmEpzkCqtbLUY74dU2wcGV8pY36tFKywezowaRIoU7j4o3H/Ljdkr6d3l8av1ONwq/H0pRJrb8q5zewaVEH7/aKPlR6tFht9RoD8S3K7IvWZtyb+HDON9kny7v6ZddyLezPup73OPLN+y9OVuD/0a4OOvyPLxxRdf4A/8gT+An/u5n8Mf+2N/LD7//ve/j3/n3/l38O//+/8+/t6/9+8FAPz8z/88fsfv+B34b/6b/wZ/59/5d95d63q94nq9xt+ff/65PcfThv2djtTZOirc9l2hmyQT2UAi6qx/rK1iJjGZEFse1Cm1ytFdLKY0mKKIiohyOEBNFwpUCKfP7WJoOgdDOspEB4S52FzsNfV3CrR1EAJef/QJ/uwZufI4aYagg6imzAFUQ8V34HFxOiwV0xSOeeFKPKSPU1Hg2XLdtBXTIBxMETDqWdCLQqRg2xu2rZn5tRfcYOfptPkD29YSGLHvrFx3gx6b9bEodHfNoyvqizlMrZaMgG5kMhqGrTADD6870wRTal6AxPT4XA/8GebSnv8AtFnGxBeHcV5w42VdjVML3kDxWb1GvAbjPoY53y72rpE34xkVHW9rMsXAA/ScEdOO0yiq9t7TYQl84vss6NXASFEzkX+2Ge34xbXxq27oTaw4npToI4Nlv7O94Mv25Jk8DGg1AWbPbbEDVTQsHHQRZEBybeYKuZ4bXo4tWDLJ79HOin5m90zBqU6a5RNNErKjlNDSKcBG7M2wBny2XyGi+Mvv3qD34doBgFI7Prsc4X4idfkGW4dns9RiVt+lsKzFnovC+8mp6X9dslxZtdqniPeghePaa2R7sJ1eefeWnpEuISt7f+JSixF1MagAwwUz1UnRVBdHR7wOYKnIDJAmoypdfZdSpvgkpnUfnl6xgiZggD0jdfMxhYY1KfYANRdWdheS14TXJn37cfPida7gFZJ8IQnSDEYciMg5Ckf2i444NyeqtEWptt8x4DOxdWrKBBSlcqPT3sC9XQUjvjACOjXKPBSP/WjPGlmHDP+DP0e4iQThEp7KbywAJOQK9/JsIfFrhDVFTRYEGMN8rm5YrBhD+VUqiH4uQyXj+hzzBmj7ePTxV2T5+Kf+qX8Kf//f//fj9/ye3zN9/hf+wl/AcRzT57/9t/92/A1/w9+AP//n//zDa/2JP/En8N3vfjf+fe973wMAaBn1SkIQr00xwUUhUiTye4DEwtCQr6Gv/J6uC52/z1aUaFyc6doTiOB1I11WZqvFRzZZ+vcIVN0h8fXBpxcoo7EHiFdlnOYLEQQIvE++VwY4cZ6GFaOUbgs9CW6Ia0RezbSIaUqljJ9xD0ask3kw7peQfB6nfv9Iua8BLpZxmceAAFPi97BkUcNImsZq6Xik4QKITfhOQ6eZPWmdAMKEz3OYMcF0zRzrwPgO+uLHtYc2H/eCRqAs2VnXoFjjyVg096xVQ4Il87XGGBgGjNI19Ro3CNzi9aH2sMw8RkYQgCkTKJ+XLWy0wu21eSppmwBIJmCrMqwdWxl04es85wwSNpa7N6r2Nl0/xz+81lrPNXlybEvxomwWxHrr251FKFuVWPyOdPyvWZLIjwLMAdF3cTc+/9e4bwngkVNmRXSkN7v7bLW6TPOa90/g0bYecRrhguW8ZovDo5be4ai3FdbxOeMya/3TkrvbLzGUSCzHZ7DwaN9GOmH5nPvTfNz8a+x57APlTfoshpJAKe/Xy+DeGeSXjJs72fg1ZNnXtnz8mT/zZ/Df//f/PX7hF37h7rtf/uVfxuVywY/8yI9Mn//oj/4ofvmXf/nh9X76p38aP/VTPxV/f/755/je974HrcXSdlScZc78eLo5gk0PSvQFBeQqkHVGeJznPksSIGgSJrdY6w9MVQzIjGvCYgt6BeB9mmIyYgHavYigWUU2iNCy2S5dXysRqvP98yUhoEnPNnyMaZHzZfRjDJXK8HmGdUDMSpRiYezh4DZwA0jBYSKKUg2Ot1YgUDBnnYu57zrQPxLwgAGPKEVeELVvLpeGy3bi7WX4gI3hsOJ0IqNSumXPFJilyFG5bjoq+yqmsYzNQwaCN6pnmeNsCEA6JlKgDHxDy9m8D6pAkdhk2inopwXsPdWhcZNLgcyRLPpFzTALkO9s7yeej9wyyCDwOLVaVkTfJgbVKoof2d/7eQXkjmDLwZkF1t+ncuK79T3e1iu+XV6CFrxKx5f9CU0LXvqOq2543y6TlrpJCzKuzYulsZGIjM/JfmzSI3PGrAfG6aG+PmpVXC4nes/kamNySwJXz/XEU0plNcFGDf2GKhak28sZY7aVDuwnRKpbQApq7fj2fsXb7YZvbbcUYGmLoUjHGzVyNvJkAEjuBh3Wo77h++2Nr2W6mTq+Va/4VjVrb4fgB+0ZZy+4dovLuLYt5ox0+OJgHLD9ieRwtBiwn5/fni2F1fv4ZjusL25VofVodYcdDkTObhaYPEdv6m1ai2c3kHvG/PcAMd8/nnFr25RizTnqVQJQHK3g5dzwxfEUxzWd+Who/epnsZg5d++S9wgK4LSMkb6LsYen6ufA2CtHWQT7LrIhT2P+rFcMqwV8rzhhskTGNczCQCXI/g6LMxDZLcMSbt93Mpv657xWL+lYCvdsXcnCnnsXxnXzz3jm/HfSD2NvLAiyssioYY2xnLkjQ06w5SKiFkdp4RCFBI9fw5zxtcDHL/3SL+EP/aE/hP/iv/gv8Pz8/HVOfbU9PT3h6enp7nMt5mrJi2bytwMDFHAxZATB9gBV0kR2hywTWo6fBBB+O3ltgDMoSJ+Nfixw0hcFF1MAKCw/daDWVcGfni9r74+7N6HcAY7sZOWiEyTLkQdqeSZAZKRkLWSCxrDgrSIRQyJeaI+ejwjuS33OL1hOGbTx1slqMiwtwLBf3o9bBooxXwlfBcj4wFjl+cygbhxoay+yfNR2DGpxpLTOVNIArRoNRZ1bgXEcKd4CQBBk8bviAqWpm8uz9SBZT0bMgHW6O5FBSSZxBhXyeFbV3ctpDKFh7ZgXfJWOohboGEXAoHHN/C+7eWxezdxelM9kgOLE/EJNlp5l2MMygMeWJHMpdczlqHmtcjcXbLTI1dIjvfaNpySznw0uDHXcv8h4Fhtbz3BarFA5ToYVbcnxkQNj4xi6SkBqdQP7pYz78OetbcPdsjwb1+LZx/zk744HVieLvTB68+LWDADY0njmLBq4uySvwddapLrTktIHZwr7qanfrSXT5iubmwqAagK1M9if+CAH6xN49HG9HD+YraCPrLdM1WcpeRpus0XEc/5tX0/90AVEIB0+jY9iet5s/HnYVsV16cp6nObnfHTS2sc8Dh+Y2nC/vNrR+/a1wMdf+At/Af/3//1/42/72/62+Ky1hv/6v/6v8W/8G/8G/vP//D/H7XbDr/7qr07Wj1/5lV/Bj/3Yj32dW6FfCs5n4Hz2ioVb0uRTo/AMrXTNavCBZlwILQcU/BPK8wVSPA2UFOThN/RyCROydKETBcg4+NkdkBaIAR8/kQtNDFSVcyDQfC3Bg2v7T2j6OwMY3prj4jns7FM5AXSnKBa/mAO8qVCQisVzHMWMRHUUIlPn3WAKXLxoVYG9o745sW0Nmxc66r6ZMKOlFFhtlw+Y1WmuFSAAiPDl8c2DWS8x8yvO24Z2IUg/MQBmrscwNBBM/tFs0g0AiwEk0Q2oXU/zmU8m7iq49YannHWSAjgnczbEK6AayKjS8dbTvt4C+KI9RV0U1nHpKpF6/VSOScicsIqIPQnIrTR85pV36Wp5dhbOXU7c1NhBD6ko6OguHEl+9t3tPd61C65tQ297CKKLM2jm+fv2dvXfewASVt+9Ms6gAWczQfS0n5EGe5x1FOwrIwhzFvYrsFMU1RDUdF3luimZrhy+ti5PJ771fMWvv7zDt7eXKdPope+DCM4tDxs6TpQQ1sDINjl7jcrG1gcNF9mhNZhNCSJp8eC4BK+JW/5uZ8XtuqP6+8S1dW0bjrTga+kuGO2zo9UgR9tLx74dKNKjYB50Dt4FEBknW+nobp3apN5Z4nLQNN1nzMDhGshZThxn/qPbhcGxt1ZxOzeczavZ3jb0VsbeRxcnkp7p72t/0lFSPu239YqwkMb+qAOUcH/sG8wikFJsuf8VD1jlsm4XvxZpHPK+K27l4HpktfXFXTH9xOhb7FPJ1RNBsf4MYc3hvk9xkqwmUWWW+381i/Rwk8gEdLJlRIE7BVvGocMa3Oc5kKZWtuIj29cCH3/f3/f34X/4H/6H6bN/7B/7x/Dbf/tvxx/5I38E3/ve97DvO/7cn/tz+P2///cDAH7xF38R//v//r/jd//u3/11boW+CTqDSiseRyEDQ+Bh/p4Ce/0cQJizwuykScgkpDf5s9bbLohTZSjjdh1fBBlM8IXx+8xmrwf3kvkZsnCNKrX5b+Auikdpzqvw4FTLGe8wrV27LC8GVxicG2V+ePV7SYyVGpHPCnwK3SwjcCweS1zTU0XZ7QW+bCcum2UOdB3QPGo7AKGddiKQDBT8ZQggwPHm72mdSHre0Fw4nwtofPgSIs2jJszn53WdNcOgtnazvT17D/Irgg1gcH/sLrSK9Kj1cvQ9ghXDNM97UBhD4vq5GUnUXJF2FxMSrLBbpONZjgAhdGEAwIsLSlbfDa4PzM+Zze25ui1gm43R3AwtnXEBrzVqxZM1RYZlZ41VyHEKO+YYjyKj9gqzLQCktN5hAxhZRNZXgq6tNDxhuIGe6jll4Nxk1MUxy8KGKh0deJjpssOuS/DIeJh43tI95mlYCHofNV+Kv1uP4l7IbEvyNuFnYaEo2EsbXoTlGivXR2bmPbUmt4vV0ukyZz11HbTufJ5cv0f9GQiKWazvdHcLn0uqhqKo/uqH4pYEZuzlOt5bFX/fSwIseT/gdsdLJZcINw+FDBIwYLjpeQGSl6U+c+/QmmpLAdHfDFbi82ny5j9FR1/vDPwyPtMsZLzDBD+hTCKBm3Q/3iP2zNwdjtMrsjC2zNdf5bv2tcDHt7/9bfzNf/PfPH322Wef4Tf8ht8Qn//j//g/jp/6qZ/Cr//1vx7f+c538E//0/80fvfv/t0PM10+1CK7ZQd0MzSZ0evkwwIioyOqtlKjTz6rOIfFf3xicu6zCTJKESLthITTKE8TKs7ISm4Jbl50UyzSeSKn6oIyywq/R7ofEPwaXDhT+Rq+YHRN8XkdGPRLj1orCjizKE/EdA7HWRSzdWjtXnTAK9Lsvqk0Qdk6pHRsW8PzxTRxAfD+sLoNLLCH3aLfv/V0s0JppdkmnUuJOwAppQMbvIKmjvlyrQVe++aurPMS2zFZz5LnxqwkDga7PZUu8UVwi34AkAfjQ7AUGp/Y5vvk2R+m8dmrx7LkFJrf2V6wlwPfre8i4BQA3nnMxV86PguLB+MlyHIJJG4PGT55wIQoMz+owQbQcXfLs5z4dn3BZ+WKZzlQ0K0qq+5417+Nd+0J329vIp6BIKirYJNRb4RxCFjM8JfS3PpRpkyMta0xV6WM9FGyr3YV3GiBcw36UKsoDHicSxEfc3tWxnxcyomzFtRyQe+Kax9l5ououSLKAIS7W512GCfGVVrsKxzLd/2Cs1d86TVd4PP/rl/uU5NhbrdHVYLJeFrUmGHPUnDZGnovOIL7xqq9HtUAyWptymN+tBqWHhHLbKq94xkypeMSJHIt0oKRU7I5toDF7bxvO97Uw+ae8+SxKrewEHVsxbKBJFyuA3gcpPxXwfXYcDtrWEdrtU2ud1c4rjW0n8i8WIU0rbYONrQAQfioMizL1o3BBZJoCRQYbpqO4PJhDatJBgHDRU0iQzVrPSozbDQszVmwGxh8sL2meyAdz19Xoq/gLPHra5KPkRjBZ+T9UlxK3vN5EzeKjb7woLwXErCJZRmGvPjI9v86w+m/9q/9ayil4Pf//t8/kYz9lbRYYCfcXz1mbYr9AEbEsg/OapkIoZOFRUK/OfAzOCuKk2Xl67ALPfcncebH8QmqgothrJq7PTeDjDXH2s/NVpNpEawCMCHku+vzZ/GTHsSv3MW8OE06AOfa6P5MdeTh577CxnPbRp6/DaeiiJnPr20E07HlzI+shSkQ1U2jj1wDCrgRZViBaBbkuFAGS3o5uB5oeeLa4R6eqPvzsIUlah1fdjRjymS9YXzFJi0sEF0leDfY3tar11Ax60VHxaFbUHIf7jr4/BgxV884cPEYDDKAAv3O8pLBDD8ndwgtH7RuvGCUfG+wlMu39QoWlqMmS1pwBo3eknacGS8N/DSQ/yEHVlqQajch6xoxNeDjqOjtPoOGz8ZPw4pCSwXuNXnGfMTzu/l/2xhjM9d1YZXhZzni/GvZpxgNzuuT25sbixt6y0CPFqMpFgQ9rn0ka0Lu4146ztrDjZSDMvkMbEytflQJuHUL8iyieDn3IL2Le2HU+Ylg2tJmQehjzwyZI/WXNV1qIiG79eoxQu5ySzErXRHur9V9BgDN3brd0/bDveqZgtn9EFZQsb0hUucBADLe1aSskmvIXxeTAeteWgCo3glnu8aKfBDWZ0vl13Q/gUCHJTy5TCZW1HR9Pk8olsv9w+Kdxy3LmSwnk7xkPEpYk5ZnyNeO/hHwcY/jOKUx71UmZf+r2l81+Pgv/8v/cvr7+fkZf/JP/kn8yT/5J//qLqxAOS3TQw9AfcFxEfR95G0LErrjwtjTHBK0ZdCbBbdTfqv7DM2+D/QoO3/fNxNyEueMc8cxYc4nwgQiqnkyuy2CMEpEO+Bg1suI1lruoQLWFhkP+AB8+D1y0Gxmgo1iKA44uNj6CcvqoMnbA0h7ZMNIuGMAhHvk+XLgsrUAHgCw1QaRguthgKIUe3maikWMrUOtLEzFTZXAQ8P0o3wpwu8By6t3ttYuqcqvAxD6R8PEelATkJhzrokJlDzYxAZYkxmIef9ZRItWB9ZWyUKfrpVZGI1S8d9vb9zaUPHuvODz6zPIhTACOq0dWlAxCLmIMRn7QKHF+7Pi6rMcuOmGm2541y/oWvCt+oKLnPisXC0AFhosq7uYIMYJvMgWloywUjj51Jt6WFVUr9i7e1Xfd8XKy787L9ir1UU5esHtNOvY7VZx3jboaZaSR+mnm7tPzl7RU3XW4m4jNoLall4KIxNTbLU7wDWYRpfOUznwttzwnfLeAki9z4eaQM1uhedyBAAi8ON808LBuBFS00fKs7u3yPuRi8FZ5Vgbl1q7vw8a74U96xDoGbxTmCvM3aXOEwKMon9P9fTMF/u7dwctDhw6zALTYMAiW6zOBBzYnrabs6P2EdNBgOvpyI1Bpd3iyEgsN21fAuN4aQK9VSNsdILJiME4MWqxxHtpWrjFz413kzEd3CaoYNJloORZ6gOocE9mHOGdyyZbmOHM0sXlEokbc2NZe7+fCoKzJCYqN4KPbfTP6rPMx06yDEkOUlb6tabrYnw/KU2u0OWYExCcURb58bolsOcAZ82O+VD7xtZ20SI26S4oleRiFNaPHjIL1RwUqjDm0dVGt0BJIcFMBjGv3QsDTKhQWAlGJsaCdHIEdJychOAqd1Xi8/GdCfv7wNSFFtg/D9ObGio1gJQzM0yIBzW6aAjfGE/AhHzRqH0Rm1oKMh1R7PxpLoHaTQhx06dfdzWt0/ebN7KWtDgWbTNLCwwo0cTK/viLoxuM3MzHLoJE08TZJrCUk85douZ0NxYcc/+YYDKBTPrYlRYimKDbk3BcgQcAz3yx6xphWMo4AYLDoUjH2/0WloW3ThBGM3k8gguiE8AhNbIWALPAHNVAxKHVioZJTSBoaLA3HQLzRTdc+47vn2/cRXAZZdvFybho7YBlwZgGDRS1Im4nRqoms2Ssv6b9Mq102wS9d8ug4vgsrbu2zVTXTHLVRbCjWRVg7eNzLQPUKl+hsT+cUf/FrBEvTNlFx9tyizEChmXj2nfs0vCXymd+rIbbhQCkuutr86yfR5khxd1K6MApJeI7csaLuSS6uS3aoFPfSg96e7rkDqkQt2bk+9HdtcZk5FpDcazHoHA9zP0dMTjBV8L1jDEXR6/hPgUMeEjpyEVQNT2nsu5Tl7lAmo73ufO9y1aLxULcNyTlbuy3fM/CLcEAVPaj85i0cXD4ghk0d17CZfNaowIsfg3BAEfECjk9d7LWJ0tP9IX7OxKYmG5o/4qmfXBRevO17NnH/fPfBB4RzpA9DwQj98v5g+2bCz5YNM2FP7k+pglZzyH6AsIkFvVRXNjksacwIWiIXOdFy50Qrqav6CrVke4bBedSn/KcTIuEyJoum7VxAQaluM1wJwFbpt69Ay9Jk/eX04BSQRSV4/N7pd+Hbw61BH/OzvoG03NYv7LlhZpN1+aa1zCn500IsJf9aMV4HrK7Rbnx+t/ciGlhYLxNntTi1rLiz7yifB7qLj2tcJPheA7wRUsvonITqzrGNL10sSZ1BPgBiFgC1jOhIFotHhHMlzRiANhhWucgcTJh/a39GuDjTT3wpt4m4cxjmZEAGN8GG9k+Txnl35/EM17AQNfBFHr1QFNq71+2p8hWuaUSyZnvYupLrwaM0hqhm4OumSwct9Kh1YRsR5muBwyh2VVgmDpRy3v8xw6giXpBvFSQLoSiTAIvAjoh4QYp2vHiwAICC8itFpRbYfPYIPhBe2OuJdZUcRDwvu0Re0JSsd2Dkl76PoGQ4hJgkw4UBONpT+9B72IF2lpBKx03d0k91RObNDw7v8zN41Ze2o6CilbnmjlHcmWxrxzDu/gRX9vZarTOB6nVq9xvIgx6ZXyH60vDgLvMQygafM8VUaICCujuZzKGQR8AD157dVnkbBgK7yYoOUvD9821jZpX/oFrXIsO69+lX6c9yjaMiA/htR7E3IWQV9wTWoYChZBDmYMjK0fSxJhc5e7r6HtZvgiuFJ2Bh5Ar6hHQyWPxEe0bCz7MZKaxMDpgJnrBiM8ABSxPGohxcmUohq/NjyMzNsBFLaOSIRXEmBkHFlzgbnoK+u5Y/Lxd4ubIaJ23Z58VZkarMKGbFwz8BaRlgqhZvfMOlRUY7g6+NGmVPtgLppXH2jPluF+Zmi8lBqEjyFRh9Oi8py7jpRYgWl5ZjGZCRlCp1zLqaQCYUiEjvVYl0nvtPjCbLQeCY97HzzAL8rGSFsRg3GyGlWad4suXNwLl+stjyDFOSuHTfuCzyw3fuhiN+q3VIBijKZ7AY5czXB4WAGqLmS6Xd/0pMkzM9eC8Ga5FP9UTb4pZPo7FRLdJC0EM4E4wmKWlBdsmQF6PigLBgTm9skjH23p1gWsBkv14xukWjg4JQrACxcUpyp9wBlsos2zsXgWndKPzzoDd55cFBunusz47cPCdlBkUZy/ohSCl4ym5NOJ5F9WQ8RItxR8drUYcA1Ohs+sk08wfqPh/2rdsntoTrrph82cjOyzvkxtdI9VVdqbtdl9kezEOmO/uFkzaVawQXrH4lO6uoq0asyrfm0zRzvlgZVm6/nJmy+nBrXCq9Vsz61i4SMtIa6Zlg2vQ0rXnujEAgi+GxGPv2z4dw0wWIxo0l2gmUTPgYcndkIKwUmTl6gEwCGWLf/IcvrN9fL5q9EDan5NeFVsfgzL7fE7cx90PQedApSjv+XRjZ4LL3Ge3oD4w7sXz3TXKHpd5OdYty8UpNoUgZtkDJwAlqX9pX1OBJS1wXPmFu1vMuvLx6OObCz48fSmCTsURXAEUiRbZJ5RAYxUwvSJMa2xKDVnvF8M8+TqO9+tmjg7p5n/FckoIJ033rYNFVdWtFjyuA2mPjIhtLQoLU/JFvfoeeYl8zw6QiTSuH/+4WtMCcWQsHijHWJNprPgPsyAP4EGiHj5geoHjOprMx0CwNZLtNKdSDtP5WOCldCjqVAuG7ix2VNiHZJXgizZtLvk8uf8XABfpBYvzMDcf0qCXEcXzfuLtfotqrQX7wn0xLB7k1bj4hs12uBAho+hMIKZ3wONtuUXq65ruuDkooSAuolNwI+M/qnhxLwDFH5QuIeOEcP990QA6X/q8kSwr15KhFm2AiwXERnzKFv0Zzz3K3JtZmnTc2WXaVKbgUHvO4VZY+Shea1PqqtdiInU5r2XWjRM9SZIqHTefk++fb3FoxRftacTSYAAsju1rLdPnX2WDv/g2x5up4++KxcSQ9bUUxZYK2s2ultVaIu7qnIEpeTZYO8Z+3+5AhQW8DjI6iL2XBffvQtdRC4ZxAmu6LoEHrTituOWKLkrJ/zSs0mF9Bh4L4tSocGR9aFb1l73RL0zZEdo+lTu6Zh7dKNfMSjLJQI7E9YUW40VBivngnwUffr60PUd/+DeVYgc/YchN1XoJSoTPXNJPzHJykl+rErYMg7nmZ8D1Ve0bCz6sQJgkWnWgPT3Y/E8AGARaWS4pZpQKJOAhy3dcdC0BmUw8BcyptsVjUApeYV5NCHP920FRblMMx/IMa2dDm+ciWgSsNKTYjtR3ALiWuHaY81qKfRAZbha2TSFbR6n+EBwQ/ozCfjp9rkBEtFPrElHLXMsARy3NLgsYghUTQC6stoYTQGeacC8p7iONcQxgAk8cg+6bQjwbAZeff44NIjQuvrAqyC/k3SZQxlhkAUaNm3EcGXjY9wWf9wu6Cl50hzFkmrvkqpsVkEtkVOTTYHorgAAe9/dDWCpyJseONlmaCCaytk/eD2r7Zu3YcWCk2r5vu9Fxe0wB0167Ct6fplGfvRj9uVtC3icukNMDaG+9xtIvArcEzi97tlzQinP0gr10XOoZ9PI5sJaZOlU6zmKcIs/1iFiRWjRI8ErpeN4HLb6Ny4YX1TkGR0tYLZ7LgapmvWlSAlCxwF2MpzPHcqzNWnLxQNb5fWCFVwtOHcGn5MtRFTzvJy614VKaA5C53wx+PbUE0BhuFqc7Vwnq/9VVRk6UvTR8looaZnDL4nV2zTbxyEzz5sGpza0dANBbQe/qoA9RdqF3i2vRJuiHBZoy5oMuzwEkYHupuxRim1RE6uew3Nrn1kUBbpj2zIiH4F6a9k4eNyzkvF9StJKiFFNJdwj3INWgdY+WjuWevn41ybYETmjFWYGMWW11vpZiUP8IZsC0mHJCpqwgaJWDC9CYLCgf0b6x4AOATZybdPqWslvy4mOQIQcjAYBYP9NkUxqnYwXhNoFS6xWPtUiAJ5n7YiHkCcjCfp28Fe1MprCEiHlaBjKC5W0Yt+N5RKTZRJjJtAwJK4T7Q66ASDdFeqGmdN9qRD8QxX0v5nGkoM6Bp0yr02mDTdH4SWOeLuv3C7znfA+d4xFzJvPP8chjnhNAiTGVMdbyYN54Sb6frKy5PvtKF08hfJc66IulQaZlYxwPu7tZLsEvkSvXZhrsEdw33ApZMPP8SZigBakZBSePMUFs5xF0MOaDVpnm3ByHGM3vqJ8ys2MCCDeB+u8v20jbpZWE7dosSybHyERFY3x14zhnlxb5S7oIimr8vZWOBsvEyIX2KsFtnWMWbJ4kxiePLefSAiwTKMIAg3sZHCpG2uZWgLSGJpC6gi0dcSrWz7Hx7W7xiCKM6R8Ap1O3wNUuXnUXc2ApMLuvaM0YVo8eQDc/VyseR+TvUVPBHnFMOgFdXl8xv/9dBaKC1m3Om9MWq1otF3XQgeSq4L7EOD5uN5OrPcVGqKT3NSth3CMF0BRHKOm4ATbSd2wy9tuppX1llTnqe1Yojo/asuDDkiuLdSWDImAGH+zXnfzBLO8k/cS47h3weKWv3A+n73+tgA8rCb9osrmlRZL9T0N4OHhYrmGcFb61KQaduCJSuPoFBg4IPDgxjL5GGnP/TNwCku8Tk8n+5plJL5OZFSUW6WRFQeo/NW+mHQOp3gki1mIyNQbytucVpnaVcUm6GALs0RR36cCu2J5PbHu7s0wcRU1DedkcdBAam1vMmCRtszs8g+DOutHH6mfVS37H4DOjZbfy6noW6M0sHnKTcBmFay2vCwcd5XgATtP8RVVfXYAfP+a5HNM0fmOSHLjBLD031+gBRAzBl+fT5D6hUKRlgRwaJJzqKvj8fDMFSVpw6YEf2d5Z2fZ6BSvN7q55Hn2brkWB/BY3u6d0HEz5hDNqQvHsqaW7nBOwualFhWTSrW/XF1R0vN8ueC97VLPtKtgwAjiLWNzCpVrKbQ4MZRYFS77n1pyKPzNidjUa76IaPBMUvi/njo3xIwmEZDCyS0MXcfrvYX3hHlpkALbT4ziey+FuJtsqD62RfQQgjb0Fku7SxtymANjvtyfkasQU0JkzZJDNkTBtWJXY2D+CNAYy01WSg2lJ9rVSyVexWBGeR3cZx4TXYor0FPcDukYNWFy9CF11UFk8qIRp4Z/fnqOoHYDI1inFTK09wEYZZRoIOlKV2SwQLe1UAsTV2/JSU5Hz/S4rq7Tw9qe5wCaQ9sKqwf2BLh6U73hddcgb7q05Di72b6IdTEJA6VnzvTzu71bsKcaC59R0r3xJ/6UNbO8HWEdKClZdwUbISQEUcmc9mfa+tQ0sGNeX/ujAD7dvLvjogCxFzO6ASAxe+ntCeysERSzKKTODaFQ5iBmtpGuvLa0vTeb8bP4bByx94Ec8WIbrxgCVYiqFnPqeWfpiEbwy9wE8MrLFR6LWqih783oSsx/d0v4EPbI0cmf8kjJ89bQG5PP5M3OE5IHKwKM3Bx6nb0gn0+8G6FjdIaGIdJlz7mX+daoU+YFxlPUZX1kTZGXlps+CWddeUcTcKHQNsFAbgAACmXCKGStTkTYxJlirOnviQA03ToVGTEZz9Y3C99kp23dpFuDrmRxstHjMFhXX9N3twswZ9mUvDddesUnDiZFBQcFYPQsiBy6SZGw10me3kM3/ADGrZSXPXwAYNcC1wwjXyGz6iL79UZpr/q75taqOTKQGCWCXQSPHg2ObfzYHgeT3yEHBOS14uj/MHXLz1GrGbcQzTxatxV2C2bI0u3OShQTj9zzuDGY2yv0+gTQAc3Cyg2KSjeW+sObQsYCfRwyY2sXea4KNxeLxyNpo8X0zMJnkg5+/1tySjjnIMptJkD8nGDGKgdg/KW9CQVniKfhLUgpzBh8j8GOPiv1ZIuh0ek483may7LvjExFY6QxN13y03Dlu601eezU0/cgyjXO0zsFXtG8s+NheTjirMcptgIKo9cKmyzhQgC8LIkhafGQjSNUtD0GNrhgMp1xwEVAZOMEvHl2w1sfH0wJNH04BPPybJekndJDAc0almpB8uhatIYF0ic4Javz9b2UuLsSX0+JE4LT2XrZ673h6c+D5cuBpN1HB+IymAhFn4CJbKvvl97LAslFEimCCFg/j7PDy6dvIalBYMNp51kE0FGZYD45tieHQNSHluPU0/mKxOT509yCLk7X0PTYrRVjPGPwc18nmXgXEheXRKm6thzn/aKbhf3E8eVCebeojHqAF6KDFg1TWF6dkZxbFqV6wTFgU7sATjslF8qL7xM3B1iBmtSjv0VGCTOz759sQljyH1g8K13f94oGVzzh6DTrxw1Non+uJl+YmfulAsXTeSzkjLuV92y0LIgcgUvtNAjZnu3CtjOMMUFFgMijzdGsTOUW6ymQF2UvD+7bj2nd8cTzh3XmJ1FOJvgDXVvGrtzfBg8FgXjLRZlI2Eq412JzTTdYheNcu3oc9LAT2ygretx23VvHl8YRTS3B0cI5v3UjWPr89TYHazWnga9FYU3RjbU5cl8FC1x4ZL5sDppH6PbJxOD+WqdIjbfcpxZEQ2NEKFzElvUSg6q0M6xctYYwFYszXtnV7t4/q2v+iCSQlMIQqrcinzErapQM98R4x4FIte8+x9niP/bsO31NpTciggT9a+jzRl+d9mcrjWv9KugwruGCykupufSaHib0KMvaRpZxD4KMziYZknQYQGZWRMePjFpmSGM8e572iRK2upFeVLo5nhxXPc1I3rEDxA+0bCz7QdEZR6YFzk/RzAm+aFhZRLNJg5ksnYTItpITsKKznExHrZj3lrn0EIqRFxrqdkE26dzYh3rU88fmd1nEJkvEEoOHLrogidKD1RywldqsWYxDrOwYXyfznF6O7CrZhRZKJfz/y+DGNJyvWxnBNx3mfNIHA0HgkEKGKxBznhbH+fYfs18XzaHzjvjKeN+GtR9dYfdwMADy0RDBa8d3wiM16lFkHgL3orN36j5xBEYGsblko6hwdkERUxh3LgzX9mIqRjWGZLja4BQWMUKMl5uhbxKDcugFQWmaANcZkjkEY4zAKic2fz5YxAK/7xr+idS50AE3HGNtzFJxuFcqppwDC0tJ6wc3r57A9wThQSGGf7eNMa6WliUX3zl6j2uuR4jdeTnOnXN2lEtaHOuJlbr3ibDX6x35VpqPCiflck2bcCxstIJyHKv3O6kF+FQBhkcrxIDGWyCCxhMUjx/ucqBZEigwUR+xTnt+ZTAxjH7jbb3H/riqRvn9XFH1zF1/aH9b3PG119/fI1xbFXSpvGtfoEvd+7lHpUGZRhnL74Dkoj6ZnfrDe41F0nGc3SvdMSi8Ud+9RPMID+bneNyvt2TqzvrKSj9fYgh9atl5r31zwITKCTS/wiqyIFK67lgYAwLCOMOsjmabGBOrd+cCYzEngLy+GrBNJIZeRZfoOefGsgpF/BiW4vVyRMrWUV1AsG3MGEBXo2T/oL5KKus8ynVcUzVORIzqc5Fqp2Bb96rdWo+4DU2SldJRLG9VxhWXv1fupHihnwvF6yKTxSOkQBznZnMz4j86Xt7r5MwMQWqRoyqyatBGYbxUYRaaqRhrzBPrznPA7ahELaKGZNJtnkU4HLCvhUlsQTgHbko5aYx1TaGRBlWuyMFWR2uS1GSfGu3rx+IERJPosxlL60vfJRfJUDg84NaDyEsGtxiHy0vfh/ilWF+XqabW0hPygPUfRs1Mrvjgv4RrI7gOm0lLg3PqGIseUskniM5Z6P3qN0vG5ii1c0HYX0DlehI0WgDfbMYJZdQYV8Oe+dQtuvbWK67mZBS8FuqIXtG4unJe2od/e4qUeuG03fFZv6HUIeLrNwmIUKas14jmKdKuQ43P9Zd/wcu54f+5W4K0Y5bhZGoaLg4BERFHFxuJ2+jj3ApEWVWqbFtyag43SBnOp9/NST1wwu7Q2t268qQfelJHJcqjxfPzqYXFGzE7K5GGsLfTSNrw7LwNgpDULwLKX3PLR1KylZyu4XXc7/kgcQXwnzzI4h7oMHiUg9i2zShgAURHbF55b6Cc4zTLat4LJYxR7RpITDZAqk8CNWD1aj7nluPU4BGySA4y/02rXJ1uBcs+qCC4ni08bJIX8GVZzHftKcGfw2nlBL5ksWX6xiHiAhmwhX107jP3jIS0OvVOqM+gKGSRuWQ4aiI9HH99Y8KE1Pfoj7fW1Z+TnnroU/nwK8sk6wOhjXaS7D2TXV+8VyBWPv5+6yYUhr3d7AjNdrIAbzXy8Rr7w+syafk2Br0F4to5hupYCUbmRz8bviheEqqWjhrWjhpBgTr4J5EfWi/sBXAvEPSoHnh+VAj8eBOmFQ0Lb6brKfum4EKPAR0nusUbu7qtLrwNcavw9/UyNgYtjjB5bAAwMWEZGaMaQiPHIxwLJxw73sRfFjtOyLbTgwFy0jIGRU2yBmIBo8HReP4Zaey75Do9RaPoooyVruSNgdD3ubmygU79oulcMS9mj9TCngd6TjsUzq8T1OYZnr+gibpEow5WBPPfcqf3Rk8afYxvCwiCWIjtVpsU8Tpy3KgqUhq27u62WaU1sU3Dq/DysexTB2QVRE4XuzCqYrGWPrpPHMKwfWNalIqxZZwA+y86htep00JgzlNZgYfZ7jPHIXFIVc6MqhiIxnTiE/3BH+wwlAKIAon6Kp9pKscB9LQVdusWR6NgroDJ4y04MpQ34ML+RjA4Y89Lcn9hHYMfexdO5tUa4ifDZP7YJx276cwCyJaOHB00y5dHe/6Hb5fM+piVA9rHtGws+zjfV5qcDcgAFgsZy8JOAs5+D5AmBcAHEE5LLIjN5dlpTWEOGXwQaJMRbRpSIb4GGjzBBCEkukC0tzgQs8qTd1ZiR9DlwxykyaQj83vtpgUcI7o7IUee5p/jnuGu1Gu/BZ/sNT9sZufoAcDsrzrN6sKgCRYYg942yNd+csgmWRehIf78AFg5vTeXDxcPLLbYjARAgQCbExsE2peVi3FcKoLBxwCEort1Mt+eLzjHXND2MneljzHU5LzaEZAGYUhvVMjaI+Gjqzu4Lu5UdT3ZSWgroCtjkKTgsTGI3WKUn3FkHWJvk2vcITGUaJ9k6q/uBrrBrrNVXARNaT+VEUZ3qj5wquJ5bCMB43pQGyqBTgKCgo6tlShztPiPjXLJdxDV2kpXtruXn1NJb2/Dl+YS1sdDZ+2YWh1NHYTMSXpWiqKkLdr/Eo9IrgAs2z2Z5cmbaTMbVFPiyPU3gscCu86Y0fKteo6ZNtnK9b0az/uI/2WrpuJ0VL7c9Aq9LUfRqgORsxnFiljNLLb5ExeQyARLOyaWeI24DMgXAvm8XXHv12CSmL5sg59o1q5DFrNx6jcyYwbL6OHW4iqIB6Mxo4eaZM0i6W017Ga4EGe9i3iN1A/S5A1tHffL4lbSXsBRDe7eZpYQKh3OGlJcyuzIETsblUnvdT9kPHRuCpKJwgUOqonMJZrnB0h1t/lhdHkRf1kfldpfBA+MrHtzGLC7j85AFKb6F+6Yd9AD/8Zoy/n51r2PMRwr8/9j2jQUflnqKr0ZeGRXKBFI/ClxmDTfQ64Iw9as6sSDTFYFOx+TP1mPxgc8nBJ5+Ku619PV4IND39NmC6vO9jb111BTJWQzGrEgT+YfHRsP2l24ry/fQqHBLd81kHQHM5ClA8JD4v3AlpTmbLBkJ/AUHwAoU89xgwnvjmuuDpWDT6UQd2S5r9sYjq0BPb/VKf26b+QBwZPUkSJmow7XEX+SdIFkZW3OwYQGZJeq0nN2KsLEYGjBiUNZG0/z7tkfZdVJ4P2oRE9AruhSQQCvHBeTnfa098mNzLMS/z+t0KhWvBqyo0XNuMqfIuKa5crYFwPFeBcafMeJs+jRekVbqrgpyZBDE0LXFmJ+uBZt0nDACNMhwjZAEbQzCCNouRS0TdBlHFg/MpG+AAZlwh7kViM+YgTH709NLynF7d14ijXzlV8mA49FcxRxGXaaM7iX2nCll87WtxRUJVEXZOoq7bktSaLpxt6NTYfXxM0VEobvaftDu96epz2sfZOwPsXLSHhpW2uUikxXiwSHT4bymLnvPI7mSPo5rZgVq/T3vla7LhdxbL/1oe8/AQ+fnoJvqY9s3F3xsQN8kqhLah0jatY9lwXAzqEwkM/4RgHFs9xLytOJr0pzVBRxnIgBNntSExEPoc5KoFWNMdsglma/B55mQbb5WHgtaYlbrhLC/IwNGoON9TmCsnAbolAWGeFA8O8dFLNK7m5ZyOyvee+l0+uz30oHtxDsxjboHCdCggw/28uUtY1nw8yy+mUpkNRSvXcFr2ncASoeoFdSzXcbGWjfzqZal1g7Hi/5Olt4Wmn99jjlGUw79sknEUC/IhBHnWnSwonpMimW71Eg/BBBZB0VYmXbEQABDALDR/x/gA8NFwRomDCQ8+oYDg+V0vdZqFu/dNN4vzqdIAx2pvPcBVeSd6FrwpbOR/uXrW3Q1Km4K9OLAlICV5vqXpb4H282zZfgNmWwBBDCYgnYhMQ5W2r3g5dhQxLJeqgvGue+jeB1rl+T+ovaw0NH68nLa2H7m9Ph5HKiS76XhSc4AHy+wd+TLdjGODAC1nnhbbhEbErwqpLsXqxTbVbD7HJ29oKDiFMUbHFCYUO10ibrA6F0gVe14j3tR9eq1kCgcxzliMbtNGCBaotDgmk77ncv7AIinGuj48rjgL37xWVgv963h7ZPHi3iWYAYhAK1B9nej1dNT5aPWlspwLx8SJeYnLJv3Rvh7vynKxWgALhcyv3LtpFM32+C1eaS9Zz32TYEmKO9H0IMop9f2xch65L3THk4Sx4j/yMpIblTa2nKNvPcvrwYVKGG/+Hfam4J0LF2LrqRclHVthftkeuaQXXHzIR5yf/h8magtyz6tgG4fjz6+ueBDEj0uhYTv8boE/kg2qyXh8wg5T+dOX4zr81ROfnz3SCghAZAFCa7HAWmx5s+zYHvt5wOFIFhEczDtchDjHKZxAR6vTIHFbRBAADhbRdtaaDQUCgLcx2sILIAUCMFBy8c4zkaseG2ddVAj4LAgNlxz48yshxT2cwwP5tQ5nr4i8hXg5XlbtId87Uf2SUlcLOGi0WT58OuEeR5z4CT/zho7S9GTdyNbP/jz1jd8kbpSPcuCtN65PbIo8Nigbee4M2sGOqwUBD7J3892Z8nxv1UNJBaYS+Bx5suHN6qRHTFAhFkfJMak+BrM8Qxfdf0AbqUDvZi5XefvM/U8MCxUpDzfpaGVEuDjfdvxvl9wcyCwlYZNnUBMzZLQNZGLYViw+HteA0xTbU45zkqvdFMGTXmyiD3a70bg6nieHLvCYOf8ncUsdXQd6bykZLf4k5ES3VSAVqd7cfwqALSxtiQpaKHoKP+WEGqrRTGEIANACfr9GXP8Gd29rQnU9wwNKwvmnwASnrz7LlsJ4m8eppgLtqVjKI986xvX4fOuLcmW6OKyH01hBY+WNff3JAen6yzHReBpllsyxlqX+wQAQToe6Ry6ph507bX2jQUfAMbi88UW2mbVGDT6m2KwCFLKMhD8I3/OgfeXQf17ODihRh0nLAWE2AKh9vnYScYxuhmzIHwULCTpuaeXkLcncuWzbqPvk/BlzZl07B3yhgGOgd49iGu3BX87Nly2hlY6eump0JhVoSysYihAqe7jT/EapaTbi7pYlIjpABDaFEFNEcuiIR14awX9VoFb8Ywgj99J2oSKz0+aD/4x1ejRNCfcr/uYhxW45jU0xabwBVSEH7f5360VnK5Nrxo/AUWkNbqwJ2OndecegLTIpjDt/ldvb7CVhi/LE/bScClnKhTX0/30ruYGuUTI03GoxT4ww4EkU0gWj6tzaJyuWVNY5ViN7qYkBhpGGmk1y0MuIPio5fiO8dkQcnTz1EpSsYKttohn2JxyfHUVTuRamoRjscwR9p1su8b+2Z17w9ONXbi/P/cgAWM2CHk8rn0zK48Yq+spfbi0VD3eo0TWEueac5//Wbpvxe2sOI5qglRHrSO+K6fHxVRR7LVNY8vnXJ+FsSYR0yIzUBvrzvYFI7UrePt0w9EqrthQSw+AfWCAvuftBERTppe1KopTFFK7cfv04rFTEu9SVACn4ObJvnH2qEKt4YalAsR949o2tLOgkTX19IvlulR5TxcFJHEGJcBwRzgZi9JB8a5TOm1Urj2d58MtQuLA486q+kCW0MKRAVHI/BUQ8Hrcq2o6QTGIM7lH8lrck3vqm98v6uRs41LRH79/yEvKpzo68WF1Ym7fWPAhqpCeMj4yUOCDpsnk7+GiyEgxTfgECHgelg9fWyBJIMl6HJYXRpeJkDGR07UqXg/U+QoYScwAYMrsCQBCtE1AMYGeoXVIecAAWjTcIDmYDDBtjBH4QPK1uu81LuNaWtbsKVh4Lk3eBkCGViaiIQyYTbO+RZyHBMgDTExj7PPyUOblDcHX1wQO83HpGh9S2mkdon+dWmZFiuHAvXvkUQvztwdK0q0QMQRi5FS7NOy1xb0eNdJ059ob4z5mYt9UsAnC8lEhADqqGD/HViz74am6aV/mOjaarhdBlzLIrfg3f7alq2u69bqdfSguZG1xnwfj3FUifZUuPlWBOijOzLy5SvgAOi1+AnBG1DnuhS6WAgXTqhuM9ZMAkMc9stIQsJeiaB6zhKVkeS0aKbub9MlqAiCq4RJ4BNdMq1a4U60oH1N9M5PuGTT2fi2fvxUkZotHBphFFK33qAs0xYKs+yC1ZyAYnucsNo091SgXTPnZtoanbS79UIriJtXqTsKLUMaLC0y1tDzNdwy6DnDBzZTnrliEafkLoAmQkOWW77Ufslzk4yelqjzY23jOg2vlbfLBLaYPbGqTbEh9D+VsBTn5vr5nDt6lB8/2SvvGgg/ANUrXKlUHk1xkNeTG9dHT4ORBSvnNAQImBLzcG0nA5OxD/3KK3UzX4L3J9hZo1D+fKgum+96h4vRZvsYUh5LlsWDUaFE7MIK31JF4VG9NwOMUuz7BhwMP2Tq2veGyncH1QWBwuDmYm8m2r9TrfikZgphD2P1lFnHmStfcREyLOM96B1B4Let6SllL45rvXfo8FzlD5U57WDKKImYnRYwP0BvDOeKE+DPNpfVXg+eAjdnjXQvqV5R9J4hgdoxxYmzBJ/GMw7Rd6cZ4Ws4p7RPAXR2Rq1OpH1qWGI8aMSmowzpDSwESGNlEUOoQLrdbDUsH+x3z5vNYXfPOgrurAGW2nEzAA1w/411bSbRCO3+lTRYPCl+MzKvjrF7ifbwnpZRXrwcgrEhPXiyOgCqCanW4MwBEPM0tbbWRdfOgmOJKtV7EhGkDoK1Yhl4yb9ZidVp2Mpa6ZYp1lDj2W2kBYK9tw/tjx5NapeFLPbGVhm9tt2BZ7RYghyYjHZtAhndvqVYTs24ysNyk4SwFVTtIGjgreekdBkZGXhaAQJSjl06GawWq+v7U8GYfVKZ7NQ6Ud2XHeXbcsFuGje97GkoX60Jh9IEYJ+GNnPkSe3u3g8rpqcArqQyVsQXgUKZMyk0CLGFh4L7P2Db2jfKQVorLvHXFNSkDs+yZDkjPyvgQt9LcKeGKWWlbZKr487FfvzZIxoCgUld3BzxsRG+aQGtCbOo/78xb3NgyggO8Rou3B/tQIEH+8QheTjfCEFKKYRnQLFABJAvIa/N3JwhX4KTpuJ4EYka11S6ifHi6sS724FL9p7hbReyF5uZGkzBgG5/FZswDRVM5QQUwggl7q+FmsX8FLNpkWXc2YCa8YeRlXNQqw0+8jsuD8Z5S5PJYyP3QrR/ENYnsT4m1JtBpAxkaxGi9FxzkQXArQNlHDZKvahSYXWuyIFha46VYkbbP6g2fbVc8lwNPMszcrDlywCwTDDbk/D05IDndlXLzctGbc7rM/bB85CKKHR21ujYsgz1T/brq9+DgEbTS5P8omNUEpLnZCHDXKSnldYsH+5stFavLhc2yPcySJ5yb7rEBgAcvlziP7qdN+t2ckaeFmihjQR5VM2Zry+drgOa4tqTfZxCuTk3eu7kub6cHr5YtgBBgrqOI1XCQdqknNlrh6E5y19ieLGgtAjO8n8sLR+BxtBKGGI4z6bzpbmKNo0ipX1+8bM4nKKDyEUSR98JdqlmEShllDOiem+KO0rgNVCFmWQHQ9wf1s3reqP3jvC+o3AtkDkz+mc9daSIYK+bDzLGJGL042cVM6mPsNTrOW/f+lVBSgQgpmPqV52NVypDAEp//EXHbOUCRrEDsA+0bCz5UBFrE/HxlCJKpUStakKn9MY552LK2miZR0jl3wIRfLeazZKlbHmK+Hy0f8uC7WNzr/vwQafL69jIp+4ABaCZNQfwAsZcW4lV0u4AsgeWpebpaD1KgUoa5/FIHp8G1mcn4sjVLK33w2OQlABzIOKA5/WBaPaiVsK7SFBOrwxweqXnpJYtxfLTeCapiHGUGHivC/wAeYIGqKXYkn57XpgsLM+vb69W6YKvdQdwINrwswn5iNvVYkO4Ty829uhZr1W1v+HZ9iRoxwIj3MM3VrhnfYRQde+l7COpbq1NMAFv1XaxIc5eCpYOWYkKTNVpoaicAEVh/92oVZTN75900SQpO5BCmeW/puDgHOp2b20oGtgb9bklDJ9NuUPmfBb2OTCICj720YGWdUnkdgHQdQK7DADKBIjBARz4/A48c7JmfI2RZUki0jcq4FlM11kWuJEw3DMc+U6lfSsNZLYuFjLlPNVlyOkB6+jxP0TdXQE4PrhWxLBswgFcUh5i7iWBbfJ2sAR2Snm0oVg70yUmUlEt7WIsdqV41O1KIlzXEG5j1LAX7ibr7SqPWisV9Pd4E7iwxvudQKZ4MODpOGsqwztkpAFg09W4PW2QYrRikHwEWRYdjNoEnSxiYMlgYpBuZien4LL/4nFnG8R8DgnloH6zY5cTIgPnI9o0FH9IV5VSUY9Ds6qMBAUL4rqbxrPFnIDFZAvhxRpH8sQCZu4HlsQ8EWZi9cj8FIz1rucb88Ev/CTpIarN2ROO0sKQEHf2m5nLZzJVSNgcfnm+vKJCto27NNQkNq8RWOp63MwIayQMwNstFu1gfQ0ZEeveOKYZw4TErj4ou6EDEn0HU06HNjcQArxVohpUpbyZZO8AY36DsB89Jx/oxfRvkQ9M6IijNoESGMKYmT3BMLfDWhqil+2MGHqaJAoiic5t0r/1RrER9GYXfcmZKZiVlSmcppum+rVeQ34MVbrsWXOsW/v439ZiLinU4mVmPrAXLcNjDOkBOiPIBrZ8pn/NED0E2j9fIjlqFXlg2MNcL4fd0ZazZL1U0qugWX5eW9u1Cv1vxwmytY2bNqF3TQ9PfEuBroti0YIu5ULMmOSFbVPIVDbbUoh2bLFlMsBgMroUqG2pJxFnNFpjpHB1aBG2byb3e1DOsTSQVI516brSAPNcDb+oxWTe20rGh4yyMLzJCuSOBvu7jZnuvAGhT0GtYVppZALtaFkq4PQDQCjEsui6wU1ZLru6tOR5LgM3LGLzZjjlQN62JUjo4gNoFevDiQLakcm+lZQPIQEjv93j1gHeCxLxfp2MEGBphcnuzZEZYPjIASNeY9pu1ZfmTwEk8R1+OXauke79jv/T70TgUh/ieKA7QbG5sT1bAKOoVKKdCUzG/r2rfXPDRADltgi0FyzX5hOamQU//RBHZJvnYaeAzIPCPVsKVCdEC86S9glRnE8l9e9Tn9foBPHh/mrTIcMpZCyic7stLeRyH+VENeJip0pCMokAF6N2sIfs+LB8WBFpCW6e2uObwr4GGGVA8LhQ3tMxoEq+vmxfH5j8dU3QAhpwel+c0AQYAU+Dp3RxmkJhfXJ2HNXyirAbs182bFE240d14fnMpqDJTxamrtYIq/eqO2Ep3F9dIvyGbJ7oJAhJXsXjc4L4wfz9rt+Tg02A2hWml0IK35YazFK+wSw15AI8KI7/i+Sl6Zfzm2i61T1b2fa09cp/M/Rzfr+sggpQXwMHvssXjtXvl73K2lroQUn4nFhzcZQQJ0xoFIKwEluHjhGAJNDEGpIriktKqIT7nkFjEI9W2RBZKF4lA77AYJDCtKEC1CrH53aPF41LPu+DYPAabwABR9RouYTGTiGvZSpsym9a2xvZw3JjW+yhdeOxxmagc0/4d1yzqioGCVaNp8hSxYNu9tsTqKnhf9qlvtRr/CS1gepP5Pn7vyZqcP8esLFJeCBDcHfRgZ9k0yZrYT0ZWSQCuZd9Z3bcfarFPcY/KIQPpvrz25KLJsSRIAGO6QTqfmTwWIjYp5wSL5p7++P5/c8HHVCowTa7cD1o2R7mLGt012lFeGC40HOm6NWVNn7R7p58JjEwCXubvJP5Ll8rHrxO7AA8FFp9D+sdn39KzLPeLYx3ol9NNw1HuPsHbfJoj4tZKBPedZ0VrRuC01f1V3zTdM4z5mLg9gMh0qR653pUlnhNvAYOzUn+YVsgWsTlMsQ6IjuBtyY2mXM6JTpOUfs2aAa1Lj/fZea7desXAs7wHq5q216oHdYriqQ6XQ1ejIj+Lp4qWNsVErGmygPMtQB98N7fdN+ECK/Ve3fRf3Sry/TZIxRiISrp2puTm4NWCkabLqq12/sjyOEtDdyF89uI1Z/zc7Yx0TgpC3qtjUIHnoM218mkOSKUwzhkZ2e0yB5jOMRu7C+AOE7bmtig4z4J+OoeM//7l7RJppM/1wHM1GIDoSQAAYmRJREFU698GYJcTT8WsBQbmZKqH85wsDNEH6Xgqxyg4V4alqzlIGQuy+HicuBZLaRUuzAzMPO6h1h4Wyqd64lLOKc6DVpcMMDnWBDtM6z6ASSifvU5Ajy7Yo1XstaP3dhdsfPYSgFV9rd98P4nYsC4o1+IK1byvqNierQWDEJFuF68oTSUqLDwyAl2f6om2F1zPanPb3EV1uNWFm3pRUwC43sJ1qmMvrb4f8f4UwP79ChQmBccBTd4bIlHC9zMx3BrfA4iU1uzK1fW666vPPoWytVhqcj8Fd24bAF7wjvdN51POMVtSBD0HBsPAjypwvpGRUPER7RsLPl5tWbMFJgE9abPkBknaMlGqaJrkPMj8NQEGFazuz+nWsUBeQ43TwfPfXOtIfaBsjWN0Blv5+lPgERelC8BA812gy2qNQnBKVDci6K3MtaCfVlvjdlYc1YTfnX9dR72WnBbJvxkzYsIGKPkhNAUoTuOS4jxkJhLivK5tHQcgjduDoeOx05jkl3dCE5jmh6cKiH/uQYCmZ6vknoClpgIIK8YpiqKJERRlyoShgAAQ1o3cGorVWfHjqwOULkyNHHU+mhYPiiT4KKO+iBZE5VmxOA3WLakOiI6+OWU1cKKEG4OZKx3OZ+HAwPglBrACstXBVgOPzaA1XBBcBz6ePD/HUjxqU2yIxzzsbtFpxQQ9BTODTeEA3G4sOFqBSEU99+mapYwMG8bZUHhfZfPnbYuFcGQOBS0mzIrEQn+rsvihFGEALpAVUsY7VlP16TwGBMDVx+7J3aeQQQnPdUVq9uJofV1z5uZDWGOKW70yQGkpyFphPCTBoZKBjO9Nd4+4KJp0u8zH+LNni4tfaPO046HMuHu52Ts3pbJQZiRzBr9eXeOUGZNF49HGovPP2IeWOEEqRWGNiL48uBzPW629aZuaLLaC+V75evx8fT5aNQBAZuoFDk+k5TIGk028oJ+zkn9s+8aCDy2CvgOdheF0XhSRC06BfUoaID8nLe7CCWppAS0TpHkBAhbFmxHuKrCAB+ljo7/TOXmR5cN5L+/bdI9lcd0t+ryQBRFcqkusCS/C+gfFqz9K17Efwl7U1gab6O22xcZ/7iU0VJbJVmAqIEduDu0FdWuo9XSftb2BORBaiqVx5kJQgJvRlQja/YusBUGGUw5aevZpvFJKrQIA3TTLXGSAMo97CiLLcZK+KU4bENK6dE1OPYvn1iouAKSeBgzSCz3M8wVnB25qhdnenYOkCpjrauT6Jd/Zrngqh5E9eawBYzz4k0RWZDPNbolTBV+eT1HWnm4C7MATTjRx9k7vMgNUrfjYZjTrbcN7F9BM7zwS22XfBZfa8FyPSUCQVpztUhpuveL9MQqrsQQ7tWXWKglXiE82x4ktUl1hAjcsHlpw65ulmZ47rudmsTCk86/OKyMahdyKWCDl+3PHm+3Acz2MUMwFeLbYcFwicwiDTI7HjJRnO45CmwXlGLDa1YDql8cFL8dmBRwpgKtbPLYepH61jJiP1fUUY4IRFMzvdjgo9nVIsjP263Q+kluzAoC0BjW3SDG7pLWCL68XvFssn6xDw0w1VQStet8AaYkOIDWzMKiVUygy9j43+dfd+D14JsF7zsarpaO6W0qCQGTsH5H+esoAQlR2Eghhf4LTA68og8AMCLj3pD0nK6vIvwvuXCYDCMCEPfUgArMsvygTc2rtA/C0yp9QuNTi2tY+PXq2SGBIx9CKgwxgPqJ9Y8EHgLvBzR9nwBof3h9656dfBcfdvXgdTddeQcOjiXnl70CjuePrdRx5RsS16AyEVgDzqCVB+8hsxs5kSvRSDHisdTRis0jpsE0F4IacNp84HpiyB/LjFdHIWsibEzD6opB0PtI17K2jcCf613UOHszbo7WgCXhgneNHw5o+V+AOCOZ+3GlM6fkxur58ZrsJQcYpNsZkK+XnL+ceguNWLa2zaMUTzoj7YMxH5vcAHCxipl3PpdNPrTi143ABXdGBMmdfnL2E1eTaK25tw63XqcgY1wVg83o4bfcNFtBayqyVm0VgcEWYcMOI3/BYgQ+1lRNmcr0gjTEMhLHPTYd1rhezXzCls7WCs2gI29pLWEuuzVbyVTpayvMiF8sq+GO8McATQRD7dfPfb56GTkF/axVnqxEcDu+zFE9NrsPawTGwjBovrKYeNCv3NXuYyWQnjiyhw61yfKacutwxUmfDUuXVhxvj0fz9XStV38UBPbJoeF/M4OjM1TpetDBElh5ZPkBi0tW5oOPah+EOEePMSJXDhXuesBPpnDWW4pW9J+8hsWX7fvPath2iRdL+ki/NPUXuj49zHsk+mbv56NrTSQmfxX3T73H4o9cxH/c1iD6+Nvj4P//P/xN/5I/8EfzZP/tn8e7dO/xNf9PfhJ//+Z/HT/zET1jnVPFH/+gfxc/93M/hV3/1V/GTP/mT+FN/6k/ht/223/a17mP+dMwTmiwQ5QTQlkJhSMKeSE0S6OA10t8TsPDPJwvEgnIzeAnEty4MjHPp5uSaniwjatlpcXjzF66KBYv6ifwp6ZkAzNJsDEOseJPlfKgxLlYV01LzGIDKYFO25uym+97wfDnwvJ3jBVdLgSziWSDeWK+lSAvkzwqvx2kb/u22GbEYh7ybAAmLxyrc1U2mTSBngRwyp3RpGtsEziaQeT4AG0ta2GQpwbhGWM/U9mrt8MybdH2CRwAowOXpxNunG7779DJVE8002pfSUOpIj+0uxN83szadzj2xVsdlITcrw+7ulZ6CRKVb5dle8EV7Aqmyq2gQSMV8wUDO+5MVas3tc9QaFgNm0bxvRiv+3supvz93XNuGd8eOs/m5DhbIv3C2GiRYdD/V0vF2uwVAuDXLzDGh5qAWiPocsZwxu12YZcM1ubuF5bv7Szzv0a367tU19798fYvraQRbLbOa9hKWBePRsDkoothqw177DBaclj4XBiSwCUHoLp5rH1vsqQbivzie8NL2KALHNfFybjhbxQ/ePUWsgjILx62LcGtNrcPlEoR2WvDSNmxa8IwDl9LwVIyjhwGyuxeyI9V7c1cfgEGl73/fHGRyfTTnrrmd9pzHseE4qpU+ACbQUS7NA3rtJRECvE2BZu8RFG6BRQCLsEoCwCHT/sqUWAKesxdbf0kRujpgy9wisT/kRoF5jLBXFWAiCFtrR/HULEv8vHBbLPJA7UUbsmi9nswKU3zGc3jJLFxkOnTsefddHfflNdf9kX2t8/jTilEO8f7oyNYs6TgMuWVj8WFlIbevBT7+8l/+y/jJn/xJ/D1/z9+DP/tn/yx+42/8jfhf/pf/Bb/u1/26OOZf/Vf/Vfzr//q/jn/33/138eM//uP4l/6lfwm/9/f+XvxP/9P/hOfn54+/GSfzQ0DKwftXgq0siNLYrCjubtgEd5ptXE/SC/Pw5Pnz7N+bEGi+Qe6nptldDn2IYKcDl34JwkeaLR8MZFs/C4rkMqL3gZQSyE0bHx57TcdTk9XY9Ef/4m/FyDpIrJPhbqF1aAGld79nUPDKsdR0Jv/vV7U874+UNh/r4n5nAo9pDKgx+g2nIMw01oqRhmrnY2gnoeF2nF78iwXMquR5oiZuGjDdsV2HwMl9MwtIw3Vi6rT+Mz6ElWFNAJcAHgwobE1Q3b10O4HNTcHV+7TBiuJt0tz9U6dx4fOtmvJsMdG7nwRmBE2bc3M00tK3DddzcwFfRpXVtOZi7BVh5s5rmACtoOIkH4a318jjchotQdCLA6IMLLsKTq/lEm6W6Nt48aUotq1HoGldrEn5njY2fQTnonsKrKPoYvEdPSTT3F+zipUJeNDyEcXb/N8QuG6Z8fvfySIqQkWGVF1rutuFXL6NfZB6lDa3yKplV3EcB1hf5nK9tKiVlGDc3yr8ef/X9ras6Mjj40KQ+0/NzzodNI6Zj13AwHr83c0etNf2tKzg3c3PK9fWDz3oX1n7WuDjX/lX/hV873vfw8///M/HZz/+4z8++qGKn/3Zn8W/+C/+i/gH/oF/AADw7/17/x5+9Ed/FP/xf/wf4x/9R//Ru2ter1dcr6N09eeffw4AqLeOcgBlx9A8+5iYFRjE4g8Ya/8KAHS3lCAdk7IkeE6MczDr+U83u03jzyjlhFKn7qTJ5SJSJLDE7zyDI5CtpAs9WiCKhDKXfqa+TgG1ri3te0OtHtooipVxERibLUR9gymeLjpSYF9upm1YTMjoSG8pWBQNtcpDQcLnUBUnGRPglOk7NM/BjzRreMqxjHFZrBf0X9KP/EG2PQq6FFUe9wbml38FHOoWKreAWPwRrNR31WAKndINteB6bsH6yXREBiM+1wOowNvNypSz9Pu744Kz27nsXjaRl14DwDw5r8QuDU0KPtuujwXissMwbdr4Ry64yahySr4ICtdb33A0i8+4nRUvtz3AIwWlVSM2X3/rBTcvUf+0GwfF0eoEao9Wgw48XAtiFPwAAgwzc6R4EGl3ywjH71Is26PCxvQmFljLkvDsM2NJzsNdRoe7ivaOUhX75TSXhuiIWVoaGTyBkVYLsChdw9vtZtam0nDrG963HZ/fnvH+3PHl7YLjrIP114Uni8RtjEPx+KneBLLZ1vD2sxf8yJuXSKnNrh4CsE3MypQzXHYZFpAnman4v3++cQvNk6Vqn3vUfzm1uKWoTLVwjqOiHRX9qFaa/s2J7dImhSXKJvga5PaW9y3bax+j+RDYmrhAGtDebzgEwGcIbpGe7tGKA/xWbWyP4sqLmOVFjOcIVdA2MRfMyVRSuXOzZP2FciB/Ecdm0JAyLWOvF0QlXPHg18kSIhg05ysmm5Qm/4hW9RwfshwPDLwTlhcFipM7spAcsySZrRKWl8z+92hAYM+kVQYb+Ue2r3Eo8J/8J/8JfuInfgL/8D/8D+M3/abfhN/1u34Xfu7nfi6+/9/+t/8Nv/zLv4zf83t+T3z23e9+F3/H3/F34M//+T//8Jp/4k/8CXz3u9+Nf9/73vesY8cDe1fWZB+hVSAWQD5n/D4jFk3AYdpe1mt8TCPC/tC56x7GYxOpDnOmKVWn512F7Qf6kv2Zj17szI+Q/7HIFmM+tDOQ0BgNz1YnrXEaz8UaElwXia9A8mauGIGk/N1/ymmARE5jF12Bx/REeYzSv/ha5n9jYB9cY21+sVfPA2agknzRKwcFP8scKVHSXucy55s0XEoLnpW5uN8AEJNP3v/l4FJaOSJrxoNT2dbsEZ6T+xSxIe5WoMDLvC13/C1AuGEiJsR/P5MGfXA9LSA1E43lz5m5wd/zv5UavqUxzzEhbHmt5nvW2kflWBmWhYhFwSjeFoGwkMn6UR0ssU95LcT9c/9SH+z+bnn0qrO0Tm6l48nTaglACMxo4XhEY79aR0YWzogZib5irLEW7/4859ZZWKo8g2Brj76PNSCDwp5rJJS+VKl8VQJikB4QY/nk5ayy1QqW18+Y4LQx+Lsa1WfXDA6/B/eajxIJSY5Me07J+85yMe5l6zOm/ewOeCx7HR79y9fs87XutrC1L9N39xvqQxn0IZn8Svtalo//9X/9X/Gn/tSfwk/91E/hX/gX/gX8wi/8Av6Zf+afweVywR/8g38Qv/zLvwwA+NEf/dHpvB/90R+N79b20z/90/ipn/qp+Pvzzz/H9773PdT3h00aGSgFQwBjLNjVihVo0I8Pi0gBwGJrWdDXhKx57TzZ+eIyXXIsij4QIoUQFxT5RCZrBsZncQtqz0XvqgTGuxzauo5zuLiyho4hrPvN2A16q2iJLbE110zPe2jb3c9sgW2wkt7ULNwiYloNEPRaC8CpVXHZTjzvZ/j8I9X0rDjUrSYseR03d8DhPADTy7mOO8fnlPvv86YWPAH2c2guOpELiaaxZeBvyRdlf8aamd5Dz8DKpvAsrPKxHWLl2b0zm1tDSGNPwfBUh5ZKYXc0c38UaMQjXLHhrHUqDPZlu0RsAim0o7AbjO/i1BHwmYNgCUjIGMqMjKPXmMcYGQJVCgPVFDQsEI8B6t25JIApyJQa/yyELYboTOsjhCW8SmuBWYxS21NcCyu48n6X7UQtIzW0iFkv1F0BUhUlaLt7osQfrrOjW4zKXgYNO7NTOG6b9GAN3ZyhNtxCteFZJejQbf6tz0/b6HvrRs9/nsW+FevbVm1NbAk47JqI1TDPJQNIezEtp/iLcPQt0qgJWNl38qHY85awbgWVex3gRryoJEEH906ymbZmqcz9LEPRyCm2m8Y+Xw4MFmcMjZ7veSgPm4Gd1gfg5jOzzlARz6ryvjbe/y5t19/5ze6tDvSitH3aL0IeMD5ixOvOvyTlkYrlncBe9rDQxzwzbyqCyS6Hy9nlgCRklEFHesR8fP5MqSgtz8asm/ibcm0dNh3PVU5BOYB6VejLI2TyuH0t8NF7x0/8xE/gj//xPw4A+F2/63fhf/wf/0f8W//Wv4U/+Af/4Ne5VLSnpyc8PT3df7FCzSTjPqT9rwt2ut4jxOeSTSGx6AKwPBJ8+dzl93wLdQDywOjw+rV4b/mI877q+wSM0My90UtB9w0k/N3pcvklipgLDLr1lURsLnmNSXC82q3QlP2EVx70DqXr8t3y2cO/87h+6J2Il3ntwINrcWPqM9W6mZPV3HmLxkyNNPaQRdMfGRIFZ2emxvgOQFxv1eiypss4Cr9LCOjQiDGO/RBXRvfF21M8xuFWD1oqGOsR8RGT1YLgwe5FF8bmgv0SgbaWaXKTGiAkP+9U1TZZN4KMTcs8xovgzYX4upcUbqK4lTr191GL8V7mjMCr1zmr5fB05Vo6au+RZZL7sUnDXkqcS2BHN0Hm0TFXlDGwTp5YWl9SnAbb+vyrJYj8LYBxwFh67RZZUXGdYJtCzBuAQXgG4JQagp0WmVo0rFsGNh3A9wLtrqakGk2rW3UOsFsnJP3qMSWrZSPeK1iMVNdknY39Jp3g96er5U54LzLj7n1fj9d0jloV9jsZ8mgfe/D9ujwDFMGVn/W8V+RbHE8FO918uofGKdY65nGYT106N9/zY9vXAh+/+Tf/ZvzO3/k7p89+x+/4HfgP/8P/EADwYz/2YwCAX/mVX8Fv/s2/OY75lV/5Ffytf+vf+nVuhb4n7WqZ5OGaQBpgjEwVYASDZY4OotxHAyRebA3z4Gr+e10Y+Z2lsMz9em0iUr9zXxQUYhhVCRPCnCwe6Z65b2H9YByKaxL9VgaZkcPWrh4sBjh7qAMD11C0i1liihEhlVxnQmEETfn23lGarMlQ2URtM21lmGFzBP86NvnPlFqbTYyiMyqfXvAyxooWrTHA4/f1ZYxnyRtdH39PUd5J24jsqAKIa8uXKOZlIODWawTGXc/NN0iJ2JDDi3QxW2JtRRTwrBkGsVqBuduUycDWVaJCKQX6nsDJidndERlH3phBcG2bxfcwDdSDNY9jc3P6sHiI97MwU6o2bNXARlS4FXMbhAVGBS9tx/tzxxfXJzDVtqigiVjw5fIiEYTkYmm0QowCe8bOetYDW2l4G9konmZ7KK7YY72LawqrG4ZjyTTiI6x+i+Dzd2Gr3V1LFSjA5jVPnpzu/LmeeGlbxPTYtT1rKbGPHn0893na9xZDI17YsU21cu4ZcHsCIz3m/9v1JY75QX/GF+0pXGpFnBdFtnDlbFvH07fPIIOzfox0YM4pg6v5TC+eIXP1AN93LxeLIXMgIEe5j+VKsXXT/rlwXkg1jpO9zm4tdUvN2cw6d57V4j5OGfxAsX/4/Rjr0YYCEvssN5ZH+zj3DxnbDjD2G9s3xF06GqDr7tX2a0/7DPd/3irLO00y6TX9IWRd6msbfViV+rzHjs94rN71Z7pVmiutVgj2Y9vXAh8/+ZM/iV/8xV+cPvuf/+f/GX/j3/g3ArDg0x/7sR/Dn/tzfy7Axueff47/9r/9b/FP/pP/5Ne5lTGmLSADwFiUryyKO8vI6wpeQsNZ2rgVJAv1D4GIFRilfoVAfNAHTb/I3YfpmgKg4yFKvUOcSx/Un4ff5+wWhVGdj374y8HxLaZhEEyED1r0nk7dzzeUz8yWYVaetVnXaItCHwWEJndT3ohoceDLovn5ZViuxhgl1SQDhvx3XD9NwjqfeYizNpDHfZkHC1S0+hoA0NXM2KyDQkFGYEYBumrbwOzDZvqnFQyzNMqnck6gYg0+LH7vBkurbMphnqu0khuE6bps1HbpXqjFS6T7vQwcYBLIj+Il+CwiGhYapiBvmkEE0EHgKJPAYyMYmDJFHGQdnv2TeZ7pfjk986V5WvCa5WJzMH5XtZRyBQI8MxXYspnG82WW8K4SKbY3UXfVLLwqPv+5PQoOFu8TwX4mcsvWktkSYunRBwz8dOXcFzQIKuY5sZ99+myThpvYffYIjh5kZk5ZPM1v7v9em7lodSYXnB+MmtUQuBNpY+xd88soBcjsycNdOPqT45GmjIJ47y3jJbs0IHydZewJ/n6zq9H33B+kZbSuJ+Uzeb8mxTHtuVRs1r0p3y/dV1PXox/p3hNA4b2SfOC1Jb7TezdybqsczPvzX2H7WuDjD//hP4y/6+/6u/DH//gfxz/yj/wj+O/+u/8Of/pP/2n86T/9pwEAIoJ/9p/9Z/HH/tgfw2/7bb8tUm1/y2/5LfgH/8F/8Gt1TPcSvkB1TTYHJekyUdaB8d2jfOpHx2aGvUf8DcinLZYOydf1fczYRTVQ9sSoCkyWUgHuU0cV4SfkYgwQw/NSnELEJfDZU045Hvyj9cIqOhu/xNqkKLBZ5L+IYmOWDMHGQuuqaQy7+3R7mVMPLR1Ukg+2erE4G6uo8eBaSLxIxR895ahzDNY5ztHfHNsY7wegIa67nHvX9MGYLwCYIEaK1TSxmiCjciytGu91N9dFt9RKxSy4qclReF+25hrpDAi+vV3xne09vlVHphjp06dsBnHG0w5c+x7HHi6IWd/jem7GyFqbxznYzycZ9WKAQXp2bZb18u42CnlZIHIZNVjKoOTvOlg2mZGxlWYaN2YAVgM3dg5/XMMqzY5+50Db99jxZXuKirMx7kxvPS3V9XaaZhwuRGos2fpFq5Cnbt7OLWKdAOB5P62eymbjw4yd62nMpe/OHbdicSAc65dzx0vbDIwk4BEWlsRbsgITqFkMz9MyjZoK9tLxhDOqAUcROC249YpNBN0rc47YIwsqqNBwwxxl7s+lMA3aLFNhwYsaPhU3IAjgOjwzzZsBAqPpb72glRLPwDVphRolPpZcnJHVV2NP8z3CDyi1YdtaWA2DvVUkMqkieJ6pwBT0vGdVKDTcPmHswLgv99EobsrHyFsmFaK0F/DcARbEg1u5P2uwuILu722cFv3AfE0FRixjHs5QytJ42sZi45YzH/PeBdtroQirP0GIxHikWLgVgCzt6wKRrwU+/va//W/Hf/Qf/Uf46Z/+afzMz/wMfvzHfxw/+7M/iz/wB/5AHPPP//P/PL788kv8E//EP4Ff/dVfxd/9d//d+M/+s//s63F8AFBJJrkTAHyDZqApMCm20Tg5LqxCqOj4blJ4kuAXyKheOu9FyIfn32U6H3D2rRlMrNdJ116tNETKgSr1tWvcz/QHJ18RjKVZkwrgQJdKAk6ZNEhk+Fm1KHp3kNL9yRWhSdIUn9t9ds3c4TB1co58DMO0+QCE8SULwb9ck89998IQTLh15M5ahnTOK6rApCUsayVnjGS3wFZ6uB9O8jwEi6wJOlqnmBYNIOizc0bHqcY4eiy5cKGJL7wNOTYkc3ycOgTm2kYsynz9/C82eh3ZUr3X0HqPs0asRxFF1aFFb9Jwag268+7zVwQRO8CpyGNaqwEKxqFEgTS/Z4FZlJiB8u684Ae3JyM0Y9yKB1zHFKa1w+eqxYQnfBy0qHFbKIz2vBVPT0a4YxjHcesbuqZA4fSc5MtYxzYDtbNVfPlysYBTBnyrs6967RkAEKnYIJG6zQE7ewEK6wgZw+xL36Nmz15Oq+2SANy1V++3DJdJsojtpYXLhUHPtVhuDNf12m5uMbJ+qVs8OavjB/e6dT9c3111BYrBu3kNwvthQjrtJ3lfSy4cFhnN/QAP9T1N8ECh875NKa6KaR8Z8XAY1uRlL1EZ+2sos4vsie4lWUCFNivgedxiCxdA4OEEC+iYL56ul/qWrxXf52dY901JIOYj2tdmOP19v+/34ff9vt/36vcigp/5mZ/Bz/zMz3zdS99fqxvqLKdPpoiZZEMgvnZimhiiVVoY8uc9TThc2PYh9MKXL+OaRLjTmGdwIOmcEHLju5isNTebC8gFIj+bFtciIMZLMsYkPY79zs9dC+huMldg1ExZJW/cyz4n8AiTZjE1QNUKTQXwCBrohL2U6bdZOGVwNsBPvn3fNIJl1bNI1DWiyZKU5nhwuC+Pkx+RL3D0QYYFC5iKEb4GQEMLy9paOq4xHdU3dqstYqXSq9QAEK0VNC/g1291pBwXhb49re7IBehdcUpBdauKET5VvG+XyeUSxc7SOmEwYTZPc/FRyFCA5jgGI0IbLXODZEBVi+JsnkrrvDDqWQjmfgEul8GOS4FOk3nrJVgpT497URcmGRAx5ZfcJrQmUFhyfKP2i3pmTq/4we0J33//HCysx1Ena914ZgJut9qIArHWTajR+nG97riJ4iVRssdr++SxMs7DcTSLN3nxCq9nyyRug28ngGUvOM+K67vd4qJuxd6PzTLNstslflZA1YjqaAXoahamVhqKZysxFujJgceoVLzhpe1B9d7VsrGOXvDuerEYmv0MkHk7qwHLzWJPLlubwAd3D7Kh8jPLsJAUU5AEXd4rgVnacfMWI1nbqnGZrLE34lbCHJytcS5ibxuC3scw7jlux31mSpX1fpp88HOdz2Pl7Aj51DHQQ/E9O+03Zvkd9xdFyIfsTaM8KodXoN1mkPAISNn+qNM4576uitej/S5kj8wHzC4yjMzCj2zf2NouuskEIl7T6qfxzgJG7r/P2vPdZPPPdMwdSpS7X+3v7BLIXwiCNGY2fWGatFgIwNSZyQKTVkmMBV+U7gCE7peUAlZOs+bgEIs3AEaktooJPLH4i6h22wpwejBh7TirFwvzjfZ22yyN7nTG0pxGx65dBrcD3QzZtEvaaDlc4NY0cHy5aX5l6es+D+9s2sQABXGTB8AqfZXBIC0vA6yNPozjdZqDuzWp9kzXVrG3LUzCdrlRcj5SPUsfbjofk5hTF8DnWdFkpr8vol6AzqjUCzSCS5siarjkLIauEp93zNYSEQ12zbyMiyieL0eYt3mdpoKrU5DT1cLy5a2VMHP3UkzTVUu95ntzbjkwFkGyxUyFihHw2s6K3ganxlrzJrhNHIi8b0aQdSnU5Gu4RMjCysJ/3YMecZqk0GqZGQRHpwzgVotJhANAbwXnl7vx0FAAbQpsHfu3nCTOSchUxWnKRwCpwlwW794/WR+udd5U+N4fBX5bI7C7KHotOLeRHn3ZbP64zpqaUkDq/q3MsUEZrOa1MVnD+igil+O22m247ZoHj99uRvh1PfpUu4dg8/Q10f14PcvYJzKuSH9PLb/DMu59NgP3VsnZqmbTQtUc5NbacTgIUL9+BLbesknc9umMzbM8EST9Lis+mWRwbQQsOnCH1ZTRad8JZTfdTwVTgKcpmfwDg36C3X8VMIxxzYozP5sUOU5JHKPjWVeTyGqW4fU6cqjVV7ZvLPjoHnD6IeARLQNkCnLGCsiDYxKytIPHIcEJkXfhR4GGPO8rQBEFZFxvcX/ys+ADyS3HGeRfeQ2fcCGyVnshBAjXlO10gnIrtoHxGvGi+apkKpzA2EWbWHrcBrS9uDC0k86jmi/1VuP66EC5WpwOqqKfJbJdAESgXrhjHPiUm5jFZE+VZFPQKZn3ciAq54caSYC0nr7D+PzhnlYYYOUCIcXNGDiVsH6pwCSi30zzmuIY+3xpK7gemwlszBkCJG3iGFgcDaIeSubL4PyyYjAAlDIyLd6dF2yl41oPPJUTkOFiqeIxNt6v7HpZgxrpynlpBbdjw3nU8OWLANenLai8AQTYOI5hLWHxwX6WIVzcR05fce8az2luAwxLgZLV1P5uwLhmE/Sj4mglrB3oCCCS66Oc3WId+Iw3VmQNOvAS19UOgBkXzQF6Z5qmuT0ARNyKBd5K9Lf8oGJ7J6gv9tnxLUV7U4DPLM6n9QIVjRoqjDEp4muhF/Qf7KjvCrYvxWMAuDbTT7FYgb4LGjp0K2h7tbHy3Ztzw2DwDsGOBlbV3ZfA5CqzhGDgcU9r9EwSlRbL1sq0nmnhimwhGcHpcW2SkzG77bT3/eFLCUyfh4ybND2rvM2A4wIJUK4eXB2Wq9rnWBLuE01Qr3BBbtweumkoZBkI8Lyo2pr75NXDp/0gyQ0tGAGnvHeyelAOCTDLI+CuDksOdteqYaV/zeIBzIrTpGSVpNCu8yDjmdh3q7P2AUGXQI78f1lY7q9piwGDB57qrO1ioNI7qu3lGjw4z5PmayUgcYcm5f5fjodQDHQbpywLIgRrmz4eF1gXUD5AxuTHYgbuNqv4Pi9U9pmAi6sqzkmd1uVfB3CaUGkAerG0m6ArPmWqEFlunqG0A9rGZlVcoyXjZXfTPE2aAoWcgKhMplfJY7805WaA9JKlSPLJxPjgBbvjFVjHIN+HY+2LZqwdXY6xehF0ZRTZJ4F/l64J+GYNZBN6FspMs4SKW29NGH5rv+Kz7YY39bjTZmkByZlG/JxabnEKbqPhHoyeCoxaHaI4HURqHYDIQMEinJrMwEP9JVF/Ng/+NQDi4+VdI9hRR78i6mmSDtTohnHrB4qVe48KtZ5BdEiNDJDnegQVfNTHSYAmCKcCuAr0ZuN2LRtqHQXrOIfhNmyC4gCdG3V/UujFUkBHlseYd7MgcH59jouib7a3SXeSLX+newXObym0mgtSNwQTJ7huxKAzeVlAgSyKSzknwHg4Z0uk50qPDKeD7iwHzNXr1hwBNO7X5x1jKd+B9EoRZGnHIBlbODVyWitdDMGPsbjG+M6VolNgdljlXOuYgnWTsiMqntZ7v3fmVNsIcgcSaMh9SJ2agNH82SoDpmvlPS6BiykbJl8jX3/dz5bHnWgGMH8HILn8E71ECksIy0sCWrLcdN1fY0x/LVg+gITWMv3tOtljn4y/Qxjgfp4md0wyjQUgzcKXx8s82Hcab/6cCyz3PX23FBadUekKPNYHCDiK+QQHJ9TkxdX9O9nKh1UYr0fcIwkMIOXcCxRqWksVKJktmwBnsRe2w3Llm9GgawVatWMimFLGhsuCUDTBEOGXY1gagDTXy3xP/smM/AXo/kyKhOwxvl/H9G7cV9C5tvRimaYnI6grAJ5tuK0b5wCAOSDThTdbbKIlZxNJCKzzLLbRAyiOSqoYX8Qbt3qwlgmZKoEBPBoKKrpr4E6Q5YOxe/0PCwblGEisE4UFwTaaMTgMS0Cxdgzg0dZFh4gr0l6QyzjnQMGZuE4iOJpaJ8fw1Iqio9YMSc+YttxUcPFaHy1cM8uzMTYpz7nC6wuZZQGaU4x7Ag/2PNysVWzN667ApqH152DdDDymVu0crQI9HXwwzuzJwVoF+kV9rxqLUwRTjMWaWcaMIjbyjjD1+r4Aoc3vVppZPZI1JbcJePTxTBI2RB7n7zuBXgA+31+yEqQ+EbQsYWxL03tsN5rSbAEEI2tOBV6Ly425lsmdKt3H1oW/AHPWY1aIXgMd6aNptLIwn/qQDn4kb/L+9AB85CnJSlA0JlvkDqU9bjo8bYJmTfZdP1v75cPb4tS/jzrQ2jcWfGTzzeTzQkKtgnlhcCNAGrdX0GKYlPiVIKwZd+mZ6fr5/o87jtmatQi9XDiIbdqT8mKZAphGVsYqdIl2Ff5dTyBjG+92PLgHcHIj5ksG+D19g6BbR28mTONZzjK0FwH0Yi6ZiNPxeg+rBY6MlbnVa65B8zroyGMVX4kJSOQgUWBYNbKLJK0Dbmh8cSdt5tGLL+PftJFgXAtqIEw3s/icZcRVZAG78p7kY6aYGKXwHTwqoNYMREAlYwvo82dcx17U4kHUM0vQA4Tc+mbl3tsWKahWTdWDJkVRdrOG1DqzqgZwcskbpHRpExWvyaO7erG9ZsPn11urJQchXXM2z9Bq3SriZnZaE1hj5ujVMnX6SLHcSkffJdJC1zax64qSWCQdMOadQIJp0Uy9hSj6rmgd6Lui74B894Ztb9i3hq22KWBXRPG8n7HEttrRdvvrPDacuEAu9nKwlpEKUF/MSqTXgr4D7dlcMM3jPvZqayFXx41Yi15Qqmf8OPCYMKR3rsGsYJYJVRMgSWCmKETM7aQq6NINHKKYu9atQQYSLXjiThDL/E8I4g4EmFuVvImtmtdQWxPMWuosIofBQHtr1anpqwlRHeuT2rmc9nu5Dmttr25h6nKX2r8uJUlrJWeslGVfxoM9b3pXeI81qDWDkrvP5wtO1pJ83wfAJb5fgIxWgLV2xgPO572mbCuf8QEg+1D7xoKPV1saDPrM+HcIfQKJfDwPe4RA1ybLzw/0Yf08BNkr54bwXO/9CmJ8ZDorjG1wU/adxSQE4gzgiEfuLzp+RvoZYy3EtcRssaH2EoDPLhBVHFNfgv3yoU/Mr9WWZ5AHQ5zHa0Hua7zGh/yT68t7Z03DA+CRT5EHJHTsdgJttHQAA3wwoJKxD3E/0YiJuVtSrzwLN9sWatuSheLcDpASgCQE0HoNWmYCWJhZf9t6AIXsDnrY0phJc/BRjc8gtGMHFmERSM//Kt05LRVpDHM12dYlYjq4xrYUiMpG68nU+CIu0oHHrVVn6R6CALpZunl/o9Ct4+n5tMyP2sKKFLE9pWNPZG0Miuy94FY73j1XaDFmztIUenMlwgWkelB47w6eEt/ICLhNz6okkMuWkZEF1CFor1FWPmgZDNq4eBic6rB3KOz37uP5yruax1poNeU+/siyPXVkjCsBlnVovG/N10Skr+vo351ioUC9meBtAAoErToPUl/uy395b1gVGv7eEa6LOwDGPqwxHh8S3L4xPAIeNqZLf2Q67dVxHP2XhyENd+0RAHl0zEe2byz4yCZx/ssPFn+vSC0dp4qJpnxCk7xO+iUu78JPloWSUSFBTlyEqLqkcx+hzYIpRenOtJavKbjXBBKKX8dkup8HFhFEKFNqw9xMkJFvgEH+w3GjQBW4tcT/LorLb3jB5XLiW8/XSJu8nRXvr5eg3uYjvbZm+8XcOmsszJ3FKoMS9i1fOGlJDN4NEyJ8M88AitdbNpBcOTcfN+ZAxnoEIkVYBZBDIMUCSd9cDrzdLfiQmRo3L+JX5b5Gy9N+PsxC2GqPNFbyG5BafCstXC5FdIAMwNksvVS7MxhlqnXGBVyKFU7bisUr1M02321rkxvozooFIAjiigKXbkKyi3lZdgMfDKlngGJDCUAz/jFocMQEdd5TFKiIdG9zJzScYjEKwe/gpFNP+wkRDe4SUm33Lh6PMXbmfhj5mnaNdzO0OX9uEQui3MX847V29F7wstnzVi9G93w5LP1zO6OfBBub9KnsO60TAuD54kG4Z8Xt2xtOBnI7gCPpnlbLdpE3Jy5vDuz7nGa6Vvrl+mkqwWramwDVjnnCmVx0rHzsYylGMHj0FkL9VXCYW0bPBCyBjZfNzPeeTMJoSgpm6yf3Vfh+3gTH1YpS3i7OwOqZPT2efxS/681B1+nAf3cA9FZN6dnEwIe7tnrFJCdsnS/veyhUiH0j78WxfeTPlv0+lNDVip6t+uvwCh6DjZ72LgCdmncCS2HtzeVFHm3M+Xik58rnhPKp45n8u49ZJmzfWPDx0S1N+MPv8guxoN/XBKKuf2RBmAf3FakaaPO1GzzSCj503Nf5TsbK1nQfLvoAEoxgzkL2q/qzgKS3zzd8+/mKv+7NFwBsE/vyvOAvieJ2brhet8HnkDWz1Ffd+DJIBMvG99x4VrD3Sr/4gk1zqwZAJL1QUNyxFGb3ycNxiB0mbzaLuwfW31LU2EGrCcKjV+DccLi2WRkQloTHXttdIbAcJ3JINaxdSNjVB8PjAjzWluNAAEQdkCo0ubsPnRkC7r6wc32IJiuCAyeV2Iwt7kVdK+9QCzAI8rqwbijdcTMde+/FUo8Zf7CsTQEmNsutdBzu2BfAM1I04j0AOIV9BhHkfLCxkOZ9Wt9p76+l2Ho8jL9ARQE4dXjvEkXzdue5qNN5I4Bzk+7cG4Lda9pg8zl5Bs5W8b529C44dk9jvlkcjR6eulbNHXa5nAFg+SzbKzEaDC6OtbBIhxEjMT7PRftWQDyuL9OPu98xBLeEW3TeQKd38rV2J4FhMWjJRVmS5ijexyrqdak6RKuvRQMa3aW/0NVQgb5bh5QK26PYpdBG+VPYpfs+r4LYQYO6xVjcijVVX89A5muMyaSUIsm29bi8L+b9Pk9C7JHyEZOTDsUDZfgr2jcWfGhGwOkzYP4M8zqYg4uQBnuJJVB+ttxzvS5kEVSrIMzXq+PcQOtr+WWCAJ6fIr8ntLr0R0Jg3sdSTH1TxAbLc2wsfQM43QJQU8cVw+/KF2FC7RrXBwDdO7ArvvvmBT/y9B6/8fkLVBey5foW3y/PuGymyW+1Ya89Mjx0FzRPK+1NLCaAmTNNUK5iWh+jrydggXhxM935aiUZaN2fuSBOZkAfXVaiGGltrt3eWaPimuntVUnXxrDQVaPe/tZ+w3ef3gMAbm3DUz0BPN8ZuZ62E3tpVnzNSbKA4RJ5f+5OKjYAxKXcAxUCjAxCKnoEFrZESd508DoUsaJnl23D0dpgH3UQUlx4n2LzV5zY6dGrQh6N46jop8UcgAJM1DMfCo4Dbl0xoHbZTsDdFdfTqLvPY4sCh1DBVhvebAfebjdspWHrVjjtB7tVxH7eTjzVE999eh9xIT84nvDl7YKL84owQNPYSSu+aM9QLUHCJHtH2Tuenm942k985/k6gAMkiv5l/hYWyQOMe+SL21PME8Hj7hDuUhou9ZwsE10FL5vN8e3Ji/d59s712AzEvwx+jTdvr/j283UUJPRj4X3bvb+nFhTtkW77VM+p+BzZcc9ecSRCPDYWpiPQAwaAbA7mTsDiQEqKS4rzbc73zc8pFnyqnZk+gr6nGIP8Hue2flaAsnXslxOfXW54qifebrelUJ+gFStOSOArl+7BvRZM2QkueH8Wf6Mr6DSOpDkeyJWPbAm/K8khM0aJfesRmHnwvFgAyPKiUUHLCuyU7edgKsYuKVbcF6dry3xs7sSrIMjlCeNUGPBr9PEfjz6+seAj2sc+Sxbg6w6fDpmqBb52/Lrg8wS9dl9e348ncHyEQAkSJP39ah/89wmIrH1YIpMf9e2RBpF/H5aRBfQ9Gv+CUb21nsYz4Z/nAmEEHqw9cbZhxh5mdl/k3TNrSKueQVlSmB4Bg7uWX7IAUOPzoDsG7l8w8ZiOR+OERSF4NK+JFyKyDeo56m7kYRQN4MHUyK49cS6UMIGvwZMMwMxttX7QvF98xzoXcrGp67R+yHCH1GD3BFSHmjqOtXOZWSAAetGIjShidNoR36LqVgikOAINS5CljtbgANFWRjVSP7aIYpeO7kGSvM5eW9TU6VrQS8OtV1zL2OLoComA3wLTygk+qq3Ly9Zw2cxyRQsGU3yr1zhhvZNnBxOnWnq1yOVVFwXrowTxnFtCLtrQS7fKowBIYc65IHuqqlVBzvEjJGN7JLkzcRiBx1dZybqWqDYcY156WI8AQHwOaUkKfhogpU3LNMcQFrTEtLeM8heP02uRj+efBWGdCzCoyQroazOvZRbJZLZSXHMb65pZfBaQ/wELRLYYcK/n59w/0t7F76YZkuV3TT/xcDo/qn2M2+NjrCsfY32ZLB2Lwvox7RsLPsrJDSsJxnXQOFn5u9eEpk+uB6vPn+ffV1CAcc/oR7pv/CsJPAqM7jh3Nb3v+QV8VPhsJctan0VTX6BAaSlzI/mt755PYOl9K9oVmH9+6TMcMTOlVp1UB3uHVI16EgE4SELWCrba8VmKYyDJUvj46ZNlmLTYwOruiNrfaGH/8hiQVriPTSs0AMnaVPoHe0Zd559AJc9D2giLB8OKSGgDYQVJIFMUaLv55YE51XLD0CKBsXHuwbORSt17HRBeg+4NBqs2L+r2+e0N+l7wVCzl9rkcZuJ3/gYAkVKJDg8wPFK2i2XL5DiE6oKGxFpFFNX96d96ahHXk830wYEB4CiK46zQvXkV5A4yswoAdQWeIIBVYe9cBV3QbxXyvqK82Po7f6zGmG2l4dBidTx8jKgB/8j+Pq7Dz744nnBtttXl+IVtP9GkGsmvKOrWse0Nz5djKhpXoBE/RQvVpVqNlCevXIyOkXnhIKH4eD7VE5dy4rmadYagsTsTIMdwtXyRx4O1aM5zrIuaQBTL2edxNIvFKL7Gz4JoLAUh79JxgmnFYx5q6dggYKXhnCK+14bLdsbzHsyWOjZzvRUFilu/YNq6thLWhXUzV0EqeYB7pZG/AEFkRpr5lzYsQ3tpKPuopsvYD8572WzNqNhGULYUlOsBeTEEefOVsW/q0jlaFh4Bhug3T2cQ6qP9GWM/WfdtFYyqv8sN+jZkJa8BvxdZR4OnY5VxvEzskUs/UiZiWFBy3Ijvk+RP+RocY99c8DE2+fR7br5Op8I88xzEcfkDeWXSvwZgu9e8V0D06GIPJiVeqnzO10G86ZnvLvpVD7SeV5bv3I8omhZU2hherasDTMILGBorBenDzJdlIwrAtyJspN8ljSEwCIsetXSNu/Xx6FjAwYbc33sFL8uGOQuBjp7AROYm2ApTHBHfn04GRcGiKndWD2C4ZRgo2LSgrCQyGAXm8j0ON7W/tM1YQBNR15oDQXB5KX7tAgtI7vfX5bNTW6aFI8cnAJZqyriIR9e4axQ6MtwdK8kUv8uEa9dyYit7xIf0B2tv+luG9SeeH3kuR8xJ/pn7n+cNMgD6lt4HO/Zxtsko/jeq/Qoev2987lfH7SNaDljOz5Gfl6nDUXhazbI5igqmbJPqG4ZrTswa0cyt4k25j/itQ+hm5Y6fY3zONPTTafCf6jweGlaQdK/pxn65ZV+xvSbtKFQy7vqs4/uv2K+nPf6r9va8nz+STx+SO+n7kIP5+ZOS9OrtZf453eorzsty+GPbNxZ89L1Aq6BXQ2NadYqfCE1fR6xC5HATQEsSSEpymXFsdi9M6FoeL1Ysn4lipLqmPvF+AAbCX7Vxtkd7UL5EzjdfX1Kk4+QBp0VeRHzmJvNKIsBLabJC5lLGXdBMqRjZOkAicXJNDhb49rTdZ25Q01VVK9Xim9Xw67vmw9gTRpU/mI+sTYznT99HBL2McRFH9R2TZan7d3fcLX58lJLO48lNkBrJMg8UNntpoTUX6ZHS+rSd7pbxQmh+fZa2z/73qfQ6ECXvKfSs0JoVVztKxe6lzAFYHRkMS8W17zi04PvHM17aHpVe399250UowAUAGmoxAU+GzwAKHUEaRybXlb1zbbSm7M73kTNB+IzsY5jKi/nn+w5ITRVffbSbH/vkwZ8G5hreVMswqtLxvl2wScelnOh18KJMlPcq/k4IsPUJ7LE/RbptlAJc6ukul3l9M87kcDfInO3SvEy9PcPZ6zTHrMzL5+NYkEiNbss7y4Z0dI9t4PNE/R8MkDsAjcT4Zf/z7sDy1u8rJO+14dc9v79bix0SNXPOXlDLAVwOvOwbWiu4XXcbS68RRSp721PEyxVoWDwiFiuDjja/l323v8+r9fP6dMyuRe9zl2GbsNiy4kGq3hcVBzGYY2BFhxTl3pIspVO5j4Q/EDEXGnJmsiov+/JDHMK+RKybRJem/f01nMn90yYnwN90iMzXjhvwo9TXPO7i19bkoswZSfa5fJTLJ7dvLPi4twslQEEhz1mMQRi/zxLhXmDbdx+4z6PuPJjMu5bvBzw20T241vTZAhpW0HEvKNeOzdcQYPiFk/lwOmD5O8x8zL/ncXw30y25cWZK6SxM8kCJWCpvKVbMbvQ/gTVGkWdE/VfT0sufh2rVDKZTVs1Dx/r7qnVAAqSrF5c7dbhSgJxhUIYwWFq2JOiyueaWM14IPELwiALaceo2pVWGRusVZrfaoWoCyMzpDU+1RdzKZA0oHUhVaV9r2ecfYwiMUujZYpCDcNg8UwbVYoFe5YmZxmwA4Yzqu5agZieZ25S9ww01CfEghBOzBpkF615TYADvmeYyQEfpeLvdcPHaKvFoXEyS/o4cS3PF9PWdkZE1lD8nmVrOZJK0Vsa6mQFTRQ8AUpYFzec8F+tWAEAVL047Mnsqlqyb0p2QTUZcDWAKBXQoN5luPXfj0TvGIekjC4zstmRkjTlOgHgMGH/a4D8kxsrAId87fSev9fOrpC+VlvT7I0uqfZz2zRV4fEh2fOzn6SEeYJG7/twROfpYEDwO4PjV7ynbNxZ8ROChYoxIti6sg/AgdgLq6PnR9fNESloDy2II0PFIcJX5nGDkS5eY3ELAVE03AFPqb772dF/FXbEzVkR9JDjZrzvCG/a7aqBlFZ2CVrUosGvyz3pfGS/STVuk1ePQgipqGp2O2IRcOr37ZmAumRaa3HndhmFHFFKcmaPLq5ZKXcc1jdv08mSrBy+mY0ym9kqcCNPYJOZL5zFGBr32Nl6PDV+UyySgj6RVtl5wiFcKTuBhekbvYHAYUEtXmYIHAWCXhm/V62T1AIBdGw6teNcvQa1e3BJQNrMUvNQde93RL7aJv9kO7LVN1Ny5dZEAVMq5hSVPtT4Et/qAZ0FI4PFmOyJCnho+AxdDiFZFv/TIjPhQED0DNE8tuPZRTfhwMPLSNnx5XIKy/jhHZWEBUDadrkWgQk2fAbMc8zxXVy9Bf2sbbm3M6ZvtwGf7Fb/p+YuYrww2c9YRuVz4+cu5T2CziIHMlgJmN+m41IbT3Ul0r0WKr/d1LxajsieXHD9Ht3kbUTLWWDGY4HmTHnEla7wPSg/LC8dPmuLcKlqDu0HE2EObQK/V9pXDrB2FVa3ZBEP79z1u1cpxWqbc2SqOoriWDdJtfXHebmeNmBCIGs5yKnWzRFtO/1qLBiqJJRqTZVoahmWWfV3bIxCVN7KUHQfA6mIRlKwAg/FsKfhVkhx4FesMzfdO8Vrl56uAL8unPEW+f4pXGi8KI8Nr/tm95/fV9o0FHwAgljcaJp+pKR6oq4+vo699zUWdv1wOWgvIrfcS3IM9Q4qPOxECclX2PgQYFxAynycB0Pj5I/OcvnK/NY32/r5iqDcY8DQWLzfITHD0qDHWg35iHtt5LaK/19pHjtOwDCWtEZZat/oko6u5mvCi4QDpZU2kOo+6xgBV1cc+eAsG/fA4ASY8wiwND6CTkdVwasFeG95uB97UAxevVrpWKo3rSbfvi1GrF3fCn8kUf5biLhoTprdeHwIitq5mcl9jHEYNGx8bMbsf5z3zUnDgsqVsGldRSO3QUmIuHo0rr0nz//t+CWF/65sBm9THXCqe59MqmInPAA/YxYjDyCmybHSNsApsdZBFYU13UEVHc+CAamC0QNCFHDiUSvM4E7RHrZ9WcSuKrWyvWs0ezdv6GcFKSxuVuXEazlQ0sHmWD7NYMtCJ8fBjV5ebSGJABWK/td8fvPc6FItpT06/q8Bq6FSLRWFmWY7DUQdsU28kbeQPxifHtWk6VtLnSL9PilHq23TPFVSlz17ddh9pXByqEGTLuE7nf9g9EwBkvebSZSpXkpTShwrZpKjpfd8/0L7R4CMEbo64BYapJw/Mo7YgSeU10/earSnK4jr+NUHCgl4FmAed10r300p/pnWY6YN3AZSPJmt9pjQOooiI5OnFEJlNY+s1yHiarvnaOE337bB6BwXA1uPYNdAPuDftTrenhuzmUfqxAxExzx4YC3sdkw/gpAzQpjx8laCj58ladDIVTlpEjicSG2vhhpT6RBCZC1JJg7FSeh+ZERGF3OqsfeYUyWCXDJ/9aVYKP4dWppe24+12w6+/fImncuJtvdmmmzqXg0wLFN+q1xQrYdaBQ6vxZZTd3BJqXCLXwyrBxtwuQaF1UQJyvEeuMcJnpIAIDVgHXwYwx3tMrobSUcU0ZugAGTZWZboHBeJL2/H58Rzf3XrFS9sDGLRecLbiSqVZ3pTvJQRl8yyddK81WJTzsCUXBJ9JVfDZfgt3CNNxKzqeyhnxN7s2XIplHTUV3PqGKi1ASJGO3utwH3TjT2GFY4XVm2EaO2NzHsWi8BnuXC6YGW8BKypn47ZN83WcmxkktgFAVovdGpMivnnexQEV9X1b7F0r4+UL5QoLAJn2OaA8NeyXE292y0h6sx3j+s2scwZKshJi9446RKmxPlF8J7CswIiwZd9k2l+kJKVOdPAF9Qf7F/ux7K+rZqziuGexIGQQYCBnyKu4XMie5X6azsv7nWJkGMX+N64r6ZqTrHnwTJJk9Me2by740DRwqzCNEUmfE6Q8EqKrtFrQtnCVU7h9ADm+dk1df+kPsjoS4lxRswgGuOBX68LyonT5Xc6La/I7LIs6NHdHQcFkIbj/SaDjtOdK68DphEzOShoR//421jQo5Ing5nj0YTuMoMKwoqQBWcjF7uae/fuIBf9wA+ALl6+Vrk0L2wRAc19WcOY/VWBjrCZwnzZL8yzwmA8ZZvzVJ78CuPxduD+KbfAXHS6RSJ0Esyo6KvJ82DV3nOhacCxxJ+/bjpe249ZrpEJvtUc2Sq65cj1MUL7c5ngJFoRjGqbAgEMpqQy9Wjlz3nctulbWf86K2hk4B8HZzA3wvu24lNOydNocIMnrZ0G7ifF1hJWuGMBCGcFMUXOmaGKQfSDEMWIsOGfk6ni73UA2UQIWpuE2GODLwt4AnwWp0v1CCwoznvIzTRbYNK4bELE5OXso9//sFRVmiVnjgg5U5MJyfE7Of8F4X2OdyljDLOrG3+3eCB6QPtT1e4WegpYvEN/r6aAHr50TlT1yWRL4zVasXJzRfsZ4eqC9iBo4qXFbVy4kticpdIM8cAmrmOafn40XunvwVz7L58QDLcdgBkK818Mm4zmQfi7dnkBGNAKs9fMEVLhHEnRpkTmM4CvaNxZ8SNcQLmvhMFokhrVBLUMDGCbfRWAE0sP4fBLyfZyTM1getTtZlAWkGwfMnC/3sSgynxMaNPvWB6K+Q8V1zkE3oCIsuTJpDtNYgmhdIGrpo3cLN/fHM0LEmUbjGazSOLCN4LcpqE1HfEcEovlGbjUmfCPLt6S1RmUJQJOYi5ijR8I/86esAGoFMAm0cE3ldWIMffO85JgXLH0IC4tvorRyPe8nPttv+PZ2RZGOs9cQ9K+BDeAejBTRCFQsWixuo57BC7K7Lz+DjgpF8RF+FtMIyW76ohvQ4TEbFe/OC17ajnfHJYIL3+7HFBDKNNz3tx3nWfHy5SUtWmYyCOQoRpW/dZTnBmwtrCHVGW3Pbtwcp5YATAAiawOteiVdoxkXGRv/2Quu54Z35wVnIVfJMP9ny8mUKiqKt9vNlq/04DaRVnBw3kS94m6P2jlrGvCUWiucr3SMC62nkq1bPbKZTtSI82Bj1sv4WaJib3YVrcHHvTurqFtgLqVFVhL7ynb0ir1Y7M+GHnwwub3H7uNjYGHzeBBaKy/OapsBMQHHrVlK8J3Vy8Fd8bnUXiYdwx4okwrayxVXSGALSK+dIgJO4z1J71L3KsU2hyNQ18YSc7aLg4mhbJp/VqUAzllh+07ai3T5N4GDWVJr0VE4L8mvofAuTdMllr1/+my1DD9CIdzDpuvLfA6VraUv2bozXYvypWBkAsoAneD++ZHtGws+YiKScM5WEH1t0hekRkEfVNp9HJLBynSZdQE8ura+8nvqJkvS5+vFOszPsyLdFf2ne9jCkOivaJpwTR9O4Iz/HgSndkzjGZp++ldOMZIgd79wzCIjgJu+b6CMPs8xDoKhjcU7BGD1E0+IcDHHcnxiuAhMiNL7CMiNy2VgmD/Lz+j3zWMxFf18tNEA83oRA4dIwYvdEWzH0A6LWEE3/s7GVEwKr5n/w+IeuNlWUadOJ332CCis4ETN/vwiHVUHYLmUM4TyUSv26mnA9QyXAgDUbhkx7dniPIozmLLYnFUPLUan7uuubg3bZsGlLLZWRS3FmCyftBz4mim+iHPAaqe1rhjB1c3LpQPA9dzSs9m1LqUN0i+Y4O1Jgr0W3BsxHt2qvTYVdAaPouJF9ogj4diY9cksHdXBdaa85+cxh5hp7fnZ0euIG3EQkvuVtXe2lfsln5PjaQrmgFO6Wzokxv7Q6tTkNYJn2ViPZorVAXBLlhkGQLNwXlv6RgsIVA0Y5PdRZbx3eR945X3zLQy5Pk/HiLeBWpbZrVdczw23sxqlv47r0/0d+6lfVABoxIT4D5W50OSH5AEeAAqVh8c9PETnnx8aBwIGyoEc46ZUMP00CwR9xUqSPotrrc9J2VDSuJXUUcUIOD0VJXnAvqp9Y8FHtm4AmAaE1g4Aw3c/nZxARVgxdJjQFJB1Uh8tkBV9PujLhMgfLMa773hC8hs+bPnePNYXXAjXnFqczqHcjkWDO1Ce7pPQzwpwiNJP00m6wthF/bhMdJV5LFoXlK+TI/tICyBCS+bY2WSSTuW4kL+jjUebsjjTOE0gC2OuxDlNNO0CkocnA710XRWT+VqHZYfjMzGJYhB2UWMHgDf1cMZTCyDtKqkqbfUN1jIL6NsvUFToBD4Ac7PQ2mH36fGzQFClBzNq14Jbqk9icSApnqGYUBRRNC143oyhk26a1kesB4EIGSgJPC7Vrsksl1zfxGIo9jDtA7a+IlYgbXxHs8BYVQlOCgZ1WoCnjR/BAIAQTNlVktuUMt4LujNn1tIBcpi4sBbRYDgtHouyu5uuJoFYxNliMbJcHhV04/tC4EEQAmCyNAjwMOWc5F7rdS0bxj7fpD0EXACC94Skc7w/rxnBz6m1njJ2fC3mVNsrtjlmxlloVdUSCLjx+q/im9Mk9HS8j5McpJtVxrOevWBPsR23XvFybrieFedZI1CW171rMb7GvDwdwv3vQSz3NCxJUXr1PtM974/JlgVeg2PzqNy9AYb76wgwAuz9mKyM0aq+djnvh3mpUHmblLikxFLZK6f904H9v7J9Y8FHCE/ArWHuU+PAQSa3SJahk2BIk3gndAhaOMDp/tMrR+GTAM30d7oGj9f1IjoOm7TsBCzuwM4iHKcCQulnLKQEyniPyXqE5JdMVgV+nhdYKFBJwIrC3ElnQT/UtYzN0xuZXVBniwizNJbU22hOLBYulzxemkyW61iuOxMQREUx/wXzS5Ou9Yi1NcYNiGAzWec4TbpiOcc3AwoVBhwyRiMzmq7CYPXxA4OLYUPDJpYdwbXHwmDNtVc7jxkMdv5kDYH5980tY5aT3UEIY1Om+J3Uv6aCZycyO3cDNTcHVCSaI0PqkeIwCDpoTXmuBzbpEdh4elYNMMcZAC58C4yDIwW5UrPP5+XYqiH0FeeyQDbpgBOWiQyNWJXmfg3Q03pB82c/u2UYbaXj1oBTLC6D8TwZRHJuDUSOgm4G9GbLx+Y1aLZe0V348xlvbUPzFPZauimcZQUm92snrysDv4P5tqHgB+051sO1b7j24XIZ7qs5oHdbXFC5wF7+jAGyR8qWsWBZQTurFXNr/r7TckItWmESk27nvF9GZ+wDbSOjqkz9NuC3l46nzd4CunzU08DtogrZABQHHiwDgA5sBY0Vv3MHXgEu9t3jYpRTWwU7/16vvwATUUS8XuxlDwRVgBeM4bxTqJe/szUkE6Tp0p+s7M/W6DRGguG2/sj2jQUfAGKwKKgnszgF9yqYgBl8+N8x6VnY82tJkyXjmOl6/tWdFo0Hfz9YsPH1im7Xz5CeMfc9Ojm+16IDiPnifKDgzOuViyc9YwCQsrhlFiwQFoYm0JM+6uIVNAeDZJjOMQqgsbx5LXMnNbMevvLyPkLo6/DHM72C2u/Mjgm5zzfDiNtZry/2H1/GsZHwhbQ3l+CLFUW7b45DwC/A4wN2WQKQ4mZ+BpGODJFidTegAUZqLNgW9Nld7fMOpJo8bi1QsxZkbo/VbXD0xDeCYc0hJ8Sp5hK5ti0sIRTYUQ+ltMH6CqAH+Yw/qwvUCEL0Mf/Qnj7zyIxx3EoDUs0PE6Lktliot/V+/JtbcVqRKcOHPCc5sLVIn+rxFFH0KgYs/N8uzVwv2Sqgg7isa8cThnumiOL9OSxCAowCff7ccwbQHGhr151TJs5epurH175PTKu5/lB2E2Zema7iGUEz78fp7sXmmTmTW66VwXTaxoROipHa+6NFBvfF0uLpHFA8aow12zxL7HZsw4rG10JgwCPFhFjAqfVBik5rbtpXp83IfzzOch+H6P3eNCnC6SbTWl8VRNdqs+VCFBOBG38KNMB1Vk4DSGQgFnIls2TPG+RrYyDLXP6aAB82PhJoivTqk4XiA6g0g40JaGSSlwRKQqnNgIBSO6NSpAn0P15FvVnq8z59/L2aHKkFTLwU6X4Tf4UjYnFhCchkxYm027VfXEwfYG1SfxkZDJatCSoATgHq0HiPXn0DHkyS1rbYIGmeP5o6CdBmhbJYtdRJa6ZxoRvlwbBOY+itb+mFTmtDmK3zCjibX6z1Jvc3nlB/up+V4p5N6ZalYtkOt2RaJwgZY3XBtW94KmdYBgBEFoQRZrlZvp6TdtogmPLr4OABqYppEvRN5+DAca8hwAyMDHfOm4IoSkcXgpVyh3EuuAlPVdCZTu0CixYPAg/eg8G0ZznvwMMYc5vk1gqux473teMoIxX4qZ5hgbn1DZ8fzxbn0C0ociJL8yHbq6W11rpHn+2n/etqGntTwXma8Gr7AQWwlx6ZMk3v3R4rs+veN5+PGShkwrHzwY5NV1JVq7JbBLidI1soX6v1gis2HG7VLFA8X2YitwMEizWydHisVbEtaA7OupMA8pgeUiW57/x5zl7x0ja8Oy7m7ji2cMERgCioZMh4bzy1Pu+H6sJf6Zbh+89jCgLA9C4W7CoaXDjFN8NSFDcn8eP9xeMkwmriIKhLMcDhwCrXoGHZeKEmOO23mISwrvtLljtZKK+gY7kOmyjuwQDBCIH5qlCRADEF1T4ERvl+K5C4kxfiirDeH5eeJebqQ5rC0r6x4GOSOpLG/nWZeXf6HeLM5y6CeUacr192whMF5q6A3Au9R8BoFXC4ByOBZnmN164j49y8GD4q4jhpluOakr4bx6jAqtrm0ztsA3At2DbTGn/T/FpcSBdBWEJ6NzZLiw9wcOMBZ5IXcwaCUz9HH6eXRRQoMgWTIX0V4/nKtb6yPTou9dX3CrMK6di0cwpl6wVdFEWqZwb1SKssMgTCsUzgapLfKPyXdKz2FQ/TIGiQD1pa2LKVhgCmwTNuBJH10f3hi4OVs5SIE6DJnq6W+wyf4oJjbENd157A7uPrhSb9TPUNmFZfZbiD4NVjswWnN4n+WAVfRe+Y0kYzLwWDXlsrqKWiVc+EQQYdJcYL8GBOHfEIOcg0p9nmGjX8yX5WUZwcPxlpz9UpyzWNb1zX3URrqXner3gdoUZBmuKBqpt7qxTPdukTtXp/cA7X7KmmfNxaxXHWAB53LVkepne4404WZnKyaatK+4J2jzdKJw+rDfs9gOWdBa3bfqSaOqOp9gub78EqLtuTgKXlfWQ0uizoqd/j0jzkflxeaV/3nFCiNJ33IVmmyz2Wc+/k7aJwrcDj67ZvLPio1w5pGoGE9lOH9v0IiHxgsENA5MVellMexXJgsULEtbw/D4V2+tjRSpilMBampXnptLi4gFaT3LQAiYB4XE+fxzgIoHqXwdG3dD4yMk4oWwisgCD+4rgn02/EdoCbqm+KKdMliJCKC9+0IbCvkqwcq3Zxhxtlni+mBa/+xzEOaZy4aSxzNb2EBZjW0APAGPfwf8GNVgDdjOcjk0xxYTFI8uXcg5uBjZv5y7LTZKHGv3OqYxCKSao94rnEBzZAgXf9YtYOiJOJ1chyAOY4iRycmtvhIOnwlN/3Tt5FIUWARC4TA6GmTd6KuyToPvJLdwiubTOuC7rwWsVxVGhPRd8U0F7QGnBmbo8lFiHXY6Ewz5aWwy1NBMytFeMoUbMaFhn8EMOCYeNc3X2UM3Zy4/xFnI+nwD65qynHUuTMlxF4apYF9vmpnnjuR1g2mldxBTBlwSgMuNyaBQW/3Q/rn8fzbNKCjK4510tXwbVvPmeDZ4TZMejm4mEQ7LVt8YzAIIZjbFNwjtQeFlOSo41Ml8TK6fsP6dWV+80DBUtt0dtp6gLfgUxfaijRkqgqeDk23M5tIg9TLwvBPogAtZqJQgRoLXE6rLKFe2juY8SU2Ydz1gkGZcJyrZxxOa7v+y9lEP97pEjRqu17ZfFnWjmK4niO5QJmct9XQHN3jdzjhEESJv21E/MhZ79DbmEZwN2cfHxTP38BCfG1LGg73WySP2lCsnntYVvvkT9eEXnuSLrXo4OylWAC6/qB8ZH0jK/cVpYxf/zHiLbP/ArcyJdbhuaGbubqV7u3Putrk51A3eu6w9IIuIDZJfWoww9e+uUys8YjiKj0h3EdOirBKkYmCeBZL32Ai+m+SSunVputIMMkPjObGgAxQX1oncDHSnaVBfSjxnllBg1Th2nG5zFnAh4RGKojsLRAra/ubiFgufUafCI0fU8mcB1GX8UAGRULkF0aXQqvNdbyGGNt/4oAurxUmWQrpwrzeTl+W3IBDPKuEQwMYAIetI5N/ZY5pmR1tVhfHj8PrU0TZ4yMjKhdmwEPEVx1tsyUsGJ19OTGo0CPdZAAEZvVbDKg3VXRHQyNTg8gZ53FyMTwatMPGwWyLsck4NPcmgovlNjUaOhbm/lS1rg5annB9fLo/utr8VWbzYPX6M6C8dEb1nLr117RpKSuB4W85PMucmV9fabPFpkyHzhuHTwpD4DWh9o3F3w0o6vtG9B3i/fo7G1+wGkxje8ibiIjaDw4b70ev9NZgX54HB4Ly4woGVsRk7dq4h8QcvMX8/lYrjeZ/z7U+FyPtPvluLtjKLEpaGEv9c01o5e2Rc0PexaJDJe7zbPYP2xqps8LzN8M35TONG6+Ua3WpLCW6GwtmcY6brq8VLrMXVJ6pmfmGnqwXkJpgAeDeaMWf+0VVWiW3nBtNQpebWUwieaWy5YDmMiuRCyS/+xlStd9LgdKMtcdYdG439Gp9WZeh+abchVFc+sMAcNTOScryF5a1Ie5YguyLwagUlMPK5ib5st58WEeBF6HM6v+4HoxDfa6W3AiXVex5gaAfW3cCM7ebjfs7uapyzEGBsb4kmFXVCClY99PK3y3H5G584It2FsfNUtfnl0gQRpW7LynpaotQRc5PgjaMofImvJKQS9+L6YwX8qgr8/xNZyzt+WGvQwyOsCyoJ7KiWs/g2qfINIsFuLuQbOgETTfmHqsBtD22sc4Lo3ul948ALUVz3TBKNzGF9L36rASpHeSy4BCUbf7e+WxP7yo3NmKWzLwOGg5afxWNdkBSNEZeKZ9N/YLfhYprTLtEdM2opj3dIKuhYJ9uO6XnsZeiiQ7ZMi2ta3H83GRsMkkg5L2lvdIDk/NnZw3SEkf/5XgqW8c+FAf/Ha+oN1e0DZBu6pxKGT+hlXArE9P01YfC3d9R3Q1i/ngr4GOd+AjA/h1QWbwEYJLXgUfOQd7FvbLRD8ACzkgi6xz+dmQ8sN7hQU/ttQvzPen2SwL/Mz3wSq6fQO0d7R3V5znDdfTmGWOs+N2AO3FfMcqClT6RaxFTMjR0M6C/qLAIUDzapKnu9mOxFibXuDVwmN25xG4to4z52R6sdbzOV48No1xHJvk+Hos57uLZS20L684+xVHu+EAcPaO61lwfnkzt8K5oZeOXtudBpuLegGYaqvUouiloe4H3p8nam3Y9hO9jMDQHM/BLAVgxBkcveOlFZza8dLm4w8MDb05bbvWc3IPndo8/bShacNxiLsEBvg4Wp2GvjvdNdulWObH0dT6cy04z47z5gyeOcaIm+lprKldb9DSY32d+80EfWs4twOtXyHSUUvDLcbBWmuK1hvOlw3n0dHeGTmadjJxntCtoZ83i/1oFf3Y0Y4Trd9wnjds2wEUc68YY6/XrXFrBYFHV4GWE3U7oXJCEvnZS1OcKDh7n7g1GtmAYYGit9ZxO4F2LdBe0c7qQkTRzhuwH9DSLOtNjIVU6gmVjgbFWU4cxw2Qhlt1llcoDq92fPSOo1uKfNcWhoWzNTQUHIfNz+0qbk3YIhh38/c6Z92YFaKiK9DOhtYE7dYMhNwsuFwOS3uVJiiHoB/jvVQqAAv4CLdGE2hT9Noh2tAuV7St4Tzs2UQU59FxnD6/PrdBWNeHGw/VgEY7OqR0c2W04pk5BTgMLNl+ZKAJpwy5wuXJRY7R71e/h+23onatR4rmI0Ne3tMeWSvWm04gI32fP7/bT1/Z9wJ8JFm29k1Ok83tptDri523gqhHXdaPOeqvYfs//o//A9/73vd+2N341D61T+1T+9Q+tU/tr6D90i/9Ev76v/6v/+Ax3zjw0XvHL/7iL+J3/s7fiV/6pV/Cd77znR92l/7/qn3++ef43ve+92nsfwjt09j/cNqncf/htU9j/8Nr/1+MvariBz/4AX7Lb/ktKOXDcQDfOLdLKQW/9bf+VgDAd77znU8L8ofUPo39D699GvsfTvs07j+89mnsf3jt/+2x/+53v/tRx32NxJhP7VP71D61T+1T+9Q+tb/69gl8fGqf2qf2qX1qn9qn9te0fSPBx9PTE/7oH/2jeHp6+mF35f/v2qex/+G1T2P/w2mfxv2H1z6N/Q+v/bDH/hsXcPqpfWqf2qf2qX1qn9qv7faNtHx8ap/ap/apfWqf2qf2a7d9Ah+f2qf2qX1qn9qn9qn9NW2fwMen9ql9ap/ap/apfWp/Tdsn8PGpfWqf2qf2qX1qn9pf0/YJfHxqn9qn9v9r795ConrXMIA/ajppppONOmOhqZlSHijLYYgscPCAhGUXZl5YhKKN0MEkDNLqxjDoopC6yy7CSsh/JBWYR6zR0hRTS1QsqRwlxbPm6d0Xe7vYKy1t517jDO8PBnR93yze71lrPl5riYwxJqlV2Xzk5eVhy5YtWLt2LdRqNd68eWPskszK5cuXYWFhIXr5+fkJ45OTk9DpdNi4cSPs7e1x5MgR9Pb2GrFi01VVVYWDBw/Czc0NFhYW+Oeff0TjRISsrCyoVCrY2tpCq9Wivb1dNGdgYAAJCQlwcHCAXC7HyZMnMTo6KuEqTNNS2R8/fnzB5yAyMlI0h7P/czk5OdizZw/Wr18PFxcXHDp0CG1tbaI5y9ljuru7ER0dDTs7O7i4uCAjIwMzMzNgi1tO7gcOHFhwz6ekpIjmSJX7qms+Hj58iHPnziE7Oxvv3r1DUFAQIiIi0NfXZ+zSzMqOHTvQ09MjvKqrq4Wxs2fP4unTpygsLERlZSW+ffuG2NhYI1ZrusbGxhAUFIS8vLxFx3Nzc3Hz5k3cuXMHtbW1WLduHSIiIjA5OSnMSUhIQEtLC0pKSlBcXIyqqiokJydLtQSTtVT2ABAZGSn6HBQUFIjGOfs/V1lZCZ1Oh5qaGpSUlGB6ehrh4eEYGxsT5iy1x8zOziI6OhpTU1N4/fo17t27h/z8fGRlZRljSSZhObkDQFJSkuiez83