Noise_Suppression.ipynb

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            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
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            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
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            "grid_auto_rows": null,
            "grid_gap": null,
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            "_view_module_version": "1.2.0",
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            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mayureshagashe2105/Noise-Suppression/blob/main/Noise_Suppression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x20MJdYDCcZm",
        "outputId": "3df67f89-9205-41f7-9426-a1c25abe986c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIMDecfiDKwy"
      },
      "source": [
        "import os\n",
        "import glob\n",
        "from tqdm.notebook import tqdm\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import style\n",
        "import zipfile\n",
        "import pickle"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BC9lIbwwQqc"
      },
      "source": [
        "physical_devices = tf.config.list_physical_devices(\"GPU\")\n",
        "tf.config.experimental.set_memory_growth(physical_devices[0], True)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1kWVzOUEERD",
        "outputId": "3a0c8f8c-0af1-4f85-fa6d-1f4fd498c2b8"
      },
      "source": [
        "if not os.path.exists('/content/Noise_Suprresion'):\n",
        "  with zipfile.ZipFile('/content/gdrive/MyDrive/Noise supression Dataset/DS_10283_1942.zip', 'r') as zip_ref:\n",
        "      zip_ref.extractall('/content/Noise_Suprresion/unzipped_dataset')\n",
        "\n",
        "  with zipfile.ZipFile('/content/Noise_Suprresion/unzipped_dataset/clean_trainset_wav.zip', 'r') as zip_ref:\n",
        "      zip_ref.extractall('/content/Noise_Suprresion/train/clean')\n",
        "\n",
        "  with zipfile.ZipFile('/content/Noise_Suprresion/unzipped_dataset/noisy_trainset_wav.zip', 'r') as zip_ref:\n",
        "      zip_ref.extractall('/content/Noise_Suprresion/train/noisy')\n",
        "\n",
        "  with zipfile.ZipFile('/content/Noise_Suprresion/unzipped_dataset/clean_testset_wav.zip', 'r') as zip_ref:\n",
        "      zip_ref.extractall('/content/Noise_Suprresion/test/clean')\n",
        "\n",
        "  with zipfile.ZipFile('/content/Noise_Suprresion/unzipped_dataset/noisy_testset_wav.zip', 'r') as zip_ref:\n",
        "      zip_ref.extractall('/content/Noise_Suprresion/test/noisy')\n",
        "  \n",
        "  print('Files unzipped successfully!')\n",
        "\n",
        "else:\n",
        "  print('Files already exists!')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files unzipped successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qm6-3peuOh-N"
      },
      "source": [
        "clean_train_list = glob.glob('/content/Noise_Suprresion/train/clean/*')\n",
        "noisy_train_list = glob.glob('/content/Noise_Suprresion/train/noisy/*')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350,
          "referenced_widgets": [
            "41e81f24ecdb4fa7a5743aaf743156c4",
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            "0bf1fc72e3844d41a094a6406ed38330",
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            "942948e72ab644998a9eb87b09a0c089",
            "0efd3ec42afd4ce4a1d9c82e218a9e2a"
          ]
        },
        "id": "ouaDOFrEcQFO",
        "outputId": "95085544-511f-4700-fb3d-ccb0f9a03d5c"
      },
      "source": [
        "clean_samples, _ = tf.audio.decode_wav(tf.io.read_file(clean_train_list[0]), desired_channels=1)\n",
        "for audio in tqdm(clean_train_list[1:]):\n",
        "  aud, _ = tf.audio.decode_wav(tf.io.read_file(audio), desired_channels=1)\n",
        "  clean_samples = tf.concat([clean_samples, aud], axis=0)\n",
        "\n",
        "\n",
        "noisy_samples, _ = tf.audio.decode_wav(tf.io.read_file(noisy_train_list[0]), desired_channels=1)\n",
        "for audio in tqdm(noisy_train_list[1:]):\n",
        "  aud, _ = tf.audio.decode_wav(tf.io.read_file(audio), desired_channels=1)\n",
        "  noisy_samples = tf.concat([noisy_samples, aud], axis=0)\n",
        "\n",
        "clean_samples, noisy_samples"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "41e81f24ecdb4fa7a5743aaf743156c4",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "  0%|          | 0/11571 [00:00<?, ?it/s]"
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        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "99bce60be9b64890bc2de3aa9f12ba49",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/11571 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(540938091, 1), dtype=float32, numpy=\n",
              " array([[-0.00186157],\n",
              "        [-0.0038147 ],\n",
              "        [-0.00354004],\n",
              "        ...,\n",
              "        [ 0.00460815],\n",
              "        [ 0.00466919],\n",
              "        [ 0.00473022]], dtype=float32)>,\n",
              " <tf.Tensor: shape=(540938091, 1), dtype=float32, numpy=\n",
              " array([[-0.01724243],\n",
              "        [-0.02206421],\n",
              "        [ 0.00415039],\n",
              "        ...,\n",
              "        [-0.00933838],\n",
              "        [-0.00518799],\n",
              "        [ 0.0065918 ]], dtype=float32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZS8r4_pl7s2"
      },
      "source": [
        "with open('clean_samples.pkl', 'wb') as f:\n",
        "  pickle.dump(clean_samples, f)\n",
        "with open('noisy_samples.pkl', 'wb') as f:\n",
        "  pickle.dump(noisy_samples, f)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYLywrC3m5Ke"
      },
      "source": [
        "pickle_in = open('clean_samples.pkl', 'rb')\n",
        "clean_samples = pickle.load(pickle_in)\n",
        "clean_samples = tf.cast(clean_samples, 'float16')\n",
        "\n",
        "pickle_in = open('noisy_samples.pkl', 'rb')\n",
        "noisy_samples = pickle.load(pickle_in)\n",
        "noisy_samples = tf.cast(noisy_samples, 'float16')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c2cMQqfWdApS",
        "outputId": "7d0365f4-8118-4ce5-a8eb-1a5073d21e7e"
      },
      "source": [
        "clean_train, noisy_train = [], []\n",
        "sampling_size = 16000\n",
        "for i in range((noisy_samples.shape[0]) // sampling_size):\n",
        "  clean_train.append(clean_samples[i * sampling_size:i * sampling_size + sampling_size])\n",
        "  noisy_train.append(noisy_samples[i * sampling_size:i * sampling_size + sampling_size])\n",
        "\n",
        "noisy_train = tf.stack(noisy_train)\n",
        "clean_train = tf.stack(clean_train)\n",
        "\n",
        "clean_train, noisy_train"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(33808, 16000, 1), dtype=float16, numpy=\n",
              " array([[[-1.8616e-03],\n",
              "         [-3.8147e-03],\n",
              "         [-3.5400e-03],\n",
              "         ...,\n",
              "         [ 5.4932e-04],\n",
              "         [-1.2207e-04],\n",
              "         [-4.2725e-04]],\n",
              " \n",
              "        [[-1.4648e-03],\n",
              "         [-2.0752e-03],\n",
              "         [-3.1128e-03],\n",
              "         ...,\n",
              "         [-2.7563e-01],\n",
              "         [-2.9297e-01],\n",
              "         [-3.0786e-01]],\n",
              " \n",
              "        [[-2.8955e-01],\n",
              "         [-1.8652e-01],\n",
              "         [-2.9877e-02],\n",
              "         ...,\n",
              "         [-7.0801e-03],\n",
              "         [-7.5378e-03],\n",
              "         [-7.3547e-03]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[-2.7161e-02],\n",
              "         [-2.4536e-02],\n",
              "         [-2.2186e-02],\n",
              "         ...,\n",
              "         [ 5.3711e-03],\n",
              "         [ 2.5635e-03],\n",
              "         [ 4.6997e-03]],\n",
              " \n",
              "        [[ 6.6223e-03],\n",
              "         [ 4.2419e-03],\n",
              "         [ 1.8311e-03],\n",
              "         ...,\n",
              "         [ 5.3467e-02],\n",
              "         [ 4.3335e-03],\n",
              "         [-4.9408e-02]],\n",
              " \n",
              "        [[-1.0516e-01],\n",
              "         [-1.6016e-01],\n",
              "         [-2.0850e-01],\n",
              "         ...,\n",
              "         [-1.3000e-02],\n",
              "         [-1.5991e-02],\n",
              "         [-1.0559e-02]]], dtype=float16)>,\n",
              " <tf.Tensor: shape=(33808, 16000, 1), dtype=float16, numpy=\n",
              " array([[[-0.01724  ],\n",
