transformer_machine_translation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7f697fec28a"
      },
      "source": [
        "##### Copyright 2022 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "GpAXuTgZ888M"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a934948f7030"
      },
      "source": [
        "# Neural machine translation with a Transformer and Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a241496dc3d9"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/text/tutorials/transformer\">\n",
        "    <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />\n",
        "    View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/text/blob/master/docs/tutorials/transformer.ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />\n",
        "    Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/text/blob/master/docs/tutorials/transformer.ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />\n",
        "    View source on GitHub</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/text/docs/tutorials/transformer.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fo1P7AN4lzdi"
      },
      "source": [
        "This tutorial demonstrates how to create and train a [sequence-to-sequence](https://developers.google.com/machine-learning/glossary#sequence-to-sequence-task){:.external} [Transformer](https://developers.google.com/machine-learning/glossary#Transformer){:.external} model to translate [Portuguese into English](https://www.tensorflow.org/datasets/catalog/ted_hrlr_translate#ted_hrlr_translatept_to_en){:.external}. Most of the components are built with high-level Keras and low-level TensorFlow APIs. The Transformer was originally proposed in [\"Attention is all you need\"](https://arxiv.org/abs/1706.03762){:.external} by Vaswani et al. (2017).\n",
        "\n",
        "Transformers are deep neural networks primarily based on various types of [attention](https://developers.google.com/machine-learning/glossary#attention){:.external} mechanisms and [feed-forward networks](https://www.tensorflow.org/guide/keras/sequential_model). The Transformer in this example uses [self-attention](https://developers.google.com/machine-learning/glossary#self-attention){:.external}. Attention allows Transformers to attend different positions of the input sequence. For example, when working with natural language, the models can make use of the surrounding text to predict a token.\n",
        "\n",
        "As explained in the [Google AI Blog post](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html){:.external}:\n",
        "\n",
        "> Neural networks for machine translation typically contain an encoder reading the input sentence and generating a representation of it. A decoder then generates the output sentence word by word while consulting the representation generated by the encoder. The Transformer starts by generating initial representations, or embeddings, for each word... Then, using self-attention, it aggregates information from all of the other words, generating a new representation per word informed by the entire context, represented by the filled balls. This step is then repeated multiple times in parallel for all words, successively generating new representations.\n",
        "\n",
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/apply_the_transformer_to_machine_translation.gif\" alt=\"Applying the Transformer to machine translation\">\n",
        "\n",
        "Figure 1: Applying the Transformer to machine translation. Source: [Google AI Blog](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html).\n",
        "\n",
        "In this tutorial you will:\n",
        "\n",
        "- Load the data with [TensorFlow Datasets](https://tensorflow.org/datasets).\n",
        "- Define tokenization functions.\n",
        "- Prepare `tf.data` pipelines.\n",
        "- Implement positional encoding and [embedding](https://developers.google.com/machine-learning/glossary#embeddings){:.external} to help learn word ordering.\n",
        "- Implement the encoder-decoder Transformer:\n",
        "  - Create a point-wise feed-forward network with Keras [Sequential](https://www.tensorflow.org/guide/keras/sequential_model) API and `tf.keras.layers.Dense` layers.\n",
        "  - Implement [encoder](https://developers.google.com/machine-learning/glossary#encoder){:.external} and [decoder](https://developers.google.com/machine-learning/glossary#decoder){:.external} layers by subclassing `tf.keras.layers.Layer`.\n",
        "  - Define the encoder and decoder, which are made up of `tf.keras.layers.MultiHeadAttention` for self-attention layers, as well as `tf.keras.layers.LayerNormalization` and `tf.keras.layers.Dense`.\n",
        "  - Put the encoder and the decoder together to create the Transformer model.\n",
        "- Train the Transformer.\n",
        "- Generate translations.\n",
        "\n",
        "Most of the Transformer components in this tutorial use the built-in APIs like `tf.keras.layers.MultiHeadAttention`.\n",
        "\n",
        "To get the most out of this tutorial, it helps if you know about [the basics of text generation](./text_generation.ipynb) and [attention mechanisms](./nmt_with_attention.ipynb).\n",
        "\n",
        "After training the model in this notebook, you will be able to input a Portuguese sentence and return the English translation.\n",
        "\n",
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/attention_map_portuguese.png\" alt=\"Attention heatmap\">\n",
        "\n",
        "Figure 2: Visualized attention weights that you can generate at the end of this tutorial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZL6uTTE5137"
      },
      "source": [
        "## Why Transformers are significant\n",
        "\n",
        "- Transformers excel at modeling sequential data, such as natural language.\n",
        "- Unlike the [recurrent neural networks (RNNs)](./text_generation.ipynb), such as [LSTMs](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.676.4320&rep=rep1&type=pdf){:.external}, Transformers can be more computationally efficient and parallelizable across several specialized hardware, like GPUs and TPUs. One of the main reasons is that Transformers replaced recurrence with attention, and computations can happen simultaneously. Layer outputs can be calculated in parallel, instead of a series like an RNN.\n",
        "- Unlike [RNNs](https://www.tensorflow.org/guide/keras/rnn) (like [seq2seq, 2014](https://arxiv.org/abs/1409.3215){:.external}) or [convolutional neural networks (CNNs)](https://www.tensorflow.org/tutorials/images/cnn) (for example, [ByteNet](https://arxiv.org/abs/1610.10099){:.external}), Transformers are able to capture distant or long-range contexts and dependencies in the data between distant positions in the input or output sequences. Thus, longer connections can be learned. Attention allows each location to have access to the entire input at each layer, while in RNNs and CNNs, the information needs to pass through many processing steps to move a long distance, which makes it harder to learn.\n",
        "- Transformers make no assumptions about the temporal/spatial relationships across the data. This is ideal for processing a set of objects (for example, [StarCraft units](https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/#block-8){:.external}).\n",
        "\n",
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/encoder_self_attention_distribution.png\" width=\"800\" alt=\"Encoder self-attention distribution for the word it from the 5th to the 6th layer of a Transformer trained on English-to-French translation\">\n",
        "\n",
        "Figure 3: The encoder self-attention distribution for the word “it” from the 5th to the 6th layer of a Transformer trained on English-to-French translation (one of eight attention heads). Source: [Google AI Blog](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swymtxpl7W7w"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OfV1batAwq9j"
      },
      "source": [
        "Begin by installing [TensorFlow Datasets](https://tensorflow.org/datasets) for loading the dataset and [TensorFlow Text](https://www.tensorflow.org/text) for text preprocessing:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "XFG0NDRu5mYQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d87f49a2-e96b-4bc6-dd92-8715adba0378"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Skipping estimator as it is not installed.\u001b[0m\n",
            "\u001b[33mWARNING: Skipping tensorflow-text as it is not installed.\u001b[0m\n",
            "\u001b[K     |████████████████████████████████| 5.9 MB 13.2 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# Install the nightly version of TensorFlow to use the improved\n",
        "# masking support for `tf.keras.layers.MultiHeadAttention`.\n",
        "!pip uninstall -y -q tensorflow keras estimator tensorflow-text\n",
        "!pip install -q tensorflow_datasets\n",
        "!pip install -q -U tensorflow-text-nightly==2.11.0.dev20220817"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ruCfRc16PW16",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6041126a-3ebc-47c6-9e2c-c74354d8b00e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 582.0 MB 13 kB/s \n",
            "\u001b[K     |████████████████████████████████| 439 kB 67.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 5.9 MB 42.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 43.1 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -U -q tf-nightly==2.11.0.dev20220817"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0GYpLBSjxJmG"
      },
      "source": [
        "Import the necessary modules:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "JjJJyJTZYebt"
      },
      "outputs": [],
      "source": [
        "import logging\n",
        "import time\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow as tf\n",
        "\n",
        "import tensorflow_text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-cCvXbPkccV1"
      },
      "source": [
        "## Download the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LTEVgBxklzdq"
      },
      "source": [
        "Use TensorFlow Datasets to load the [Portuguese-English translation dataset](https://www.tensorflow.org/datasets/catalog/ted_hrlr_translate#ted_hrlr_translatept_to_en){:.external} from the TED Talks Open Translation Project. This dataset contains approximately 52,000 training, 1,200 validation and 1,800 test examples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset 124.94 MiB (download: 124.94 MiB, generated: Unknown size, total: 124.94 MiB) to ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0...\u001b[0m\n"
          ]
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          "data": {
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              "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
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            "text/plain": [
              "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteR4BVDA/ted_hrlr_translate-train.tf…"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation examples...:   0%|          | 0/1193 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8955b31dbb4414f912488ca6957a2d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteR4BVDA/ted_hrlr_translate-validati…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "173fceed2ec845a782c3eff2a107b9b6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test examples...:   0%|          | 0/1803 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25dc32c911c9484d8b47720fb7376d35"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteR4BVDA/ted_hrlr_translate-test.tfr…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7e009df90044ddeb8bb2603adc70788"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDataset ted_hrlr_translate downloaded and prepared to ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "examples, metadata = tfds.load('ted_hrlr_translate/pt_to_en',\n",
        "                               with_info=True,\n",
        "                               as_supervised=True)\n",
        "\n",
        "train_examples, val_examples = examples['train'], examples['validation']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZA4cw7F_DmSt"
      },
      "source": [
        "The `tf.data.Dataset` object returned by TensorFlow Datasets yields pairs of text examples:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "CZFAMZJyDrFn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "525883f8-d64f-499f-aa1e-c66bb0fee08e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> Examples in Portuguese:\n",
            "e quando melhoramos a procura , tiramos a única vantagem da impressão , que é a serendipidade .\n",
            "mas e se estes fatores fossem ativos ?\n",
            "mas eles não tinham a curiosidade de me testar .\n",
            "\n",
            "> Examples in English:\n",
            "and when you improve searchability , you actually take away the one advantage of print , which is serendipity .\n",
            "but what if it were active ?\n",
            "but they did n't test for curiosity .\n"
          ]
        }
      ],
      "source": [
        "for pt_examples, en_examples in train_examples.batch(3).take(1):\n",
        "  print('> Examples in Portuguese:')\n",
        "  for pt in pt_examples.numpy():\n",
        "    print(pt.decode('utf-8'))\n",
        "  print()\n",
        "\n",
        "  print('> Examples in English:')\n",
        "  for en in en_examples.numpy():\n",
        "    print(en.decode('utf-8'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJxTd6aVnZyh"
      },
      "source": [
        "## Load the tokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mopr6oKUlzds"
      },
      "source": [
        "Now that you have loaded the dataset, you need to tokenize the text, so that each element is represented as a [token](https://developers.google.com/machine-learning/glossary#token){:.external} or token ID (a numeric representation).\n",
        "\n",
        "Tokenization is the process of breaking up a sequence, such as a text, into tokens, for each element in that sequence. Commonly, these tokens are words, characters, numbers, subwords, and/or punctuation. The beginning of sentences and end of sentences are typically also marked by tokens IDs, such as `'[START]'` and `'[END]'`.\n",
        "\n",
        "Tokenization can be done in various ways. For example, for a text sequence of `'how are you'`, you can apply:\n",
        "\n",
        "- Word-level tokenization, such as `'how'`, `'are'`, `'you'`.\n",
        "- Character-level tokenization, such as `'h'`, `'o'`, `'w'`, `'a'`, and so on. This would result in a much longer sequence length compared with the previous method.\n",
        "- Subword tokenization, which can take care of common/recurring word parts, such as `'ing'` and `'tion'`, as well as common words like `'are'` and `'you'`.\n",
        "\n",
        "To learn more about tokenization, visit [this guide](https://www.tensorflow.org/text/guide/tokenizers)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJr_8Jz9FKgu"
      },
      "source": [
        "### Subword tokenizer\n",
        "\n",
        "This tutorial uses a popular [subword tokenizer](https://www.tensorflow.org/text/guide/subwords_tokenizer) implementation, which builds subword tokenizers (`text.BertTokenizer`) optimized for the dataset and exports them in a TensorFlow `saved_model` format.\n",
        "\n",
        "Download, extract, and import the `saved_model`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "QToMl0NanZPr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "342d8cd9-81c4-4bf8-e75c-9099f3d20fc8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/download.tensorflow.org/models/ted_hrlr_translate_pt_en_converter.zip\n",
            "184801/184801 [==============================] - 0s 0us/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'./ted_hrlr_translate_pt_en_converter.zip'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "model_name = 'ted_hrlr_translate_pt_en_converter'\n",
        "tf.keras.utils.get_file(\n",
        "    f'{model_name}.zip',\n",
        "    f'https://storage.googleapis.com/download.tensorflow.org/models/{model_name}.zip',\n",
        "    cache_dir='.', cache_subdir='', extract=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "h5dbGnPXnuI1"
      },
      "outputs": [],
      "source": [
        "tokenizers = tf.saved_model.load(model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CexgkIS1lzdt"
      },
      "source": [
        "The `tf.saved_model` contains two text tokenizers, one for English and one for Portuguese. Both have the same methods:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "s-PCJijfcZ9_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e2ddba8-ceb3-4080-d008-9ab32abe955e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['detokenize',\n",
              " 'get_reserved_tokens',\n",
              " 'get_vocab_path',\n",
              " 'get_vocab_size',\n",
              " 'lookup',\n",
              " 'tokenize',\n",
              " 'tokenizer',\n",
              " 'vocab']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "[item for item in dir(tokenizers.en) if not item.startswith('_')]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUBljDDEFWUC"
      },
      "source": [
        "The `tokenize` method converts a batch of strings to a padded-batch of token IDs. This method splits punctuation, lowercases and unicode-normalizes the input before tokenizing. That standardization is not visible here because the input data is already standardized."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "z_gPC5iwFXfU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2ead6f0-8451-4286-fe41-7e57721af104"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> This is a batch of strings:\n",
            "and when you improve searchability , you actually take away the one advantage of print , which is serendipity .\n",
            "but what if it were active ?\n",
            "but they did n't test for curiosity .\n"
          ]
        }
      ],
      "source": [
        "print('> This is a batch of strings:')\n",
        "for en in en_examples.numpy():\n",
        "  print(en.decode('utf-8'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "uSkM7z8JFaVO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f30faee4-e612-4bdd-fbdc-3a84fda8caea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> This is a padded-batch of token IDs:\n",
            "[2, 72, 117, 79, 1259, 1491, 2362, 13, 79, 150, 184, 311, 71, 103, 2308, 74, 2679, 13, 148, 80, 55, 4840, 1434, 2423, 540, 15, 3]\n",
            "[2, 87, 90, 107, 76, 129, 1852, 30, 3]\n",
            "[2, 87, 83, 149, 50, 9, 56, 664, 85, 2512, 15, 3]\n"
          ]
        }
      ],
      "source": [
        "encoded = tokenizers.en.tokenize(en_examples)\n",
        "\n",
        "print('> This is a padded-batch of token IDs:')\n",
        "for row in encoded.to_list():\n",
        "  print(row)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBkv7XeBFa8_"
      },
      "source": [
        "The `detokenize` method attempts to convert these token IDs back to human-readable text: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "-CFS5aAxFdpP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b198d13-6e19-4089-9707-f9f20ead849d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> This is human-readable text:\n",
            "and when you improve searchability , you actually take away the one advantage of print , which is serendipity .\n",
            "but what if it were active ?\n",
            "but they did n ' t test for curiosity .\n"
          ]
        }
      ],
      "source": [
        "round_trip = tokenizers.en.detokenize(encoded)\n",
        "\n",
        "print('> This is human-readable text:')\n",
        "for line in round_trip.numpy():\n",
        "  print(line.decode('utf-8'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-2gMSBBU-AE"
      },
      "source": [
        "The lower level `lookup` method converts from token-IDs to token text:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "XaCeOnswVAhI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2037a608-2569-4458-ca0f-76eec0b56517"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> This is the text split into tokens:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.RaggedTensor [[b'[START]', b'and', b'when', b'you', b'improve', b'search', b'##ability',\n",
              "  b',', b'you', b'actually', b'take', b'away', b'the', b'one', b'advantage',\n",
              "  b'of', b'print', b',', b'which', b'is', b's', b'##ere', b'##nd', b'##ip',\n",
              "  b'##ity', b'.', b'[END]']                                                 ,\n",
              " [b'[START]', b'but', b'what', b'if', b'it', b'were', b'active', b'?',\n",
              "  b'[END]']                                                           ,\n",
              " [b'[START]', b'but', b'they', b'did', b'n', b\"'\", b't', b'test', b'for',\n",
              "  b'curiosity', b'.', b'[END]']                                          ]>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "print('> This is the text split into tokens:')\n",
        "tokens = tokenizers.en.lookup(encoded)\n",
        "tokens"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pR3vZJf1Yhg_"
      },
      "source": [
        "The output demonstrates the \"subword\" aspect of the subword tokenization.\n",
        "\n",
        "For example, the word `'searchability'` is decomposed into `'search'` and `'##ability'`, and the word `'serendipity'` into `'s'`, `'##ere'`, `'##nd'`, `'##ip'` and `'##ity'`.\n",
        "\n",
        "Note that the tokenized text includes `'[START]'` and `'[END]'` tokens."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_4vdnhSaATh"
      },
      "source": [
        "The distribution of tokens per example in the dataset is as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "KRbke-iaaHFI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfbb7c19-c3c9-4678-b3f0-fd6c93dac399"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "..................................................."