NFcYkzZ1WmZCQENLpdML3s7Oz5ObmRjk5OUasyrxkZ2dTUFDQomODg4NkbW1NhYWFwrEPHz4QANLr9RJVaJ4AUFFRkfD93NwcKZVKun79unBscHCQZDIZFRQUEBFRa2srAaC3b98Kc54/f04WFhb09etXyWo3dT9nT0SUmJhIMTExv3wPZ78y+vr6CABVVlYS0fL2mGfPnpGlpSUZDAZhzu3bt8nBwYF+/Pgh7QJM1M+5ExHt37+fTp8+/cv3SJn7qvqXj6mpKdTX10Or1QrHLC0todVqodfrjViZ+Wlvb4ebmxu8vLyQkJCA7u5uAEB9fT2mp6dF18DPzw/u7u58DVZYV1cXDAaDKGtHR0eo1Woha71eD7lcjt27dwtztFotLC0tUVtbK3nN5qaiogIuLi7w9fVFamoq+vv7hTHOfmUMDQ0BAJycnAAsb4/R6/UICAiAq6urMCciIgLDw8NoaWmRsHrT9XPu8+7fvw+FQgF/f39kZmZifHxcGJMy91X1V22/f/+O2dlZ0cIBwNXVFR8/fjRSVeZHrVYjPz8fvr6+6OnpwZUrV7Bv3z40NzfDYDDAxsYGcrlc9B5XV1cYDAbjFGym5vNc7H6fHzMYDHBxcRGNr1mzBk5OTnw9/lJkZCRiY2Ph6emJzs5OXLx4EVFRUdDr9bCysuLsV8Dc3BzOnDmDvXv3wt/fHwCWtccYDIZFPxfzY+z3FssdAI4dOwYPDw+4ubmhqakJFy5cQFtbGx4/fgxA2txXVfPBpBEVFSV8HRgYCLVaDQ8PDzx69Ai2trZGrIwx6Rw9elT4OiAgAIGBgfD29kZFRQXCwsKMWJn50Ol0aG5uFj1Txv7/fpX7fz+vFBAQAJVKhbCwMHR2dsLb21vSGlfVf7soFApYWVkteOq5t7cXSqXSSFWZP7lcjm3btqGjowNKpRJTU1MYHBwUzeFrsPLm8/zd/a5UKhc8bD0zM4OBgQG+HivMy8sLCoUCHR0dADj7v5WWlobi4mKUl5dj8+bNwvHl7DFKpXLRz8X8GPu1X+W+GLVaDQCie16q3FdV82FjY4Pg4GCUlpYKx+bm5lBaWgqNRmPEyszb6OgoOjs7oVKpEBwcDGtra9E1aGtrQ3d3N1+DFebp6QmlUinKenh4GLW1tULWGo0Gg4ODqK+vF+aUlZVhbm5O2DjYyvjy5Qv6+/uhUqkAcPb/KyJCWloaioqKUFZWBk9PT9H4cvYYjUaD9+/fi5q/kpISODg4YPv27dIsxMQslftiGhsbAUB0z0uW+4o+vroCHjx4QDKZjPLz86m1tZWSk5NJLpeLnr5lfyc9PZ0qKiqoq6uLXr16RVqtlhQKBfX19RERUUpKCrm7u1NZWRnV1dWRRqMhjUZj5KpN08jICDU0NFBDQwMBoBs3blBDQwN9/vyZiIiuXbtGcrmcnjx5Qk1NTRQTE0Oenp40MTEhnCMyMpJ27txJtbW1VF1dTT4+PhQfH2+sJZmM32U/MjJC58+fJ71eT11dXfTy5UvatWsX+fj40OTkpHAOzv7PpaamkqOjI1VUVFBPT4/wGh8fF+YstcfMzMyQv78/hYeHU2NjI7148YKcnZ0pMzPTGEsyCUvl3tHRQVevXqW6ujrq6uqiJ0+ekJeXF4WGhgrnkDL3Vdd8EBHdunWL3N3dycbGhkJCQqimpsbYJZmVuLg4UqlUZGNjQ5s2baK4uDjq6OgQxicmJujUqVO0YcMGsrOzo8OHD1NPT48RKzZd5eXlBGDBKzExkYj+/eu2ly5dIldXV5LJZBQWFkZtbW2ic/T391N8fDzZ29uTg4MDnThxgkZGRoywGtPyu+zHx8cpPDycnJ2dydramjw8PCgpKWnBDzmc/Z9bLHMAdPfuXWHOcvaYT58+UVRUFNna2pJCoaD09HSanp6WeDWmY6ncu7u7KTQ0lJycnEgmk9HWrVspIyODhoaGROeRKneL/xTNGGOMMSaJVfXMB2OMMcbMHzcfjDHGGJMUNx+MMcYYkxQ3H4wxxhiTFDcfjDHGGJMUNx+MMcYYkxQ3H4wxxhiTFDcfjDHGGJMUNx+MMcYYkxQ3H4wxxhiTFDcfjDHGGJPUvwCzmGu7BYfsVwAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure()\n",
        "plt.imshow(processed_example[\"labels\"].T)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bWMT9X6w237M"
      },
      "source": [
        "\n",
        "If we run a vocoder on the log-mel spectrogram, it should produce the original audio again. We'll load the HiFi-GAN vocoder from the original [SpeechT5 checkpoint](https://hf.co/microsoft/speecht5_hifigan)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "a60987317acd40278489533a2d9dd021",
            "c9662066695c43e7901ba019a0dc9e8c",
            "4ca762251949444f921863535a18e5c5",
            "d7142a382f884564921bcfcf65909287",
            "13226fa6805041fdb7e3976e4577a66e",
            "7c992442c78045e0962299728b49bbb5",
            "a5fe768b739d4df896d15370e981b656",
            "4a7bd718c8874fc2bbd7a566e1da124d",
            "ea8fbaa445bd442b82bb60a67b0efcea",
            "db1394f796f84b5295e16f1f8b2a526a",
            "f44278459f804c7e9412f72c8cb4eef0",
            "33e739de4df54f96a276027d2d1f66ea",
            "2d3eb78c7b994f3cb2d57d80458c35d6",
            "de8ee1e97a0f4bbfa17281a0c067b5b1",
            "5ab5b7305633479494e991c26c2268a9",
            "ab27bce16ed24effbddb7596ffe2c3c1",
            "5d30def7a2bb463baa858eb0eee9d094",
            "06508d700e9c426b85f374381d3439d9",
            "af4aeb90cfbc4937860ffd47ef7987e5",
            "8b5abf1e77284e549e1ed7cdd100e2af",
            "e550d62c5fb54e8c9be548e252a4ddc3",
            "b51084470848412d9360ed3487aaf3f6"
          ]
        },
        "id": "2Yu-rqwa1zns",
        "outputId": "3b909915-c86b-4ee3-d2bb-12b1d2b2ede6"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a60987317acd40278489533a2d9dd021",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/636 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "33e739de4df54f96a276027d2d1f66ea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/50.7M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import SpeechT5HifiGan\n",
        "vocoder = SpeechT5HifiGan.from_pretrained(\"microsoft/speecht5_hifigan\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "byzHH-Jl2XeL"
      },
      "outputs": [],
      "source": [
        "spectrogram = torch.tensor(processed_example[\"labels\"])\n",
        "with torch.no_grad():\n",
        "    speech = vocoder(spectrogram)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "MgovGHdT2aJv",
        "outputId": "fc483835-189f-4c0d-ef81-8cc75a8981dc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import Audio\n",
        "Audio(speech.cpu().numpy(), rate=16000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPLVad8w3CvV"
      },
      "source": [
        "That all looks and sounds good! We can now process the entire dataset. This can take between 5 and 10 minutes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "2ebee62340c24076b6edeb96d233b5e0",
            "dc0e721504184b778486802d47239e00",
            "4744d62b21e645b8bad84578aed387d3",
            "ef635c73df96463ea145fad659b04028",
            "00adec96a5654f3a9d08c8331366f704",
            "3fd3b45e98ff4292a1444f89fc6ae95e",
            "876341d2a2484e60a72caad3dc02c1b4",
            "4699dc096aa644b6add210252efa7334",
            "f2f329976e13413c9bcef0e56280d563",
            "bd10c6180edc414f877b5ac363801a42",
            "b818deafc75f4bfa8139ae6192dab50d"
          ]
        },
        "id": "ekis02SC2hBN",
        "outputId": "dab33b54-b3a0-43fe-b624-aab4ec391873"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2ebee62340c24076b6edeb96d233b5e0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/4112 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (9497 > 600). Running this sequence through the model will result in indexing errors\n"
          ]
        }
      ],
      "source": [
        "dataset = dataset.map(\n",
        "    prepare_dataset, remove_columns=dataset.column_names,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7z_QGJ1u3LTa"
      },
      "source": [
        "Some of the examples in the dataset are apparently longer than the maximum input length the model can handle (600 tokens), so we should remove those from the dataset. In fact, to allow for larger batch sizes we'll remove anything over 200 tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "2993094c7ff6425eab9bb0b1bdf1d7ac",
            "fd6cb6748b1a4d83bbd3c1f9bc9293c9",
            "a15ad78eb7e249a2ae8f1a4e0b0e359e",
            "39f6a7a3035a4c53bebc34e7a90ad14c",
            "078823ec9ed64a58abc1ee0a7d55976a",
            "b710b2c93fcb437eb220dbd57ba1a235",
            "4acb801fd07e4e1db048921d83fad83f",
            "3bd4aa82001248acb3758f0ff98fe64c",
            "494b73ce426e44da9601305d859f484c",
            "c350945401cb48968758c0674598a855",
            "0194367f515647b1ae5bc638d212c2ce"
          ]
        },
        "id": "Sy4kcOco3Ks3",
        "outputId": "516034d9-6b6f-44ff-d18a-452e18dff79d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2993094c7ff6425eab9bb0b1bdf1d7ac",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Filter:   0%|          | 0/4112 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def is_not_too_long(input_ids):\n",
        "    input_length = len(input_ids)\n",
        "    return input_length < 200\n",
        "\n",
        "dataset = dataset.filter(is_not_too_long, input_columns=[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KRaX-WqeHiDy"
      },
      "source": [
        "How many examples are left?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xi6DLMeEHlZ6",
        "outputId": "65ff941a-d5b6-4f50-81a4-5c9912f3dd9b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4111"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAcyUJ6IHVug"
      },
      "source": [
        "## Train/test split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UnDAXO5KTTE-"
      },
      "source": [
        "Create a basic train/test split. For our purposes, it's OK if the same speaker is part of both sets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IUuFNOjZ_viW"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.train_test_split(test_size=0.1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZmUOJ956k8P"
      },
      "source": [
        "What does the dataset look like now?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xUBq8Y8X6htL",
        "outputId": "8fa224c6-af43-4d14-b21b-a8a9f827e6d4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'labels', 'speaker_embeddings'],\n",
              "        num_rows: 3699\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'labels', 'speaker_embeddings'],\n",
              "        num_rows: 412\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLvJ4HRb3aCp"
      },
      "source": [
        "## Collator to make batches\n",
        "\n",
        "We need to define a custom collator to combine multiple examples into a batch. This will pad shorter sequences with padding tokens. For the spectrogram labels, the padded portions are replaced with the special value -100. This special value tells the model to ignore that part of the spectrogram when calculating the spectrogram loss.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rZOX8bTe3Kq5"
      },
      "outputs": [],
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Any, Dict, List, Union\n",
        "\n",
        "@dataclass\n",
        "class TTSDataCollatorWithPadding:\n",
        "    processor: Any\n",
        "\n",
        "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
        "\n",
        "        input_ids = [{\"input_ids\": feature[\"input_ids\"]} for feature in features]\n",
        "        label_features = [{\"input_values\": feature[\"labels\"]} for feature in features]\n",
        "        speaker_features = [feature[\"speaker_embeddings\"] for feature in features]\n",
        "\n",
        "        # collate the inputs and targets into a batch\n",
        "        batch = processor.pad(\n",
        "            input_ids=input_ids,\n",
        "            labels=label_features,\n",
        "            return_tensors=\"pt\",\n",
        "        )\n",
        "\n",
        "        # replace padding with -100 to ignore loss correctly\n",
        "        batch[\"labels\"] = batch[\"labels\"].masked_fill(\n",
        "            batch.decoder_attention_mask.unsqueeze(-1).ne(1), -100\n",
        "        )\n",
        "\n",
        "        # not used during fine-tuning\n",
        "        del batch[\"decoder_attention_mask\"]\n",
        "\n",
        "        # round down target lengths to multiple of reduction factor\n",
        "        if model.config.reduction_factor > 1:\n",
        "            target_lengths = torch.tensor([\n",
        "                len(feature[\"input_values\"]) for feature in label_features\n",
        "            ])\n",
        "            target_lengths = target_lengths.new([\n",
        "                length - length % model.config.reduction_factor for length in target_lengths\n",
        "            ])\n",
        "            max_length = max(target_lengths)\n",
        "            batch[\"labels\"] = batch[\"labels\"][:, :max_length]\n",
        "\n",
        "        # also add in the speaker embeddings\n",
        "        batch[\"speaker_embeddings\"] = torch.tensor(speaker_features)\n",
        "\n",
        "        return batch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffNanldmrlbD"
      },
      "source": [
        "In SpeechT5, the input to the decoder part of the model is reduced by a factor 2. In other words, it throws away every other timestep from the target sequence. The decoder then predicts a sequence that is twice as long. Since the original target sequence length may be odd, the data collator makes sure to round the maximum length of the batch down to be a multiple of 2."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CJKY4DVO4khr"
      },
      "outputs": [],
      "source": [
        "data_collator = TTSDataCollatorWithPadding(processor=processor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McZfpBzL4mRK"
      },
      "source": [
        "Let's test the data collator."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "78-XVnD04ouJ"
      },
      "outputs": [],
      "source": [
        "features = [\n",
        "    dataset[\"train\"][0],\n",
        "    dataset[\"train\"][1],\n",
        "    dataset[\"train\"][20],\n",
        "]\n",
        "\n",
        "batch = data_collator(features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vuWoBSpW4pY9",
        "outputId": "cfaf09be-cef4-4328-f96d-8c3eda7ec71a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'input_ids': torch.Size([3, 106]),\n",
              " 'attention_mask': torch.Size([3, 106]),\n",
              " 'labels': torch.Size([3, 580, 80]),\n",
              " 'speaker_embeddings': torch.Size([3, 512])}"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "{k:v.shape for k,v in batch.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVMpz-tpP-vw"
      },
      "source": [
        "Looks good!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_iOqGsqq4uJv"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQiZlXN9ICmc"
      },
      "source": [
        "It's always a good idea to upload model checkpoints directly to the [Hugging Face Hub](https://huggingface.co/) while training. To allow this, first log in to the Hub by entering your Hub authentication token:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "9defb63d40414377962be7a45fe04863",
            "4bdb55d30a0b4b648c082f2cf5921383",
            "b9d136e1b7944a08a3b206be697e8bcd",
            "9a84e7b2ed2e4854b3a27c4a7a6d7b29",
            "17f05d9f756e466eba646e8dca6a89e3",
            "9d901610487e4d7389072038d0e5d72d",
            "8e0bf7d4611641c091e87fbacbf10a19",
            "c4660f3058d14daba1940d89e200b65d",
            "31ed87b6e80b4822b430c51ae043edcf",
            "4edcc714fca4485190e973ed83325af1",
            "8c8333ae0d3d4ba7813d4d91245823db",
            "7dc5299d9e264f4d9dda9ff552d5d5cc",
            "034918f3b833496397f34caa9a0bfbb4",
            "0a595220c4004b6ca316c47babe4e5a7",
            "1e34bc54a39b42b1a5a2ece20823ca3f",
            "efeef3aa079d4bf4bc4c5049f9c74e16",
            "821bc686077d4d4faf73a98e60ef2262",
            "e6a2cb79b3a14ceb96e44b93529a1b17",
            "18f235d673c1422abc9a7a897295aaf4",
            "e87df5c1d1fb4d19bd7e8663f39a9073",
            "346edbb27fec4479ad74331561fe6d4f",
            "382582721b924a77a6ed9ebf2d0e78c6",
            "f1e85d48269a4675aa3eb1484d5ebbf7",
            "bd810380dabe446d874fbcdbf0a2a815",
            "5942dd51d8014d32a6400698fa84f88e",
            "813e7e5696b84a0091524b551d94b1a6",
            "59f995c679f843eba17be9f2cab2b5ca",
            "aa7bf7b3cc7441888151beffb6d30321",
            "a903a041f8f34e95b7eb95e7680e68af",
            "499ae3562b0e4636a5526230245d502a",
            "c5b791315d534952b9087a66d94a49a4",
            "d3d5fd89f4df4f278529a4d959f64d5f"
          ]
        },
        "id": "1z3DEgUOIBz7",
        "outputId": "ade902b9-4eda-4a99-fd24-3f62259bf680"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9defb63d40414377962be7a45fe04863",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Et5pSY56QEFA"
      },
      "source": [
        "The `use_cache=True` option is incompatible with gradient checkpointing. Disable it for training, otherwise it keeps complaining about it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DmjX8QsH4vUW"
      },
      "outputs": [],
      "source": [
        "model.config.use_cache = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRWLpbljIagm"
      },
      "source": [
        "Define the training arguments.\n",
        "\n",
        "We won't be computing any evaluation metrics during the training process. Instead, we'll only look at the loss. The lower the loss, the better the model.\n",
        "\n",
        "Note: If you do not want to upload the model checkpoints to the Hub, set `push_to_hub=False`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fXg9cfJ7JQT",
        "outputId": "9e4f0c98-efb1-4dd9-f58c-05204b4c294e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import Seq2SeqTrainingArguments\n",
        "\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"./speecht5_tts_urdu\",  # change to a repo name of your choice\n",
        "    per_device_train_batch_size=16,\n",
        "    gradient_accumulation_steps=2,\n",
        "    learning_rate=1e-5,\n",
        "    warmup_steps=300,\n",
        "    max_steps=10500,\n",
        "    gradient_checkpointing=True,\n",
        "    fp16=True,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    per_device_eval_batch_size=8,\n",
        "    save_steps=1000,\n",
        "    eval_steps=500,\n",
        "    logging_steps=25,\n",
        "    report_to=[\"tensorboard\"],\n",
        "    load_best_model_at_end=True,\n",
        "    greater_is_better=False,\n",
        "    label_names=[\"labels\"],\n",
        "    push_to_hub=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksyDvPosI1Vg"
      },
      "source": [
        "Create the trainer object using the model, dataset, and data collator."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ekIPeQuD7RwA",
        "outputId": "c6c0e4b8-8685-4ba1-a3d6-c3f97cfb14fd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ],
      "source": [
        "from transformers import Seq2SeqTrainer\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    args=training_args,\n",
        "    model=model,\n",
        "    train_dataset=dataset[\"train\"],\n",
        "    eval_dataset=dataset[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=processor.tokenizer,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l9OkZG-ZJAh5"
      },
      "source": [
        "And with that, we're ready to start training!\n",
        "\n",
        "Training will take several hours depending on your GPU or the one allocated to the Google Colab. Depending on your GPU, it is possible that you will encounter a CUDA \"out-of-memory\" error when you start training. In this case, you can reduce the `per_device_train_batch_size` incrementally by factors of 2 and increase `gradient_accumulation_steps` by 2x to compensate."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "87U167Vf7V5Q",
        "outputId": "48f86de9-beeb-4a6d-bdc3-c77ab1a53889"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2353' max='10500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 2353/10500 41:28 < 2:23:44, 0.94 it/s, Epoch 20.28/91]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.578200</td>\n",
              "      <td>0.507094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.524800</td>\n",
              "      <td>0.486292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.512500</td>\n",
              "      <td>0.474619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.508100</td>\n",
              "      <td>0.472668</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='10500' max='10500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [10500/10500 3:07:45, Epoch 90/91]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.578200</td>\n",
              "      <td>0.507094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.524800</td>\n",
              "      <td>0.486292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.512500</td>\n",
              "      <td>0.474619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.508100</td>\n",
              "      <td>0.472668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.496700</td>\n",
              "      <td>0.468271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.490500</td>\n",
              "      <td>0.464520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.479400</td>\n",
              "      <td>0.466818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.482900</td>\n",
              "      <td>0.464720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.477000</td>\n",
              "      <td>0.464455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.463700</td>\n",
              "      <td>0.470984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.474300</td>\n",
              "      <td>0.468265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.459500</td>\n",
              "      <td>0.469457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.473500</td>\n",
              "      <td>0.468382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.461300</td>\n",
              "      <td>0.472399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>0.467800</td>\n",
              "      <td>0.473160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.453800</td>\n",
              "      <td>0.472332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>0.453600</td>\n",
              "      <td>0.474660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>0.458700</td>\n",
              "      <td>0.473995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>0.453600</td>\n",
              "      <td>0.476193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.460600</td>\n",
              "      <td>0.476838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10500</td>\n",
              "      <td>0.452800</td>\n",
              "      <td>0.479585</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=10500, training_loss=0.4873873142969041, metrics={'train_runtime': 11266.2505, 'train_samples_per_second': 29.824, 'train_steps_per_second': 0.932, 'total_flos': 2.251261023307164e+16, 'train_loss': 0.4873873142969041, 'epoch': 90.51724137931035})"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4z3CzVGSs000"
      },
      "source": [
        "If we do one more `push_to_hub()` after training we can get a nice model card built for us. We simply have to set the appropriate keyword arguments (kwargs). You can change these values to match your dataset, language and model name accordingly:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NpXOJAZIs2-n"