              "         [-0.02206  ],\n",
              "         [ 0.00415  ],\n",
              "         ...,\n",
              "         [-0.0824   ],\n",
              "         [-0.1072   ],\n",
              "         [-0.1105   ]],\n",
              " \n",
              "        [[-0.1228   ],\n",
              "         [-0.1348   ],\n",
              "         [-0.1205   ],\n",
              "         ...,\n",
              "         [-0.2113   ],\n",
              "         [-0.2194   ],\n",
              "         [-0.2391   ]],\n",
              " \n",
              "        [[-0.212    ],\n",
              "         [-0.1056   ],\n",
              "         [ 0.0572   ],\n",
              "         ...,\n",
              "         [-0.0984   ],\n",
              "         [-0.0777   ],\n",
              "         [-0.091    ]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[-0.002625 ],\n",
              "         [ 0.001923 ],\n",
              "         [-0.006897 ],\n",
              "         ...,\n",
              "         [ 0.006256 ],\n",
              "         [ 0.002289 ],\n",
              "         [ 0.003387 ]],\n",
              " \n",
              "        [[ 0.005127 ],\n",
              "         [ 0.003845 ],\n",
              "         [-0.0003662],\n",
              "         ...,\n",
              "         [ 0.01016  ],\n",
              "         [-0.05817  ],\n",
              "         [-0.1072   ]],\n",
              " \n",
              "        [[-0.1442   ],\n",
              "         [-0.216    ],\n",
              "         [-0.2778   ],\n",
              "         ...,\n",
              "         [ 0.00473  ],\n",
              "         [-0.001495 ],\n",
              "         [-0.00482  ]]], dtype=float16)>)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "FnSsUYV1ZGVM",
        "outputId": "54de2f6d-3e83-4fe4-edab-cc3f1da91ff2"
      },
      "source": [
        "fig, axes = plt.subplots(2, 2, figsize=(20, 10))\n",
        "\n",
        "axes[0][0].plot(np.arange(noisy_train[10].shape[0]), noisy_train[10], color='r')\n",
        "axes[1][0].plot(np.arange(clean_train[10].shape[0]), clean_train[10], color='b')\n",
        "\n",
        "axes[0][1].plot(np.arange(noisy_train[100].shape[0]), noisy_train[100], color='r')\n",
        "axes[1][1].plot(np.arange(clean_train[100].shape[0]), clean_train[100], color='b')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dw3hQgwlp5CA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1b9ebf4-84ee-427e-a6cc-5d6e8cfd4855"
      },
      "source": [
        "def input_pipeline(X, y, batch_size, shuffle_buffer, train_split=0.8):\n",
        "  train_split_index = int(X.shape[0] * 0.8)\n",
        "  ds = tf.data.Dataset.from_tensor_slices((X, y))\n",
        "  ds = ds.shuffle(shuffle_buffer)\n",
        "  train_ds, val_ds = ds.take(train_split_index), ds.skip(train_split_index)\n",
        "  train_ds = train_ds.batch(batch_size)\n",
        "  val_ds = val_ds.batch(batch_size)\n",
        "  return train_ds.prefetch(1), val_ds.prefetch(1)\n",
        "\n",
        "train_ds, val_ds = input_pipeline(noisy_train, clean_train, 64, 50000)\n",
        "train_ds, val_ds"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<PrefetchDataset shapes: ((None, 16000, 1), (None, 16000, 1)), types: (tf.float16, tf.float16)>,\n",
              " <PrefetchDataset shapes: ((None, 16000, 1), (None, 16000, 1)), types: (tf.float16, tf.float16)>)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1BRkHRNX1Hm",
        "outputId": "eca240e5-5a78-4aee-b90c-67be747b1fcc"
      },
      "source": [
        "def build_model():\n",
        "  inputs = layers.Input((16000, 1))\n",
        "\n",
        "  conv1 = layers.Conv1D(2, 32, 2, padding='same', activation='relu')(inputs)\n",
        "  conv2 = layers.Conv1D(4, 32, 2, padding='same', activation='relu')(conv1)\n",
        "  conv3 = layers.Conv1D(8, 32, 2, padding='same', activation='relu')(conv2)\n",
        "  conv4 = layers.Conv1D(16, 32, 2, padding='same', activation='relu')(conv3)\n",
        "  conv5 = layers.Conv1D(32, 32, 2, padding='same', activation='relu')(conv4)\n",
        "\n",
        "  deconv1 = layers.Conv1DTranspose(32, 32, 1, padding='same')(conv5)\n",
        "  concat = layers.Concatenate()([conv5, deconv1])\n",
        "  deconv2 = layers.Conv1DTranspose(16, 32, 2, padding='same')(concat)\n",
        "  concat = layers.Concatenate()([conv4, deconv2])\n",
        "  deconv3 = layers.Conv1DTranspose(8, 32, 2, padding='same')(concat)\n",
        "  concat = layers.Concatenate()([conv3, deconv3])\n",
        "  deconv4 = layers.Conv1DTranspose(4, 32, 2, padding='same')(concat)\n",
        "  concat = layers.Concatenate()([conv2, deconv4])\n",
        "  deconv5 = layers.Conv1DTranspose(2, 32, 2, padding='same')(concat)\n",
        "  concat = layers.Concatenate()([conv1, deconv5])\n",
        "  deconv6 = layers.Conv1DTranspose(1, 32, 2, padding='same')(concat)\n",
        "  concat = layers.Concatenate()([inputs, deconv6])\n",
        "  deconv6 = layers.Conv1DTranspose(1, 32, 1, padding='same', activation='linear')(concat)\n",
        "\n",
        "\n",
        "\n",
        "  model = tf.keras.Model(inputs=[inputs], outputs=[deconv6], name='auto_encoders_for_noise_removal')\n",
        "\n",
        "  return model\n",
        "\n",
        "model = build_model()\n",
        "print(model.summary())"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"auto_encoders_for_noise_removal\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_12 (InputLayer)           [(None, 16000, 1)]   0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_55 (Conv1D)              (None, 8000, 2)      66          input_12[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_56 (Conv1D)              (None, 4000, 4)      260         conv1d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_57 (Conv1D)              (None, 2000, 8)      1032        conv1d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_58 (Conv1D)              (None, 1000, 16)     4112        conv1d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_59 (Conv1D)              (None, 500, 32)      16416       conv1d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_transpose_29 (Conv1DTran (None, 500, 32)      32800       conv1d_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_25 (Concatenate)    (None, 500, 64)      0           conv1d_59[0][0]                  \n",
            "                                                                 conv1d_transpose_29[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_transpose_30 (Conv1DTran (None, 1000, 16)     32784       concatenate_25[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_26 (Concatenate)    (None, 1000, 32)     0           conv1d_58[0][0]                  \n",
            "                                                                 conv1d_transpose_30[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_transpose_31 (Conv1DTran (None, 2000, 8)      8200        concatenate_26[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_27 (Concatenate)    (None, 2000, 16)     0           conv1d_57[0][0]                  \n",
            "                                                                 conv1d_transpose_31[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_transpose_32 (Conv1DTran (None, 4000, 4)      2052        concatenate_27[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_28 (Concatenate)    (None, 4000, 8)      0           conv1d_56[0][0]                  \n",
            "                                                                 conv1d_transpose_32[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_transpose_33 (Conv1DTran (None, 8000, 2)      514         concatenate_28[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_29 (Concatenate)    (None, 8000, 4)      0           conv1d_55[0][0]                  \n",
            "                                                                 conv1d_transpose_33[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_transpose_34 (Conv1DTran (None, 16000, 1)     129         concatenate_29[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_30 (Concatenate)    (None, 16000, 2)     0           input_12[0][0]                   \n",
            "                                                                 conv1d_transpose_34[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_transpose_35 (Conv1DTran (None, 16000, 1)     65          concatenate_30[0][0]             \n",
            "==================================================================================================\n",
            "Total params: 98,430\n",
            "Trainable params: 98,430\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-Difeywc08K"
      },
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "              loss=tf.keras.losses.Huber(),\n",
        "              metrics='mae')  "