          ]
        }
      ],
      "source": [
        "lengths = []\n",
        "\n",
        "for pt_examples, en_examples in train_examples.batch(1024):\n",
        "  pt_tokens = tokenizers.en.tokenize(pt_examples)\n",
        "  lengths.append(pt_tokens.row_lengths())\n",
        "  \n",
        "  en_tokens = tokenizers.en.tokenize(en_examples)\n",
        "  lengths.append(en_tokens.row_lengths())\n",
        "  print('.', end='', flush=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "9ucA1q3GaK_n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "c9d661a2-a8ae-48ba-a4cd-895dbabd62ee"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "all_lengths = np.concatenate(lengths)\n",
        "\n",
        "plt.hist(all_lengths, np.linspace(0, 500, 101))\n",
        "plt.ylim(plt.ylim())\n",
        "max_length = max(all_lengths)\n",
        "plt.plot([max_length, max_length], plt.ylim())\n",
        "plt.title(f'Maximum tokens per example: {max_length}');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EoMA1_h8FOW3"
      },
      "source": [
        "### Create tokenizer functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_EXJPJmDlzdv"
      },
      "source": [
        "This section shows how to define custom functions for transforming/tokenizing the text in the dataset into tokens. You will need these for building an input pipeline suitable for training.\n",
        "\n",
        "The following function drops examples longer than the maximum number of tokens (`MAX_TOKENS`). Without limiting the size of sequences, the performance may be negatively affected."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "81fPafMpSVJ-"
      },
      "outputs": [],
      "source": [
        "MAX_TOKENS = 128\n",
        "\n",
        "def filter_max_tokens(pt, en):\n",
        "  num_tokens = tf.maximum(tf.shape(pt)[1],tf.shape(en)[1])\n",
        "  return num_tokens < MAX_TOKENS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qEZnF2Ulzdw"
      },
      "source": [
        "Next, define a function that tokenizes the batches of raw text:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "6shgzEck3FiV"
      },
      "outputs": [],
      "source": [
        "def tokenize_pairs(pt, en):\n",
        "    pt = tokenizers.pt.tokenize(pt)\n",
        "    # Convert from ragged to dense, padding with zeros.\n",
        "    pt = pt.to_tensor()\n",
        "\n",
        "    en = tokenizers.en.tokenize(en)\n",
        "    # Convert from ragged to dense, padding with zeros.\n",
        "    en = en.to_tensor()\n",
        "    return pt, en"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Yb35sTJcZq9"
      },
      "source": [
        "## Set up a data pipeline with `tf.data`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dAroQ6xelzdx"
      },
      "source": [
        "In this step, you set up a `tf.data` pipeline that tokenizes that dataset using your `tokenize_pairs()` function with `Dataset.map`. Make sure to use buffered prefetching `Dataset.prefetch`, so you can yield data from disk without having I/O become blocking. These are two important methods you should use when loading data:\n",
        "\n",
        "- `Dataset.cache` keeps the dataset elements in memory after they're loaded off disk during the first epoch. This will ensure the dataset does not become a bottleneck while training your model. If your dataset is too large to fit into memory, you can also use this method to create a performant on-disk cache.\n",
        "- `Dataset.prefetch` overlaps data preprocessing and model execution while training.\n",
        "\n",
        "You can learn more about both methods, as well as how to cache data to disk in the *Prefetching* section of the [Better performance with the `tf.data` API](https://www.tensorflow.org/guide/data_performance.ipynb) guide.\n",
        "\n",
        "The `tf.data` input pipeline that tokenizes/processes, shuffles and batches the data looks as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "bcRp7VcQ5m6g"
      },
      "outputs": [],
      "source": [
        "BUFFER_SIZE = 20000\n",
        "BATCH_SIZE = 64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "BUN_jLBTwNxk"
      },
      "outputs": [],
      "source": [
        "def make_batches(ds):\n",
        "  return (\n",
        "      ds\n",
        "      .cache()\n",
        "      .shuffle(BUFFER_SIZE)\n",
        "      .batch(BATCH_SIZE)\n",
        "      .map(tokenize_pairs, num_parallel_calls=tf.data.AUTOTUNE)\n",
        "      .filter(filter_max_tokens)\n",
        "      .prefetch(buffer_size=tf.data.AUTOTUNE))\n",
        "\n",
        "# Create training and validation set batches.\n",
        "train_batches = make_batches(train_examples)\n",
        "val_batches = make_batches(val_examples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YS75Y-9-lkzn"
      },
      "source": [
        "## Embedding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "279u2DiDlmdS"
      },
      "source": [
        "Now that your sequences of text are converted into sequences of tokens, both the input language tokens (Portuguese) and target language tokens (English) have to go through embedding layers. Token embeddings learn to represent each element as a vector/tensor. Embeddings represent tokens in a d-dimensional space where tokens with similar meaning will be closer to each other.\n",
        "\n",
        "Converting tokens into embedding tensors is done with the built-in `tf.keras.layers.Embedding` layer, which is shown in the encoder/decoder sections of this tutorial.\n",
        "\n",
        "Next, the input embeddings need to be added with the positional encoding, which is covered in the next section."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBQuibYA4n0n"
      },
      "source": [
        "## Positional encoding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4gcCNZP7lzdy"
      },
      "source": [
        "Positional encodings are added to the embeddings to give the model some information about the relative position of the tokens in the sentence. Now the model can _learn_ to recognize the word order.\n",
        "\n",
        "This section shows how to implement positional encoding.\n",
        "\n",
        "Attention layers see their input as a set of vectors, with no sequential order. As discussed earlier in the tutorial, the model doesn't contain any recurrent or convolutional layers.\n",
        "\n",
        "Without positional information the order of elements would be unknown. This would be similar to a [bag of words](https://developers.google.com/machine-learning/glossary#bag-of-words){:.external}, where, for instance, `how are you`, `how you are`, `you how are`, and so on, are represented identically.\n",
        "\n",
        "The embeddings on their own do not encode the relative position of tokens in a sentence. Therefore, after adding the positional encoding, tokens are closer to each other based on the similarity of their meaning and their position in the sentence, in the d-dimensional space.\n",
        "\n",
        "The formula for calculating the positional encoding (implemented in Python below) is as follows:\n",
        "\n",
        "$$\\Large{PE_{(pos, 2i)} = \\sin(pos / 10000^{2i / d_{model}})} $$\n",
        "$$\\Large{PE_{(pos, 2i+1)} = \\cos(pos / 10000^{2i / d_{model}})} $$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "WhIOZjMNKujn"
      },
      "outputs": [],
      "source": [
        "def get_angles(pos, i, d_model):\n",
        "  angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))\n",
        "  return pos * angle_rates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "1Rz82wEs5biZ"
      },
      "outputs": [],
      "source": [
        "def positional_encoding(position, d_model):\n",
        "  angle_rads = get_angles(np.arange(position)[:, np.newaxis],\n",
        "                          np.arange(d_model)[np.newaxis, :],\n",
        "                          d_model)\n",
        "\n",
        "  # Apply the sine function to even indices in the array; 2i\n",
        "  angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n",
        "\n",
        "  # Apply the cosine function to odd indices in the array; 2i+1\n",
        "  angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n",
        "\n",
        "  pos_encoding = angle_rads[np.newaxis, ...]\n",
        "\n",
        "  return tf.cast(pos_encoding, dtype=tf.float32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ra1IcbzFhnmF"
      },
      "source": [