      },
      "outputs": [],
      "source": [
        "kwargs = {\n",
        "    \"dataset_tags\": \"m-aliabbas/common_voice_urdu1\",\n",
        "    \"dataset\": \"common_voice_urdu1\",  # a 'pretty' name for the training dataset\n",
        "    \"dataset_args\": \"split: train\",\n",
        "    \"model_name\": \"SpeechT5 TTS urdu\",  # a 'pretty' name for your model\n",
        "    \"finetuned_from\": \"microsoft/speecht5_tts\",\n",
        "    \"tasks\": \"text-to-speech\",\n",
        "}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooEluBcXs5hJ"
      },
      "source": [
        "The training results can now be uploaded to the Hub. To do so, execute the `push_to_hub` command:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "456aa13fa38c4c01b9f43570e31556fe",
            "ec7ac27d32c44b40ac8fd18a7700962d",
            "fbeb5a0f2394434fb67644f452dcb6b2",
            "f22ab8b84c9a406d9383a1253576cf62",
            "3915aa47172546afb3f09ea1f7c5b947",
            "308b5dd72e6f4ea39d9be4c2e4a02512",
            "9aa2c30314774289a458d92cabf2e9e1",
            "4d5bdff9982e40138888bc9c72f9233e",
            "2c9002098b114841b511b97c5fa39f14",
            "fe75e4e2a67749b49efe29817c9148db",
            "1660e3387e094116b87f3a8f04782124"
          ]
        },
        "id": "mpodV89js9KT",
        "outputId": "ecfd3656-aacf-4f48-f2bc-718285ecdcc6"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "456aa13fa38c4c01b9f43570e31556fe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "events.out.tfevents.1721676197.d34fde1d2e44.268.2:   0%|          | 0.00/101k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/pocketmonkey/speecht5_tts_urdu/commit/9be7dcf5e5852928f45804bd67da59f3a93d610f', commit_message='End of training', commit_description='', oid='9be7dcf5e5852928f45804bd67da59f3a93d610f', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub(**kwargs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "ielUv3WZXGVj",
        "outputId": "4b5a89ea-1b86-4ce5-d942-fc7ce816aee7"
      },
      "outputs": [
        {
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: ''",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-5c3faba020d7>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\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      2\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"your_model_save_directory\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36msave_pretrained\u001b[0;34m(self, save_directory, is_main_process, state_dict, save_function, push_to_hub, max_shard_size, safe_serialization, variant, token, save_peft_format, **kwargs)\u001b[0m\n\u001b[1;32m   2476\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2478\u001b[0;31m         \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_directory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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   2479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2480\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpush_to_hub\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/os.py\u001b[0m in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m    223\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m         \u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\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    226\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m         \u001b[0;31m# Cannot rely on checking for EEXIST, since the operating system\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: ''"
          ]
        }
      ],
      "source": [
        "model.save_pretrained(\"\")\n",
        "tokenizer.save_pretrained(\"your_model_save_directory\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojrUgMeEs_LP"
      },
      "source": [
        "You can now share this model with anyone using the link on the Hub."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5K1AQd8B7b-p"
      },
      "source": [
        "## Evaluate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrqUYy3-Qr1w"
      },
      "source": [
        "After training finishes, let's use the model to synthesize some speech!\n",
        "\n",
        "I'm loading the model from the Hugging Face Hub, as the Colab notebook was terminated before training finished (which is why it's a good idea to use `push_to_hub=True` when training)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "af584652d0304a899e66314d61726323",
            "3b74fdd0043949548e50b1d87413505b",
            "958d0637ccad4af49815a2d137fee333",
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            "68a15f79b7ab42eaa0b14086f2b54ed6",
            "6916935a59f94d6e95449be55732c80c",
            "cef8c2e1224b4f0eb21e4963d53431b5",
            "7597054472514485afa120d6266c91a5",
            "6035b946f770456fbfd123adbb5e3be9",
            "64eb31cf8d59468ea56a8e58c71d8122",
            "0900daeba5cf4cb29d00a9c5ed5ad330",
            "054b003700514935960c7ee0895b8c8e",
            "2dcbcbfcbb3c455a8beee0d7f4c12c05",
            "be44a4398328449daebb6bd48e2519f7",
            "bcd4e9d178c944f6973b2d62fa1e5538",
            "d8419ef35db5403c862e8321572ad376",
            "af01cdbb16dd490984a749d2da6eff34",
            "54783e715ad0408eaa512f7d28a374b2",
            "d0f6e0eefec74d9aa2a0a00868c77a55",
            "bd858f248a124f14833b044f7b0b71e5",
            "d432e91ec63b4655a50bc775ee74aab4",
            "af1e15c4f52e4ea5886d1631de2fbe39",
            "a617fca4345c4d21a6c44d5ef8246f91",
            "3109d5c2ffe04d5ebf296cc26a334a38",
            "4d97d63c12ab4c37b53207cf91ac9794",
            "a3f2bf1d001c4f2d8571d2a4db8972d1",
            "e27fcac6c1be4ecc89028aa8368e6e89",
            "c1b4ea7cfc0d434dbb68027af48a5f07",
            "5e1fd59b51d1459da614ab5d51116d59",
            "ced524c3f07b40e792db0bf1e9c3fe92",
            "b257195cf3314cb4bc3aeddc4da142b9",
            "95446dc9a4cc46469d1efa27c7896bfc",
            "3cd2ea08c82e49a08396e31dfead7f6f"
          ]
        },
        "id": "bos-fW6CHijQ",
        "outputId": "ad9f4982-f54c-4faa-c710-f3f7df98d480"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "af584652d0304a899e66314d61726323",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/2.11k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "054b003700514935960c7ee0895b8c8e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/578M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a617fca4345c4d21a6c44d5ef8246f91",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model= SpeechT5ForTextToSpeech.from_pretrained(\"pocketmonkey/speecht5_tts_urdu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AePsNMjKHhBH"
      },
      "source": [
        "First we need to obtain a speaker embedding. We can simply grab one from the test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08GkZiwq7dE7",
        "outputId": "293bf06a-dc5f-46ad-9d42-c93785ab7ca7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 512])"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example = dataset[\"test\"][304]\n",
        "speaker_embeddings = torch.tensor(example[\"speaker_embeddings\"]).unsqueeze(0)\n",
        "speaker_embeddings.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dUg-8t6xQ-k1"
      },
      "source": [
        "Define some input text and tokenize it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T6uROO_97nnJ"
      },
      "outputs": [],
      "source": [
        "text = \"زندگی میں کامیابی\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LpuE7d9WOoE0"
      },
      "outputs": [],
      "source": [
        "def urdu_to_roman_urdu(text):\n",
        "    urdu_to_roman_dict = {\n",
        "        'ا': 'a', 'ب': 'b', 'پ': 'p', 'ت': 't', 'ٹ': 't', 'ث': 's', 'ج': 'j', 'چ': 'ch',\n",
        "        'ح': 'h', 'خ': 'kh', 'د': 'd', 'ڈ': 'd', 'ذ': 'z', 'ر': 'r', 'ڑ': 'r', 'ز': 'z',\n",
        "        'ژ': 'zh', 'س': 's', 'ش': 'sh', 'ص': 's', 'ض': 'z', 'ط': 't', 'ظ': 'z', 'ع': 'a',\n",
        "        'غ': 'gh', 'ف': 'f', 'ق': 'q', 'ک': 'k', 'گ': 'g', 'ل': 'l', 'م': 'm', 'ن': 'n',\n",
        "        'ں': 'n', 'و': 'w', 'ہ': 'h', 'ء': 'a', 'ی': 'y', 'ے': 'e', 'آ': 'a', 'ؤ': 'o',\n",
        "        'ئ': 'y', 'ٔ': '', ' ': ' ', '۔': '.', '،': ',', '؛': ';', '؟': '?','ھ': 'h'\n",
        "    }\n",
        "\n",
        "    roman_text = ''.join(urdu_to_roman_dict.get(char, char) for char in text)\n",
        "    return roman_text\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XCgo3oV5FH34"
      },
      "outputs": [],
      "source": [
        "text=urdu_to_roman_urdu(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "wc2MziRoFUcq",
        "outputId": "4beacf3e-45dd-458f-d82a-3b3134449180"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'zndgy myn kamyaby'"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JCY1Hr987oox"
      },
      "outputs": [],
      "source": [
        "inputs = processor(text=text, return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5ShECAkRCUv"
      },
      "source": [
        "Tell the model to generate a spectrogram from the input text."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZvA6z5w47q7L"
      },
      "outputs": [],
      "source": [
        "spectrogram = model.generate_speech(inputs[\"input_ids\"], speaker_embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "IVN17Mt67sTe",
        "outputId": "f9e3e63b-6a9b-4ac2-b5a5-1ee8aa4d7899"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure()\n",
        "plt.imshow(spectrogram.T)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McG3EnpYRJVj"
      },
      "source": [
        "Finally, use the vocoder to turn the spectrogram into sound."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MpogdOe_7uoe"
      },
      "outputs": [],
      "source": [
        "with torch.no_grad():\n",
        "    speech = vocoder(spectrogram)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "5H_FaVuW7wXt",
        "outputId": "ce585e70-1e90-4cf5-af17-9ac311dee7ee"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import Audio\n",
        "Audio(speech.numpy(), rate=16000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "id": "PV0nUOmO7xvv",
        "outputId": "3e0c913d-86ac-41e6-dba1-144238fa147d"
      },
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-82-d52c7ae4212c>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msoundfile\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"output.wav\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeech\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamplerate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first."
          ]
        }
      ],
      "source": [
        "import soundfile as sf\n",
        "sf.write(\"output.wav\", speech.numpy(), samplerate=16000)"
      ]
    }
  ],
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