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6kpVieskv3O"
      },
      "source": [
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-6 * 10 ** (epoch / 20))"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1rxELImtk-Du",
        "outputId": "1394f9fe-09c0-4a56-9129-e1b32ad01c6d"
      },
      "source": [
        "history = model.fit(train_ds, callbacks=[lr_scheduler], epochs=100)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "423/423 [==============================] - 42s 95ms/step - loss: 0.0034 - mae: 0.0531\n",
            "Epoch 2/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0033 - mae: 0.0524\n",
            "Epoch 3/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0033 - mae: 0.0519\n",
            "Epoch 4/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0032 - mae: 0.0510\n",
            "Epoch 5/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0030 - mae: 0.0500\n",
            "Epoch 6/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0029 - mae: 0.0486\n",
            "Epoch 7/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0027 - mae: 0.0465\n",
            "Epoch 8/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0023 - mae: 0.0432\n",
            "Epoch 9/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0018 - mae: 0.0382\n",
            "Epoch 10/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0015 - mae: 0.0351\n",
            "Epoch 11/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0014 - mae: 0.0335\n",
            "Epoch 12/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0013 - mae: 0.0324\n",
            "Epoch 13/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0012 - mae: 0.0314\n",
            "Epoch 14/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0011 - mae: 0.0304\n",
            "Epoch 15/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0011 - mae: 0.0295\n",
            "Epoch 16/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 0.0010 - mae: 0.0286\n",
            "Epoch 17/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 9.3865e-04 - mae: 0.0278\n",
            "Epoch 18/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 8.6962e-04 - mae: 0.0268\n",
            "Epoch 19/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 8.0819e-04 - mae: 0.0259\n",
            "Epoch 20/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 7.4559e-04 - mae: 0.0249\n",
            "Epoch 21/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 6.8981e-04 - mae: 0.0240\n",
            "Epoch 22/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 6.4548e-04 - mae: 0.0232\n",
            "Epoch 23/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 6.1357e-04 - mae: 0.0225\n",
            "Epoch 24/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 5.8412e-04 - mae: 0.0219\n",
            "Epoch 25/100\n",
            "423/423 [==============================] - 40s 93ms/step - loss: 5.6199e-04 - mae: 0.0215\n",
            "Epoch 26/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 5.3610e-04 - mae: 0.0210\n",
            "Epoch 27/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 5.1724e-04 - mae: 0.0206\n",
            "Epoch 28/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 4.9802e-04 - mae: 0.0202\n",
            "Epoch 29/100\n",
            "423/423 [==============================] - 40s 93ms/step - loss: 4.7357e-04 - mae: 0.0197\n",
            "Epoch 30/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 4.5608e-04 - mae: 0.0193\n",
            "Epoch 31/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 4.3950e-04 - mae: 0.0189\n",
            "Epoch 32/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 4.2598e-04 - mae: 0.0186\n",
            "Epoch 33/100\n",
            "423/423 [==============================] - 40s 93ms/step - loss: 4.1177e-04 - mae: 0.0183\n",
            "Epoch 34/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 4.0140e-04 - mae: 0.0180\n",
            "Epoch 35/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.8980e-04 - mae: 0.0178\n",
            "Epoch 36/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.8154e-04 - mae: 0.0175\n",
            "Epoch 37/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.6995e-04 - mae: 0.0173\n",
            "Epoch 38/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.6212e-04 - mae: 0.0171\n",
            "Epoch 39/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.5289e-04 - mae: 0.0168\n",
            "Epoch 40/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.4661e-04 - mae: 0.0166\n",
            "Epoch 41/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.3720e-04 - mae: 0.0164\n",
            "Epoch 42/100\n",
            "423/423 [==============================] - 40s 93ms/step - loss: 3.2579e-04 - mae: 0.0161\n",
            "Epoch 43/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.2258e-04 - mae: 0.0160\n",
            "Epoch 44/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.1694e-04 - mae: 0.0159\n",
            "Epoch 45/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.0842e-04 - mae: 0.0157\n",
            "Epoch 46/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.0409e-04 - mae: 0.0155\n",
            "Epoch 47/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.9580e-04 - mae: 0.0153\n",
            "Epoch 48/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.9106e-04 - mae: 0.0152\n",
            "Epoch 49/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.8343e-04 - mae: 0.0150\n",
            "Epoch 50/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.7862e-04 - mae: 0.0148\n",
            "Epoch 51/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.7376e-04 - mae: 0.0147\n",
            "Epoch 52/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.6874e-04 - mae: 0.0145\n",
            "Epoch 53/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.6097e-04 - mae: 0.0143\n",
            "Epoch 54/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.5990e-04 - mae: 0.0142\n",
            "Epoch 55/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.5157e-04 - mae: 0.0140\n",
            "Epoch 56/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.4771e-04 - mae: 0.0138\n",
            "Epoch 57/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.4222e-04 - mae: 0.0137\n",
            "Epoch 58/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.3892e-04 - mae: 0.0135\n",
            "Epoch 59/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.3826e-04 - mae: 0.0135\n",
            "Epoch 60/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.3476e-04 - mae: 0.0134\n",
            "Epoch 61/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.2820e-04 - mae: 0.0132\n",
            "Epoch 62/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.2784e-04 - mae: 0.0132\n",
            "Epoch 63/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.2331e-04 - mae: 0.0131\n",
            "Epoch 64/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.2089e-04 - mae: 0.0130\n",
            "Epoch 65/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.2130e-04 - mae: 0.0130\n",
            "Epoch 66/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.2048e-04 - mae: 0.0130\n",
            "Epoch 67/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.1756e-04 - mae: 0.0129\n",
            "Epoch 68/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.1866e-04 - mae: 0.0129\n",
            "Epoch 69/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.1322e-04 - mae: 0.0127\n",
            "Epoch 70/100\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.1114e-04 - mae: 0.0127\n",
            "Epoch 71/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 0.0057 - mae: 0.0400\n",
            "Epoch 72/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 4.6278e-04 - mae: 0.0199\n",
            "Epoch 73/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 4.1513e-04 - mae: 0.0185\n",
            "Epoch 74/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 4.0351e-04 - mae: 0.0182\n",
            "Epoch 75/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 3.9535e-04 - mae: 0.0181\n",
            "Epoch 76/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 3.8795e-04 - mae: 0.0179\n",
            "Epoch 77/100\n",
            "423/423 [==============================] - 39s 93ms/step - loss: 3.8345e-04 - mae: 0.0178\n",
            "Epoch 78/100\n",
            "423/423 [==============================] - 39s 93ms/step - loss: 3.7594e-04 - mae: 0.0176\n",
            "Epoch 79/100\n",
            "423/423 [==============================] - 39s 93ms/step - loss: 3.6815e-04 - mae: 0.0174\n",
            "Epoch 80/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 3.6756e-04 - mae: 0.0174\n",
            "Epoch 81/100\n",
            "423/423 [==============================] - 39s 93ms/step - loss: 3.6342e-04 - mae: 0.0172\n",