        "Test the positional encoding function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "AKf4Ky2dhg0L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "de591b9b-97c0-4e55-87b0-f23e2308e552"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 2048, 512)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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dCuihlw62ToA+D+Z5kANTUD+YN71+XBoOh3he0R+VN1mPZ9a5cLuodxbiOP5F4Bdn8v6nmfU/O+e4Xwc+fSGFyNl3hDM4cQ9gTm/gKCdwWOz+OAeQh/9s/mngL1T9/O2yIk2dS5kD7gnUJ8DXlcl+aS8gD/k0recUfaruJzBPHcIk5j4P9pPwz0TFHwX6LC/ZR2ZayQuFLoCeqfgsTJPPz+2bB3eq3gNfxNIDf1q555dJuCVO8jJ17gVzFXse/hPYh5nSP0ypR154AOqhFx6r0I8D+lEKfR7w8/vMpo8D+bMAcUUCLuCc+XG3j5t97J3BiXoAMz2BeQ7gsB7AYfCfB/7TQF9R5Sm1L6vy9LY5Kl+dgbaawVuegvgs6PUZ6GuyPAF44lw0WZpAX54GfLr/rJJXcuBWpINAT9dTsEsJnCehlwDJyyv6YLItnKwT+lNAjwNvAuoc4KdgnuSncfPIDzKQp4BOoX1wXRwT5gHvhXMBHvpRpsjz++ThHfrhicF9HLQPA3ke2KeF9ZMC9exvBqXvdQDo8nReuu+8dTl3fD5fkWb2719MuS/qDeRnzT7WzuAwR3BUSOigkj/aCZzVAQjYHw9/WZWzEI+iJvsmPYI8xNUM7MoB6OuHgH8W+qoiT6l6LVnPK/p0/xT2qZqfDAznGqKcm6WTg72AeTAZTE3zoiAD/QT6AvSx702HZZK8zAmksM9BPq/W0zBM5AfZYGge8rOAF0CfhF1CL8rUearM4wTkGfi96IAqD73oxGA/Wq1PoH6S2Hl++1H5Z7XDQJ7Pnwfu00B8ennUtqQdKUmPWFeQFQlJlsVSkVB0Bb5y/u8Nl87gI2+HOYJ5vYHZUNC8MNBRTkDW9AMOIB/SSdP5cM4s/LN8Rc5UfwryCeDFUlfkGYcw4wAyqMvHQj8D/iFwn5+XxOYzqIciHOMHYjkH9ul6HuJx4B0E/YyajwNPqPgU5J6frechn1fxaXxd7BNmsfJUrUcz6/nt85R7GESnhvtJwH6aMEs+/6w2D9z5/DzM56Vlzg5yPQsRTmCdwlzRRFpSZBRNFm1AF0sp2Z5Cf7JdQdFUJEVG1lRkXUOWZWRdRU7y+Uo2IefsJk1eJPy42cfWGRwZDsqBfdYJzBsLSB2Aouongn8K8HmAT3sEiiJnIR5FPQh9XZUT6CsZ0POOIP1YuugJmIp8APZ5yJv5nkAO6lqalnJv8OYgr0rMKHpPhG7ygA+83PZEyY9dEaZJ4B7l4e65B8CeqnexTEI2c6AutoWZIo+8cArokRcdVPTRRNnnlboXHYS0Fx0OcRDpFOJHKfOTxMlPaodBOx9KOUp9H6Wo87DW5el8TZYPqO0U1IquiHqsKxnIFU1B0eWpbbKmomRAFsBOAZ3mi7SWQFxFVjVQNSTDQlI1JFUDVUfSNCRVT9Yn25FVYlklzqWRFbFU9Wyd/+FvnO0B5ExiMhPv42YfS2dwGkcwGxI6LMwz6whSByCr+gH1ryjyAQeQbVPlqbyj4D/Jm4Z/CnVNFttT8JvqtMI3kmmcqpJv4NIB6GtyEsYJvYmqH3tIUYAU+hNFH/gT4OeBnqQz0OfCOlHgE7reobAPXDEYG7peBv7QC0WsPQF9GnePvIjQDycqPucQ8oo9D/lJ+milngf8WcIwJ7Xjwijp+mz6OKjr8kHVnQf6LLBT1Z3l68oUzFPoT0Fb11A0FcXUDwe5biJpCbx1U8Bc00HVkHVTQFwT2+ZCXNGIFRUUAfFY0YhkhTCKCZJn6UcxYeK0g1xa5Me44wg/jPGjiHEQ4UcefnhBbyBLXA4gfxTtJI7gNE5AVnVxbJI3UfaHw17OtoGqiWOm4S8cgKUpU3mWrmT7pao/Bb+hKmhyovZz4BezgyZqP7+uzir90J9AP/Qnqj+cOAABfAH4KFH7U8BP1H/kBwLmuZBN4HiZso88P4N9qupDLyTMwT0Ffab2p5S/UPMC7LOwnw93L5oOxRw2wJqmUzst4E8SIz8O7LOKPA/0dFse6oouI8nyFLjlXDqFvWaqSIqEauqJGtemlHmar2gaSrqPqiEZ5jTMDVMo8gT0km4m0NZBUYgTcKNoxKouwJ5sD5EySE/DPCbIQdwLYnw/hXeMG4T4oc84tKfA7oURjhfiBRFeEDFK02GU5IWMg8k+YZAIiYt6A5mLm1r6rNnHzhkcNWh8EicglP4E/nkHoKjqlPJPQz3z4K/qE+Vv6UoO8iq6IlPQlQz46dJUZAF6RcJIlb4iZ+kU+FoC+TQtlmJdlcgpfA/J8xLwe5NwTpgAfeyKpeeKME6STh1A6HqJeh8TeULFT6A+DXjfDaaUfJhX9ZkDSPJzCj4F+3w1P1n6R8yUyafT9ZParCpPFXa6bWqQkvkQn15OA1zR03i2ciS8FV1BNSfhE8U0MhUu6wm4NRXVMsQ+hjENbd1EMkyhzNN8zRCgVrUppY2qi3xFI5IU/CjOPmEEQZIOopzaDgSM06U/TtbDPKTHeIGD4yVA9gWQHS+YC+jAF+kwjIiSZRjEufQE5FHgiToahUSBWIbJMo7CbFv2gl6an74TckEmId7a/zjax84ZpDZvoPgwR5CP+SupA0jy870AVROxUKHwp51CmicrMnoCeiOFvypjacoBB2CqSgb5PPA1WaagKehqAhflIPQ1RazLoS9g77lIgVD5hB5SIBxA7I+Jx44A/owDyEM/dD0B/UTVp6AP3TGB62VAD5wgg3zgBAdUfqrk/SiaAv009KfT89T7UQOrJ7HDQi9HAX4W5vmlnky5TeEuKxKqqU7BXUvWZV1BM1VkXZlA3DSmwK6ahlDlpp4BPA/1Sdo6CPVUgadpRceLIYxivDDGnwN0L4gZh2EO6g7j0M4A7wZhBmvHC4XizlS4WHe8kCCICPyQMIgJQ5E+CuRhEAh4hwLisyA/DuLpr42m+Wn6tByYFYlntssw0UfD5oWF5jkCWdWmHEEaCkp7AqkTyKv9FPaqpiTKPwGCpmTwL+iKCPnoCmVDOIGSqc4Ff0GbrBc0BV2RppS+rkycgByHSMFYqPuxOwG+P0bynUzlR65NlIf92CV0RhnoQ9fLQJ+B3/UIHJ/ADQi9KFmGM8uJok/BPgv4fAjnMLgfNsh6lOWhPjtgmoJbZp5SF/unIJ+FuaIpqJaKoslimQO4ZqoopoZq6ciahlY0RZxcU9GKlkib+gTWuolsFScAt4oC4KpBnKpw1RCKXBVg92PwwxgnignCBNhRTBCCH0WMfAHukR8xHoWZCh/5IY4X4HjjDNpOkpcCe+yFAtReNAXsVIkLmE/DOvKnQZ0H+FGgPgukD0zTnmmjsqqjyPKUKMsLtXxvXdW1+T1yTUnarZwJuV9+/W+fqHzHlB75Av7c5lm0j5UzyNtxjmBeBUudQL6C5Z1AGvNXNZFvJE6gbGoC/Iaaqf+SqWYOwErAX9AUCpqMoYh14QAQg8WKUP26IifwdwX4/cQJ+C5SMBZK37GFondsYlekY8cmHAvg+7aThXTEcpw4AR/fDQicYAr2qcIXqn+i6iefo4Gf5sPplPxx6n0W9LNKfQr0xrQiF0uh1jPYF3VUU0cxjWQp1vOAz6BuFkXYJVlKZlFAXTOI1SRmrpnEqkGkGowTVZ5+/Gw9wh1HjIYC6CPfZhwMBOzDiKEb4CQQH3khw3EwBfbAD6egHiSD5UEK/BTsOaBHgTcX6FH6nsYMzGFaeR/XrvLtKwPzKduYoqo5YB8UWrNtTLQrFUtTKCcCy9IVirp6oI3lxZaqkAgBiWt/4vQcOfD9L3sGHx2b1x2cV0lFhZzjCDQFVVcylZFWUM1Qs3XhANQDlbNkivWCpkxVzpKewl/OFL+uJCBTJFQiAX8vgX8wWWagd21ixyZyR2LpuQS2OwV+33azsE6q9j3bF9BPHEDWA3CCDPpOeLjS9w9R+KcB/izs8wOmmnQ44PVkymyq5lOwq8lyaj2BeQp2rWiimgZq0USxChnQJ7AvIFtFYs3KAB+rBiSAD2VNDEqmgM/BfjROwR4y8m1GfgL3IMzgPnADHD9k6PoTxT4OCIMIfxxmEA/8vHIPM6Uejp25UI+y3zc6u0I/TJlPesgnayep4k7bRfbJtZOCrlAytSPbSSqSzKyXfFQ7mW4fkj8Q4VBnQGRPhFHkjojHDpE7IrBdAnd8WpQccf8uncGZLPmvz9eBzTiOf2/yk60/BzSBrwF/OI5jT5IkA/h7wOeBFvAH4zh+cOLrzI4RyApKfv6/qmWVOa3cim4hazqqbkypFN1QMqegJemSqWYOoGSIdMkU6bRSl5NxgrKuYmqiQotB4UmF1uIgAf9oUrE9h3icQH40mCh+xyYYOQL4IxffFp8gcQCe7WeQ94ZeBvtU4bthCvtoSt17UQr5+YO1cDTk84BPQzHZVEZpPtSttFFn4RkFvaRNgV0raqhWGpYx0AomWtFEK1oTqFtF5EJZLM0icrE8AbpWmIBds/BjcIOJQh+nMPdDhl4oYO4INW77IUN3xNDtT4F84AaMvRBvHGTg9scBgZco9QTqoedkyjzwnJnQS6Lc58D7pOCe7enKqo46A21Vt0S9NqwDwNYMJVHck/ptJKKmZGpZnbZ0hWpBSwA9+ZR0JQtnGoqUAdtUJbQ8pD0ngzVjm9jpEaVCZjQg7tqEzigTMr7t4o8mddq3PcZOwNAN8IcHe7KeH06JFyec1ONJvZ4emxL1+WJmE0nJdOyPo30YPYP/BngXqCTr/yvwl+I4/jlJkv4W8MeAv5ksO3EcPy9J0o8k+/3B01xottGkebOOQMk1GlXXphSNZqhTDaZc0DJ1U7M04QBMlUriBFJnkDYWQ512AKYqo4Rj0VicEZLvCFXj2UT2QCh+uy8aSg7+3mCmwdjjTOnPNpLACXDDKIH+tNJPQzp5tX8S6M+bHpnNkpHmg95S5EzJa0V9AvmShl4U4NeKxhTg07RWKiIVK8hmCvwKklUEo0isWwL4ekGodsXATcYvxkEsnF4YY7shw37IyPcYjkeM/JD+WIBl6AYMxgG9kSfCMW6APw7wEpU+C/jA8yfq3HOEQs/CMP5kEPQUcJ96TyUJrwhhok1i5YY1ESuGhaJbGdRF3RRhFN1Qp4RKraBj6cpUHS1ndVOloE1ESlo/TSV5iVGRkH0XyR8l41AjZM8h9hxRNzuirsajPpFjE4xcvL6Nb7s4tos/Suuohzf054oTfxweWj8Pg3ge5ofV0Ykoma6nxeRXcS1FTuop0DkNTY647qUzOL1JknQV+D3A/wL8t5KYoPs7mPyJw98F/izCGfxwkgb4BeCvSZIkzfyO94ktVU/prKG8I0iVk2boB9SSZqgYloqhK9QKWqaaagWNakGjmDiAkq5S1kVDszQZU02ngwrlpEWecAAje9K43CGR3SdMG5Y9IHRGeH17LvzHfS9rWL49cQCpOnLC6EDD8g9R+yeB/uwAbAr9CewnwFctVQDeEopeT+CvF4Wy18tF1KKJXilMw75QRi5WRJimVBOgV00io0isFYg0MxksnYB+HMQMRwG9dsDItxl6fQaeAHxv5NN1/EzFD0Y+gR/ijQXg/bGIs4u8IFPwoecIBZ8DfKre8zNc4PCwyyzcFU2fEh9TYE/qnaprBwSHbol1y1SzOpdC/aR1zlJllMhH8myhznNAjwYdAXPXTurfkMB28QY2/f4oq3PewBX1y/YzmKe9z3yd8yIysJ8F5IfVuRTiWS9yTp1TTTXrUepFDa2kz61zermIWrAmAqNY4b/4Lb//LCiZfuZIl87gjPaXgf8eKCfrTaAbx3GQrG8g/hgacn8QnfzxQy/Zfz9/QkmSfhz4cQC0osibM400nT46CQVZWYPUTDMDv25MHIBmiMZXK+iZA2iUdMpJQ6waKmVDzBSyVBlTkbA0WTTEcIzkDZBsB3k8RBoPiewB0bBLaPeJ7D5Bv483sPH6I7zBCL9vJ9AfZw0whb4/9LGDtAHGBxpfPsZ/XMPLz6xJG6ApT9S8aHAirRlioFUvaRnkjYqOVjTQK0X0ckE0tkoRrSLUu1yqoZRrSMUKmGVi3SLSi8RGiUgzcfyIYRAzDiMcPxYg90J6I59hN2TgBfRGQ1rDNsNEvXdHPuNxwNgJMqh744DAC/HHIqaeAj1V8PJqqyoAACAASURBVCnQw6npivPVej5mnge5ppsolYUz1ZmqpVE21VPWGRt5bIuw4XhINNglGg2I7D5Rv0+wcXidGeTqTArti6wzs9Nt83WmoStZndELWgbpfJ0xKhZq0TpZnTGKRJpFbJQIVRM3iHCCGCcQYiCrM67P0BN1pj0U9eRAnRkG+K2DdeYiTJJAv/w5itOZJEm/F9iN4/hrkiR930WdN/lD6S8CyIWFrDofNkUt7YrnG7Vo0KKbbVgamqFQsLRE/es0izrVgkbd0qiYGmVdoW5plHUVQ5UoajJW0s2WPRupPxBKbGwL+A+7RINu5gDG3UHWmMfdgYiL9oTy92wvC/24YcQwiKZU/3QjProhz3sZSpMOAr+kTkPfqBjoxWRZsdDLRYx6Cb1cRK+VkMt1oejLNeRyfdJ4jSKxXsSTdRxfqPmRL+att7s+Pddh6A3pjf2s4fYcn9ZwzNANGDs+nhPgjYNsGfghgTM8APrQc7OwzGS64+GQTwc8FS0d/NQSZZ4HfAHNUDPA65aGYakUMqgb1CzRI6wX9KweVE01C71YqlDmBU1G9kZCCHg28rhNPOoRtpO6MOhkYmDcH9HvDglsB7c7whv6mSBIQy2OF2Z1IA92d85b2EfVhVnVXdem60NJldE1RTz7BOZmxcCoGGhFA6NWRi2aGLUSRr2MUiwjl0Q9kMs1sCqiLiTO31cMxmGM7Uc4QUTPj+i5AQMvpO/69MYBPcenPfBobXsMXZ/WcMjY6eCPQ8aOj5/06tKeXOAMs3EYUR8mTv+oupDngqzqZ8HNwfNJ4j2fj6M9yZ7B9wI/JEnS7wZMxJjBXwFqkiSpSe8g/yfQ6R9Eb0iSpAJVxEDyqS3rqmuzXXQjcwS6pWKYovGXCxrNkkHV0miWdBZKxlTDLxsqRU2eNH4CpPFAKLqxDSPR4L1ei3jUx+90Mgcw7g4Yd4d4to/bcUWjH4pG77pBBv9hMGns0/P4Tw5+Uxbx0ZKaa+yGGKA1KgZGRUcv6lh1cwr6Rq2MXp3AXq40kEs1AXujTGRWCFWTvi/mvTtBxGAc0hsG9FybjtOl4/j0Rj4t26M9HNMd+YxsL2vgKez9sXcA9qHnTin6tIHPe67Z5IAkBKPo5iTsZ5ZQrRKqbqBbWubwzYKOZihUS/rUc24UdaqGSsXUaFgHn7MhRcLZuwNktwPugKjXItroEA27BP0ebquH2x/R6w6Es+8L1Z46+9nnnIL9tM85D3VTllnQp4FeVBW0xLGnzzlV50atnDl3o1GdPOdSDbnSOPCc7dxz3vMiemNfPN9xQNcRjr217tEbebSGDq7TZ+wET+Q5T8K7pnjGxSqaWTrgzA1LxbA0dFOlWtKpFcTzbZaEsKsaGv/Z5//qWXAyXTYuxwxObXEc/xngzwAkPYP/Lo7j/1SSpH8M/H7EjKIfA/5ZcsiXkvXfSLb/69OMF0y/QaxlsysU3RIVyNAxEuWnWxpmQcvg0CzqLFYM6pboDSwU9ET9KZT0xAHEHrLTQRrayE6PqNci7LWI+q0M/m5LKD9nf5CA38vgkAJhGAgYDINoSvWnSm/WxCCYNAWCicKXqGoKekFLGr5QdFbdxKgXMWplzGZFqLtaA7nSRKk2BQCsKpFZJjbKOLFCxxeq3vZD2o7Pvu3Tb/ns22N2+xu0bY/WcMwgme3h2h6eEzB2fXzXJXCG+O4wC9+Ennukcptt8KpuoRUqmXpXE7CLZ6ahWypmQaSbJZ3FsimWFYOGKXpwCwWNSg7oRU1G9YZITg95PAS7Q9jbFEDfbzF+r4ebOGu31WPcH7OXOGwBdA87CKee20kgnnfUk+c2eWYrpkZRVTCqk2dm1k3MmoVRL2PUSljNqnDQ1SZyVTw3CjUiq0pslgmNEkMvSp5ZRNf16bkBjx2fvZEngD30aNtjWkOPke3h7vp4D4NEfQd49jaBe28K1uk4ymEhttkxEkU3cz0t8cw006RYKaAvVzAsDbOoUbA0liomS2WDRklnoagnYks8t2IyvbSoSVhSKMSW00N2B0T9tmhrwy5hr4Xb2mTcGeK2e4w7NuP+GCd7bh6O7U2JrF4Ys39Bs4mQLscMLtL+FPBzkiT9eeAbwN9J8v8O8H9JknQPaCP+CPpElv8BuuxNYk2fmjGUqkTd0iiUdGolfapyLhb0zBFUTIWiKgtHgI/kDJDdHvJIVEavs0fU2cXrDRjtdgRIukPcjptVSrfj4roBPX869OMeA5K80rcUeQoipeTFNqtuohU1CguFDPxmo4rZrGA2qwL41SZKfQmKdSKzQlSo4ykGth8x9CKGXki77bM/GtAaeewNxuz2x7RtsXRtH3c0Ab8/9vDtXgb8wLWP7apnP+WRhWfMKWjohSJmQcewVMyijlnQsufSLOqs1EwapnbguZQNBTP2EsWePJfOHuF6i6izy7jdw2n12Ekgnz6XtGc2+1zsMDoW8LPPpaLKc59LCnajomMtlDEbVYx6CWuxjlJfQi7XJs/FqhJZtey5DMYRrXFIx/XZH3n03ICdvsvu4zHtu2N2+0Ncu4M78nBtP4H62Z/LZFxEPBervpLBPP9cdFOlUTGy9rJYMVgo6NTN5LkYAuQlXc6ei+J0wO4K0dTZJRp0Ge+3ce73Muc72ncY98e0Oy5bF/RczKRnXFLFuMa1qoGW9JL+5y+/f1KkHM4aQJEvZsxAkqQfRERLFOBn4jj+6ZntfwT435hET/5aHMc/k2z7MeB/TPL/fBzHf/e85flQnEEcx78C/EqSvg98z5x9XOA/Oes1JDkdK5CzHoKiT7qSelKxCyWdhYrBYtlkqWKwXDFZKIhK3ShoVJLeQFGVkJ0O8qiD7A4IW9t4rS3CXgtnr4Oz22XcHeC0hlmldjou44FHzw/p+ZMKnR/Im6ci9dzAXAr/qiY+pqli1k0KCwWMik5xqYzZFOC3luqo9UUB/uYqcakhuvuFOqNYoe8J8Hcdn509h9aox77tsdV12e45tIYeztDDtRO4uD7eyJ6CS+i5BEncfhYseZWoGhayqiUwKaGaJYxyBc1QKZT0DPYLFYOVqiXufdmgWdDFvbc0yoZMSZMpyqFQ824PadgWMLm/S9DZw2316O922W31GLVs3I7LaH+UKcIUJsNAON7T3PtKApGqJpZWUcesm1h1k8KChVkrYDarWEs1zGYVtb6I0lxFrjaJSw2iQp3IrDIKJYYJ3DdGHm3HpzXy2BmM2d0ds33XYb/fxx21GA3GuCMfz/Hx7AH+qEeQxMmP6l3N3vsU6IXmFbRi9YCjtUo6S0m9X62ZLBR1lkoGCwWNmqlR0RPxI4Wizo864PQJW1tEvRb+/g7Ogw7j7pDRbgenZeN0XPodl92c8BH1PpoD8qPvfTqLqKrJXC9oGGURzkzvv9UsZfW+sLyQiR2luUJkijofWXXsIKY/Dhn6Ee2Rz47t0XF89odj+J0vnRUv02W/gJ5B8v7VXwe+HzGR5quSJH0pjuN3Znb9+TiOf2Lm2Abwk8BriH92/lpy7Lkmz37k30DOzySSc/OzVbOEXixjFnTRTa0YlMsGqzWL1arJSs1kqWiwWjJoWBpVI1GcoYNsd1BGHcLWY4LWNmFnl9F2C3u7jdvqYe/YjPZHuF0Xpzum7YUZgOzcdM9ZJZOGfOYpmFJJqJfCQoHCgkVhqYK1WKewVMdYEtBRmitQbhIWm0RWnb4X0RtH9MchW8Mxu9tjdgdjNtofsDdw2e26jPoCNgL4I3y7hzfqZbDJgz5v6b1UDQuzuoBiWOiFKnq5gWFpWCWdQtnAKums1kyu1gus1EyWS0I1Lpd0qoYi4C75yHYb2ekRd7YJ9jaJ9ncZvb2Ps9sV93R3wPrOKFPw/cSh9vwwg3r6gtHsPZ2FSkmVuVXUMrVuLVgUFgoUl8q5e7og7ml9Eam2TFhsEltVhpFCbxwy9CK2BmMejzy2h2M22g7bPYedrsto6OFsJ70n28W33517T6PAm3tPFU1HNYtJ76iKVqxiWAbla0tYpWsUSjrLNZPFssnVhsVK2WSpmLunukJZjVHsloD2YJ9gd5Ows8t4dwN7+5vinj4aiHracRntO2LWTQLsx2HE3UMc5TyBUtUUqppMRVMScWJh1U0azzcpLNWxlmoUlhdQmisozVWk2hJRUThIBy2pqyF7tseu7bEzHLPZdtgZjNnqOgz7Y1zbY5SIk7Se+naPcMsleJDW0z5x1AHuZu0/7xDz91S3LAplA7OgYZUvZgBZlqSLmk30PcC9RBwjSdLPIabXzzqDefa7gF+K47idHPtLwA8C//A8BfrIOwPITyeVJ11fwxKDS6YiegRFEX5YrZpcaxRYKuqslg2aiSOo6DKy00EZ7BF3d/Bb24R7m9iPd3F2u9jbLeEEWo5oWMNJD6AfHK7+8y++FHOKv66rQm3WTYrLRUqrVcxGldLVRfRmE2VxDXX5ulA8xSaOWmQwDumMQ3ZaHrvDFpt9dwKpjoOdhHec4ZjxoI8/6uHZPQLXPhJQYtDVQjWLaFZJAL9YwizqFCsGxYrBcgL8tYbFckk40eWSmGFTNWT0cQ951EHqPSLc3ibY3WC8u89ot0Nru8Vwq89o32HUGjHsuBmU8rA/DEop5K+YCiVVEs6zbmLWTErLRQpLJQpLdQorDazVZZTmKurSmnCcpUUCs0bfC+m6Ifdtn117zNZwzKP9EVsbDo/bfez+Hu7Iwxl4OIOBgFF675KY+qw6zztM1SxilBtJj6iGWdApVMW9q5UNrtYL3FgosFQyWCkZXKkYVHSFmpkIkFEHZbhHsLcpeqE7W4zudBhttxhu9Rjtj2jvO2zsO/T9MBMg9sy7Jvl7p0iTGWT5une9IepdaalAcbmItVSnuNKgsCJ6mOriGlJjlahQJyw2GQQSvXHI/shn1/Z4fzDmcddhq+ey0R7R67q4ex72++PEOT5iPHzjSMGRh3jeMerlBmZRp7G8RqFym1rVZLVmcbVucaVqslI2WS0ZVE2FuqlQkkNku4Vst4nbWwQtITbszXcY7XZxHncZ7ooe5G+7IN7k/23uGFuQJOn13PoXk9mQkJtKn9gG8IU55/iPJUn6D4D3gD8Zx/H6IceuzTn2VPaxcQbpyz2qbqFaJcxiAaukU6yY1OsmN5pFrjYsrtcs1iomi0WdhqVQ00CxWyitXcLWY7yddcaPN7G3Wgw39xhuDbB3bewdm47t0/Mj2l44Ff7JA0zM5pEyhdrQFRq6QqFhUVwuUF4tUVxtUFpboHhlSUD/yi3iyhJRsclIKbDthrSdkEc9h92Wx4P9XR62bLbaDsOey6g3ZjQc4w3ajIftZPDWzqZh5u9L2uCMch2j3EArVDErVYpVA6tkUG9YXG0UuNEscLVmsVIyuFoxaVgKVUNBtfdRhvtC0W+9zviOuDf25h7DrT57Ozb27ojeYDxzbwTgZ+9NOtupqMg0dJnrBYNS3aS4VKR8pURhqUJpbZHi2iLa8jXUletQXSIsL+NoZXrjkLYTsNEf83Dg8qA1Evem6zJoO9gPxokzfJ/x8KtJyMue+lXO2XujmsUktFLFrNaprzSxSquUamZ2b240C6yWDNYqJg1ToWoqGOMeSn+HuLtDsLOOv73OaPseg0c72BsDhl8ZMty1GfXH7I2FcPggjHgrEQ/zRIOlSMm9EfWmXDUoLRUpLFgsfmKZ4toixZUm+upV1OXrSPUVwsoSnlmnN45ouwEbPZdd2+Nhe8RGx+GNfZt+0ku0+y5ur5Op7uB9m+AdhygYEkc94D5A1sPWElAbpQZmtYlVEg6uVDNZrpl814061z5XYLVscL1qUjdVaqaCFdjIgx2k/h7B4w8IW9sM17cZbu5hb3cZPB5i79o43TF744C2F2VtKnVu+XvTkSVcRWZflVnXFBq6TLVsUFwqUFgoULlapbDSpHR1EWN1jebLn2ehsUpYFu2q70VQK52fNaf7OYr9OI5fO8fl/h/gH8ZxPJYk6Y8jXtL9Hec435H2sXAGwGQaWjqDyFCzmQyrNYurDYu1qnAEV8oGNVOhIvso3W2k/i7+4w8Idh4xXN9h8GiH4VaPweMhw60h/ZGfNWY7mQk0W1nT+fsp5BYNlULDonylROVqmeJKk/L1Zcxr11AX15CXbxJWlgisBjtOwJ4dsPV4zHpvl0etEff3hjzes5MGPGbU6wv4D9pTSj8PN0W30ItVtEIFvVjFqC5SrBQoVA1KVZMbSyWu1i1uLxa5XrVYLRk0C4lDHO6h9DcJdh4RvPkAe2OLnc19hpst+usDRq0Rvbab3IdwqjeUQi2v5Bd0hYau09AVqg2T0mqJ0nKR0lqT8vUlCqvLqFduoq7eJiwtEJaXabkRXTfkXt9lc+Byf8/m/teHbLU7DHtbDLsuzsBh3NvL7oPvDI+EvFldpHr9E1jlIsWKQblhsVy3uL1Y4uZCkbWKyfWqAHzDUlHtfdT+NuHeBsHWW7hbOwze2mG4uc9ga8ju4yHvt0a0vZC2F9EPwgOOL1XlaX2oajKLhsL1hkVxqUD5Sony1TqltUVK15ZRV2+hrlwnql0hKi/RDxVaTsDO0ONRz+U3WzYbHYf7u0Ph8N4eY/+Gy7j3Ad7oDXy7j59M45ztuaRA18sNAfRKlcWbVyhWbrPQFPfhxkKB61WL61WLhiXug+X1UIZ7xLuPCPY2GW8+YvDoq9hbLQbv9BhsDbF3RuyPA9peyNeDiH87x8mlbxGL+6CwaChUTZXicpGFl5oUlkq8dn2Z8vVltJVraFduEjeuEpYWsSWTlhOyNxL34XHf5e7OkIf7Nr2uy7DrMuw6jAddxo/beN/uEHg24fibRMFXp+7DRb1nABc2tTSdSp9afpo9AHEc56fW/wzwF3PHft/Msb9y3gJ95J3BVIgoqfx6Mu84HTRbrZqJIzBYKes0LYVi7KJ2Nol3H+HvPMJ5+JDh5h69D7bpb4jeQLvlZE4gbfR5J5Cq3Iqaq+gJ+Go3qhRXmlRurWJev4m6fB2WbhBWVumjszcK2GiNedTb5f6ezd2dATsdh97+iFF/zKjbxe3vZco2cIYHgKcXq+ilOnqxillfoVgxKdVMqg2LGwtFXl4tc6MmGvlSUWOxMIFdsHmPYPsRw4ebbDzYYrjVE997x2Z/6LE3FsAfzjTwFPglVeaapVHVZJZNjdKVEuXVEpWrVUrXl6ncXEW7+jzq8jXC2hpBZYW2E7I38nmj47A5cLm7PeTuWwN63fv022+Lhp2A3h/154Je0a0klFWn0FzDqjUo1cT3XmoWeHG5zM2FIjdrFlcrJs2CQl0JUAa7SJ1N/I17+Nvr9F/fov9gSwB+fcD7rRF7YxF+SR2dE04/61nV/ulli/JqidKVEpXrC1RurlC4egV17TnkxeuE1RVGWoW2G7I99HjQcXij63B3Z8j97QHDnsvwKy52p4c3+Drjwb/CG/UP9PCyel2oYJQb6OUGxXqVhbXnMif/wnKJW40CN+sWKyWdhqlQCIYogx2i7Q8INt9ntPE2g/Vdend3GT4eMtga0u6LMa+3vJAvz1HlaZipkvRyFw2FcrNA5VqZlc8u87mbq1RurQqQX32OuL5GWF6iE6q0RiEbfZcHXYcH+zbf3Bmw2xolEHdxum3GO228ex18p03obRJHvzZVxzWrhJZ8b6O6SKlmUWlYVGsmr720yAsrJdbKJrfqFosFjYaloPa3Ubqbore2cY/h+g7DzV2e//IF8ObiXjr7KvBC8sOdm4jZkz+a30GSpNU4jreS1R9C/MYbwL8A/oIkSfVk/QdIpvGfxz7yzgAmv0Ok6hZ6oYhV1kUXf6HAC8tlXlwq8UKjyGJRZclSULsbAgwP3sW+f5/B+i7de8IJ9NcH7Lg+bS+i44dTvYB0ALiqKdQT+DfrJrUbVSpXy1SfW6N8fRnj1suoa88T1K4w0ms8HAVs9sfc3xzxzusb3N8bsrNr098fYfdHjFqbeIP2AejnIVBevolZX6FUK1JdKLC4UOCV1Qovr5S5VhXqdrmoYoxaqN0N/I238T64S+/9TQaPdti83+XdjQHt/phtN5j6binzUsin3+2lmknlapnazSrl64vUnlvDvPkc2tXniJo38Cur7DsBjwced1sjPmjZ3Nnqs7ln03trxOBXHZzOm7i9X56COxwEu1VfyQDXWHyV20slXl4t82KzmDgzlWo8QulvET1+H//Rewwfvknv/U06X2vTXx/Q3R+x7QbseCF35zgx4bgFyFdMhdpqicrVCrd+53NUn1ujeP0q2rUXYeU2YWWVdqix7wTca42417K5v2fz+uM+3T2bYdfFbrdw+3t4ux38h0PCX24h3pN8KwOZXhLhuUJzmXJdgOzWaoXf9ZlVbjYKvNAoslLSaJoyancDdgW47fv36T/YpvfBLt2HfYb3h+y4/pSTztfLjiLjqzJ7WvLdatP1svrcGuXPfJ7a73mRK/WrjNQSO6MAqeMy6Dp0twfc2erTaTv090cM2l3c3h7eoI1n96d6HbKqowwt9M0K5nARc1MIkepCxMpSmxeXfV5eKXO7XuC5hsVvuVZO6mUf/8EDvMcP6b2/Sbf7mH53QH9jwF5P9DrTepl3xOmLdRVV1Mvlsi4E180q1ZvLQnDdfA795suE1Sv4lVU25CY7xivcLf5WPliwuXtlAPz8uVkjcTEDyMlP7vwEAuwK8LNxHL8tSdJPAa/Hcfwl4L+WJOmHgAAx3f6PJMe2JUn6cwiHAvBT6WDyeUw64+/APRMmFxZi49N/cAKU2gqlhQaVusXSSolPrVV5YVk4gutVg6WiitZZh+338dffY/De+wImH7TpPezRbjlsuyH7XjBVIdMwUKqO1iw1A0njpRXqL17DvH4T/fYnCWtruKVltocB632Xb+/bvLnR4/7ukNbOkN7+CKfbxuls4w07BwApBtIqmNVFCs1Vyg2L2mKR51YrvLxa5qXFEs83CiwWVKphH7X9CH/9LuMPvk33vXV6D3bo3O/SXx+IF5DGIf3kxal50F8xFRarAvqNFxpUbq5Se+E62u1PIq/cIqhfZ99X2Rv53Nm3eXd7wN2dAQ8fD+jtjxh2bUatTca9/QOhihT2RnUhg321WWBlpcSLy2U+eaXCSwtFVks6ywUFtf0Adh/iP3yX4QeP6L63Tvtei/5Gn719Z8qJzXs2i4bKmqVSXilSv12jcr1J/cXrFF54CfXq80TNG9jWAnujkPsdh7ttmzuPB7y72aO7Z9NvO4zaezid7QPOK3XMRqmOWV3Eqi9RaRaoLRa5vVLm1WtVXmwWuVGzWCmqlMKheDYP3mH88B7d99bpvL9N72GP7qM+m06Q9EIOfzaLhsJKzaR6o0LtRpXai9eovngL7erzSGsvENavs+/JPB543Nm3ubc35FubPR5vi3DSoN3H6Wwz7u3j2b2pXlYaUkwdcblRo7ZUZHGhwKfWqnxipcztRoGrZZ0FS0HtPIKte/jr79F/7z7de5t0P+jQfdhjt+1kIO/5s+Jp8mxWTIX61QrVq2XqL61Sf/EaxrVb6Lc/SdC4jmPU2Rr63Gs7POiOeHO9x93HffqtEb3WCKezL77PsDPVe0rbTfpsCs1lqs0ClWaBF66IdnOrXuD3ffrK184Zw+fKi5+K/8u/+gsn2venfvCVc1/vw7SPhTMwynX0coNCc43aUplqs8BL12q8eq3KC40izzUsrpQ0tN4m0tZdvHtvMvhgnfa7D2nfFRV6MwmNdPxwSlGm0z+XDZVrBY3KtTKN5+vUX7xK7cVr6Lc/hXTtFYLaVXacmEe9Md/eH/LGek8orZ0h3T2bUWsLt7fHeNDJgDmJ81ew6iuY9RVqi0L5v3Ktxmev1XiuUeB23eRKURXK8fF7ePfepHv3Ed33Ntj/disD/7YbZCGOMJ5ujCumylpJp3K1TPOFBvVXrlO9fRX9+Vfhyov49WtsDX3We2Pe2Rvy5kaP9x73aSfldzvboiEOOhlU0vIbCVCs+hK1xSK1xSKvrFX57LUqLzSL3KyZLFsSaucR8ca38e5/S4D+zjqtux36mwM2nYCdcZBN0YVJ+VOndaViCAf8Qp3GKzcoP3cD/flXiVdewK+ssjnwudce8X5nxDcedrm/PaCzO6S7O0jKv4Nn9wg9B2Cq/IXmGlZ9gdpikaXVMi+vVvjM1Sq36wVu1QwW9RClu0G0fofx3bdE+d97TPtum+7WkE0nYN8T4yl5R5Uq2hVTYa1uZU638fINys/fRH/+VaKl53BLyzwe+txtOby3P+TN9R4fbPVp79j093tTUM+XP4WgVV+h0FiktlRkZbXMK6sVPr1W5YVmgWsVgwVljNpZJ3z0Lu69d3Ll79DesZPyHxRBE9GgstoU5V94aZH6Kzco3r6N/vyrhAu3cKwmmwOfb+/b3GvZfONhJxMM/f3OAacEEwdrVhKx0GhSXy6xslLileT+v7wgek4NHJTOBsH9N3Hu3aH7/iadb2/Rutuhuz/i0cg/4IxmHesfbd85N5zXXvxU/Mf/+j850b4/+QMvf6ScwUc7TCRJk9iiWcIsmhQrJmuLIl7+8kJJgLQgo+3cIXjwNqN332L/zXu077Vo322z3puETdJGoMsSDV04gOsFjdr1CoufWKD5yVuUX34R4+XPEyw+TxuL+x2Xt9aHvPHr93jnYYfu3ojOditTy57dy9SYapYwE5VcWVmhsVzixpUyr91q8NnVCjdrJmsWqO0HBO+/yejrb9G+84C9d3Z4906Lbdtj0zlY1qomyvrZmpmVtfHKDSqfeFmUtXmTllTmg67LnX2bf/CgwzsPO7S/ZtP7f9vYu/8Ib9g5UNYUkpWVFVZuvsKtq1/gC881eXmhxItNiytpWe9+g+E7b9G9+6vs/toWvYd9HrUdHrsBbx9S1jVLZeFWjZXPLvPKH3iN6qc+gf5SUlaKbPQ9vrnd52uPunxto8f+4wG93ba4r+/v473ZI47eQZK/FsHA9gAAIABJREFUPQX0yopwqM9fr/K7P3eFV1cqvNi0WC2qaLvvEbz/TZx7d9h/8x577+zQvd9l/c7BOhDIEvc0ma6usl8QZW2+0KD5qZtUP/UJ1v6972d56QX24iLr/TGtnQGPH3R4b7NHa2tIf7+Hvfdoqg5IsoLiWRiP6hSd65TWF2jekbh9dZ9XrwV87kqVTy8V+Z03SmjXh4SPthm9+yb7o3u01vdo3z+8vlY1mQVd5VZRo/lcnfrtGguv3hb19cXPEiy/xF7teR5wjW/q/z5vVXp8a6nD/q0Bg3Yfe+8RTmcnC1OmjjKtr+WlZZqr5ay+vrpSmbSt3buY3/pVVt99C+3Ne1y51+JTR7SttA5kbWv58Lb1mxs93ljv5tqWxKhVYdzz8GydWL8KVw5vWzdvNXgl6U2zXOHcdvnnNs+upT9BoRiWmD1U0FipWlxJZg0tFlTUzgPC9Tu4779L6+377L4lQikPbI/HyW8GpWoibVTXCyrL16ssfWJBAODVT6O99BrB4nM8cGXeeTziW9uP+fL7LdYfdunu2Qx2HuIm6jNVP1qxillZoLh0nfpKk4UrZT5zo8733Kzz6nKZaxUNq/OA6P4v4/zqt1j/xnvsvbND606bB7bHTvKiUL58V0ztyPJtujJvtBy+8bjHl3+lxebmm7S3BkeWr377M9RXmtSXi3zX7Sbfc7POJ5dK3KzqonyP3sF559+w+/fvsP/tPd6+0+afH1K+uqZwq6jx4nOLNF+os/S556i++mn0Fz6Lv/wSm67M3ZbDrz3u8Wt399neGtD+0oDBzr/D7fyTA+UzSnVKK7eorzS5/ZnrR5TvG+y+/i/Z/1d7tO60WR/5/DvX558n5csr3VtFjeXrVa5971W+6zO355bvG497/PrDDn//YZf21gB7b4PRLz7G+8ditkpemafla6yU+P7vvcHnrtX47GqZm1Uds/+Y+IM3cN75Bq1v3Wfnm9+k9W9E+R67Yrry+1HMg5nyLa6UWPr0Ioufuc3aD/42jFe+G3/5JXY8hXf3Rry+0eXrDzs8Wu9Nytd6LO7fWx7S2yMU/duY1RallftUFuosXq3wuVsNvnCrwR//LTdy5TMYvdWn9fYWu29tZfcvLV96/yxFxtMVWkUNeaXEKClf5RMvU/2B30fhD7+E4Sl090bsbXT5ZlK+zs6Q4c76pHyBJ9qtbGFuLVCKb1HZrrN4z+Nzt7az+/ejn17iP78REK/dY3z3LXadO+w+2qL9QYdHA29u+Rb06fvXeOU69U+9eDG8+Rj/n8FHO0xUXIyLn/8xzPoypeVbLKxVeOFWndduNfjutRovL1hUvTbSB19n9OZX2H/zHjtvPGbn2y0+sP0MZJD2BhSuWSrXk2lvy689R+O7XkV/+bvxVl7h4TDkWztDfvNBh6/c22d/c0B3a4fhzgdZ+AeESrHqyxSXrtO40uTazTrffavBF67X+eRigUXVQ914k/Fbv8H+G++x/Y2H7L29P7dyVzWFa5bGzZpB8+UmK5+7wcJrn8R45TXCtU+x6Ru813L4zUcdfu3uPrubfVqb+9i7idJzhwCZyis016ivXWH5epXP3KjzvbcbfGalzNWijL79Lt57X6f9+jfY/cZ99u+0ebg1YD2Jb+eBumyoPF/SaT5XZ+Xzayx9/mXMV74Lbn6WrrHA3bbDl9e7vP5B+/9n772Dq6zWv+9Pes9OLzttp/dCCCEhEAi9gyggHEVBlKJoBEURG0dseKwIiqKIgoogvZc0SC+k9152stN7T9b7x4Z9Ds953vfnjGfO855nzpq5Z5LZ99zruta6ru/3e62197opq+qgteGe+rwHBmJ8DHVN7Xubq1IkUgcs7Y3xdjIlwtWcQBsjXE110GuvZLwqj+676TRnlNJc2Iq8pouqvhHV/gEobboPAtaOEmyCrDH3dcZ4QggaHiEMmDhS0TFERkMXadUdFFS201zXRW9zA30tdQz1tKsUsfKbKxZI7FwxtTbE3dmUaR6W+FkZ4WWhh8mAAmrz6M9NozmjmOY8Bc3l7ZT3jtA6/GBMWdzbY3K0NrhnkxNmIRPQ8p7MiKUb1T1jZMm7yajtJKuyDUVtF91NTcqq4n8TU0ZSN0ytjbF3MiHC3YIJUgm+lvpYqfWi0VjMUF4yivRCWvIbVDFVNzBC18iYaunQTFsDqa4ypqz8rbD0s8cixBfdwKmMWrrRMKJDXnMf2Q1dJJS00NbYQ1tDK71NVQx2tf5TTBlaOyOxtsDaUUK4uwUT7CUE2hjhoDuGVks5Q/lJdOQW0Xy3EkVeC/Wt/f8UUxItDSy0NXAz1MbS2xwrf1usQ33Q9QtDSL1o1TKnvH2Q9IZOEkpaaGjoprWhm57G8geqr/sxZWDpgLGNVBVToTJTnp4s+9PLNvae/uKFb878oXt3znD/j1om+o8nA8NJGzC0liGR2mHjZEKktxVhTqYEWBtiNdICVdl0pyciTyxAkdtCpbyHuoER2oeVyaGnoaYqr6VOEuwm2WE7NQBdvzDGnEOoHdTiblMPsaUtZJe20trQTWd9Of1tckb6uoC/J6qJoxdmtkb4ulswy8uKYFtjXCUaaNdnM5iXjCI5B3lKNc3l7ZT0KFX1wJh4IEFdrPSx8rFAOsUTk9AwNDxC6DB0IK+5j8yGLm4WKGio7KCzUUFPYwWDXS2qJNCVWGBg6YiZgx3WjhKivK2IdDbH01wXs345Y0XJdGVlIk8upim7maq2AdVYgHIs7oO81Mscm2B7bKYEoRs8gxEbbyp6BCn1nSSWt5Ff2oqitp1eRRX9rXIVQGgZSJTgbueKjZMpPs6mzPS0JNTOGDudMTRrMunPuo0irRB5ej01FR3U9iv3C+4Dg5KUtXCRGiGdaINViAfGk6aA+2TaNE3JVfQRV95KWkUb8qoOOutrHwB0dU1tJRlbOmLhZIurmzkR7hZMczLDw1wXg85qRvNu05qcjiKrmqZsBSWdgw+IA0NNdaS69+LC3wrpZBcsQvzQDpzOgLU3VV3DJNV2ElOkoKKqg5a6NrrrSxjsalUB+H1QkkidsHKUEO5lRbizGcG2RtiqdaNek0tvegKNyfk0ZSuoqe2mun9EBZL3BYqrgRZ2UiOkoVKkEQHo+YUg3EJpGDMgr7mPuLJW0opbaJV301lfTV9LnQocNbT10DO1xtjeE3NbCZ7u5sz0siLEToKHmS66jfkMFabSkphBQ2olLUVtqti8T7T3q1EPM10sfSyQTvHALDQELe9QekxdKWwdIP1ebNZWdtDe0ExPYzmDXa2qCuD+WJg5OGHpYMx0H2umuZjjbaGP1UgLoiyNnrvpNCYX0pjVRK2iTzUW9/PUVEtJFg5uptgE2WI7NQC9iVGMSn2pHdQivaGLpMp2MktaaK7tpLux8oE8Hck+8qfB2dHLX+z47tz/fCMQPdX1v2Tw72rqBpbCaPLTGNt5YGZnhZuHOQv8bAi1N8HDRBOt6nQG7ibQEJ9NQ0o9tQ09VPQNqwLs/nfl3SQ6SCfaYDPZHfOp01DzmkKzpjmZjT1cL2omu7yNxqp2OmuLVWr7foAb2bpi6WyPg8yUef42hNub4m2hi25DNoN3E5DHZ1CfUkdddScVfSO0DI2qgttBT0sJvBOssZvmhdmkENT9IunQs+FuUx+3SltILW1BXtlBR20JAx0KRvq6UFPXQNtAgqG1DAtnZ6wdJczzt2GmiwUe5jroK4oYzrtDU0I69YmVyCs6VAl+v+/7oO8QYIX9VA/MJwWhGTiDbmMnshV9xJS1kljSQmNVB+21FfS11KmSSstAgpG1DHMnF6wcJcz2tyHK1Rwvcz0k7WWMFCTRfDuN+qQK5MV/B5f7AGetowRZmY8F9lNcsZwcgPaEmXSZuVPcNkBsRRsJJS3Ul7fTVlOpAnpQEq+hjQwzR1esHSVM87Fmjocl3hZ6mPXWMpoTR1t6NvV3SlHkt1DYPfQA6ar6djfDPsIF68n+6ITMptfKm/L2IWIqW7mW14SitovWqqoHSEZT1xB9Cymmjp5YOUiI8LVmloclflYGWA01MZ6fQHt6Bg23i1HktVDYMYh8cETVt6WOJg56mri6mGIf5oBNuB/6k+cyaBdERecQ8dXtXM5pRF7bSUtVPb2KahXZa2grD6EzcfTC0l5CiLcVc72tCLQ2RDreznjBbbrvZlAfX0BDWiMVvcqKoHd0/AGCdXMwxi7MHpvJPhiGz2ZENonqnjHu1HRwJb+J6qoOFJUN9DRWMNTTzvjoMOqa2uibSzG298TSwZQJ3lbM9rIi2NYIR80+KE6kJzOF+oR85GlyKjoG71UjSjJRCh1NPKWG2IfZYRPqg1HYDMbcwqgd0CCprpNrBU2UVXWgqFKoSPV+3/fJzMLODB9PCxb42hAsNcZZbxSNsmT6spNpiM+hLqWBmpZ+FYnA34nMTaLD4vq8P08G3v5i5/fn/9C926a4/JcM/l1N3cBSGIdvwkTmj6WDKZP9bJjvY80EG0OsB+oZybhGS/JdauJKqShtp7r/72As0VJHpq+Nl7ME6SQ7HGZNQndiFH3SQLIa+4ivbONWbiMNFe101pXS11zH2PCAKjhNZP5YO5owxd+GhT7WBFjpY95dyUjeHRpjkqiJK6euupPSe99SAlQqz9neGMdIGdKoUHSCZ9Jl4UVWk1LtxuU20VTTQUd1Ef1tclWf95PRzs2SSH8bZrhZECI1xKStlOHsWOSxqdQmVFFZ00VF39+TwUxbA5m+Fu4yCU4zXLGZEoRO6DzaTd3Ja+7jalEzt/OaaK7tpKO6gIEOBWPDAypFaSrzRepqRriXFfO9rZhoa4hhaylDaddoiEmjPqWOsuouKu6dGPqPfrq7m2EX5og0ajJaIXPpkjiTIe/lcqGCjJIWGspb6KotZKBDwfjosArwTGXe2DiZMiPAhnmeVvha6iFpLWYw5TKNibnUJlRTUtutqmrug52HoTYuXhZIw2TYzpyClv9U2oxdSGnoIa6slaS8Jhorm+muL3mgTwMrB8xlXtjITJgVYMsCTyu8LHQxkOcwmBlL3a10am/XUN7YR3W/0s/74O5xT6k6zfDAKmoqmn5TUejZk6vo5VKBgvQCBYradrpqC+lvk6tIxcDKARN7N+zdzZkXaEuksxlB1gbo1KQzkBlL3c0MGtIaKa5XVgv3Qf0+mbn6WuI4wxOL8EloBM2iUduaTHkPVwoVZBUoaK5pfgBU7/dpLvPA3t2cKG8r5ntY4WuuhXZ9Nr2J16i9mUlDeiPFTX0PEImljibeRto4+lliP8UNqxlTUQ+IQqFpQVJ9N5fzmygoa6WpXE53Q6mKwLQMJBhYOmAuc0PqbMqiCVJmu1rgbqaDdmUK3fFXaUwuoDaxnsLmPtX+3f3lNR9jHRwnWGMX7o7F9EjUvacg17QksbaTWyUtZBcoVMT1j30aWcuwcHbFzsWUJROkTHU0xcNMFwN9vT8Nzk7eAeLVI3+MDLaGO/+XDP5dTd3ASphOfwELV1/s3c15JNSBOa7mOGr0MJ5xBfmVW9QnVVNQ0k7FvRdeaKurIdPXwttcD+fZzjgumIpOyByajN1Ia+jmdHYD2bkKWquq6KovZXSwF3VNbQwsHbB098Pe3ZyVkxyY42qGPV2MZ1ym4UoMdYk1FJV3qMBCT0MNqa4W/tYGOM9yxj4qGJ1pD9Gk70RqQze/320gN1dBa1UF3fIKVT+GNjJMHT1x8bViZYg902SmuKh1MpZ+iab4FKquF1NQ2akCiPv9BEgNcZnlgt3MULSnLKHV0JG46k5OZtZTXtqGoqxM1c8/AqCrrxWPhNgzzdEUmWhlPC8O+dUYqq6XUFLb/U/j5m9vhEOEA44LpqIdvoQWfXtiqzr4LbOeitI2FGUl9DZVq/rRN5di6e6Hu68VCwNsmedmjsNoM2O5cciv3KLyehll9yq2+/446GnhY6UcN8eF09CeOJsmQxdiqjq4kt9Ibq4CRVkRfc11D/hj6eaDp68ViwNsmeVihr3oYCz9Eg3X4qlNqKKgspOKvmEGxgR6GmpKMWBjgMssF+wXRKIVPAu5vhOxVR1czmskP1dBW03lP82PpZsPju7mrA51YJazGdKxVkbTLtFwNZ7ahGoKa7oemB8HPeW4Oc9yRTo9BO2IZTTo2JFc38XJzHoK8xS0VZfTI69QEbGhjQxzmQcu3pasnuRAhKMJTmPNjKZdojE2hcrrZRTUdatA+x/7cZnrriTgyYtR6EqJqerg96x6yotbaa4ofaAfI6krZo6uuPtasSrEgamOEhxGmhhJvYg8NpWqmxUU13f/07j5Ohgr42DxDDRDF6HQtuZWVQenMuupLG6luaKY3qZqVT//6/zMcTXHbqiBkaybNFyNp+pWBSXy3n/qx18mwSHCEftFUWhNmEmDrgO3qtq5lNtIcX4zirIClVC7T3hWrl74BViz0N+WKGdTbEdaGMuJQW/exn8JGbz2w4U/dO/msD+/R/HvbP/xZGA++2VsPH3w8bHk0RAHomQSjBX5dFw5Sfm5DCqyFeTf+8n9fUALcJQgi3LGfuk81EOXUDagy+/5jdzIllNTUE9ndT6DXS2oqWtgaC3DzNkHr0Ab1kxyINLJBNvucnpvnaL6WiaVCbVkdSp/PXlfuU200Md1nguOcyajHfkIVRo2xFS182tyDdWFzXRW59HfJgdAz9Qac7cJyHyseGiSPQvdLXAYb2M06QzV52KpSagjs7EX+eAIYwJsdDUJNtHFYbIUtxVT0I96mGYjF25WdnAspYaaklaaS3Lob5MzPjqsPJvHwRtHX0eWTHZgoacVXrp9jKecQ37jNhVXS8iuU+6jDI8LJFrq+Bnr4B0qxXGmH+aLH6HTbiLJ9T2cyKwjK6uRlqoyFaBoGUgwcfDG3tuZiEBbVgVJCbLSQz37MopLl6m+VUx+URulvUMMjAkMNdXxNtLGw9Mc10UBWC1axqjnNJLk/fya1UBmXhPy4nK6GysY6etSLQvZenoxOVjKUn9bpjsZo1cST0fcNcovZFGYraC09+8kLNPXJsDVBKcZrtgtmQ8hiynp1eC3HDk30uuRlzXQWZPPUE+7CuBtPP1w87Jgbagjs51NMGkppOvaKWpu3qXkdh359w6b01ADmb42fhZ6uC1wx2HZXDQmzKECc84WKriS1UB1gVxVCaipa6BvLsXcLRBXP2tWTLRnvps59v1VDKVcofJsPJUxNeR1DSEfHAGUMRRsqofDVHvcHopAJ/JhmvQcuF7ZzvGkGmpLWmkty1ZVGnqmNpi5BWPvbs5DYY4s8bRCptbBaPJZGq7foeJ6OXcbepAPKpfqLHU08DbSwTPMDrflkzGavphWS39uVnZwLkdObk4TzSV59LXUMT46jJaBBFMnPxx8ZcwItOWRACn+RsOQF4P84hXKLxY+QBgSLXW8jXTwDrZBNscPi4XLGXKN4HZtN79k1pNToKCxuJSu+lIViJs4emPtJiMi2I61E+2YYGOAZt51FBcvUHOrmIKCFlX1aaipjkxfCz93M9yXBGA5MwoRvIg0xTAXCpqIyahHXlxJV0MpI31dKjHXfuX1Pw3OMu8A8frRP0YG/4oN639n+88mA0NrYTXvNez9vIgIkrIuxAF/g37GM65Qc+oSZRfLyOlUrl9qqyuV0wQnCZ4PBWA9bw7jIcuIrenmVLaclLR6miuK6ZFXMD46jJ6pDRYeE/EMsuWREHsWu5tj3phFx/WzlP6eTk5Os0rNmmlrKAE0wh6XJaEYzl1Nla6MGxVt/Hy7iurcGrrqihjsakFT1xBjqSt2Pp7MDXNkhb8tgUZDjN3+jforCZReLCX73sautroaUl1NQmQmeCz1w2bhvAdsTs1ooLEwR5W0StCZgFuADatDHVjobo6VIpuO62cpOZn6TzZ7G2njN9UB95VT0Yt8iBo9GdfK/9nm+wrSwc/nQZsTf6f+UiylF0tJv3cUhIYaOOhpMdHRGM/lAVjPm40IXU58XR+/58hJSqv/J5tNXYLwCLJlfYSMGTITlc2lp9PIu6sE+n+02SfcHs9Hp6E3dRk1Bi6cLWrmXFodVXl1tJVnMdLXpbLZzsebOWGOrAiwJVgyytidkyqbM1v7kd+z2dVAG397I9yX+mG34iHG/Of8jza7BtiwIUJGlLMp1q15tJ79hYrL2f+vNnusjEB/+kPUGrhw5h9s7qjMYainHQ1tPUwcvbHxcGVOmCPL/W0IMRljPOl36i/HUnaxhHRFH4qhUeCfbR73m8Vt+SBHU2vIzlXQWJRHb1P1/2hz1bVccjIaKepREraZtgaBEh3cJtspbY5cTr2RG2eLmzmTVkdlTu3/1uaoUAfWhdjjYzjKeOJJqk9fo+pm1QM2y/S1CZAa4rHUF7uHlyP8Z5PYNMyRlL/b3COvQIyPoW8uxcIjGJmPFU9MkTHT2RTb9gLazv9C5eXsB2yWaKkTbKKLa4it0ua5a2jQdVDZXFPUQvW3K/88GfgEiDd/vPiH7n1qktN/yeDf1TQMrYXtsvdwDXZl7VRnVnhbYloWS+OZ0xT+dpfkum6aBkfR01AjUKKL32QpPk/MRnPOExQO6HMwsZqExBqaS3LoVVSjrqmNiaM3sgm+LAl35LEgKQ4d+bRdOEHB8RRSilqpG1CqN28jHQJ9LPBbNw2jpU9QpWXP0cx6rqbUUpOdS3d9KQCG1jIcgiYwfZIDz4Q54TlWx0DMSfJ/iCUzs4nS3mGGxwWuBtpMcJLgsyYE6+WP0Gwfxm8FCk7eqaEyq5iO6nzGhgfQN5di6x9KyEQpW6Y6E2I4wFjSaUqOXiI7rpb87iF6R8ex1NEgzNIAn9WB2C1bxEDwMs6XtHE0qZrSjCpaS9IZHexFV2KJpVcobgE2PDvDlZkyCVppp6k6dpLyyxUk3Tuh836yBSxyQ/bwPNRnPMaNukF+Sq0hM62e5sJUFdmZyvxwCXZn40w3FriZYVZ5m7rjxym/VEhydRfye3PiZ6xDcKQj7iunojP3SdL7DPgpo564xBqaS/LoaaxAXVMbY3sPnCf4szrKheXeVsi6ilD8dpSys3dJLWilom8YDTXlnEycaIP78hBMVmygVteRo5kNnE+ooqGwiK7aItWc2AdMYOE0GWuD7fAWTfScO0Lp6VTS0+SqOZHpK0nN59EQbNeso9k6iFOFzRy7VUFdQSXtlTmqObHxnURkhBN/mWhPuOkwY0lnKP3xIndvVavmxEZXkxBTPbxX+uL42FqGfOdwvqSNb+IqqMproLVUeeS2jpEZFl5huN6bkwUuxqhnXqDqp98oPF+iqkTvg7b/fFdc162AyQ8R0zDEwYRKirIbH5gTc7dgnPyc2DDLleWeFhhX3qH+5+MUncgmq7mfuoERVdUWFOGA95Pz0Jr2CNmDEo5l1nPzdjXy/Az6WupQ19TGVOaHvZ8Hq2a4sMbfBtuuUlpPHqHgWCrZVZ335kQNVwNtJk2wxn35RMxWPEmZrjO/5TZyLqGKmuxsepuqEeNjGEldsfX2Z8E0GRtCHXAbV9B3+Sj5R+LJzW+hqGdINSehrqZ4PBSI7Zp1NFpN4EJpKz/GVFB9t4jO2iLGhgcwsHTAynMC4eGOrAt1IMJWF11D4z8Nzs4+AeKvxy7/oXvXTXT4Lxn8u5qGobWwX/UpAWHOPD3VmXn22oxdOUTJL7GkxNWS3z0IgIehDuFT7PBaPRXdJc+Q0KHD98nVJN+pobkwkZG+LvRMbbD0CmHqVBnbZ7jirdFO37lvyf0ujozCVkp7h1UAFjLbGc+Nj6AWsYpzFT0cuFFKZXY1raXpqkB0DJ7MrHBHtk5xwqWrAMXJn8j/MYXE6i6aBkeRaKkTYa6P5xIPXJ5aR4fXHM6XtHL4ehk1OUrwF+NjGNt7IAsOZMM8D5Z7WWJZEUf1kaMUny5SAbWljgYRdsb4rwvFZvVj1FoEcSKviRM3y6nJTFclsLlbMM5BLmyZ7c4KL3PUkk9S9t0JMq5WqgDLQU+LKZ5mBDwVicmSxyjWdubz25UkptTRkJtKf5scTV1DrHwjmDhFxsYpMmbZqDF86WuKj8eTklhPae8wY0LgbaRDcJA1Ac/MRnvhM2R0afFxTDmZSdW0laYz1NOOnqkNVt6TiJruzLZpzniptdJ96muKf00hKVtBRd8w2upqBJvoEjLfBdc1C1Gb+SRnyzr5Nr6SktRS2sqzGBsewNBahtPEScyf4sTTkx1w6q1AcewQeT+mklrXjfzeuE8y1SNwhTfOG9fT7RHFiYJmfrheRk1OIR3V+aipa2AkdcV10gQenibj8SBbzItvUH/yd/KO3yWxrZ+uESW4R8gkeC4PwP6pTVRKfDmR28jJW+XU5ykFgYa2HqYyP9xDvXl2jgcL3UzRvPMzJYdPURBbQ3rHAANjAgc9LSJ8LAh8ajpGSx6nUN2e71JruZ5QhTwnhYGOJrQMJFj5ROAdLGX7LHcizUcYjf2Z/G8vk5bcoBp3P2NdgoKtCdw0H62ZfyGpW59PY8vJy2iguTCZoZ52dCWW2PqHM3O6MxsnOxGooaDjxNcU/5ZGYm4z1f0j6Gkox91/uiNeTy1HzFzP1fIOvrhZRmmqMkZHB3sxsnVF6hfEilmubAixx6GrGPnRQ+QdTSNdodwYNtRUJ8Jcj4DV/jisWkFPwGKO5Tbxa3wVFek5KqKWOHrjOimQR6c7s9bfBkneRep+O03uiTzSOwZpHx7DRleTydYG+D8RinTd09Sb+vBdej2nb1Ugz8+mp7FCJUo8JnuxIdKFRe5mmBsb/GlwdvEJEHuP/zEy+Evwf8ng39Y0jGyEy7qvWDLXnU3hjrg1p1P+xQHyzpWQ2NbP8LgyMaYucMHz+afo8VvA12n1/HypRAVs+uZS7AIms3aRJ49PkGJTco2SL74j83oV6R0DaKsrq4rwh71xe/5ZGqWT+eR2NReul6EoSGGwqwVDaxmOwSFsXOLNal9rjNNciCB6AAAgAElEQVROkP/5z6TdriOnaxBDTXUmmeoSum4iTltfoMLQkw9iyomJqVCRkcTRG5eJQTy32JuHvMzRuP41dz89Q0qWsnow09Zgirk+EzZNwXpjNIVqUvZcLSYttojW0nTGR4cxcwnENcSXHYu8WCjTZ/j0p2R+cY2Uolaq+0ew0dUk3MGY4C3TMXnsRVJ6DNhzuYjC23m0V