            "Epoch 82/100\n",
            "423/423 [==============================] - 39s 93ms/step - loss: 3.8357e-04 - mae: 0.0177\n",
            "Epoch 83/100\n",
            "423/423 [==============================] - 39s 93ms/step - loss: 4.8776e-04 - mae: 0.0205\n",
            "Epoch 84/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 4.1525e-04 - mae: 0.0185\n",
            "Epoch 85/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 3.9756e-04 - mae: 0.0180\n",
            "Epoch 86/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 3.8114e-04 - mae: 0.0174\n",
            "Epoch 87/100\n",
            "423/423 [==============================] - 39s 93ms/step - loss: 3.8240e-04 - mae: 0.0175\n",
            "Epoch 88/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 3.6165e-04 - mae: 0.0170\n",
            "Epoch 89/100\n",
            "423/423 [==============================] - 39s 93ms/step - loss: 3.5434e-04 - mae: 0.0168\n",
            "Epoch 90/100\n",
            "423/423 [==============================] - 39s 93ms/step - loss: 3.5402e-04 - mae: 0.0169\n",
            "Epoch 91/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 2079955.2500 - mae: 2079956.3750\n",
            "Epoch 92/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 784.9503 - mae: 785.4500\n",
            "Epoch 93/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 367.0874 - mae: 367.5872\n",
            "Epoch 94/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 454.1250 - mae: 454.6252\n",
            "Epoch 95/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 69.2991 - mae: 69.7991\n",
            "Epoch 96/100\n",
            "423/423 [==============================] - 39s 93ms/step - loss: 75.6247 - mae: 76.1246\n",
            "Epoch 97/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 82.5359 - mae: 83.0358\n",
            "Epoch 98/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 88.6942 - mae: 89.1941\n",
            "Epoch 99/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 93.8182 - mae: 94.3181\n",
            "Epoch 100/100\n",
            "423/423 [==============================] - 39s 92ms/step - loss: 98.4755 - mae: 98.9754\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "VOtC0dG8lI_K",
        "outputId": "90466499-c6e3-41f5-fdce-cf97aa3a9106"
      },
      "source": [
        "style.use('ggplot')\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.semilogx(1e-7, 1, 0, 1)\n",
        "plt.plot(history.history['lr'], history.history['loss'])\n",
        "plt.title('learning_rate vs loss')\n",
        "plt.xlabel('learning_rate')\n",
        "plt.ylabel('loss')\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7xEuqECy1SzX"
      },
      "source": [
        "model = build_model()"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6nrulkZO1ez1",
        "outputId": "26a32230-4117-4e7b-fa51-19a1cfa69151"
      },
      "source": [
        "tf.keras.utils.plot_model(model, 'auto_encoders_for_noise_removal.png')"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t2Q0-yq31l3A"
      },
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(0.0023),\n",
        "              loss=tf.keras.losses.Huber(),\n",
        "              metrics='mae')  "
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4SfPYABW2YMk",
        "outputId": "2f5303cb-7a06-4bd5-a458-3e67329915c6"
      },
      "source": [
        "history = model.fit(train_ds, epochs=20)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "423/423 [==============================] - 42s 94ms/step - loss: 5.3746e-04 - mae: 0.0203\n",
            "Epoch 2/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 3.0889e-04 - mae: 0.0157\n",
            "Epoch 3/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.6621e-04 - mae: 0.0145\n",
            "Epoch 4/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.4342e-04 - mae: 0.0138\n",
            "Epoch 5/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.2875e-04 - mae: 0.0133\n",
            "Epoch 6/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.2118e-04 - mae: 0.0131\n",
            "Epoch 7/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.1604e-04 - mae: 0.0129\n",
            "Epoch 8/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.0995e-04 - mae: 0.0127\n",
            "Epoch 9/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.0738e-04 - mae: 0.0126\n",
            "Epoch 10/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.0535e-04 - mae: 0.0125\n",
            "Epoch 11/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.0412e-04 - mae: 0.0125\n",
            "Epoch 12/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.0266e-04 - mae: 0.0124\n",
            "Epoch 13/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 2.0155e-04 - mae: 0.0124\n",
            "Epoch 14/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 1.9939e-04 - mae: 0.0123\n",
            "Epoch 15/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 1.9781e-04 - mae: 0.0123\n",
            "Epoch 16/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 1.9707e-04 - mae: 0.0123\n",
            "Epoch 17/20\n",
            "423/423 [==============================] - 40s 94ms/step - loss: 1.9656e-04 - mae: 0.0122\n",
            "Epoch 18/20\n",
            "423/423 [==============================] - 40s 93ms/step - loss: 1.9561e-04 - mae: 0.0122\n",
            "Epoch 19/20\n",
            "423/423 [==============================] - 40s 93ms/step - loss: 1.9458e-04 - mae: 0.0122\n",
            "Epoch 20/20\n",
            "423/423 [==============================] - 40s 93ms/step - loss: 1.9271e-04 - mae: 0.0121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "m43QZwgI6Z7T",
        "outputId": "2f82ea3b-793a-46b9-bf03-bb61ce0910a7"
      },
      "source": [
        "epochs = np.arange(len(history.history['loss']))\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
        "axes[0].plot(epochs, history.history['mae'], color='b')\n",
        "axes[0].set_title('epochs vs mae')\n",
        "axes[0].set_xlabel('epochs')\n",
        "axes[0].set_ylabel('mae')\n",
        "\n",
        "\n",
        "axes[1].plot(epochs, history.history['loss'], color='r')\n",
        "axes[1].set_title('epochs vs loss')\n",
        "axes[1].set_xlabel('epochs')\n",
        "axes[1].set_ylabel('loss')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZAXXmVmNmQJ251ZgZUGduNWbmnEMiIqIhFh8fj7q6OtTX18Pj8aCsrAxOp9OnTVJSEvbt2wcAKC8vR0JCAjQaDZxOJ8rKytDe3o76+nrU1dVhwoQJV+2zqakJALxzFuPi4gAA58+fhxACQMc8SFmWYTQa/fQpEBGRWnHkkIiIaJDodDosXboUa9euhSzLmDFjBuLi4rBjxw7Ex8fD6XRi5syZKCgoQFZWFiIiIpCdnQ0AiIuLw7Rp05CTkwOtVotly5ZBq+34Dre7PgFg48aNuHDhAgBg7NixeOCBBwB0FJ1vv/02dDodgoKCkJ2dDY1Go8AnQkREaqIRnV8tjiDcykI91JgZUGduNWYG1JlbjZkB9eVWYisLteNWFuqixtxqzAyoM7caMwPqzK3GzLytlIiIiIiIiHrE4pCIiIiIiIhYHBIRERERERGLQyIiIiIiIgKLwz6rqjKgvJwrvhEREXWl+eILBJeWAufOKR2FiIj6icVhH/3qV5H45S91SscgIiIKKPrjx2FetAiaigqloxARUT+xOOwjh0NCTQ1HDomIiLqSbTYAgObzzxVOQkRE/cXisI8cDhmffQaMvN0hiYiIeiZZLB0/1NcrG4SIiPqNxWEfxcRI+OorDZqa+NERERF5hYRAjoyE5uxZpZMQEVE/scLpI4dDAgDU1vKjIyIi6kqyWgHeVkpEpFqscPro6+KQi9IQERF1Jdts0PC2UiIi1dL7642qqqqwZcsWyLKMtLQ0zJ492+f19vZ2FBQU4MSJEzAajcjOzobNZsOHH36I4uJieDwe6PV6LFq0CDfeeCMA4MSJEygsLERbWxtuvvlmLFmyBBrN0C4W01kc1tWxOCQiIupKtlig+fhjpWMQEVE/+WXkUJZlFBUVYfXq1Vi/fj0OHDiAmpoanzalpaUIDw9Hfn4+7rzzThQXFwMAjEYjHnnkETzzzDPIzMxEfn6+95jnn38eDz74IDZu3IizZ8+iqqpqyM/FapWh1wsWh0RERJeRbDbeVkpEpGJ+KQ6rq6tht9sRHR0NvV6PlJQUVFy2D1JlZSVSU1MBAMnJyTh8+DCEELjuuutgMpkAAHFxcWhra0N7ezuamprQ0tKCiRMnQqPRYPr06Vf0ORR0OiAmhiOHREREl5OtVmguXABaWpSOQkRE/eCX4tDlcsFsNnsfm81muFyuHtvodDqEhYWhubnZp817772H8ePHw2Aw9KrPoTJmDEcOiYiILidd2utQ19CgcBIiIuoPv805HKgzZ86guLgYjz76aJ+PLSkpQUlJCQBg3bp1sHTuxdRPsbEaHDwYNOB+/E2v1zOzn6gxtxozA+rMrcbMgHpzk//Il/4+tPX1kOLiFE5DRER95Zfi0GQyobGx0fu4sbHRe6vo5W3MZjMkSYLb7YbRaPS2/+1vf4vMzEzY7fZe99kpPT0d6enp3scNA/xGMyYmGm+8ocG5cw0Y4vVvBpXFYhnwufubGjMD6sytxsyAOnOrMTOgvtwxMTFKRxhx5M6Rw3Pn0K5wFiIi6ju/3FYaHx+Puro61NfXw+PxoKysDE6n06dNUlIS9u3bBwAoLy9HQkICNBoNLl68iHXr1mHBggWYPHmyt31UVBRCQ0Px6aefQgiB/fv3X9HnUBkzRqC1VYvz51VUGRIREQ0xyWoFAGjPnVM4CRER9YdfRg51Oh2WLl2KtWvXQpZlzJgxA3FxcdixYwfi4+PhdDoxc+ZMFBQUICsrCxEREcjOzgYA7NmzB2fPnsUrr7yCV155BQCQm5uLUaNGYfny5di0aRPa2towZcoU3Hzzzf44HcTGCgAdi9JERXn88p5ERESBTr60FgCLQyIidfLbnMPExEQkJib6PJeRkeH9OSgoCDk5OVccN3fuXMydO7fbPuPj4/HMM88MbtBeiI3t+N+6Oh1uuIHFIREREQDAYICwWKCrr1c6CRER9YNfbisdbsaM+XrkkIiIiL4moqOhVdHcVCIi+hqLw36w2wGtVqC2lsUhERGRj+hojhwSEakUi8N+0OsBm03myCEREdFlRHQ05xwSEakUi8N+cjgkFodERESXi46Gtr4eEELpJERE1EcsDvupozjkx0dERNSViI6GtrUVmosXlY5CRER9xOqmn2JiJNTW6vjFKBERUVfR0QDQMXpIRESqwuKwnxwOCW63Fs3NGqWjEBERBQxxqTjUcd4hEZHqsDjsJ4dDAgCuWEpERNSV3Q4AXJSGiEiFWBz2U0yMDIB7HRIREXUlbDYALA6JiNSIxWE/dY4csjgkIiLqwmyG0Om41yERkQqxOOyn6GgJGo1gcUhERNSVTgfZYuHIIRGRCrE47CeDAbDZZG5nQUREdBnJauWCNEREKsTKZgA69jrkyCEREVFXstXKkUMiIhVicTgADofE1UqJiIguI1utnHNIRKRCeqUDqJnDIeHvfw9WOgYREQWQqqoqbNmyBbIsIy0tDbNnz/Z5vb29HQUFBThx4gSMRiOys7Nhu7TC565du1BaWgqtVoslS5ZgypQpV+2zsLAQR48eRVhYGAAgMzMT48aNgxACW7ZswcGDBxEcHIwVK1Zg/PjxfvsMJJsN2oYGQAhAw/2AiYjUgiOHA+BwyGhu1qK5mRc+IiICZFlGUVERVq9ejfXr1+PAgQOoqanxaVNaWorw8HDk5+fjzjvvRHFxMQCgpqYGZWVlyMvLw6OPPoqioiLIsnzNPhctWoSnn34aTz/9NMaNGwcAOHjwIM6ePYuNGzfigQcewAsvvOC3zwAAZIsFmvZ2aM6f9+v7EhHRwLA4HICYmI7tLM6e5a2lREQEVFdXw263Izo6Gnq9HikpKaioqPBpU1lZidTUVABAcnIyDh8+DCEEKioqkJKSAoPBAJvNBrvdjurq6l71ebnKykpMnz4dGo0GEydOxMWLF9HU1DRUp30F6dJIKBelISJSFxaHA8C9DomIqCuXywWz2ex9bDab4XK5emyj0+kQFhaG5ubmK441mUxwuVzX7HP79u14+OGH8eKLL6K9vd37HhaL5ao5hpJstQIAF6UhIlIZzjkcgK+LQ9bYRETkfwsWLMDo0aPh8Xjw3HPP4fXXX8e8efN6fXxJSQlKSkoAAOvWrfMpKPtLr9cjcuJEAMDo1lbIg9DnUNPr9YNy7v6mxtxqzAyoM7caMwPqzK3GzD1hcTgA0dEdxSFXLCUiIqBjtK+xsdH7uLGxESaTqds2ZrMZkiTB7XbDaDRecazL5fIe21OfUVFRAACDwYAZM2bgjTfe8L5HQ0PDVXMAQHp6OtLT072Pux7TXxaLBY16PRwALh4/jouD0OdQs1gsg3Lu/qbG3GrMDKgztxozA+rMrcbMMTEx3T7PIa8BCA4GLBbudUhERB3i4+NRV1eH+vp6eDwelJWVwel0+rRJSkrCvn37AADl5eVISEiARqOB0+lEWVkZ2tvbUV9fj7q6OkyYMOGqfXbOI+ycsxgXFwcAcDqd2L9/P4QQ+PTTTxEWFuYtJP1BjBoFERTE20qJiFSGI4cD5HCwOCQiog46nQ5Lly7F2rVrIcsyZsyYgbi4OOzYsQPx8fFwOp2YOXMmCgoKkJWVhYiICGRnZwMA4uLiMG3aNOTk5ECr1WLZsmXQaju+w+2uTwDYuHEjLly4AAAYO3YsHnjgAQDAzTffjA8++AArV65EUFAQVqxY4d8PQqOBZLVyQRoiIpVhcThAMTESTp/mx0hERB0SExORmJjo81xGRob356CgIOTk5HR77Jw5czBnzpxe9QkAjz/+eLf9aDQaLF++vC+xB51stXLkkIhIZXhb6QA5HDJHDomIiC4jW63Q1dcrHYOIiPqAxeEAORwSzp/Xwu3WKB2FiIgoYEg2G7QqW6CBiGikY3E4QJ3bWdTW8qMkIiLqJFssHcWhJCkdhYiIeokVzQB9vdchby0lIiLqJNls0MgytC6X0lGIiKiXWBwOEItDIiKiK8lWKwBAy3mHRESq4bdlNquqqrBlyxbIsoy0tDTMnj3b5/X29nYUFBTgxIkTMBqNyM7Ohs1mQ3NzM/Ly8lBdXY3U1FQsW7bMe0xZWRleffVVyLKMxMRELFy40F+n48XikIiI6EqyzQYA0DU0wKNwFiIi6h2/jBzKsoyioiKsXr0a69evx4EDB1BTU+PTprS0FOHh4cjPz8edd96J4uJiAIDBYEBGRgYWLVrk0765uRlbt27FY489hry8PJw/fx6HDh3yx+n4CAkBTCbudUhERNSVZLEA4MghEZGa+KU4rK6uht1uR3R0NPR6PVJSUlBRUeHTprKyEqmpqQCA5ORkHD58GEIIhISEYPLkyQgKCvJp//nnn8PhcCAyMhIAcNNNN+G9997zx+lcgdtZEBER+fKOHHKvQyIi1fDLbaUulwtms9n72Gw249ixYz220el0CAsLQ3Nzs7f4u5zdbkdtbS3q6+thNpvx/vvvw+Pp/saVkpISlJSUAADWrVsHy6VvM/tLr9f79DF2rA41NboB9zvULs+tBmrMDKgztxozA+rMrcbMgHpzkzJEeDjksDBoWRwSEamG3+YcDraIiAgsX74cv/vd76DRaDBp0iScPXu227bp6elIT0/3Pm4Y4L5LFovFpw+zeRTefTdkwP0Otctzq4EaMwPqzK3GzIA6c6sxM6C+3DExMUpHGPFkq5XFIRGRivilODSZTGhsbPQ+bmxshMlk6raN2WyGJElwu90wGo1X7dfpdMLpdALoGB3UapVZfNXhkNDUpENLCxAaqkgEIiKigCNbrdBxziERkWr4pZqKj49HXV0d6uvr4fF4UFZW5i3qOiUlJWHfvn0AgPLyciQkJECj0Vy13y+++AIA8OWXX+Ktt97CzJkzhyT/tcTEdKxYevYs5x0SERF1kmw2jhwSEamIX0YOdTodli5dirVr10KWZcyYMQNxcXHYsWMH4uPj4XQ6MXPmTBQUFCArKwsRERHIzs72Hp+ZmQm32w2Px4OKigrk5uYiNjYWW7ZswalTpwAA8+bNU+wWoq7bWVx3naRIBiIiokAjW63Qvvuu0jGIiKiX/DbnMDExEYmJiT7PZWRkeH8OCgpCTk5Ot8cWFhZ2+3zXAlJJ3OuQiIjoSpLVCl1TE9DWBly26jgREQUeZSbpDTMOhwwAqK1lcUhERNRJtloBAFoVLWRERDSSsTgcBGFhAqNHc69DIiKirqTOvQ5ZHBIRqQKLw0HicEioq+PHSURE1Em+tC+mliuWEhGpAquZQdJRHHLkkIiIqJPcOXLIFUuJiFSBxeEgYXFIRETkS+ocOWRxSESkCiwOB4nDIaGhQYevvlI6CRERUYAICYEcGcnikIhIJVgcDpKYmI7tLM6e5eghERFRJ8lqhY5zDomIVIHF4SDp3M6Ct5YSERF9TbbZOHJIRKQSLA4HSefIIYtDIiKir8lWKxekISJSCRaHg8RuZ3FIRER0Oclq5cghEZFKsDgcJBERApGRMvc6JCIi6kK2WqFtbgZaWpSOQkRE18BKZhBxOwsiIiJfUudehw0NCichIqJrYXE4iBwOCbW1LA6JiIg6yZ17HXLFUiKigMficBBx5JCIiMiX3DlyyHmHREQBj8XhIIqJkXDunBZtbUonISIiCgyS1QqAI4dERGrA4nAQORwyhNCgvp6jh0RERAAgm80QGg20nHNIRBTwWBwOIoeD21kQERH5MBggR0VBx5FDIqKAx+JwEHUWh7W1/FiJiIg6yTYb9zokIlIBVjGDiCOHREREV5KtVi5IQ0SkAiwOB5HRKBAeLnM7CyIioi4kq5Ujh0REKqBXOsBwotF0rFjKkUMiopGrqqoKW7ZsgSzLSEtLw+zZs31eb29vR0FBAU6cOAGj0Yjs7GzYLm33sGvXLpSWlkKr1WLJkiWYMmVKr/rcvHkz9u7di61btwIA9u3bh61bt8JkMgEA7rjjDqSlpQ31qfdItlo7VisVouNiSUREAYnF4SDjXodERCOXLMsoKipCbm4uzGYzVq1aBafTidjYWG+b0tJShIeHIz8/HwcOHEBxcTEeeugh1NTUoKysDHl5eWhqasKaNWuwYcMGALhqn8ePH8fFixevyJKSkoJly5b558SvQbLZoG1thebiRYiICKXjEBFRD3hb6SBzOGQWh23BitYAACAASURBVEREI1R1dTXsdjuio6Oh1+uRkpKCiooKnzaVlZVITU0FACQnJ+Pw4cMQQqCiogIpKSkwGAyw2Wyw2+2orq6+ap+yLGPbtm1YuHChv0+1T2SLBQD3OiQiCnQcORxkDoeE+notPB5Az0+XiGhEcblcMJvN3sdmsxnHjh3rsY1Op0NYWBiam5vhcrlw/fXXe9uZTCa4XC5vP931uWfPHiQlJSEqKuqKLO+99x4++ugjOBwOLF68GJZLBZoS5Eu3zerOnYM0frxiOYiI6OpYvgwyh0OCLGvw+edajBkjKx2HiIiGKZfLhXfffRdPPPHEFa8lJSXh1ltvhcFgwDvvvIPCwkI8/vjjV7QrKSlBSUkJAGDdunWDUkDq9for+tFMnAgAGNXaCqFgkdqT7jKrgRpzqzEzoM7caswMqDO3GjP3hMXhIIuJ+Xo7CxaHREQji8lkQmNjo/dxY2Ojd1GYy9uYzWZIkgS32w2j0XjFsS6Xy3tsd32ePHkSZ8+excqVKwEAbW1tyMrKQn5+PoxGo7d9Wloatm3b1m3e9PR0pKenex83NDQM4Ow7WCyWK/rRGgywA7h44gTcg/Aeg627zGqgxtxqzAyoM7caMwPqzK3GzDExMd0+zzmHg4x7HRIRjVzx8fGoq6tDfX09PB4PysrK4HQ6fdokJSVh3759AIDy8nIkJCRAo9HA6XSirKwM7e3tqK+vR11dHSZMmNBjn4mJiXj++edRWFiIwsJCBAUFIT8/HwDQ1NTkfb/KykqfBXGUIEdFQeh00HHOIRFRQPPbyGF/l/Zubm5GXl4eqqurkZqa6rPy2t///nfs2rULGo0GUVFRyMrKQmRkpL9OqVssDomIRi6dToelS5di7dq1kGUZM2bMQFxcHHbs2IH4+Hg4nU7MnDkTBQUFyMrKQkREBLKzswEAcXFxmDZtGnJycqDVarFs2TJotR3f4XbX59Xs3r0blZWV0Ol0iIiIwIoVK4b83K9Kq4VssXCvQyKiAOeX4nAgS3sbDAZkZGTg9OnTOHPmjLe9JEl48cUXkZeXh8jISGzbtg179uzBPffc449T6tGoUQKhoVyxlIhopEpMTERiYqLPcxkZGd6fg4KCkJOT0+2xc+bMwZw5c3rV5+U69zgEgAULFmDBggV9iT3kJKsVOhaHREQBzS+3lQ5kae+QkBBMnjwZQUFBPu2FEBBC4KuvvoIQAm63+4p5HUrQaDq2s6itZXFIRETUSbZaOXJIRBTg/DJyOJClvXu6TVSv1+PHP/4xHn74YQQHB8PhcGD58uVDdxJ94HBIHDkkIiLqQrZaYfj4Y6VjEBHRVah2tVKPx4O3334bTz75JKKjo7F582bs2rULc+fOvaLtYC/Vfa3laseP12HfPm3ALWmrxmV21ZgZUGduNWYG1JlbjZkB9eamwCDZbNA2NABCdNxmQ0REAccvxeFAlvbuycmTJwEAdrsdADBt2jS8/vrr3bYd7KW6r7VcbVSUEbW1Efj88wboAmgAUY3L7KoxM6DO3GrMDKgztxozA+rL3dMy3aQM2WqFpr0dmvPnIaKilI5DRETd8Mucw4Es7d0Tk8mEmpoaXLhwAQDw4YcfYsyYMUN2Dn3hcEiQJA3OneNOIUREREDHgjQAuCgNEVEA88vI4UCW9gaAzMxMuN1ueDweVFRUIDc3F7GxsZg3bx4ef/xx6HQ6WCwWZGZm+uN0rqnrdhZ2u6xwGiIiIuXJl4pDbX09MHGiwmmIiKg7fptzOJClvQsLC7t9/vbbb8ftt98+eCEHSWdxWFurw803tyuchoiISHmyzQYA0Kno1mQiopGG9z0OgZiYjtFCrlhKRETUQbq0mJG2vl7hJERE1BMWh0MgKkpGSIhgcUhERHSJGDUKIiiIex0SEQUwFodDQKMB7HYJdXX8eImIiAAAGg0kqxU6jhwSEQUsVi9DxOGQOHJIRETUhWy1dux1SEREAYnF4RBhcUhERORL5sghEVFAY3E4RGJiJJw9q4PMnSyIiIgAAJLNxjmHREQBjMXhEHE4JLS3a9DQwI+YiIgIuHRbaWMjIElKRyEiom6wchkiMTEdFz7eWkpERNRBslqhkWVoXS6loxARUTdYHA4Rh4N7HRIREXUlW60AuNchEVGgYnE4RByOzpFDfsREREQAINtsAAAdVywlIgpIrFyGiMkkIyhIcOSQiIjoEsliAcCRQyKiQMXicIhotYDdLqG2lsUhERER0GXkkCuWEhEFJBaHQygmhnsdEhERdRLh4ZDDwjhySEQUoFgcDiGHg8UhERFRV7LNBi3nHBIRBSQWh0OoszgUQukkREREgUG2WKDjyCERUUBicTiEHA4ZbW0auFz8mImIiABAstmg5ZxDIqKAxKplCHE7CyIiIl+y1crikIgoQLFqGUKdxSFXLCUiIuogWa3QNTUBbW1KRyEiosuwOBxCMTEsDomIiLqSrVYA4KI0REQBiMXhELJYZOj1giuWEhERXSJxr0MiooDF4nAIabWA3c7tLIiIiDrJFgsAcN4hEVEAYnE4xLjXIRER0ddkjhwSEQUsFodDzOGQWRwSERFdInWOHHKvQyKigMPicIh1jhwKoXQSIiKiABASAnnUKC5IQ0QUgFgcDrGYGAmtrRo0NWmUjkJERBQQJIsFOo4cEhEFHBaHQ6xzr0PeWkpERNRBttm4IA0RUQBicTjEWBwSERH5kq1WLkhDRBSA9EoHGO5YHBIRjSxVVVXYsmULZFlGWloaZs+e7fN6e3s7CgoKcOLECRiNRmRnZ8N2aQXPXbt2obS0FFqtFkuWLMGUKVN61efmzZuxd+9ebN269ZrvEQgkqxXBLA6JiAKO34rD/l4sm5ubkZeXh+rqaqSmpmLZsmUAgJaWFjz22GPe410uF7773e/i/vvv99cp9YrNJkOnEywOiYhGAFmWUVRUhNzcXJjNZqxatQpOpxOxsbHeNqWlpQgPD0d+fj4OHDiA4uJiPPTQQ6ipqUFZWRny8vLQ1NSENWvWYMOGDQBw1T6PHz+Oixcv+uTo6T0ChWy1QtvcDE1LC0RoqNJxiIjoEr/cVtp5sVy9ejXWr1+PAwcOoKamxqdN1wvZnXfeieLiYgCAwWBARkYGFi1a5NM+NDQUTz/9tPefxWLB1KlT/XE6faLTAdHREmprWRwSEQ131dXVsNvtiI6Ohl6vR0pKCioqKnzaVFZWIjU1FQCQnJyMw4cPQwiBiooKpKSkwGAwwGazwW63o7q6+qp9yrKMbdu2YeHChb16j0AhXRrF5LxDIqLA4pficCAXy5CQEEyePBlBQUE99l9bW4sLFy7gm9/85lCeRr9xr0MiopHB5XLBbDZ7H5vNZrhcrh7b6HQ6hIWFobm5+YpjTSYTXC7XVfvcs2cPkpKSEBUV1av3CBSy1QqAxSERUaDxy22l3V3Yjh071mObrheyyMjIa/ZfVlaGadOmQaPpfruIkpISlJSUAADWrVsHy6UNePtLr9f3qY9x43Q4dEg74PcdqL7mDgRqzAyoM7caMwPqzK3GzIB6cw9XLpcL7777Lp544ol+9zHY10egd38nmuuvBwCMbm2FCIC/KbX+basxtxozA+rMrcbMgDpzqzFzT4bFgjQHDhxAVlZWj6+np6cjPT3d+7hhgBvvWiyWPvVhMkWipiYM5841oIf61S/6mjsQqDEzoM7caswMqDO3GjMD6ssdExPj9/c0mUxobGz0Pm5sbITJZOq2jdlshiRJcLvdMBqNVxzrcrm8x3bX58mTJ3H27FmsXLkSANDW1oasrCzk5+f3+B6XG+zrI9C7vxOtwQA7gIvHj8MdAH9Tavvb7qTG3GrMDKgztxozA+rMrcbMPV0j/XJbaV8ulgCueiG73MmTJyHLMsaPHz+4oQeRwyHB7dbiwgUFK0MiIhpy8fHxqKurQ319PTweD8rKyuB0On3aJCUlYd++fQCA8vJyJCQkQKPRwOl0oqysDO3t7aivr0ddXR0mTJjQY5+JiYl4/vnnUVhYiMLCQgQFBSE/P/+q7xEoZLMZQqOBVmX/Z4qIaLjzy8hh1wubyWRCWVmZ95vOTp0XsokTJ/bpQnbgwAHceuutQxV9UHTdzmLUKI/CaYiIaKjodDosXboUa9euhSzLmDFjBuLi4rBjxw7Ex8fD6XRi5syZKCgoQFZWFiIiIpCdnQ0AiIuLw7Rp05CTkwOtVotly5ZBq+34Dre7Pq+mp/cIGAYD5Kgo6OrrlU5CRERd+KU4HMjFEgAyMzPhdrvh8XhQUVGB3Nxc7xLe7777LlatWuWP0+i3mJiO4rC2VofJk1kcEhENZ4mJiUhMTPR5LiMjw/tzUFAQcnJyuj12zpw5mDNnTq/6vFznHofXeo9AIdtsXJCGiCjA+G3O4UAuloWFhT32W1BQMDgBh1DXkUMiIiLqWLGUI4dERIHFL3MORzqbTYZWK1gcEhERXSJZrZxzSEQUYFgc+oHB0FEg1tXx4yYiIgI6Rg619fWAEEpHISKiS1it+InDIXHkkIiI6BLJZoO2tRWaL79UOgoREV3C4tBPWBwSERF9TbZaAYCL0hARBRAWh37icEiorWVxSEREBHxdHOpYHBIRBQwWh34SEyPhyy+1aG4OnE2IiYiIlCJ1jhxyxVIiooDB4tBPuJ0FERHR12SbDQBHDomIAgmLQz9xOGQALA6JiIgAQI6KgtDpOOeQiCiAsDj0k69HDvmRExERQauFbLGwOCQiCiCsVPwkOlqCRiM4ckhERHSJZLVCxzmHREQBg8WhnwQFAVarzBVLiYiILpFtNmgbGpSOQUREl7A49CPudUhERPQ12WLhyCERUQBhcehHLA6JiIi+JnWOHAqhdBQiIgKLQ79icUhERPQ12WqFpr0dmvPnlY5CRERgcehXDoeML77Q4uJFjdJRiIiIFCdZrQC41yERUaDodXHY3t6O7du342c/+xkWL14MAPjnP/+JPXv2DFm44SYmhttZEBERdZIvFYdazjskIgoIva5Sfv/73+PMmTNYuXIlNJqOka+4uDi8/fbbQxZuuOnc65ArlhIRBa7Dhw+j/lKx0tTUhIKCAmzatAnneevjoJNtNgAcOSQiChS9Lg7ff/99rFy5EhMnTvQWhyaTCS6Xa8jCDTedxSHnHRIRBa6ioiJotR2Xx5deegmSJEGj0eC5555TONnwI1ksAAAti0MiooCg73VDvR6yLPs8d+HCBRiNxkEPNVxFR7M4JCIKdC6XCxaLBZIk4Z///Cc2bdoEvV6PBx98UOlow44YNQoiKIjFIRFRgOj1yGFycjIKCgp8brUpKipCSkrKkIUbbkJCALOZK5YSEQWy0NBQnD9/HkePHkVsbCxCQkIAAB6PR+Fkw5BGA8lq5V6HREQBotfF4YIFC2Cz2fCf//mfcLvdWLlyJaKionD33XcPZb5hh9tZEBEFtjvuuAOrVq3Cxo0bMWvWLADAxx9/jDFjxiicbHiSO/c6JCIixfXpttL7778f999/v/d20s65h9R7MTESamp6/bETEZGfzZ49G1OnToVWq4XdbgfQMcf+Jz/5icLJhifZYoHus8+UjkFEROhDcdippaUFra2taGlp8T4XHR09qKGGM4dDxvvvc+SQiCiQxcTEeH8+fPgwtFotbrjhBgUTDV+SzQZDVZXSMYiICH0oDmtqarBx40acOnXqitd27NgxqKGGM4dDwvnzWrS0aBAaKpSOQ0REl3n88ccxf/58TJ48Ga+99hrefPNNaLVazJo1C3PmzFE63rAjW63QNjYCkgTo+OUpEZGSej3n8IUXXkBCQgI2b96MsLAwbNmyBd///veRmZk5lPmGna+3s+j1R09ERH505swZTJw4EQDwl7/8BY8//jjWrl2Ld955R+Fkw5NktUIjy9ByaywiIsX1ukI5deoUfvSjHyE8PBxCCISFhWHhwoUcNewj7nVIRBTYhOi4q+Ps2bMAgNjYWFgsFly8eFHJWMOWbLUCALRcsZSISHG9vq3UYDBAkiTo9XoYjUY0NDQgPDwcX3755VDmG3ZYHBIRBbZJkyZh8+bNaGpqwne+8x0AHYUi9/UdGrLNBgDQnTsHbhZCRKSsXheHkydPxrvvvovU1FQkJydj7dq1CAoKQkJCwlDmG3ZYHBIRBbbMzEy88cYbiIyMxF133QUAqK2txQ9+8AOFkw1PUufI4blzCichIqJeF4c5OTnen+fPn4+4uDi0trbie9/7Xq+Or6qqwpYtWyDLMtLS0jB79myf19vb21FQUIATJ07AaDQiOzsbNpsNzc3NyMvLQ3V1NVJTU7Fs2TLvMR6PB0VFRTh69Cg0Gg3uvfdeJCcn9/aUFBEaCkRFSaitZXFIRBSIjEYjFixY4PNcYmKiQmmGv87bSnUsDomIFNfr4tDtduPPf/4zTp48idbWVu/z77//PnJzc696rCzLKCoqQm5uLsxmM1atWgWn04nY2Fhvm9LSUoSHhyM/Px8HDhxAcXExHnroIRgMBmRkZOD06dM4c+aMT7+vvvoqRo0ahQ0bNkCWZdXc4upwyBw5JCIKUB6PB6+++ir279+PpqYmREVFYfr06ZgzZw70eu5TO9hEeDjksDDOOSQiCgC9vsrl5eVBlmVMnToVQUFBfXqT6upq2O12736IKSkpqKio8CkOKysrcffddwMAkpOTsXnzZgghEBISgsmTJ3sXBuhq7969WL9+PQBAq9UiMjKyT7mU4nBIXK2UiChAbdu2DcePH8ePf/xjWK1WnDt3Djt37oTb7cb999+vdLxhSbbZoG1oUDoGEdGI1+vi8NixYygqKurXt6Yulwtms9n72Gw249ixYz220el0CAsLQ3Nzc48FX+eqcTt27MDRo0cRHR2NpUuXYvTo0Ve0LSkpQUlJCQBg3bp1sFgsfT6HrvR6/YD6uO46HT78UDvgHH010NxKUGNmQJ251ZgZUGduNWYG1Ju7r8rLy/H00097F6CJiYnBddddh1/84hcsDoeIbLFAx5FDIiLF9WlBms8++wxjx44dyjy9JkkSGhsbMWnSJCxevBh/+tOfsHXrVmRlZV3RNj09Henp6d7HDQP8dtJisQyoj6ioCJw7F4mamgaEhAwoSp8MNLcS1JgZUGduNWYG1JlbjZkB9eWOiYnp13GdW1mQ/0g2G/TV1UrHICIa8XpdHK5YsQL/8z//gwkTJlwxOjdv3ryrHmsymdDY2Oh93NjYCJPJ1G0bs9kMSZLgdruvumy40WhEcHAwpk6dCqDjVtTS0tLeno6iYmI6Viz9/HMdxo6VFE5DRERdTZs2DU8++STmzZvnLYh37tzZ6wXP+rsAGwDs2rULpaWl0Gq1WLJkCaZMmXLVPp999lmcOHECQgg4HA5kZmYiJCQE+/btw9atW73X2jvuuANpaWmD9RENOtlqha6sTOkYREQjXq+Lw+3bt6OxsRFWqxUtLS3e5zUazTWPjY+PR11dHerr62EymVBWVoaVK1f6tElKSsK+ffswceJElJeXIyEh4ap9azQaJCUl4ejRo7jxxhtx+PBhnzmMgaxzO4vaWhaHRESBZuHChdi5cyeKiorQ1NQEk8mElJSUa34RCgxsAbaamhqUlZUhLy8PTU1NWLNmDTZs2AAAPfa5ePFihIWFAQB+//vfY8+ePd7CMSUlxWeF70AmWa3Qnj8PtLUBfVzXgIiIBk+vi8OysjJs2LABUVFRfX4TnU6HpUuXYu3atZBlGTNmzEBcXBx27NiB+Ph4OJ1OzJw5EwUFBcjKykJERASys7O9x2dmZsLtdsPj8aCiogK5ubmIjY3Fj370IxQUFODFF19EZGQkVqxY0edsSuBeh0REgeXw4cM+jxMSEpCQkAAhhPeLyo8//hg33njjVfsZyAJsFRUVSElJgcFggM1mg91uR/WlWy176rOzMBRCoK2tbRA+CWV0bmehbWiA3M/bgYmIaOB6XRxGR0dDp+t/MZOYmHjFPlEZGRnen4OCgnz2UuyqsLCw2+etVit+9atf9TuTUhwOGQCLQyKiQPHss892+3xnYdhZJBYUFFy1n4EswOZyuXD99dd725lMJrhcLm8/PfW5adMmHDx4ELGxsbjvvvu8z7/33nv46KOP4HA4sHjx4oBeTEi6dFut7tw5FodERArqdXH43e9+F0899RTuuOOOK+YcXuubVPIVHi4wapTM7SyIiAJET19CqsGKFSsgyzI2b96MsrIyzJgxA0lJSbj11lthMBjwzjvvoLCwEI8//vgVxw72at5A/1a11Vwqikd/9RWEAkWsWlfiVWNuNWYG1JlbjZkBdeZWY+ae9Lo4fOuttwB0zD3sqjffpNKVYmIkjhwSEQ0zA1mA7fJjXS6X99hr9anVapGSkoI//vGPmDFjhs+Cbmlpadi2bVu3eQd7NW+gf6va6gwGRAO4ePw43AqsiKu2lXg7qTG3GjMD6sytxsyAOnOrMXNPK3r3ujhU87eqgcjhYHFIRDTcDGQBNqfTiY0bN+KHP/whmpqaUFdXhwkTJkAI0W2fQgh8/vnnsNvtEEKgsrLSe7FvamryrhFQWVkZ8Au2SZe+cddyr0MiIkX1fUd7GhQOh4SqKgOEAHqx4CsREanAQBZgi4uLw7Rp05CTkwOtVotly5ZBq+2YftBdn7Iso7CwEG63GwAwduxYLF++HACwe/duVFZWQqfTISIiIvAXbAsJgTxqFLTnzimdhIhoRGNxqJDvfKcNxcXhKCsLwq23qneFOSIi8jWQBdjmzJmDOXPm9KpPrVaLNWvWdNvPggULsGDBgr5GV5RksUDH4pCISFFcEUUhP/xhC0aPlrF1a7jSUYiIiBQn22wcOSQiUhiLQ4WEhgL33OPG7t0hqK/nr4GIiEY22WqFjnMOiYgUxapEQQsXXoTHo8H27WFKRyEiIlKUZLVCq7LV/oiIhhsWhwqKj5dw221fobg4DJKkdBoiIiLlyFYrtM3N0LS0KB2FiGjEYnGosPvuu4jPPtOjtDRY6ShERESKkWw2AOC8QyIiBbE4VNjtt7ciOlrCSy9xYRoiIhq5ZKsVAPc6JCJSEotDhRkMwPz5buzdG4zTp3VKxyEiIlJEZ3Go47xDIiLFsDgMAAsWXIRGAxQXc2EaIiIamSSOHBIRKY7FYQAYM0bG97/fiv/3/8LQ1qZ0GiIiIv+TzWYIjQY6zjkkIlIMi8MAcd99bjQ06LB7d4jSUYiIiPzPYIBsMnFBGiIiBbE4DBDTp3+FsWM9XJiGiIhGLNlqZXFIRKQgFocBQqsFFi50o7w8GJ9+qlc6DhERkd/JVit0nHNIRKQYFocBJCPDjaAgga1buTANERGNPJLVCi1XKyUiUgyLwwBiNsv44Q9b8PLLYXC7NUrHISIi8ivZau1YrVQIpaMQEY1ILA4DzKJFbjQ3a/Haa6FKRyEiIvIryWaDtrUVmi+/VDoKEdGIxOIwwHznO22YPLmdt5YSEdGII3OvQyIiRbE4DDAaDbBo0UV8+GEQqqoMSschIiLym87iUMd5h0REimBxGIDmzm1BWJjMbS2IiGhEkThySESkKBaHAchoFPiP/2jB66+H4Px5LkxDREQjg2yzAQB03OuQiEgRLA4D1H33XURrqxavvMK5h0RENDLIUVEQOh20LA6JiBTB4jBA3XijB4mJbdi6NYwrehMR0cig1UK2WFgcEhEphMVhAFu06CKqqw0oKwtSOgoREZFfSFYrdJxzSESkCL2/3qiqqgpbtmyBLMtIS0vD7NmzfV5vb29HQUEBTpw4AaPRiOzsbNhsNjQ3NyMvLw/V1dVITU3FsmXLvMc88cQTaGpqQlBQR/GUm5uLUaNG+euUhty//3sLfvWrUXjppXDcemub0nGIiIiGnGyzceSQiEghfikOZVlGUVERcnNzYTabsWrVKjidTsTGxnrblJaWIjw8HPn5+Thw4ACKi4vx0EMPwWAwICMjA6dPn8aZM2eu6HvlypWIj4/3x2n4XWgocM89bmzeHI76ei1sNlnpSERERENKtlhg+OgjpWMQEY1IfrmttLq6Gna7HdHR0dDr9UhJSUFFRYVPm8rKSqSmpgIAkpOTcfjwYQghEBISgsmTJ3tHB0eahQsvwuPRYPt2LkxDRETDn2SzQdvQAMj8QpSIyN/8Uhy6XC6YzWbvY7PZDJfL1WMbnU6HsLAwNDc3X7PvTZs24Re/+AVeeeUViGG4ckt8vITbbvsKxcVhkCSl0xAREQ0t2WqFpr0dmvPnlY5CRDTi+G3O4VBYuXIlTCYTWlpa8Mwzz2D//v343ve+d0W7kpISlJSUAADWrVsHi8UyoPfV6/UD7qMvfvYzDe69V4+KCit++MP+F8D+zj0Y1JgZUGduNWYG1JlbjZkB9eYmdZGsVgCArqEBHpNJ4TRERCOLX4pDk8mExsZG7+PGxkaYLvsPfmcbs9kMSZLgdrthNBqv2S8AhIaG4rbbbkN1dXW3xWF6ejrS09O9jxsaGgZyOrBYLAPuoy+Sk4Ho6GgUFkpITnZd+4Ae+Dv3YFBjZkCdudWYGVBnbjVmBtSXOyYmRukI1A/ypeJQW18PTJyocBoiopHFL7eVxsfHo66uDvX19fB4PCgrK4PT6fRpk5SUhH379gEAysvLkZCQAI1G02OfkiThwoULAACPx4N//OMfiIuLG7JzUJLBACxY4MbevcE4fVqndBwiIqIhI9tsAAAdVywlIvI7v4wc6nQ6LF26FGvXroUsy5gxYwbi4uKwY8cOxMfHw+l0YubMmSgoKEBWVhYiIiKQnZ3tPT4zMxNutxsejwcVFRXIzc2FxWLB2rVrIUkSZFnGt771LZ/RweFm/vyL2LAhAsXFYVi16tpzMYmIiNSo87ZS7eefK5yEiGjk8ducw8TERCQmJvo8l5GR4f05KCgIOTk53R5bWFjY7fNPPvnk4AUMcGPGyPj+91uxfXsYcnKaERysdCIiIqLBJyIj4Rk3DqGvLtGPPwAAIABJREFUvYaLDz4IXOUuIiIiGlx+ua2UBsd997nR2KjDnj0hSkchIiIaGhoNmrOyEHToEIIvLSZHRET+weJQRaZP/wpjx3rw0kvhSkchIiIaMi1z58LzjW/AuH49MAy3qSIiClQsDlVEqwUWLnSjvDwYn3yi6l1IiIiIemYw4MusLAT9858ILi1VOg0R0YjBCkNlMjLcePppI7ZuDcN///cFpeMQEdFlqqqqsGXLFsj/v717j4+ivvc//tpLNsnmvru5EIyKAS/FUxGiYLxxk18rtqVIxUu1CMqhUDDSeqxg/dlWLIKA3OyxilQ9VNFW1Proz1qMqCWgAQHFqhCoRzCBkGzuIWR3Z35/LCxEEkAuu5nwfj4e+5id2e/Mvndc8+Wz850Zw2DIkCGMGDGizeuBQIBFixaxfft2UlJSKCoqImv/FTpXrFhBcXExdrud22+/nT59+hxxm7///e/Zvn07pmnSrVs3Jk2aREJCwhHfwyqaR40ief58UubNY9/gwTr3UEQkCnTk0GK8XoPrrtvLn//sprlZHaWISGdiGAZLlixh2rRpzJs3j9WrV7Nz5842bYqLi0lKSmLhwoUMHz6cZcuWAbBz505KSkqYO3cu06dPZ8mSJRiGccRt/uQnP2H27Nk8+uij+Hw+3njjjSO+h6W4XOGjhxs2EL//VlciInJqqTi0oNtua6ahwc4rryTGOoqIiByirKyMnJwcsrOzcTqdFBYWUlpa2qbNunXrGDhwIAADBgxg8+bNmKZJaWkphYWFxMXFkZWVRU5ODmVlZUfcptvtBsA0TVpbW4/6HlbTfMMNBLt3J2XuXJ17KCISBSoOLaigoJXzzw/w7LNu9ZUiIp2I3+/H6/VG5r1eL36/v8M2DocDt9tNQ0PDYet6PB78fv9Rt/n4448zfvx4ysvL+e53v3vE97Acl4vGn/0M14cfEv/uu7FOIyLS5emcQwuy2eDWW5uYPj2djRvjuPjiQKwjiYhIjEycOBHDMHj66acpKSlh0KBBx7zuypUrWbn/dhEzZ87E5/OdcB6n03lSthMxaRLm4sVkLFxIcOTIU3Lu4UnPHCVWzG3FzGDN3FbMDNbMbcXMHVFxaFHXX7+XGTNSee65JC6+uDbWcUREhPDRvurq6sh8dXU1Ho+n3TZer5dQKERzczMpKSmHrev3+yPrHm2bdrudwsJCXnvtNQYNGtThe3zd0KFDGTp0aGS+qqrqxHYA4PP5Tsp2DuX+6U9Jnz6d+hUraL3qqpO6bTg1maPBirmtmBmsmduKmcGaua2YOTc3t93lGlZqUSkpJiNH7uXVVxOordWFaUREOoP8/HwqKiqorKwkGAxSUlJCQUFBmzb9+vVj1f4LrKxdu5bevXtjs9koKCigpKSEQCBAZWUlFRUV9OzZs8NtmqbJrl27gPA5h+vWrYt09h29h1U133QToZwc3fdQROQU05FDC7v11ib+53+SeOklN3fe2RTrOCIipz2Hw8HYsWOZMWMGhmEwaNAg8vLyWL58Ofn5+RQUFDB48GAWLVrE5MmTSU5OpqioCIC8vDwuu+wypk6dit1uZ9y4cdjt4d9w29umYRgsXryY5uZmAM466yzuuOMOgA7fw7Li42n42c9Iv/9+XKtX03rFFbFOJCLSJdlMK16+7ASVl5ef0Pqd6dDx977no67Oxjvv7DnqaRidKfexsmJmsGZuK2YGa+a2YmawXu6OhsxIx060f4RT+D1paSH78ssJnn021X/5y0ndtNW+2wdYMbcVM4M1c1sxM1gztxUza1hpFzVmTBPbtsWxbJk71lFEREROnYQEGidOJH7tWlwlJbFOIyLSJak4tLgf/nAvV165jwcfTKWszBHrOCIiIqdM0803E8rKCt/3UERETjoVhxZnt8O8eTXEx8PkyRkccg9kERGRriUxMXz0cM0aXGvXxjqNiEiXo+KwC+jWzeDRR2v56CMXc+YcfqlyERGRrqLpxz8mlJkZvnKpiIicVCoOu4jvfreFm29uYvHiZNasccU6joiIyKmRmEjjT39K/D//iau0NNZpRES6FBWHXciDD9Zz1lkhpkxJ170PRUSky2q+7TZCPh/JOvdQROSkUnHYhSQlmSxeXENlpYP77kvXfYJFRKRLMhMTaZwwgYR33yVu3bpYxxER6TJUHHYxffoE+PnPG3jttUT+8pfEWMcRERE5JZp/8hNCHo/OPRQROYlUHHZBkyY10r//PqZPT+N//1e3txARka7HdLtpmjCBhFWriPvww1jHERHpElQcdkEOByxYUIvNFr69RTAY60QiIiInX9OYMYQyMnT0UETkJFFx2EWdcUaImTPrWL/excKFybGOIyIictKZSUk0/ed/klBcTNyGDbGOIyJieSoOu7ARI/YycmQz8+alsG5dXKzjiIiInHRNt9+OkZ6uo4ciIieBisMubsaMOrp1CzF5cgb19bFOIyIicnKZyck0jh9PwltvEbdpU6zjiIhYmorDLi411WThwlp27nQwdaouTiMiIl1P09ixOnooInISqDg8DVx6aStTpjTy3HMOXnstIdZxRERETiozJYXGO+4g4R//IO7jj2MdR0TEslQcniaKihq45BKDX/4yna++0n92ERHpWprGjcNISyNZRw9FRI5b1KqEjRs3ctdddzF58mReeeWVw14PBALMmzePyZMnM23aNCorKwFoaGjg17/+NbfeeitLlixpd9uPPPIIP//5z09pfquLi4M//jFIIAB33ZVBKBTrRCIiIiePmZpK4x13kPj3v+PcvDnWcURELCkqxaFhGCxZsoRp06Yxb948Vq9ezc6dO9u0KS4uJikpiYULFzJ8+HCWLVsGQFxcHKNHj+bWW29td9vvv/8+CQkaKnksevaEhx6qY82aeJ54Qre3EBGRrqVp3DiM1FRSHnss1lFERCwpKsVhWVkZOTk5ZGdn43Q6KSwspLS0tE2bdevWMXDgQAAGDBjA5s2bMU2ThIQEzj//fFwu12HbbWlp4fXXX+f666+PxsfoEm64YS/Dh+9l1qwUPvpIt7cQEZGuw0xLo2ncOBL/3//D+a9/xTqOiIjlOKPxJn6/H6/XG5n3er1s3bq1wzYOhwO3201DQwOpqakdbveFF17ge9/7XruF46FWrlzJypUrAZg5cyY+n+94PwoATqfzhLcRC06nk8xMH089BQUFcNddPt5/P4DbHetkHbPyvrZabitmBmvmtmJmsG5uOb003nEHSU89Rcq8edQ8+WSs44iIWEpUisNT4YsvvmD37t2MGTMmcn5iR4YOHcrQoUMj81VVVSf03j6f74S3EQuH5p4718WNN3qZMiXAzJl1MU7Wsa6wr63CipnBmrmtmBmslzs3NzfWESQGzPR0msaOJWX+fBo++4zg+efHOpKIiGVEZVipx+Ohuro6Ml9dXY3H4+mwTSgUorm5mZSUlA63uWXLFrZv386kSZN44IEHKC8v58EHHzwl+buiK65oZcKEJp57Lok334yPdRwREZGTpvHOOzGSk3XuoYjINxSV4jA/P5+KigoqKysJBoOUlJRQUFDQpk2/fv1YtWoVAGvXrqV3797YbLYOtzls2DCeeOIJFi9ezG9+8xtyc3NVHH5D99xTT+/eAX7+83QqK3V7CxER6RrMjAyabr+dhNdfx7llS6zjiIhYRlQqAofDwdixY5kxYwZ33303l112GXl5eSxfvpx169YBMHjwYBobG5k8eTKvv/46t9xyS2T9SZMm8cwzz7Bq1SomTJhw2JVO5fjEx8PixTU0N9u5++50DCPWiURERE6OxvHjMd1u0h54AAKBWMcREbGEqJ1z2LdvX/r27dtm2ejRoyPPXS4XU6dObXfdxYsXH3HbWVlZzJkz58RDnoZ69QrywAN1TJuWztKlSYwb1xTrSCIiIifM9Hio//WvSf/FL0gvKqJ24UKwa5SMiMiRWPaCNHLy3HZbM8XFCcyYkUrfvq1cfLF+YRUREetrvukm7H4/qQ8/jJmWRt2MGXCEU1ZERE53+glNsNlgzpxasrJC3Hijl7Vrj3xrEBEREatonDSJhokTSXrmGVJmz451HBGRTk3FoQDg8xmsWFFFt24hbrnFy1tv6QqmIiLSNTRMm0bTzTeTMn8+SX/4Q6zjiIh0WioOJaJbN4O//KWaXr0CjB3r4bXXEmIdSURE5MTZbNTNnMne4cNJ+/WvSVy+PNaJREQ6JRWH0obXa/Dii9X07dvKpEkZvPBCYqwjiYiInDiHg5qFC2m56irSf/ELEt54I9aJREQ6HRWHcpjUVJM//cnPVVft4+c/z+DJJ5NiHUlEROTExcdT89RTBPr0IeOnP8X13nuxTiQi0qmoOJR2JSaaPP20n2uv3cuDD6Yxd24yphnrVCIiIifGTEqi+tlnCZ5zDp5x44jbuDHWkUREOg0Vh9Kh+Hj4/e9ruOGGZubMSeU3v0lVgSgiIpZnZmRQ/ac/YXi9eH78Y/j001hHEhHpFFQcyhE5neHbXIwd28gf/pDMf/1XGqFQrFOJiIicGCM7m+rnn4e4OOKGD8exY0esI4mIxJwz1gGk87Pb4Te/qSclxWT+/BQaG+3Mn1+DS7dDFBE5zMaNG1m6dCmGYTBkyBBGjBjR5vVAIMCiRYvYvn07KSkpFBUVkZWVBcCKFSsoLi7Gbrdz++2306dPnyNuc8GCBWzbtg2n00l+fj7jx4/H6XTyySefMGvWrMh2+/fvz6hRo6K4F6whdPbZVP/pT2SOGoX3xhupeuUVjMzMWMcSEYkZHTmUY2KzwX/9VwO/+lUdr72WyLhxHvbujXUqEZHOxTAMlixZwrRp05g3bx6rV69m586dbdoUFxeTlJTEwoULGT58OMuWLQNg586dlJSUMHfuXKZPn86SJUswDOOI27ziiit47LHHePTRR2ltbaW4uDjyPhdccAGzZ89m9uzZKgyPIHjBBQRffRX77t14b74ZW11drCOJiMSMikP5RiZMaOKRR2p5++14br3VS0ODLdaRREQ6jbKyMnJycsjOzsbpdFJYWEhpaWmbNuvWrWPgwIEADBgwgM2bN2OaJqWlpRQWFhIXF0dWVhY5OTmUlZUdcZt9+/bFZrNhs9no2bMn1dXV0f7IXYI5YAA1S5bg3LoVz5gx2PTrp4icplQcyjf24x83s2hRLR984OLGG734/SoQRUQA/H4/Xq83Mu/1evH7/R22cTgcuN1uGhoaDlvX4/Hg9/uPaZvBYJD33nsvMgwVYMuWLdxzzz08/PDD7ND5dEe17+qrqVm4EFdpKRnjx0Nra6wjiYhEnc45lOMyYsRe3G6DCRM8jBrl4/nnq8nONmIdS0TktPTUU09xwQUXcMEFFwDQo0cPHn/8cRISEvjwww+ZPXs2CxYsOGy9lStXsnLlSgBmzpyJz+c74SxOp/OkbCeaIplvv52QYZAwcSLZ995L6I9/BIcj1vE6ZOl9bTFWzG3FzGDN3FbM3BEVh3Lchg3bx3PPVXP77R5GjvTxwgvV5OXpUqYicvryeDxthnZWV1fj8XjabeP1egmFQjQ3N5OSknLYun6/P7Lukbb50ksvUV9fz/jx4yPL3G535Hnfvn1ZsmQJ9fX1pKamtskydOhQhg4dGpmvqqo63o8e4fP5Tsp2oqlN5h/8gOSvviJ1xgxaEhKoe/jh8In3nZDl97WFWDG3FTODNXNbMXNubm67yzWsVE7I5Ze38sIL1dTW2hkxwkdZmX5vEJHTV35+PhUVFVRWVhIMBikpKaGgoKBNm379+rFq1SoA1q5dS+/evbHZbBQUFFBSUkIgEKCyspKKigp69ux5xG2+9dZbbNq0iaKiIuz2g116bW0t5v4b05aVlWEYBikpKdHZCV1A48SJNEyaRNKzz5Iye3as44iIRI3+JS8nrG/fAC+9VMXNN3v54Q+9PP98NRdeGIx1LBGRqHM4HIwdO5YZM2ZgGAaDBg0iLy+P5cuXk5+fT0FBAYMHD2bRokVMnjyZ5ORkioqKAMjLy+Oyyy5j6tSp2O12xo0bFyn42tsmwJNPPklmZibTp08HDt6yYu3atbz55ps4HA5cLhdFRUXYOunRr86q4b77sNfWkjJ/PkZaGk3/+Z+xjiQicsrZzAM/LZ5GysvLT2h9Kx46hlOfe/t2Bzfe6KWhwc5jj9UybFjLCY/E0b6OHitmBmvmtmJmsF7ujobMSMdOtH8E631P4AiZQyEyJk4k8fXXqZ09m+abb45+uCPoUvu6k7NibitmBmvmtmJmDSuVU+6cc0KsWFFFt24hxo718IMf+Fi92hXrWCIiIsfH4aBmwQJaBg4k/Z57SPv5z7E1NMQ6lYjIKaPiUE6q7t0N/v73PcyaVUt5uYMbbvAxerSX9evjYh1NRETkm4uPx790KQ1TpuB+8UUyr7kG1/vvxzqViMgpoeJQTrq4OLjllmb++c/dPPhgHZ9+6uT7389kzBgPn3yi01xFRMRiXC4a7r2XqpdfBocD7/XXkzJjBuzbF+tkIiInlYpDOWUSEuDOO5tYs6aSe++t54MPXAwblsVPf5pBWVnnvW+UiIhIewKXXMKeN9+k+ZZbSHn8cTKHD8f5r3/FOpaIyEmj4lBOuaQkkylTGikp2c3kyQ2sXBnPoEFZTJ2azs6dKhJFRMQ6zKQk6h55hOpnnsFeVUXm8OEk/f73ENJ9fkXE+lQcStSkp5v88pcNrFlTydixTbzySiJXXJHF9Olp7N6tr6KIiFjHvqFD2fPWW7QMGULaQw/hveEGHDt2xDqWiMgJ0b/IJep8PoNf/7qe997bzQ03NPM//+OmsDCLGTNS8Pt1Hy4REbEGw+ul5sknqZk3j7jNm8kcOpTE5cvh9LtLmIh0ESoOJWa6dzeYNauOd96p5NprW/j975MpLMxm7txkGhpUJIqIiAXYbOy94Qb2rFxJ4D/+g4ypU8m44w7s1dWxTiYi8o2pOJSYO/vsEAsX1rJy5R6uuGIfc+akctllWfz+90k0N8c6nYiIyNGF8vKofvFF6n71KxKKi8kcPJj4f/wj1rFERL6RqN1XYOPGjSxduhTDMBgyZAgjRoxo83ogEGDRokVs376dlJQUioqKyMrKoqGhgblz51JWVsbAgQMZN25cZJ0ZM2ZQW1tLKBTi/PPP54477sBuV71rVeefH+Spp2rYtKmRWbNSeOihNBYuNLnuujSuv34vl1zSiv7ziohIp2W30zRhAvuuvpqMKVPwjhlD0803U/9//y9mcnKs04mIHFVU/qltGAZLlixh2rRpzJs3j9WrV7Nz5842bYqLi0lKSmLhwoUMHz6cZcuWARAXF8fo0aO59dZbD9vu3XffzezZs5kzZw719fWsWbMmGh9HTrGLLgqwbJmfFSuquO46g5dfTmTkSB+FhVnMmpXCtm26wqmIiHRewQsuYM/rr9MwaRLu558nc9gwXKWlsY4lInJUUSkOy8rKyMnJITs7G6fTSWFhIaVf+yO5bt06Bg4cCMCAAQPYvHkzpmmSkJDA+eefj8vlOmy7brcbgFAoRDAYxGbTeWpdyaWXtvL00yE2bdrN/Pk19OgRZOHCZK66KpvrrvOxdKkbv1+HEkVEpBOKj6dh2jSqX34ZTBPvyJGk/O530Noa62QiIh2Kyr+s/X4/Xq83Mu/1evH7/R22cTgcuN1uGhoajrrtGTNmcOedd5KYmMiAAQNObnDpFJKSTEaN2svzz/spLd3Nr35VR0uLjfvvT+fii7MZM8bDX/+aQEtLrJOKiIi01Xrppez5xz9oHj2alEWLyPzud3E/+yy2r/07SESkM4jaOYenyvTp02ltbWXBggVs3ryZb3/724e1WblyJStXrgRg5syZ+Hy+E3pPp9N5wtuIBSvm/npmnw8uvBDuv9/k448DLFtm54UX4vnHPxJISzO5/nqDW24xKCw0Y3p+YlfY11ZhxdxWzAzWzS0Sa2ZyMnWPPkrLsGGk/u53pN93H2kPPEDLoEHs/eEPabnmGkhMjHVMEZHoFIcej4fqQy7pXF1djcfjabeN1+slFArR3NxMSkrKMW3f5XJxySWXUFpa2m5xOHToUIYOHRqZr6qqOs5PEubz+U54G7FgxdxHytytG/ziF3D33bB6dTwvvZTI888n8PTTceTlBRk5ci/XX99Mfn4oyqm73r7uzKyY24qZwXq5c3NzYx1BpI19w4ax55prcH7yCe6XXybx1VdJfPNNjORkWq69luaRI2ktLASHzq0XkdiIyrGV/Px8KioqqKysJBgMUlJSQkFBQZs2/fr1Y9WqVQCsXbuW3r17H/EcwpaWFmpqaoDwOYcffvgh3bt3P2WfQTovhwOuumofCxfWdnh+4h//6Na9E0VEJPZsNoIXXkj9Aw+w+4MPqHrhBVqGDyfhb3/Dd+ONZF96Kam/+Q3OzZvBNGOdVkROM1E5cuhwOBg7diwzZszAMAwGDRpEXl4ey5cvJz8/n4KCAgYPHsyiRYuYPHkyycnJFBUVRdafNGkSzc3NBINBSktLuf/++0lOTmbWrFkEAgFM06R3795cc8010fg40okdOD9x1Ki97Npl55VXEvnzn91Mn57Oww+n8qMf7WXMmCZ69QrGOqqIiJzuHA5ar7yS1iuvhBkzSFi5ksSXXybp6adJfuIJAueey94f/pC9P/whoby8WKcVkdOAzTRPv5+lysvLT2h9qw2tOsCKuU9W5g0b4li6NIm//jWR1lYbl1++jzFjmhg2rAXnKfiJ5HTe19FmxdxWzAzWy61hpd/cifaPYL3vCXS+zDa/n8TXXydxxQriP/gAgH2XXsrekSPZe911mBkZQOfLfSysmBmsmduKmcGaua2YuaM+UvcBkNPCxRcHWLCglnXrdvPLX9bzxRcO7rzTw4AB2cyfn0xVlf5XEBGRzsH0eGi+7TaqV6xg99q11N97L/aaGtJ/+UtyLr6YjLFjSfjrX2H/6TUiIieL/kUspxWv12Dy5EZKSipZssRPz55BZs1K5ZJLspk8OZ316+N0ioeIiHQaobw8GqdMYc/bb1P597/TdPvtuDZuxDNhAnHdupF5zTWk/upXJLz+OnaLHbkQkc7H8reyEDkeTid85zstfOc7LZSVOXnmGTcvvujm5Zfd/Md/tHL77U18//t7dWVxERHpHA5cyObCC6m//35c69aR/tFHGG+9hfv550l++mkAAj170tq/P60DBrCvf38MXaxPRL4BFYdy2uvZM8hvf1vPvfc28Je/JPLMM0lMnZrBb36Txk03NXPbbU2ceWb0b4chIiLSLoeD1v79MYYPp/rOOyEQIO7jj4lfuxbX2rUk/vWvJC1bBkDwzDNp7d+ffQMG0Nq/P6Gzz4YjXA1eRE5vKg5F9ktONvnJT5q57bZm1qxx8cc/JvGHPyTx3/+dxJAh4QvYXH31PuwajC0iIp1JXByBvn0J9O0LEydCKITz00+Jf/99XGvXEv/WW7hfegmAUE4O+/r3jxxdDPbqhTo2ETlAxaHI19hsUFjYSmFhK+XldpYtS2LZMjc//rGXvLwgffoEOO+8AOedF+TccwOcfXbolFzxVERE5Lg4HAQvvJDghRfSNG4cmCbOsjJc+48sxq9di/vVVwEIZWTQeuWVtAwaxL5BgzAyM2McXkRiSf+kFTmC3FyDe+5p4K67Gvjb3xJ59dUEPvoojr/+9eDJiPHxJvn5wUjBeN55AQYMgORk/RgrIiKdgM1GsFcvgr160XzrrWCaOL78MlworllD/DvvkPjaawC0XnQR+wYNomXwYAJ9+oDDEePwIhJNKg5FjoHLBSNG7GXEiL0ANDfb2LrVyeefO/n88zi2bHHy/vsuVqxwR9ZJTMyhV69gpGAMT4Pk5oZ0uoeIiMSOzUborLPYe9ZZ7B09GgwD57/+RcJbbxH/9tskL1hAymOPEcrIYN/AgewbPJh9AwdieDyxTi4ip5iKQ5Hj4HabXHRRgIsuCgB7I8sbGmxs2eLkq68yWL++hc8/j+Pdd+N56aWDRWNyssG55wbp1StIz55BevYMkJ8f5KyzNDxVRERiwG4neOGFNF54IY133YWtpob4d98lobiY+Lffxr1iBabNRqBPH1qGDGHfoEEEvv1tDY8R6YL0T1GRkyglxaRfvwD/5/8YfP/79ZHlNTU2tmyJ4/PPnWzZ4uSzz+J4++14li8/WDTGxZn06BEuGPPzDxSO4ecpKbr5ooiIRIeZkUHLD35Ayw9+AIYRvhJqcTEJb71Fypw5pD76KCGvNzL8dN9VV2FmZMQ6toicBCoORaIgI8Okf/9W+vdvbbO8rs7Gtm1Oysqckennnzv5+98TCIUOjj3NyQlFCsZevQKR5926GRqiKiIip47dTuCiiwhcdBGNd9+N3e8nftUq4ouLw1dB/fOfMe12An370tqvH6EzziB4xhmEuncndMYZmGlpsf4EIvINqDgUiaG0NJO+fQP07Rtos7y1Fb78MlwsHvp4+eVEGhqSIu2SkgxyckJ4vQYej4HXa5CRYUTmDyw7ME1MNFVMiojIcTM8HvaOHMnekSMhFCJu40YS3n6b+OJikp55BltLS9v2KSmEDikW7eeeS0JGBqG8PEJnnIHh8+m+iyKdiIpDkU7I5SIyrPRQpgmVlfZIsbhtm5Pdux1UV9v54gsn69fb8fvtbY46HiohwcTjCbUpGg888vPtpKe7OPPMELm5IeLiovFJRUTEshwOAv36EejXj4Zf/AJME3tVFY6dO8OPr77CsXMnzv3zrg8+wF5fz6GXtTETEgjl5oaPNh4oInNzMbKzCWVlYWRlYWRk6PxGkShRcShiITYbZGcbZGe3cvnlre22MU2or7dRXR0uFA88qqsdhzwPT7/4wonfb6eh4UCn6wPAbjfJyQmRlxfijDNCnHlmiLy8IGecEV7WrZuKRxER+RqbDSMzEyMzk8DFF7fbxBcXR+2mTZHi0blzJ44dO3B89RVxb75HY3UDAAAbPUlEQVSJo6rqsHVMpxMjMzNSLLY7zc4m5PNBfPyp/pQiXZqKQ5EuxmYLD1dNSwtxzjmhY1pn3z4IBHx89FE9O3Y42LHDyY4dDnbudLBmjYsVKxwYxsGjkQ6HSbduB4vH8DTImWeGyMkJkZZmkpxs4HKdqk8pIiKWlJZG8FvfIvitb7X/+t69OHbtwlFZib2y8vDpV18Rt2ED9upqbObhF2sz0tMJZWeHi8nMTMzUVIyUlIPTlJS28wemujmxCKDiUEQI/9DavTskJ7d/NLK1FSoqHG0KxwPF43vvxbN7tx3TPHwoa0KCSUqKQXKySWrqwWlKSnh5R9PU1HC7zExD918WETmdJCYS6tGDUI8eR24XCGCvrg4Xjbt349izJzytrMS+Zw+O3btxrV+Pvb4eW0MDttDRfyw1kpPDxWNq6sFpcjJGaiqObt1Idrkw0tPDj7S0yHMzPR3T7da5k9IlqDgUkaNyueCss0KcdVYIOLyA3LcPysvDBeOuXQ4aG+3U19toaLDT0BCeNjbaqK+3s327s82y9orKA5zO8PDW7t3Dj9zcttPu3UP4fKfwg4sch40bN7J06VIMw2DIkCGMGDGizeuBQIBFixaxfft2UlJSKCoqIisrC4AVK1ZQXFyM3W7n9ttvp0+fPkfc5oIFC9i2bRtOp5P8/HzGjx+P0+nENE2WLl3Khg0biI+PZ+LEiZxzzjnR3REip1JcHEZODkZOztHbmia2lhZs9fXYGxrC08bGtvOHLm9owF5fj726Gvu//x2er6sjNRDo+C3i4iJFo/m14vFAAWmkpUWOXprJyRhJSZHnZkKCikvpFFQcisgJi4+HHj1C9OhxbMNYDzAMaGqyUV9vixSUB6a1tXYqKhx89ZWD8nIHpaUuKiocBINtO8/UVJPc3Mx2C8fu3cPDXHV+pESLYRgsWbKE+++/H6/Xy3333UdBQQFnnHFGpE1xcTFJSUksXLiQ1atXs2zZMu6++2527txJSUkJc+fOpaamht/+9rfMnz8foMNtXnHFFUyePBmA+fPnU1xczLBhw9iwYQO7du1iwYIFbN26laeeeoqHH344JvtEJOZsNszERMzERIzs7OPahM/rpfrLL7HV1mKvrcVeVxee1tZiO/C8pubg8spKnFu2hJ83NBx1+6bD0bZgTEoKF5JJSeHlB56npGAkJWFkZGB060Zo/4V7SEg4rs8l8nUqDkUkZux29g8lNQHjqO1DofDVWsvLDxaN1dXJbNsW5KuvHGzYEEdNTdtxqDabic9nkJ0dIjs7fOuPrKwD8yFycgyyskL4fAZO/UWUE1RWVkZOTg7Z+/8BWlhYSGlpaZvicN26dfzoRz8CYMCAATz99NOYpklpaSmFhYXExcWRlZVFTk4OZWVlAB1us2/fvpHt9uzZk+rq6sh7XHXVVdhsNs4991yampqoqakhQzcqFzk+Nlu4OEtKwuje/ZutGwyGh7fW1ISPTDY2Rqa2hgbsTU3hoa9NTQeXNzaG16moaNveaL+vNNLTCeXkEMrJCV+cJzsbe34+CcnJ4eX7z8NURydHo2+IiFiGwwHduhl062bQr194eI/Pl0hVVU2kzd69Nr76yk55uZOvvgoXkbt329m1Kzz96KM4qqoOP0fSbjfJzAwXjQeKx0MLyZyc8C0/0tIMkpJ0v0hpn9/vx+v1Rua9Xi9bt27tsI3D4cDtdtPQ0IDf76dXr16Rdh6PB7/fH9nOkbYZDAZ57733GDNmTOQ9fIeMufZ6vfj9fhWHIrHgdGJ4PODx8M3G13yNaWLbuzdcUPr9OHbvDp9nuWtXm+dxn32Gfc8ebKFQ29uG7L+abCg7G+NAwbj/ViFtHh4PRkYGZlKShrqehlQcikiXkpho0rNniJ49O+6CAwHYs8dOZaWD3bsd7NplZ/duB5WV4Wl5efgoZHV1+1fDcTjCF8wJXxU2fAGdtDTjkMeB1w+2OXS5yMn21FNPccEFF3DBBRd8o/VWrlzJypUrAZg5c2abgvJ4OZ3Ok7KdaLJiZrBmbitmBmvlDgGhUAhnTQ2hHTugvBxbeTm2igqoqMBZXg4VFdg2bcJWWdnhdsy4OPB6MT0e8HrB48H0etssi0wzMsDtDg9vTUwMP47znA4r7esDrJi5IyoOReS0ExcHubkGubkG0PEFBlpbw0Xk7t3hIrKm5uD5kPX1durqbNTV2amrCx+pDC+z09p65F9a4+NNnM4c4uIgLs7c/6DN1OkEl+vQ6eHL4uNNkpPDw3KTk4020wNXiT2wTLf+ig6PxxMZ2glQXV2Nx+Npt43X6yUUCtHc3ExKSsph6/r9/si6R9rmSy+9RH19PePHj2/zHlWH3C+uvRwAQ4cOZejQoZH5qnbuMfdN+Xy+k7KdaLJiZrBmbitmBmvm9vl8VAHk5XXcKBQKnyfp92OvqQkPff36Y/9r9s8+iyyzHeHiPAeYDgdmQkLkQXx8m/mOHo70dFoCAXC5MF2ucJG6f2q6XEd9HlmWmBi+imwULntuxe9Hbm5uu8tVHIqIdMDlgu7dDbp3P3IReSjThJYWqKuzR4rF2lpb5HldnQ3TTKK+fi/BILS22ggGbQQCEAgcPm1psdHYaNvfjsg0ELCxb5+NhgYbodDRh/24XIcWkIcWj+Fl6elG5JGRYZCR0XaZLupzbPLz86moqKCyshKPx0NJSQlTpkxp06Zfv36sWrWKc889l7Vr19K7d29sNhsFBQUsWLCA6667jpqaGioqKujZsyemaXa4zbfeeotNmzbxwAMPYD/kHm0FBQW88cYbXH755WzduhW3260hpSJyOIcjPIy0nR+POmSa4fMjDykcbXV14SvC7t0bnn79sW8ftpYWOGSZvaqq3Ta2fftIPYkf0UxIwEhMDJ8z6naHp/vnja/NH3jdcLvDzw+8npQUvljQ/gsEmW53l70vpopDEZGTyGY7MKLGICen/SGkPl8CVVX1J+X9DhSjB24b0tj49enB24Z8fbprl52yMif19eEjoEcqMpOTDbxeGykpvjaFY0ZG26LS4zHwesOPlJTT79xMh8PB2LFjmTFjBoZhMGjQIPLy8li+fDn5+fkUFBQwePBgFi1axOTJk0lOTqaoqAiAvLw8LrvsMqZOnYrdbmfcuHGRgq+9bQI8+eSTZGZmMn36dAD69+/PqFGjuPjii/nwww+ZMmUKLpeLiRMnxmaHiEjXY7NhJicTSk4mdKSjksfJ5/VStWtX+Ohka+vB6aHPA4Hw8337Dj7f34ZA4GCh2dyMrbk5fNGf5mZsTU3hArapCXtFBc4Dyw+81sEFf9pjHFI42tPS8MbHRwpHMykpfN9Mt7vNVWgNn4+Qz4eRmRkuyDvhL6820zTNWIeItvLy8hNa34qHjsGaua2YGayZ24qZwZq5O2Nmw4DGxvCQ2ZoaO7W14SOeNTUH51taEqmoCLR5rbbWjmG0XwG6XOYhxWL4irCHFo/h+VDk+ckuJjsaMiMdO9H+ETrn9/torJgZrJnbipnBmrmtmBlimNs0Yd8+7IcWi/uLR/uB5/sf9qam8NVj97+e0NpKoKYmvF5jY9v2oY6vgRDKyMDw+cKPzMxw4fj15/vnzcTEk/pxNaxUREQ6ZLeH7xmZmhrizDPb78h8PhdVVf42yw4UlQeKSL/fTnX1oQ9H5PkXXziprrbT1NT+UJy4OBOvN1xAfu97e5kypfGkf04REZF22WyQkICRkADfZJgt4YK2ur2C9kDB2dSEra4OR3U19j17wo/qahx79mCvqsJeVUXcxx8TX1XV4X0xjaSkSLFYs2ABobPPPo4PeXQqDkVE5LgdWlSeddaxXaR9717w+w8WjVVV4emBwrKqykFCwmk3qEVERLqaQwtOr5fQOeccfZ2WloNF5P7C0fG156bbfcoiqzgUEZGoSkyE7t1DdO9+Qnf8EhER6XoSEgh1706oe/eYvH3UisONGzeydOlSDMNgyJAhjBgxos3rgUCARYsWsX37dlJSUigqKiIrK4uGhgbmzp1LWVkZAwcOZNy4cQDs27ePuXPnsnv3bux2O/369eOWW26J1scRERERERHpUqJyDVbDMFiyZAnTpk1j3rx5rF69mp07d7ZpU1xcTFJSEgsXLmT48OEsW7YMgLi4OEaPHs2tt9562Ha/973v8dhjjzFr1iw+//xzNmzYEI2PIyIiIiIi0uVEpTgsKysjJyeH7OxsnE4nhYWFlJaWtmmzbt06Bg4cCMCAAQPYvHkzpmmSkJDA+eefj8vlatM+Pj6eCy+8EACn00mPHj3a3CRYREREREREjl1UhpX6/X68Xm9k3uv1snXr1g7bOBwO3G43DQ0NpKYe/TaYTU1NrF+/nmuvvbbd11euXMnKlSsBmDlzJj6f73g/ChAuRk90G7FgxdxWzAzWzG3FzGDN3FbMDNbNLSIiIsfG8hekCYVCzJ8/n+9+97tkZ2e322bo0KEMHTo0Mn+i907RfWOix4qZwZq5rZgZrJnbipnBerl1n0MREZFvJirDSj0eT5shn9XV1Xi+dv+QQ9uEQiGam5tJSUk56rafeOIJcnJyGD58+MkNLSIiIiIichqJSnGYn59PRUUFlZWVBINBSkpKKCgoaNOmX79+rFq1CoC1a9fSu3dvbDbbEbf7wgsv0NzczJgxY05RchERERERkdNDVIaVOhwOxo4dy4wZMzAMg0GDBpGXl8fy5cvJz8+noKCAwYMHs2jRIiZPnkxycjJFRUWR9SdNmkRzczPBYJDS0lLuv/9+EhMTefnll+nevTv33nsvAN/5zncYMmRIND6SiIiIiIhIlxK1cw779u1L37592ywbPXp05LnL5WLq1Kntrrt48eJ2l7/44osnL6CIiIiIiMhpLCrDSkVERERERKRzU3EoIiIiIiIiKg5FRERERERExaGIiIiIiIgANtM0zViHEBERERERkdjSkcPj8Mtf/jLWEY6LFXNbMTNYM7cVM4M1c1sxM1g3t0SXFb8nVswM1sxtxcxgzdxWzAzWzG3FzB1RcSgiIiIiIiIqDkVERERERAQcDz744IOxDmFF55xzTqwjHBcr5rZiZrBmbitmBmvmtmJmsG5uiS4rfk+smBmsmduKmcGaua2YGayZ24qZ26ML0oiIiIiIiIiGlYqIiIiIiAg4Yx2gM9u4cSNLly7FMAyGDBnCiBEj2rweCARYtGgR27dvJyUlhaKiIrKysmKUFqqqqli8eDG1tbXYbDaGDh3Ktdde26bNJ598wqxZsyI5+/fvz6hRo2IRt41JkyaRkJCA3W7H4XAwc+bMNq+bpsnSpUvZsGED8fHxTJw4MaaH78vLy5k3b15kvrKykhtuuIHhw4dHlnWWff3444/z4YcfkpaWxpw5cwBobGxk3rx57Nmzh8zMTO6++26Sk5MPW3fVqlW8/PLLAIwcOZKBAwfGLPNzzz3H+vXrcTqdZGdnM3HiRJKSkg5b92jfpWjnfvHFF3nrrbdITU0F4KabbqJv376HrXu0vzfRzDxv3jzKy8sBaG5uxu12M3v27MPWjeW+ltiyWv8I1u0jrdY/gvrIWGRWHxm9zF2+jzSlXaFQyPzZz35m7tq1ywwEAuYvfvELc8eOHW3avPHGG+YTTzxhmqZp/vOf/zTnzp0bi6gRfr/f3LZtm2maptnc3GxOmTLlsMybN282f/e738Ui3hFNnDjRrKur6/D19evXmzNmzDANwzA///xz87777otiuiMLhULmHXfcYVZWVrZZ3ln29SeffGJu27bNnDp1amTZc889Z65YscI0TdNcsWKF+dxzzx22XkNDgzlp0iSzoaGhzfNYZd64caMZDAYj+dvLbJpH/y6dSu3lXr58ufnqq68ecb1j+XtzqrSX+VDPPPOM+dJLL7X7Wiz3tcSOFftH07RuH2nl/tE01UdGK7P6yFPjdOwjNay0A2VlZeTk5JCdnY3T6aSwsJDS0tI2bdatWxf5lWjAgAFs3rwZM4ancGZkZER+LUxMTKR79+74/f6Y5TmZ1q1bx1VXXYXNZuPcc8+lqamJmpqaWMcC4OOPPyYnJ4fMzMxYR2nXt771rcN+8SwtLeXqq68G4Oqrrz7suw3hX+m+/e1vk5ycTHJyMt/+9rfZuHFjzDJfdNFFOBwOAM4999xO+d1uL/exOJa/N6fKkTKbpsmaNWu4/PLLo5JFrMGK/SN03T6yM/ePoD4yWpnVR54ap2MfqWGlHfD7/Xi93si81+tl69atHbZxOBy43W4aGhoih8ZjqbKykn//+9/07NnzsNe2bNnCPffcQ0ZGBrfeeit5eXkxSHi4GTNmAHDNNdcwdOjQNq/5/X58Pl9k3uv14vf7ycjIiGrG9qxevbrDPwyddV/X1dVF9l16ejp1dXWHtfn6/wMej6fTdDbFxcUUFhZ2+PqRvkux8Pe//513332Xc845h9tuu+2wjuZY/t7EwqeffkpaWhrdunXrsE1n29dy6lm9fwTr9ZFW7R9BfWQsqI+Mjq7aR6o47IJaWlqYM2cOY8aMwe12t3mtR48ePP744yQkJPDhhx8ye/ZsFixYEKOkB/32t7/F4/FQV1fHQw89RG5uLt/61rdiHeuogsEg69ev5+abbz7stc66r7/OZrNhs9liHeOYvfzyyzgcDq688sp2X+9s36Vhw4ZFzqNZvnw5zz77LBMnToxZnm/iSP+og863r0WOhdX6SCv/f6Y+MvrUR0ZPV+0jNay0Ax6Ph+rq6sh8dXU1Ho+nwzahUIjm5mZSUlKimvPrgsEgc+bM4corr6R///6Hve52u0lISACgb9++hEIh6uvrox3zMAf2bVpaGpdccgllZWWHvV5VVRWZb++/Ryxs2LCBHj16kJ6efthrnXVfQ3g/Hxh2VFNT0+6v+V//f8Dv98d8n69atYr169czZcqUDjvro32Xoi09PR273Y7dbmfIkCFs27btsDbH8vcm2kKhEB988MERf33ubPtaosOq/SNYs4+0av8I6iOjTX1k9HTlPlLFYQfy8/OpqKigsrKSYDBISUkJBQUFbdr069ePVatWAbB27Vp69+4d01+XTNPkv//7v+nevTvXXXddu21qa2sj532UlZVhGEbMO+yWlhb27t0bef7RRx9x5plntmlTUFDAu+++i2mabNmyBbfb3SmGzBzpV6POuK8PKCgo4J133gHgnXfe4ZJLLjmsTZ8+fdi0aRONjY00NjayadMm+vTpE+2oERs3buTVV1/l3nvvJT4+vt02x/JdirZDz/354IMP2h02dSx/b6Lt448/Jjc3t81QnkN1xn0t0WHF/hGs2UdauX8E9ZHRpD4yurpyH2kzY32GeCf24Ycf8swzz2AYBoMGDWLkyJEsX76c/Px8CgoKaG1tZdGiRfz73/8mOTmZoqIisrOzY5b3s88+44EHHuDMM8+MdMI33XRT5BfFYcOG8cYbb/Dmm2/icDhwuVzcdtttnHfeeTHLDLB7924effRRIPxLzBVXXMHIkSN58803gXBu0zRZsmQJmzZtwuVyMXHiRPLz82MZm5aWFiZOnMiiRYsiQ5MOzdxZ9vVjjz3Gv/71LxoaGkhLS+OGG27gkksuYd68eVRVVbW5TPe2bdv4xz/+wYQJE4DweQsrVqwAwpfpHjRoUMwyr1ixgmAwGDkXoVevXowfPx6/388TTzzBfffd1+F3KVray/3JJ5/wxRdfYLPZyMzMZPz48WRkZLTJDe3/vYlV5sGDB7N48WJ69erFsGHDIm07076W2LJa/wjW7COt2j+C+shoZ1YfGb3MXb2PVHEoIiIiIiIiGlYqIiIiIiIiKg5FREREREQEFYciIiIiIiKCikMRERERERFBxaGIiIiIiIig4lDktFdZWckNN9xAKBSKdRQREZFORX2knG5UHIqIiIiIiIiKQxEREREREQFnrAOIyOH8fj9PP/00n376KQkJCQwfPpxrr72WF198kR07dmC329mwYQPdunXjpz/9KWeffTYAO3fu5KmnnuKLL77A4/Fw8803U1BQAEBraysvvPACa9eupampiTPPPJNf/epXkfd87733WL58Oa2trQwfPpyRI0cCUFZWxlNPPUVFRQUul4srrriCn/zkJ1HfJyIiIqA+UuRUUnEo0skYhsEjjzzCJZdcQlFREdXV1fz2t78lNzcXgHXr1nHXXXcxefJk/va3vzF79mzmz58PwCOPPMKgQYO4//77+eyzz5g1axYzZ84kNzeXZ599lp07d/LQQw+Rnp7O1q1bsdlskff97LPPmD9/PuXl5UybNo1LL72UM844g6VLl3Lttddy1VVX0dLSwpdffhmT/SIiIqI+UuTU0rBSkU5m27Zt1NfXM2rUKJxOJ9nZ2QwZMoSSkhIAzjnnHAYMGIDT6eS6664jEAiwdetWtm7dSktLCyNGjMDpdHLhhRfSt29f/vnPf2IYBm+//TZjxozB4/Fgt9s577zziIuLi7zvj370I1wuF2effTZnnXUW//u//wuA0+lk165d1NfXk5CQwLnnnhuT/SIiIqI+UuTU0pFDkU5mz5491NTUMGbMmMgywzC44IIL8Pl8eL3eyHK73Y7X66WmpgYAn8+H3X7wN5/MzEz8fj8NDQ0EAgFycnI6fN/09PTI8/j4eFpaWgCYMGECy5cv5+677yYrK4tRo0bRr1+/k/VxRUREjpn6SJFTS8WhSCfj8/nIyspiwYIFh7324osvUl1dHZk3DIPq6moyMjIAqKqqwjCMSOdXVVVFt27dSElJIS4ujl27dkXOvThW3bp1o6ioCMMw+OCDD5g7dy5LliwhISHh+D+kiIjIcVAfKXJqaVipSCfTs2dPEhMTeeWVV2htbcUwDL788kvKysoA2L59O++//z6hUIi//e1vxMXF0atXL3r16kV8fDyvvfYawWCQTz75hPXr13P55Zdjt9sZNGgQzz77LH6/H8Mw2LJlC4FA4Kh53n33Xerr67Hb7bjdboA2v7yKiIhEi/pIkVPLZpqmGesQItKW3+/n2Wef5ZNPPiEYDJKbm8vo0aP57LPP2lyJLScnhwkTJnDOOecAsGPHjjZXYrvpppu49NJLgfCV2P70pz+xZs0aWlpaOPvss5k+fTq1tbX87Gc/4/nnn8fhcADw4IMPcuWVVzJkyBAWLFjARx99xL59+8jMzOTGG2+MbFNERCTa1EeKnDoqDkUs5MUXX2TXrl1MmTIl1lFEREQ6FfWRIidOx71FRERERERExaGIiIiIiIhoWKmIiIiIiIigI4ciIiIiIiKCikMRERERERFBxaGIiIiIiIig4lBERERERERQcSgiIiIiIiKoOBQRERERERHg/wPGWKFHLhqjcAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NkvawiW2jpl"
      },
      "source": [
        "model.save('auto_encoders_for_noise_removal.h5')"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "LmVo8jVU5wIV",
        "outputId": "8c977acc-6327-4663-ecae-4a7eb8022d2a"
      },
      "source": [
        "from IPython.display import Audio\n",
        "Audio(np.squeeze(noisy_train[22].numpy()),rate=16000)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Amss5FGW6C2p",
        "outputId": "54e148cb-97f9-411e-e808-43af99584f42"
      },
      "source": [
        "Audio(tf.squeeze(model.predict(tf.expand_dims(tf.expand_dims(noisy_train[22],-1),0))),rate=16000)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wfuo25g36Hvr",
        "outputId": "9951a8c0-76d8-4ea6-e754-11b50788cc5c"
      },
      "source": [
        "model.evaluate(val_ds)   "
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "106/106 [==============================] - 7s 42ms/step - loss: 1.9169e-04 - mae: 0.0121\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.00019169217557646334, 0.012121148407459259]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unZ2TRDUAjrf"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}