+YAYCrzw2daILsXejHVZIieXz8n60CMisTuk8XE5+eh83A0NxpG+PBiISVJd+mqLVKRTkiUL2/M8yJQQ0Hz959x91Aiic19tA+P4WqgzZQgaya8sAS1Jc9zpqiVLy4VU5aUSk9jBVoGEiy9wpgx051XZ7nh3l9O3defk/FDBintA/SOjuNnrENouB0B0Y/SH/EYJwtb+Pp8IdXpKfS11KFjZIa13xQWzXFne6Qz9ooMKvd/ScrJArI6BxkeF0qCme2M9wvrafFZxE/Zco5dKqE24zaDXS2qKmzVfA+eDXPAtPQWJZ8dIulyBfndQwBEmOsRuNQDt21bqZaG811aHaevltKYl6yKDYegEDYs8WLDBFu0U05S+MUxEmJqKO0dQk9DnUgLffweDUD27PMUGHiz/3YVN26WPxAbsuBAtizy4i/epozfOkL2Z78Tnyqnun8EiZY6UQ4S/B4LwXbj86SOSfk8roLkuBJaS5QCxVTmh+skf7Yv8mKJvTrDl78h64urxOU2Ix8cxVJHgygPc/zWhWP6eDQ3O/T5LKac/NsFtJVnAWDhMQn3Se7sXuTNdKNues98S+aXt4ivaKdlaAypriZRQdYEbIxC75FoTlcP8dXNMoruZKliw8o3goAwZ96c70WwqKX1xGGyDiYQJ++ha2QcVwNtIsPt8N84F/Wl0RzLa+a7m+WUJafTXV+Kpq4h0gkzCJ/iyK7ZHnh151F/5DCZR9JIaO1nYEzgZ6zD1NkyvJ5azmDkE5wqbOG7m+Wk7J7958nAN1C89/MfI4M1Qfb/JYN/V9MwthVeG7/hqYd8eDLIFr3Yw2S8f0KlbBz0tIgMtCLklUcYmLWJn3Kb+PZMITVpcQz1tGNs74HvjDCen+vBUuthun87QNrfrnOroYfe0XECJbpEPeKF66YNVDtF8dbVYpISKmnKiQWUyRExx589C7zw6CmkbN8+Ms8Uk9jWj56GOmFmeoRtnoL1lldJ6TfhjXP5FMSl0VVbhJaBBMdJUSyf7cb2aTIME45QcPA0N+Nrqe4fwVJHgzmB1gRtno326lc4nNPMkcslVCTfob9NjoGlA24REbz8kB/LnPXpP/YBqX+7RkJ9N+3DY3gYahM1S4bvK5to857PB7GVnL9SgiIvgbHhASw8JuEV7s0Hy/2YOFJG/eGvSP4ulZT2AcaEEuRC1gQgi95JqpoT718vJfNWNm3lWahraiOdMJOHF3mxJdwRu5Ir5H3wLckJymUqiZY6M2UmhL2yEL2HtnK6TvC30/lUpWfQ01iBvrkUp5ApbH3Ilyd9TRi7coi0D89wK7+ZliElWURMtCF412N0h/+F77Pk/HC2kLrMOEb6ujCR+eE7PYSd8z2ZY9xJ48F95Hyfxq3mXsYEBEp0iFzth/PmZyi1DuONy0WkJ5TSXJCImroGlt5hzJzry5tz3XFqzqTkg4+JOVdKUY8SnGc7SgjcOAWLja8Q067LO2cLKL6TSnd9KTpGZsjColg5x40XpjiidfUAOQcuEJMip25gBKmuJrMm2hL07DzUlr/EgbR6jl8rpTIpXkUOnpER7Fruy3xb6PrhQ9I+uUWCopeukXGCTXQJn++C987nkTtNZe/Ncq5dLaSlKIWx4QGsfCLwj/Dgg6W++HbnUnPoKxJ/uktK+wAaampEWeoz+blIrJ/YSvyQDR9eLyXnVgbtlTlo6hpiFxzF2qXePBPqgEXmb2R/+COJKcqqwkZXk0h3M0JfWYzGwzv5Jb+Z/WcKVAR7v/KKXuHHGjc9hs59SeqHl4gpb1fF3KwFrnhtXkNL0Ao+iK3kWkwFDVmxjA72Yu4WTEDUBF6Z68F0nSZqP99H9rEsYlv6AZhkqkvE4xNwin6FPE0nXrtQyN1bWbRX5qChrYeVbwQL5nuxa6Yr0qp4iv52gJhLFRT1KGNujrs5QZsiMV73Cicqhzl4tYSShER6FdVoGUjoT/zsT4Ozq2+geP+XK3/o3tWBdv9RZPAffTaRGqCtp4m9RA/DsV7aCkpQVHaiGFKuSQdb6eP5cAjqM5/g53wFRy4VU5MWx3BfF2YugYQtmMxHS31xbkwmf8vHxN+opqhnCAc9LVZMsWfiWxtpCHiI5+MqufjF7zQXJqKhrYdsygK2rPLnqQm2jJ94n6QpmzhS10XvqDKZX9o+DYftr5PQb8bK33IpXvcjfS11GNm64jcrglcXrWOuXhOVH7xD4roC3mgfwFJHg+muZjz//QbUlr3IJ4m1bPg9H/k3cYx9GY3thNnMmOnOuRdfxSLlJ3I+/Y1rHxwn5u1R6g21mbvUnblXvsTByJ/tZ/I4H5/Lq6Xp6O3OxDFElxdX+fPRe2F0/5xM0vsx3My+Cb9B4/sGtO+ah82Od+h7eJzbF4ooux3P1x1NSKq9CbzUz+cPa3FuxjiF8Re5XlpGae8w0txrzKtwwFZjFS3h69i/xo046zIa795EXVOb3yfOYp2jL89qmbC09EssTp1UKcgwMz0i3ItxsXmD6/W6fFAbSr7VCD3eFRhYOuA1IxKXJd5oWvTR985mnH/MZFZrP4aaSgU8Zak96qsn8d3dRl78qpjaNAPGbMOwmhXBgiVBTI9yxaHgPHlv/Y1rN6qw7B9hq74W8+Y44//WdqrtI3jlQiERW47SXpmDtoEnrs9uYeMSb572ldD1zR4yv4zj+huXGR4XbLPQJ/LFKCyf3MXpFgPeP5HDJx/+xN42OSYyP3yW/JWPfggguDeH8s++4MaJAn5aexhLnSPMD5Hy7AtL6Hv1LfbequDSjTLunjnJ6nOnsfaPZNWyjbxW9C4z446Q+vYxruc1k/VrIcFXo5m6xJ2v39xNYsg89lx2JOdGMor8BLobSnlK0csbKwNY8NoHRBm/g8bBRFLaB0hsG0D/x1R0TAyJWP8mq0MdaKjsoEdRzUhfF32tTZQp7OkaGsPKyBQNrb+fzW+goY6h1BBtF18qu4ZJKG2luVJ5npCWgQRbnwk8MtOV1T4W9P2wh8wvb3G1tI2BsXEizPWYsXkKNs+/wc12Pd7+MpmShHj62+QY23vgGRHKO4/4M0O9hur9L/DD0SzSOwaRaKnzUIAVYXv+wujsp/kwvprj7xbQlPMNYnwMu4lzWL56GnvmuCHJ/J3MPdv49rEGWobG8DbSYd4qb57dtpV8i1BePJlLUfxd2o+8ir65FFnoFL58fwOPuOgycOozTBL/Nbij8X/n0UT/2WSAmjqGJrp4WxqgXpVMbUwhWZ2DDIwJZlsZEP7aInQf283bsTUc+zWN5sJEdCWWhKxcw56H/YkaKSBz3TJ+S6ijbmCEYBNdXn9zDpYvfcS3+R2s++kudS9vR0NbD4+o+Xyzey9zDFspfe1Vzs8t5qX+EQIluizaEs6sF17n+xpNPj2Zx3O3r6CWeACHkJm88kQw6560p/7z97j49Y/kX/mWnDe1cF7ji9ue9ynaZkryj1mUxV9l3/AAjglePG5WwyuTLVkXe4nb5ckktPbjUBTHPLkjtsFvc91lBX+N8qBg5DpDPe1Y+UTQvnYyb0il2PzwDhv+eomE1n7MtDVYMsOJoCUBFNoaE3U4i6I4Cb02oVjNjGD2oglELPFC45e93ApZRHZdNzPU1fjIz5IpHz9LU+AKtpzMJfKp/fQ0VmAim8XsA2+zd6EXtklHSHjpB15f9hFjYh/rfCw5uPsh2vd9ylvXy7j8eyJ7d17kU0sHghbO4pvYLbxRcZ3U7Z9wMbOR9EMZLDj3GDM+Wsm0LS/zspMp504m0VqaTlFMLPtGx7FdF8yk1SvpLFdQdK2SlqFRWroGGWjpwFhdjYHhMQb7BhkbHkDbQIKNm5S/TLTHpaeEvIPHuHmzmur+EcLM9Ji+PgS71z7ki4IBvtkbQ3XSFTR09HCbvpQ3nghmpUkrVR+9woGj2ZT2DuNtpMNzz05Gtn0XJzstmH00i+qVBxkbGsB56kLefGs9z3nrUP/uTmLf+Ybvtg8SZ6DN3IWubMn+hfgxB3b+lMVnsTcY/qAM22sX2PyXYPI/XkDX/liu77tJSvol9I9rUjXPBf83XkD71EVu/ZhFSUI837fJsRiaxIocfT5YoM8Z45vEp//OlaYeDDXVWTp8m0lzo6m0mMNm08fJniClr6UOc7dguldOxW+OO7rnP8Vx7znmlLSioabGHGcTIv8SREO4F7svFRF7rpLORieMps1k8tIodq0MwObuSRK37uN83ksYC/hOZkL4a4sYXfUa0ecK+er72+x5KRFDaxk+6/Zx8PFgvCqucPevh/jqozi63rtFlKUBV99egtrZN/kwvppjv6aRcfIXll+9hu/cuby9+TOe3FRC4Pa3uRZXy4lsBY1PfU3k1jze2PEh2prq/KipTl3qFZoLk0k20yPN14b5npMwdTmFQUYjLSi/Kq5rLgErZzLqupBXtNMtr0BDWw+H4HCeW+7LKpsB5B+8Qfy3/4LXnKF805mWxp9/uc3/H9t/OBmooW+gjYWeJsOV+bQUtKo2mDxnOmG46HFOlXVy+mopzYWJaBtI8Jk9i2+fDMGt4irxW/7G2YIWtNXVWBfpSMh7z5FqO4slHyRTfvsWo0MDeM15iF1rAlllUEf6lkfZe6eO9uExlniY8fzeNTRM28RfjmaQv+5H+tvkWPtH8td9LxLtqUbdh2/w+4ydPNc3TJiZHo++sxjx5DvsvlpK5C8xdC7+CDOXQB5ZG0XaG+/S9dXrXHhvF+kHBzlgqM2KreGsLLxKVe4gX/+QxJ78BAyjY4lYocnxZ0Kxdk7m+osXuXn3BtqnNKlY6Y3X228xPOUF4n/Opjz+Ip8PSvC4acpXTwliQutJO3eN03lytAviWFBijbHrK9ye+SJvN0WRf/0yY0MDXJq6kPesJrG0v4r3buzh95hy2ofHWNiawcz1mrSoe7O4egKZjs30G8iRTpyH61OhTPXSY/CNDUz4KQet7iEiLfSZ8bAOxs9M4rmzRVw/10zroCfmy9Ywe1kY01f40vP5S8Q6TUS09vOasQ7z1vrj9N57fFM8wNq9N5FnXkPfPJSJH77BkccmYJl0lLjnvuHiq16YaWtwZr4rAac/4nyPFa99l868R19HQ0cPv7nbeffdIKI7k0nc8j5ff5HI8GcRrJgk5dl3N5G74322fJdGaXwMT96+xN7pi3l5/ce8sKOF7Odf4dLNKj7Zn8LsE2tZ8ukaJr/2Ai+etSTh1HUq4s7ySXMLAxumsfO1D5ij+xa9B5Io6hki9UYVNiFHiNi6l4ggKfX5Dgz1KI/eLlN40D08jqa+LmMCxoRSjUucLRB2XqSUddJU3cJAhwIDSwdcgr3YNk2G+pUDJL5/geuKXsy0NVg8zZFJB/ZyatCZdz+5Q2nsJTR19PBduJIvngplcuMtMpY/z6dJ9QyPC5b7WjL1wHbK3Rcy+4cMcv+2h+G+LmRTFvHF26t5xLKXoh0vcuTFUpoGR1loY8i7v2yhe+6zbPglh8fPJ9L15XPYBEaxatVk3o96iIa3nuPa+z9z8NVBwsz0mLVlCm+ev8zzF0p44UwaLd+kYHbzWyIWhZFzcBVaZ6q59vJJbsafIGOvFk5rfQn84Tuyq9X4/vsMvk+9hG68GROMcznyxER2ijvE38zk/N12JKlnsMmWwbsvU/3KtxwzT6cy8ToA3+ku4u1OY57UycEz4yOuZDUxMDbO0uEMwmc+RZHDarZZPUluiDNce+vPQw6grvZ/Z2nwH00Gamrq2JroYaY1Tmt+GaU9yk09P2Md3FbPIR9b9l/Joi4jBk09Qzyj5nB4/SRkt7/mxvZjXKjvxttIhyVPTcT2/e95+UoZP+/7nq7aImwCo9i9JZKnLVvIfXkDuy6UMSZgWYgtEUf38UO3AxOOZlG19wWM7T2YvmoBx9YGMnDoNU4te5dtXYNEmOux5dBj1M+OZvPPd9l26gJjv7yK+/T5XPp8PX65P3Pn5e85u/JTvtDXZulaPx6rTqI2uZmPDt/k1etZWNZeJXrzTKren0Tpa7/zy2/x9N48QvWXljge/Ssj1zcQ90Mm5fHn+azZgeDLg5x4wowlHkn8/n0c6R2DTMo9h5vNcppWvc7ex11J0bvEcF8XFyPms988jIjaK3xw5gCnchRItDRY61GHh00wnxfp84nuKpq9EzFzCcR041zmBusytPtJpv+Qje/wGItcTYlaP59bpvqEfpRJcaoZWoFLCF62mNfXhyC59hnnnCaiew/sV70xn/Gn1/Ps7/k4Lv+A/rZhHFe9y74tYSweSCftxY/YbxGOnoYaZxa64X/5AD/UafPO14m4HTmCxNGb1R//xKezbJC//zK/fpnEYedlLLAx4u4P0aRve5ftP2WSe+ksS69cIHDJMn66cp23k49w9ZlDHE9pIH3ZXlZtv83Nlz9nh7cVZ3+8TFnsOd7uHkR9cxhrP9zN2Pa/8tutahJa+5F+f4uQ8MU8OsmBvAxfehXV98C9l3F9WzR0tRm7t/dmpq2BsYcLDYPqZJS0qJSqY4Av26e7Ikn/jZsfXiOlfQAHPS0e2TwZm73f8tKVMn7+4eoDsbfBaZSCLSvZc6GMgTHBylApEUf3cXnMDe/9yVTd+Q59cynznn7877EX/CLH78Xe2z88Sc+iHaw5kkHm7guMDMThOWspMcd2E9gQQ/yGdzh7oI1iHU3WPB7ALkUa7yU3s+nwTdrey8Ly9HGiN8/k7Kq/UB69iV9+fZv2H8dI9LMi8uhf8XnuC748kMyR+PPo3DEj9LtMTjwRzL7O3/j9WhLp2TFMitVFbWg5I4+9ybe9c0g5dYnBrhZ+Nl7K/m4DntS5i2vqvr/Hnl0+9k8c4ku9GXwyCZoNlLGnvnEu/u5S3F5+ilU/ZCMfHOUhdzOi5k4lz1CHkK9GKR71R2tyOMHLFrNjfQhj1z4jLyAcr9Z+5utr/fmXBN9r/7cuEyGE+I+9tEwdxfsxpWKsLFkkRk4TW9WcxGdGHqLhnc2iurVHTHzzqtAN3SpM5rwpnj2VIwa72sTdFXNFtIZM7NJ2EWXPrhQdPX1i8aEkYThth9AN3Soi/xYrOnv7Rf3bz4g9uq4iWkMmEiOnieHGcrH5ZLawWLpPaAdvFCFvXxO58k7RcWiX+MzIQ2zCSdzwCRHDKWfEd+k1QvroIaEVtF64bDolbpQ2i6E7J8Rpax+xCSfxo7mX6L/wpbhQ2CS8os8JraD1wm7tYbEvrkyMFiWIOxFTxVY1J7Hf2EPI39sqKlq6RfDrV1S+PH86Vwx1KETWsjkiWkMmXtdxEeXPrxat3X1iwVeJQj8iWuiFbRPTP4kTPX39ou7NjWKPrqvYoeksUmbPEMNNlWLDL1nCfPH7Qidkkwjdc13ky7tE6/6XxGFTT7EJJ3HLf5IYTjsnvk2tFrarDgqtoPXCdcvvIq68RQzGHhMnrZS+HLfwFgNXDolzBY3C8/mzQitovbBe8Zn4OKFcjBbEioSwKWKrmpM4KPEUTR8+J8qau0XgrktCJ2STMJ23R7x4Nk8MtTeKjMWzxTZ1pS+VOx4TA329Yu6BOypfZnwaL/r6B0TNq0+Kt+75kjovSgw314gnjmcKswV7hU7IJjH5nRuiqKlLtHy+XRwyUfoSExQqRrKuiIPJVcLmkf1CK2i9cNt6WiRUtIrBW0dVvvxq6S0Grh0Wp/PkwuO5Mypf9idVipHsayI+NFxsQulL8yfRoljRJQJeUfpitmCvePVigRhqqRPpC2aKnVrO4i1dV1G1c50Y6O8Xs/bfFvoR0UI/IlrM2n9bDPT3i6qd68Rbuq5ip5azSF8wUwy11InHfkpX+RLwyiVRrOgSzZ9Ei4MSpS/xoeFiJPua2J9UKaxXfCa0gtYLj+fOiOTqNjFw7bD41dJbbMJJnLTyEYO3joqEilbhtvW00ApaL2we2S8OJleJkawrIiYoVGzCSRwy8RQtn28XRU1dwu/liypfdl8uFMPNNSJ1XpTYoan0pebVJ0Vf/4CY8Wm80AvbJvQjosXcA3fEQF+vqNzxmHhdx0Xs1HIWGYtni6H2RrHmaJownbdH6IRsEkGvXRZlzd2i6cPnxEGJp9iq5iQSwqaI0YJY8XFCucoXz+fPirSadjFw5ZA4bqH05bS1jxiMPSbiyluE65bfhVbQemG76qD4NrVaDKedE7f8J4lNOInDpp6idf9LIl/eJXx3XBA6IZuE+eL3xYZfssRgb7dA+W7hP4U57r6B4mqx4g9d/1N/wHygBCgHXv3ffL4dKARygVuA0z98NgZk37vO/yvw9P84oP8pMjCXiR8z68TAlUMqoI0JChVjJXfEqxcLhGTWbqETsklM/yRODPZ0iqKnloloDZnYZ+AuGt7ZLAoau1QA5vDEj+K79BoxeON7ccTMSwWIIw3FYvflQmEy502hHxEtIv8WKwZ7u0XG4tlih6az2GfgLjoP7xZFTV0qYLd/7Ii4XNQkBm8dFUfMvMRWtXvPqssXG37JEiZz3hR6YdvEsm+TxUB/v8haNkdsU5eJ9/XdRMehXaJU0f0ASZwraBR9v30ofjRX2pUYOU2M1uaKp0/cFUbTdwq9sG1i8aEkMdRSJ+6umCu2qcvEXj030X7wFZFc3SZ8XjyvIqbTeXLRd+ojsd/YQ/WsobYGsflktupZC75KFENtDSJ75XyxTV0m9ht7iJbPt4u0mnbhsumU0ApaL2RPnxTJ1W2i/8wn4qDEU2xTl4k7EVPFUEudePZUjjCO2iWMpu8Ucw/cEUPtjSIpKlJsU5eJz4w8RN+v74m79R2qxHZ66leRWNUqOg7tegAohhVVYsV3KcI4apcwnLZDPHE8Uwx1toiU2TNUxN/78zviclGTCvA8nz8r4spbROfh3eKQifJZGYtni+GmSrHqSKrQj4gWhtN2iMd+ShcjDcUidV6U2KqmBJKeY3vE1WKFcHjiR9WzbpU1i67v3xD7DNzFVjUnkb5gphjs6RRrjqYJ/YhoIZm1W6w5miZG5KUidmKY6lndR98SN0qbVc/yeO6MyJd3ie6jb4mPDd1FtIZMpM6LEoPdHWLm5wnCcNoOIZm1W7x8Pl8MN5arnnXIxFMMXDoo4spbhOP64yoiy2noFIqPnlc9q+DxxWKos0XM2n9b6IZuFcZRu8SLZ/PEsKJKRWKHTDxF/4UvxS/Z9cLpqV+FVtB64ffyRXG3vkM0fxIt9ui6im3qMpG3dqHo7usXcw/cUT3r2VM5YrQyUySETVERZ/+5z8WJnAaVWPB7+aKobOkWLZ9vF3v13MROLWeR++gC0dPXLxZ8lSj0wrYJswV7xeaT2WK06q646j5BJSiG7pwQp/PkQvroIaEdvFH47rggKlq6Rc2rT4q9em5ih6azyF45X/T1D4iID2KEXtg2YTpvj9h7s0SMVmeL617BKqE1lPCLOJhcJezWHhZaQeuFz4vnRWJV67+GDPwCxY3S5j90/X/1B2gAFYALoA3kAD7/yz1RgP69v7cAJ/7hs95/NZ7+Hwf0P3NpW7iIW2XNouXz7WKfgbvYqeUs6t9+RlS2dKtA3uO5M6KypVuUbn5Y7NRSgnffbx+KW2XNQvroIZUyHujvF3lrF4qtasqAGrhySOxPqhSm8/YI3dCtYtm3yWKkLl+VWNe9gsVYyR3x9Im7Qj8iWhhH7RJ7rheLsfJUcc7GV2zCScRODBND7Y0i8m+xQjd0q7Bc/rE4ldsgBq4dFvuNPUS0hkzkPrpAKDp7hd/LF1Uge7VYIVr3v6RSjXVvbhR36ztUPgXuuiTKmrtF2bMrRbSGkkT6fn1PHM+qE3ZrD6sql/6BAZEye4bYpi4TR8y8xEjGRfF1SrUwnbdHGE7bIRYfShIj9YXiikuQ2ISTuOo+QYwWJYjNJ7OFfkS0MJq+U6kQU86ofEqdFyWG2hrE9E/ihHbwRmG5/GPxS3a96Dm2RxyUeIpoDZnIXjlftHb3iYBXLqnAPqehU7QffEW8pesq3tJ1FbW7N4ichk4VKAW8ckm0dveJ8udXi2gNJZn1/vyO+CW7Xtg/dkRoB28U0z+JE339AyJ1XpTYhJM4YuYlhlPOiN2XC4XZgr1CPyJabD6ZLUbkpSqgueISJEbqC8Wzp3KE4bQdwnTeHvHqxQIxnHZORfops2eI/gGl4tUO3ijs1h4Wx7PqRN+v76l8Knt2pWjr7hOBu5Q+Oa4/Lu7Wd6iqrrd0XVWqVPb0SRU4Nnf1iYroNSJaQ0mq3UffEqdyG4Tl8o+FdvBGEfm3WDHU3ijSF8wUm3AS52x8xVDSKfHm1SJhvvh9oR8RLZ4+cVcMN5arAO+yU4AYqcsXy75NVvl0X/H/aK4UH0lRkaqKRCdkk5A+ekj8mFkn+n77UOwzcBfb1GWidPPDorKlWwS9dllFfhm1HaL+7WfETi1n8bqOi2j5fLsoaOxSiQDfHRdEY2evyFu7UOzQdBafGXkoxVieXFiv+Ezohm4VU/fFiKEOhSpXTlv7iKE7J8TemyXCOGqX0AvbJjb8kiXGylPFDZ8QVa6M1uaKFd+lqCrgz+5UiIFrh8VxC2+xVU0pXAZ7u8Xkd26ofLpR2iz6Tn2kyv/ipx8S1a09D+R/cnWb6D761r+EDDz8AsWtsuY/dP0PZBAOXPuH/3fB/8Pem0dFeWUL348DoyCOUROTYgZxSEkITSROcWqySExMYqvLjpJgl1aCq7wkGILB8Emkg8HgokPCJ5HWDlEjQREFHAAlCI6IIIPMiMwgUFDF6Pp9fzxwEjK19/18b6/Vt/84i6p6ztlVnEqlkGEAACAASURBVL3Pnvc+kv/vzJ8rSdKl/wiD3xjGj9lS1dLJdc+lqEfIB7+3vZkFn6ULBlPf3kXW4gXCjdOt1wvG+0xgCr3364W2c9H1Odq79IKQ3D45T19DhTBDC/7sSWN7FzZbvhcHub/mNkcmy8Rasvk1qls7Ubx9BCMXFUsifuBB+TVhHVT4rudCWTPT18dg7Kpm24l8BgrSibRwwN/Qmnsf/4XEwgamrY7E1F1D0Nli+q4mEG5uzw4jaxr3bOVw7j3GLQvEbL4vYRlldH4TRIipLUHGNnSflk3nsYv9GbvYn/1XqmiL8ifI2IYQU1t60r8hLKMMs/m+TFkVzuHcezTu2SoOdd/VBILOFmPqrmHa6kgSCxu49/Ff8BlpSaSFAwMF6azcn42xq5rp62MoadRS4bse9QgFidNm8aD8mmA+irePUN3aScnm11CPkAVNf81tgRubLd/T+BPcpM5+lm69HrdPzgvG1N6lFwxlCDfPBKYI3HTr9QI3WYsXUD+IGwOll2zBdbSSbK0U1kFVS6cQPksifuBB2RUSp81CPUJBpd+bFDd2CNys3J/NQP55jj3mhM9IS+59/Bdu1bYL3GyIvUHf5eOEmdnhO9qKpr0aYnNqmPxKGGbzfWW3ZOpBQkxlDbktSsbHRM8Qxi725+tr1egT/0aQsQ2hY+zo+naXwM24ZYEcvVWL7rtP2WFkTbi5PfqEfQSmFAntOqmogZpAb/wNrQVutp3IF7i5UNZMhe96fEdbETPBkQfl13jrcI7AzfW7bRRvelVo+f33Cnk+NA1DZ29stnxPU4eO/HUvCgHV11jJH3adw0DphaMmAa1OtmhVkmzF9bY14rwjWQjBbl2XYPSXly6ip0srlAPnHcn0tjcLJSRn5TKqWjqFNbz8i0z6mqpJnDZLWCrFjR3YquMxdPZm1deX6a8t5sjkGWhGWVK86VVu3mtj2upIjFxUAjeHJjriO9qKcs1aSpsejZvIYdbTZJS3PNSQJKlKkqTrPxl/+Qkzf12SpOifvP+zJEl/+x1h8DdJknb85P3AIMzLkiS98r9eGJhMtUWn7xZEVbb1TyQWNjD5lTDGLQskpbiRhk/fxWekJWcdneltrmHO9tMYOnuz6POLDOSfJ3SMHTuMrOk+HUnohVKMXdVYbjpGaZOWy0sXoZJk10BRQwdTVoVj4uZDeGY5zfv+C39Daw5NdKSvoUIwuaf9T9PXWEnspBn4jLSkaa+G8MxyLJYEMGVVOEUNHZRsfk0wMK1Oj7UqDmNXNW8dzpF/xxg7QsfY0Z97hrcO52Do7M2s90/R3qXnrKOzEDznSpoYtyyQsYv9SSiop1yzFp+RliRbK+lrqBAa34LP0nlwJ5MwMzv8Da3RH9/LO3G3MHHz4SmvWFq0OqGZZi1eQHmzlmmrIzF2VROSVkJblD87jKxl66K2mBf2ZWCg9GJeSCq9rbUcmTwDPwMr6nar+epyFeOWBTLp5VDy6top16xFJSnIdH8efXc3Nlu+x9hVzdqDV+lJPchOYxuCTWzpu5rA8i8yMXT2xmnbSVq0OlJnP4t6hILCjS+RUd7C+BVBmC/04+itWir93kQzypJT02fT06UVQmLV15d5UH6NiLH2+BlYoTuym/dP3sbUXcP09THUt3dxc9VyVJKC/HUvUt3ayeNrojCb70tgShHagzvxHW1F1DgHBu7msfyLTAyUXvxh1zl625uJn+KE72jZAv379buYzfdlomcI1++2kbfGQwiuni7ZOjVyUbFyfza9mUcJMrYhyNiG3syjrNyfjZGLCoetJ+jpbCf9GTdUkoLqDzZytfo+Ez1DMJvvy6EbNdQEeuM72oqEqTPp6WjFNegsBkovPL68xEBVLlHjHNhpbIP24E4CU4owclHxxLpoqls7xW+6uWo5dW1dTF8fg6m7hvdP3qbr2134jrYiYqw9AxU3mBeSioHSC5ePz9B/r5DEabPQjLKk+oONHL1Vi/lCP8avCCKjvIWCP3uiHiG7ZZs6dDhtO4mhszdb4/Pou5pAsIkt4eb29KQeZO3Bqxi5qLBVx6Pv7ibT/XlUkoJyzVpu1bYz6eVQxi0LZP+VKup2q1GPkGMevffrheL2wr4M+muLiR7vwA4ja9qi/AlJK8HYVc201ZGUN2uFYnHdcykF9R085RWLiZsP78Tdou9qwqMRBrOf5lJly0ONf2IZPLQwkCRp/SDTN/rJZ08M/rUeFDo2/6uFwZjH7ehrqBCadW9WHAv3XsBA6cXCvRfozTxK6Bg7Ii0c6GuoEJrN+ydv0306Et/RViRbK+npaMVaFYeRi4rQC6VCG05TulLerGX6+hjGLvYno7yFsq1/QiXJDOpWbTvjlgUybXUkt+s6BJFX+K5n/5UqzBf68eSGQ+i7u0md/SyaUZbojuwmJK0EQ2dvlB8m0dvWSMwER4JNbOk+E43Hl5eE5tufe4aIsfaEmdkxcDeP50PTMFB6senoTXozDuNvaC1rbU3VOGw9gYmbDwFJhTTt1eA72ooUu7nUtXVhuekYZvN9SSyUtckh5lDSqGWiZwiTXwkju6pVCITiTa9y9FYt45YF8viaKFq0OhEIbo8OIDK7EmNXNTN9E+nW6zkyeQY7jKzRxe1h7cGrGDp747wjmQfl14ge70CwiS0PSrNZEvGDYNj9OcnsNLYhdpKslQ4FLzcfy6XjwEf4GViROG0WbZ067N89jombD7E5NTTu2SrcVTWtnUxZFS405SHGl7fGg5TiRiZ4BDNlVTg1rZ3CtdS4ZyuxOTWYuPlg/+5x2jp1JE6bhZ+BFR0HPmLzsVyh3fbfKyR20gx2GtvQn5PMqq8v/2hVlGYTbGJL9HgHHpRfw3lHMobO3qw9eBVd3B52GFnLAWm9npm+iRi7qonMrqQ9OkDERFq1Oh5fE8W4ZYEczr0nNPVrHi+QXdXK5FfCmOgZQkmjltw3/ohKUlAT6E1iYQNm832x3HSMurYuUuzmCuskIKkQQ2dvHLaeoK+pmoSpM+WzkXGYTUdvYqD04vnQNAbu5hFmZkfEWHv6c88Ii2z5F5l0n4nGz0C2KHrbGlF+mIShszfB5++gO7IbzShLUmc/i767myc3HMJ8oR/7r1RR4bteCP7bdR2MXxHEuGWB3KqV43VDCltGeQtjF/szfX0MpU1a0pSuwvoKvVCKkYsKa1UcPR2tQrvvPh3J+ydvY6D0wjXoLH2NlURaOBA6xo7ezKPi3C/4LJ3erDhhMf303D8KYeA4+2myq1ofajwKN5EkSUslSSqSJOmx34H1d0mSXv9fLQzMn3SgN+MwPiMtOefkwtXq+5i6a7BWxdFfc5udxjYcmuhIY3sX41cEMfmVMPTd3URaOBBiaktvay1PrIvGfKEfJY1aMtzm4TvaioGCdOaFpGLkoiKxsIGit1eiHqFAf3wvG2JvYKD0Ivj8HZr2alBJCmp3bSY8s1xokN3JUahHyH7Q1NImjF3V2L97nAfl1/A3tObYY05Ut3ZisSSAaasj6dG2EWZmR5iZHT1aObBqsSSA6tZO0pSu+Bta86D8Gi4fn8HYVc2Fsmby1nigHqGgOzmKlfuzMVB6EZFVwb2P/yIYevD5O+Lw64/vFX7XxMIGjFxUOG07yUBBOr6jZcZb0qhl0suhTF8fQ29rLTETHIm0cEDf3Y3i7SOMXxFEY7vMfHYa29Bfcxvlh0mYumu4Wn1fZAR1HPiIJRE/YOjszbYT+VT6vSmE5NCBfmFfBp3fBAnGfjj3HobO3rJldTWBncY2JFsryatrZ4JHME95xdJXX0akhQPR4x1o69QxfX0MEz1DaO/Sc2r6bIKMbehrqGCmbyJm832Jz68ja/ECWQh/9ykLPkvH0NmbwJQiSt95QzDXIQHgGZXF/cjtQljuv1KFiZsPzwSm0JP+Df6G1qTOfpbsqlbGLQvEWhXHwN08ws3tiZ00g/r2Lh5fE8WUVeFUNGuJn+JE6Bg7HpRm47D1BGMX+3OupImLrs/hZyAzOLdPzmPkoiIso4zCjS+hHqGg4dN3Wf+PaxgovVh78CqNe7aiHiG7KcMyyjB2VctustOR+BlYcdH1Oc6VNDF2sT8OW0/woOwKoWPsiJ/iRLeuiymrwpm2OpK6ti5iJ80g3NyegapcbLZ8z7hlgWRXtZI6+1n8Da3pST2Iy8dnMHHz4avLVUII3Y/cjmdUllBGhpSKks2vEXS2GCMXFQs+S0f33af4jrYia/EC4vPrMJvvy0zfRPoaKgg2seXU9NlodXpBZ61aHdHjHYi0cKC/rgTF20eY4BFMXl27oLO+y8d52l+26GNzaoTSoj24U1ip75+8LYRRpd+bbDuRj6GzN0sifqA38yg7jKwfkTBQcrX6/kONfyIMRkuSVCFJktVPAsgzfzZn7mCQ2e5nn48fshIkSZokSVLpz4PP/+uEwQSrGXQc+EiY16u+voyhszcRWRVcXroIf0Nr+nOSsdnyPZNeDqWurYtICwfipzjRotUxdrE/yg+TGCjKENkwX1+rFib4EGNtiXiPBZ+lY+LmQ3x+HamznyXYxJaBqlymr4/h8TVR6Lu7CTG15dBER0qbtJi4+eC07SQ96d8I5hJ0tlgcppLNr+Ez0hJd3B6eCUzBfKEfV6vvkzhtFjuMrOltlYOMU1aF09tcww4ja05Nn82lyhaMXFS4fHwG3XefopIUlL7zBm8dzhFCKmflMtQjFPRmHsVRk4Cpu4byZi0xExwJHWNHp07PuGWBPLnhEAOVN/EdbUWa0pW4vFqMXdUs3HtBCLq63WrhKjl0o4YMt3loRlnyoDQbxdtHGLvYn1atjnBze6LGOVDT2slEzxBs1fH0Xz+Fv6E1V1YsJjyzXGjOQ8JBe3Anbp+cx9DZm3MlTSRbK/E3tKavvgxbdTwTPUPo6WglapwDxx5zIq+unbGL/Xna/zTdpyPRjLKkcONLbDuRLxjCkJ+7OzmKhXsvYOyq5nZdB2lKV4KMbejpbGf6+himrY6kv7aY0DF2pNjNJaW4EVN3DfNCUoX2Xv3BRiFQI7IqKH3nDXxGWtKfk4zLx2cwm+9LXVsXidNmEW5uT4tWx5RV4VhuOkZvay07jKzJdH+eq9X3MXJR4fHlJXRxewRNDWnq8fl13Fy1XFZEqnJx2naS8SuCKG3Scmiio5yK3N3N+BVBsgCvysV3tBU3Vy0nPr9O0FRLxHuoJAW6uD14fHkJIxcVX1+rJtP9eXYYWTNQlIHlpmNMWRVOyyDOTk2fzb37XZjN95XdQznJqEfINBWRVSEsueoPNqIeoaDjwEfMC0nF1F0jp1DazZVdmrXFTFsdyZMbDtHT2U6QsQ1pSldu13Vg7KpmzvbTdCdHidjbkFKw7UQ+BX/2RDPKku7TkTztf5qxi/3Jq2vn2GNORI1zoEfbxkTPEKatjqSvvgx/Q2uSrZWcK2nC0Nkbt0/Ooz24UwiC1TFXMHT2JjyzXGSL6Y7sfiTCYMZsJdfvtj3UeIjU0hclSSoZZPgBg5/9P5IkvTz4+rwkSY0/TyGVJGmeJEn5gwIkX5Kktx8FP/2/xqglSTKWJOnq4A8ukCQpaPBzK0mSrkhybu1RSZIMBz83GnxfNvjc8p99x+N2M8lf9yI7jW3oba5h7GJ/Fu69QFuUP+oRCvrvFTJ9fQy26njBOPWJf8NaFcdEzxAGKm8KjdX9r2kYu6pp69Sxw8iai67P8cGpAoxcVKSWNhEzwZGzjs6kljZhoPQiLq+Wi67PETHWnpv32jB2VbPp6E3KNWtlrU+vZ6JnCC4fnxFCZaAgnSmrwuUDMygk2qMDcNp2Uib0pmoRMPP48hJm832paNYSYmrLdc+l4nDerusgYepMEqbOJDanRjCrZGslIaa2VDRrMZvvi8eXl8hf9yI+Iy3pa6rGYon8XUPMrif9G57ccAhbdTwDBenCynH5+AwTPUPo1uvxM5ADcJuO3sTYVU2rVkfEWHsuuj5HQFIhBkovsT8xExxJLW3CyEXFB6cKuOj6nOzf7dRh7KrG/a9pVPiux8/AioHKm0z0DOGZwBT0iX+T9yf/PLbqeKavj6H/XiHqEQraomScjl3sT29zDTuNbchf9yKeUVkYOntTPrg/ydZKIrMrhdBKmDqTcHN7Cuo7MFB6sf4f17juuZRgE1t6tG1ifxr3bJX3p7GSaasjcdp2UigYPakHmbP9NFNWhTNQlIFmlOzKcA06i6m7RuzP5aWL2HwsF2NXNdfvthFp4cCRyTMEow69UMo5Jxeixztw734XRi4q3j95m+JNr+JvaE17lyyc54WkCkE5UHEDxdtHsNx0TOyP7shu7N89zvgVQWJ/Sja/xqLPLzJ2sT/17V3sNLYhb40HwefvYOgsu5RiJ80gSTGH7KpWsT+XFswnzMyOooYOTN01rD14VdSt9HS0Yr7Qj6f9TwuloO9qAo+vieLxNVH0XU1AJSlo3vdfPO1/msmvhNHb3oxmlCU1gd4ihVff3U2YmR1Zixfw9+t3MVB6cbX6PkmKORyZPIOEgnoMnb0JSSvhnJMLQcY21LXJ+7Po84tif/prixm/Igj7d4/T9e0uWdifjsRy0zEsNx3jQfk1VJKCrm93MS8kVd6f2mL8Da0p3vSqUBYehTBwmqPkVm37Q41H8X3/k+P/pjAYIUmS2eBrg0EG7yZJ0neSJK0Z/PwrSZK2DL5WS5L01eDrNdJPcmp/ayhmzCZ19rPET3ESTLq8WUvEWHtyVi5j4d4LmLpr6GusFNqYzZbvcdh6QrhyetsaGb8iiOVfZFLhu56dxjYU1Hdg5KIiMlsOBB97zInwzHKMXFQUN3aIApwX9mUwwSOY/pxkmXmcO4D9u8exVceLQ9TbXIOpu4YX9mVwzeMFosY5UNKoxUDpRUZ5C8cecyJN6cqG2BsYuajQ6bsJMrahwnc9T/ufZvyKIMGou09HMm11JK5BZ7kfuV32E3foMHXXsO1EPil2c4kYa09iYYNg0mFmdpxzchE1Ce1dejSjZFeO845knlgXjf74XlSSggdlV3h8TRTPBKZQsvk1NKMsaevUYegsu1LOObmQMHUm50rkva5q6ZSF4arlLPgsHbP5vvTVl+E72or7kduxVsUxbXUk3acjUY9QMFCUwbhlgXhGZVGuWUuQsQ15de0YuajYf6WKQxMdiZ/iRFhGGQZKL0qbtOwwsua651IWfX6RiZ4h9DVVy3ud/g226njs3z1Ow6fvyri8X4+Jmw9LIn7gyorF7DS2oaihQwjLI5NncNH1OSKzKzFyUdGt14vCsDnbT8u4zD0j7/WZaKa+HoHNlu9p3vdf+I62oq+pWgRfr3sula3MQYZ/oayZ0DF2pCldRdZOp07e63LNWpQfJjF9fQwDRRmC2U9bHYnLx2co3vQqvqOtaNHqRBD2rKMz4eb2JBXJuEwpbiTc3J68NR5sPpaL2Xxf+muL0YyypD06AMtNx2QLNWEf6hEKHpRmM25ZIM47kil95w2CTWxp79Jj6OzN36/fJXX2syRMnUlIWgkGSi8qmrX4G1qLczPp5VD6GirEubFWxeGoSfjFufH48hJZixcQNHhuDJRefHW5ithJM8hwmyfOTUmjjMu7AW/9eG6unxJCd8qqcOzfPU7jnq34GVjR21wjYmA/PzeXKuVzk/6MG+v/cQ0jFxX67m58RlqKc/PkhkMM5J8XVqLD1hOPTBjk1bU/1PiPMPh1wWAqSVKOJEl/kCSpRZKk0YOfiyCKJElnJEl6bvD16MF5I34Prt3Mp4mZ4Ej+uhdZ/kUm01ZHCq23qUMnTPBgE1saPn0X5YdJ2L97nLKtf8Lf0Jrrd9swUHpR3NiB72g5n1/x9hH+sOsclX5vEmZmJ9xG+u5uYYJPejmUtQevcs3jBY495iS0wv6a2zLTK5AZ48r92aTYzeWi63O8f/I2FksChEXQ21orgnLR4x0o3PgSL+zLkC2Ey8dRj1BQ394l4hZBgxrf0/6ncdh6Au3BnfgbWgttr7RJi2aUJbW7NvOUVyw2W76nwnc94eb23Kptx0DpRbeuC9/RVuiP72WiZwiuQWe5smIx8VOcOHRDPnydOr3MrIoymPxKGKu+vkyytZIjk2ewNT6PccsC6a8tlv+HtkbMF/oRklZC1DgH0pSuLPr8IhZLAujNihMWiZGLipTiRoGrWe+fYurrEbRHB7DDyJoWrU4wpGATW+p2q1F+mIStOv43caVP/JvA1eWliwgzs+Pv1+9i4uaDTj+Iq+8+Fbi67rmUY4858U7cLYGrIWtkKE13CFcL916Q/4eMw0KgGzp7E3S2mJgJjhT82ZMlET8wbXWksEIb27swUHqRUFBPsIktuW/88Re4ulp9HwOlFyWNWmFlPOUVi9sn5wWu9l+pknGl1w/DlcvHZ7jm8QLxU5xEAHygKlfgymy+r8BVhts8tp3IH46r+/UCV9Hjf8TV42uiBK6GtPKkoh/pbc7200x9PYKOAx8Nw1X5T3A1ZF2Wa9YKS9lA6UVPl1b+HxL2Ybnp2O/i6sGdTCa9HMrqmCskKeYIXA1ZQSpJQX/uGcwX+hF6oZQUu7mkP+P2I64yj+Iz0pLe5hqMXFScK2kahquxi/0fmTC4XdfxUOM/wmC4EBg16OvqkiTp08FgR9lPnj8pSdLtwde3JUma/pNn5ZIkTfoVmH+RBvN2H5/+JL6jrXhQdgWz+b5cKGvGZ6QlPakHMXZVU1DfIbsk8s8Lk1kzypKBu3mYumu4frcN9QgFfZePY+Si4nDuvd+FV9QwHF5SkQyvv0ZOXbx5r40dRtYC3tFbtcPgZZS3/Dq8gvRh8PSJf8PEzedHeFcThsMrvybgDbl7jF3VFDf+CG/sYn+SihrwGSn7Yk3cfLhV2/678C5Vtsh+9UF4f79+dxi8lOLG34UXlyfD0333qYC389fgFWVgvtDvR3jJUQKev6E1/ddPYbEkgLi8Wjn4G7dnGLzejMMPBS8yu3IYvPj8OgHP1F1DdlXrMHiHbtQMg3eupEnO8von8PTH9w6Hl3mU8SuCBLzOb4KGwzsTPRxeTvKvwgu9UPoLeL6jf4SXWjoc3leXq4bBSyioJ3SM3W/Ci82R4XV9u+t34bVF+Q+Hl7BvOLysOMYtC/xVeEHGNvScO8BEz5Bh8IbSoUNMbQW8sIwyOV42CG/oPHZ9u0uk/g7Bm+ARzP4rMrz26ADGLvYnsbDhV+E17dXwlFfsI2HOM+coKajveKjxH2Hw60JhnCRJ6ZIkPf//Vxj8dMydK1eZljbJ5mNP6kEiLRz44FQBNlu+J8VuLldWLGbq6xEczr3HTmMbBipvCk3fZ6QlX12Wi7SqP9hI1DgH3D45zwv7MkhSzKFpr9xLJj6/Tk5LHNT++nPPEDrGjvDMcp7ccIiLrs9x0fU5ntxwiOVfZBJiakt/rpyR0dShQyUpuHe/C1N3DU17NYSZ2bHo84u4fXKeNKUr1R9sFEViPiMtGSiSMyQGKm6gHqEgNqdGzgbZrSbFbi42W77H/a9pRIy1pyf9G8wX+lHerJWDsjo9xq5qHpRmE2Rsw+oY2fWTtXgBxZtexXLTMYLP38F3tBW9WXJV6ZA74FyJnPnUHh1AwtSZeEZl8UxgCocmOlK29U+MWxbIzXttsl9Z24ahs7fwzb4Td4vJr4Rxc9VyTk2fjaMmgXfibhE9Xm6BYOjsze26DjmYfq9NNu2P7+XQREfeOpzDTN9EEqbKMaCJniG8f/I2fgZW9DVUYOjsTW9bI76jrciuamX8iiCKN71K7KQZKD9MYtXXlzn2mBPagzsxdlWTUd4iNHoDpRc96d/gO9qKgKRCbLZ8T+HGl7iyYjHTVkey/h/XZLqouDHM3VDc2MHYxf50n4kWdGH/7nGSFHNo3LOVKavCicurlTXzu3mCLnxGWhKRVYHFkgAqfNdz0fU5XIPOsvyLTELH2NES8R4mbj40tnfJAfo2mS76riYQZmbHC/syULx9hDSlK5nuzzN9fQxfXa6SExYG6aJVq0M9QsHh3HtyEHuQLhZ8lo77X9OItHDg3sd/wXyhH3+/fhf1CJkuDJRePCi7QpCxDX+/flfQxTknF6xVcQSdLSbc3J7ezKPCDaiSFLRqdYIugk1s8YzKwnlHMpnuz1P6zhuMWxZI6IVSEW8wUHoJukgsbGDSy6G0RLxH4rRZOGoScA06K+jCYkmAoIueLi2Gzt7oj+9lh5E1m47exGnbSa57LiVvjQf27x5n24l8OSZ3JlrQhUpScLX6PuNXBMmV+IN0MWf7aY495kThxpew2fL9IxMGRQ0dDzX+Iwx+WyAESpL0/qN0Ezk/PRufkZZsO5HP9PUxJE6bRc+5Axi7qulrqibYxJat8Xm4Bp2VA4fnDsgm+JloQkxtcQ06y7YT+QSb2ArG0X0mmsRps3D5+AzbTuTL/tHmGoxd1VT4ridx2ixR4akeoaC4sYNJL4dS4buevDUe4plK+vFZ+jNu5K3xwHyhn3DDRGZX4rTtJGlKVzoOfISB0gudvlvOjx58FmnhIJ4NFKTjb2jNkogfWLk/m0gLBx6UyVkTAwXpxExwxP7d46zcny20fUNnb1oi3hPP/n79Lr6jrWjV6rBYEkBLxHtkuj/PBI9g/n79rjj4FksCuO65VDwbYgpJRQ085RXLdc+l1AR6Y+SioqqlE5+Rlmw+lstTXrHET3ESz/rrSvAZacmmozeZF5IqFxJlHJaZRV0JoWPscN6RzLyQVLkYq61RFtSJfxPPhrTB3rZGTNx80CfsI9layeNroghMKUI9QsHtug5M3HwofecNCje+xONrosiuahXPpqwKF8/M5vtytfo+KkkhGqSdc3IR2me3rosdRtaEXihl1vunxLMhN9sOI2sW7r3ArPdPycVaVXJa6lAriIV7L7D24FWRvmno7E3/9VMcmuiIzZbvWXvwKppRljS2d2Ho7E3jnq1kLV6AzZbvOZx7D5UkuwfHkkHLOwAAIABJREFULQsUz8avCOLoLVnwxOXJ9R9XViymbrea8SuCuHe/C80oS+LyarHcdIwjk2dQt1uNkYuKujb52YbYG1ir4jgyeYawXPtrbhNubs/6f1xjwWfpchrnVbkKWPfdp4Sb2/O0/2kWfJbODiNrejrbxbOzjs7M2X6akLQSmZF3tmPqrqHo7ZWcdXRm6usR3Kptl4vA7raJZyWbXxPP1CPkZ9NWR5JiN5eSza9h6q6hRytb7EFni1F+mESK3Vxh3fR0yNbX0LMwM9nyMVB60ZsVx05jG54PTeOtwzmEmdnRf09Ocujp+P28/4cWBk8rKW7seKjxH2HwI/OfLEnSuMHXJpIk/SBJkqckScd+FkBWD75+52cB5O/+2Xc4O8pVsRM9Q8irk10Wtup4IrMrUY9QsOCzdDyjslBJCgKS5MIm39FWnCtpYtrqSJEOaL7Qj2RrJV3f7sLQ2ZvcN/5I8aZXMVB60RLxHmlKV6G9RY93YNLLofS2N+NvaI39u8cpaZS18oV7L+Dx5SVUkkzIM30T8TOwIqO8hamvRxAx1p62Th1m833lPkCDjCR/3YuCoNui/Dnn5IKpu4b+3DNEjXNgyqpwerRt+Bta46hJoKhB1qRe2JfB/itVqCQFIWklrI65IuIIU1aFE2nhgFanx9Rdw1lHZwYq5BqJwo0vofvuUwyUXnQc+IiyrX/CbL4vAwXpRIy1Z+rrEfR0afEzsMJp20mhuS3/IpPI7ErBSFfuzxb+/DnbTxM1zgGdvhtTdw2ps5/lQalc/1C86VWxt53fBFGy+TXMF/rx4E4maUpXOcis1xM93kE0TlNJCjyjsgjPLJc7uGZV4PHlJXYYWXOrtp2ZvolEj3egW69n6usRpCldhe+89J030B7ciaGzN7ojuyl6e6X8fYO9cB5fE0WnTk/UOAfmbD/N1er7+Btas3J/NqEXSlFJCr66XMWSiB/YaWzD7boOnLad5NBER3o625myKpyLrs+JeEPZ1j/RHh0gM8q4PRT82ZOxi/0ZqLjBWUdnnlgXTXuXnkgLB5QfJnGpsgU/AytWx1wh+PwdVJKCr69Vs+jzi6gkBUUNcgPF2Ekz6OmQi88y3Z+nPycZU3cNFb7ruR+5XRacCfvIX/eirDDczSPFbi5m831F1pfzjmQulDXjZ2DF+n9cIzClCJWk4NCNGhbuvSCnkTZpsX/3uOyiaWtk0suhRI93oO9qAiZuPlR/sJHmff8lC8TTkeS+8UdZyN0rJEkxB/OFfjR1yKmqQ/2CfEdbMWf7aT44VYBKkq2Y50PT5LYszVpstnwvUqgneobIBW5ZcZi4+XDR9Tka92zFQOlF2dY/kbNymfx9dSWcmj5bZE6FmdnxxLpo4V592v80207ko5IUrI65wryQVDlFeLCm51Ew51lPz5X7gj3E+I8w+FEYzJEk6aYkt1+9LUlS4ODn1pKcclo2KBiGiieMB9+XDT63/mffMdfqcS4vXSQOYeOerXLGz6ZX6c9JZoJHMCl2c+nWy/2GwszsyChvwePLS/iMtOT9k7cFM3UNOkt4Zjl+BlZMXx9DUUMHUeMcMF/oR2+rnEZqoPSi59wB7ga8haGzN5V+b9KT/g0WSwLkpnRtjYxdLHfevF3XwZMbDsmdTS+U4vaJnNmw4LN0ApIK0YyyFJ0/w8zsGLcskJ4uLWcdnZngEUzf1QRKNr8ma3e71egT9mGg9CJr8QL66sswdddwaKIjFYOtI/wNrfn6WjXOO5LF4dt09CbqEfLBSCpqINjElpm+iaLqdvIrYTy4k0neGg85rTbKn5yVyzBQepGzchn3I7dj7KomfoqTCPAFGdvQ3qXHadtJuedQcSOrvr6MSlKw+VgusTk1qCQFzjuSuXe/C39Dax5fE0V/zW1iJjhi6q5Bd2Q3lxbMF4KpdtdmOXCpmEPfZTl18pyTCz2d7diq4wk3t+dq9X1e2CendwamFBGRJbu25oWkUtIoByqf8oqlr6maSAsHORW1vZk0pausNWYcpsJ3PYbO3twNeIvuM9FyVbnbPHqba3jKK5aocQ4UN3YwLyQVPwMrwjPLcQ06i0pSsOjzi2w7kY/PSEtstnxPRnkLoWPsGL8iCH233BLF0Nmb/twzFL29EiMXFU17NULoXlmxmP57hXIl9aQZ1LR2MvX1CHYYWXPohtwmRSUpWLk/mw2xsntw/T+uEZ9fR5CxDXO2n6apQ0fC1JlMWRXOg/Jr3Fy1HBM3HzoOfCSEUd4aDx6Uyv2jEqfNYiD/PBM9Qwg2kVu3OGoSUI9QcKGsWShK207k8/W1avwNrXH5+AxVLZ0cmujIE+ui6a+TA85m833RH99Lhts8OQi++TVqAr0xdPYmxW4uvZlHGbcskDAzO3rbm7FWxaEZZcmt2nYW7r2A72grgs/fISyjDJWk4PnQNArqO9CMskTx9hF679cTbm4vJ1mkHuSckwuGzt5Uf7CRsq1/wkDpRe2uzXSfjsR8oZ/cVr6hgic3HCJmgiPlzXIDO39Da766XCWE0PIvMnkn7hYqSfHIhEF5s/ahxn+Ewf/gmD1lIu3RAXJwbaIjSUUNRGRVoB6hkCt7zx0g940/ymmiFg60anWCoSg/TKK3uUZYAGlKVx6UXcFWHY9KUrDp6E2KGztIsZsrtLH+2mImeATjO9pKdtVkVxJmZoexq5ruM3KK5tB3/f36XZZE/IBmlKWcptdUTcnm1zBx8yFN6UpVSydrD15FJSlw1CQwcFdOJzRQelGuWUt/XYmwZDyjsqhr6yLc3F4OjP/ZE33CPqasCsdnpCVfX6vm6K1aosc7MNEzhK5vd1H6zhuYuPmIuoO1B6/iZ2CFw9YTDFTlUhPojYHSi1PTZ5NX1y6YkceXl2hs7yLT/XmMXFTkr3uRno5W8V3Ph6YRlyd/15Cm35t5lOnrYwgxtSX4/B3W/0OutLZYEsBA5U3hvz41fTa3att5/6ScyaN4+wgD+efJdH8eA6UXRybPoEfbhmvQWdQjFLj/NQ19dzcxExwxdPaWO6ZmxTF9fYywvjLKW4if4oTFkgCa9mpE8HeHkWyxfHCqgCBjGxRvH6E/J1lo1LGTZpBS3Mgfdp1DPULB42uiRDtxQ2dvLi9dRHuXnic3HBLC7VKl/F0GSi8a92zlwZ1MoeFuPpZLQX0HQcY2ctrn9VO0RfnLgdpJM0gqahBWzhBt3ly1XNBLW6eORZ9fxGekpdzIrbWWhKkzMVB6kf6MGw/KrmCz5ftfpc1KvzdFLv4QbUZkVRBmZidbXT+jzUM3asQ5mPRyKL3NNb+gzdUxVwRtljRqBW1W+K6nv66Emb6Jv0qbhRtfQp+wj8mvhA2jTc0oSyZ6hqA7slvQZugYu2G06ahJYKAql7sBb2Gg9CJJMYe8unZmvX9K0GZ9excZbvPEOehtbxa0ueCz9GG02fXtLnozj2Li5sM5J5dHJgwqmrUPNf4jDP4Hh73pGNo6dey/UkWIqa1cgWtiy1eXq9Dq9NyP3I75Qj9UkoLJr4TRk3qQvoYKNh+TKzgNnb05NNGRS5Ut3K7rIH6KE0YuKjSjLFkdc4WBqlz6r59i2mo5T97UXUPdbjV1bV0czr1HmJkdBkovdhrbEHqhlG6d3IXTYkkAKkmB8sMk9Il/o/d+PTN9E/E3tBZad0pxI6VNWk5Nn42xqxqfkZYs/yKTB3cyqf5gI9PXx6AeoeDJDYeo/mAjFc1a2U1j4SCYeEBSIb3tzVz3XMoEj2BUkoJZ759Cd2Q3+oR9KD9MYqexDeOWBXJ56SLi8moJSSsRTCTc3J6Fey/Qn5NM7a7NKN4+Ihhj2dY/MVB5k1VfXyZmgiNGLirip8jpfhnlLeSt8WDSy6FCwLRHB9Bz7oDQpIe0bq1OT2R2pdDQQ0xtmReSSqtWR/O+/xLCd8qqcAo3vkR/bTFvHc4R+ImdNIPrd9u4VdtO8aZXBX5stnxPf81terPicP9rGipJgdl8X1JnP0tTh45DN2oIHSPjJ8jYhvDMcnT6bjoOfMTYxf6oJIVcWJccRW9zDVvj8/AzsMJA6UX0eAdSS5sobpSL+4bw4xmVxYPya/TnnuGJddGoRygwcfOhJtCb6tZO4vPriBhrj4HSi2RrJUFni+nRtnFlxWLGr5Bbb8zZfhpd3B56OlqZs/00O4ysheWQUFBPVUsnydZKTNx8iBhrzwv7MhgoSKcm0JunvGLxGWnJE+uiqfBdT0mjFs+oLKLGyTSROG2WfFlQay03Vy1nomeIcPVpD+6kO1muIQkytsFiSQCZ7s8Tm1NDeGY555xc5BRbMzueD02j77Kc82+tikMlKZj6eoRscdfcZv0/rnFookwTRybPYNPRm1ytvk/hxpeY/EoYvqOtsFXH0xLxHr0Zh4VVbL7Qj/Rn3GjV6vj6WrWwtoNNbPnDrnNodXraovyxf/e4OLP5614UZ9bPQKaJmAnymS2o76D0nTcETVhuOsZA5U36r59iwWfpw85sf07yI2HOs5+eS1VL50ONf0thIMnVweskSfpwMBAcOOT2+VcOq9HGRGZXMlBxg65vd+Hx5SVhXqokmRBy3/gjgSlF9OeeoTs5ivX/uEaG2zyMXFSoJAXGrmqyFi9g87FcWrQ6erPi2HYin3Bze0zcfFBJsiaX/owbq76+TLdez0BRBqEXSomZ4CiEzaSXQ0m2VpJU1EBvay39Nbf5w65zHHtM1lp9RloyblmgyOnvv1dIb7Pst01SzGGiZ4g4MIUbXyIso4wHpdnovvuUlfuzSZ397LD/K2flMtGXv+fcATYdvcmlBfMxdlWjkhRMXx9DpvvzvHU4h/YuPf3XTxGQVEikhQOm7hrBgFNnP4tnVBY9ne08KLuC8w45EGq+0A/NKEsmeARzavps4vPr6Guqpr+2mHkhqSRMncm4ZYGoRyiwWBLAkckz2H+lioG7eXQc+IgX9mWQYjcXA6WXYNT5614kJK2EgYJ09An7WB1zhfRn3AQuTNx8uObxAttO5FPf3kVvxmHeibslrC+VJAuqDLd5rD14lU6dnoH88wSdLSZqnOzKGGIiQ9Xive3NDFTexOXjM8ROmsHYxf74jLRk/IogEqfN4uitWvrqy+hrqOD50DQSp81igkcw6hGyQCve9OowGlv+RSZnHZ1/QWNBZ4tlGjstZyiFmNqK/+vJDYdElfJ/h8ae9j8tXDS+o62G09j9egbu5v0+jdUWiwruJMUcDJReqEf8SGPhmeWCxjyjsn6TxmpaO+k5d4C3DucQOsZuGI1dWjCftw7n0Napo//6KT44VfCrNJZR3iJoTPlh0jAam+gZQpJiDgkF9YLG3D45T/wUp2E0Vrb1T3x9rXoYjSVbK3+dxooyBI0FGdtg5KJ6ZMKgurXzoca/qzBIkeRWEX6SJPkOjX/1j398hBHKD5PIdH+ery5XoYvbw/4rVej03fR0aenU6en8JojwzHI6vwlizvbTpCldmfX+KXYa2zB2sb/QzFSSggkewYSOsWOCRzBXVizmD7vOiSZq3WeiOXqrlqYOHb3362nq0NES8R6BKUW0RLyHw9YTInjnb2gtDsMQ7EkvhxJubs/kV8LIfeOPLPgsndw3/igf7Kw4UoobqW7tpK++jK3xedTtVrM1Po9T02djrYojcdosTNx88B1thYmbD+oRCgydvVGPkLW26PEOTFsdSeHGl1j+RSb9uWe4VNnCQP55BqpyKW7sYKAqlw2xN6j0e5P1/7jGsceceMorliOTZ2DkosJnpOWwv7GTZjB9fQyxk2aw6uvLlGvWsjrmCg/KrnC7roMH5dd4cCeTm/faGCjKwDMqi5LNr/H4mihiJjjy+JqoYb/T2FWNZpSliEMo3j5CwtSZovHZpqM36b9XSHmzlgtlzfRfP0VqaRP5615k0ecXyV/3ogiMT1kVPmx/Td01+BlYYequIUkxB1t1PI17tvL+yds07tlKXVsXfU3V1LV1kVBQT0/6N8Tn15GzchnzQlK57rmUiZ4hhJnZCcFsoPTC39Aas/m+7DCyxmnbSc45uYiWHiFpJbRHB9DWqaOno5W2Ttki0Sf+DZePz5C1eAEuH58hxNSWccsCCTaxFb93yGKxWBJAkLENT/ufJsNtHsoPk9Ad2c1Xl6vQd3fTrdej7+6m69tdRGRV0PXtLp72P036M27M2X5a0PFOY5thsENMbRm/IojLSxfhGnSWy0sXEZtTQ/fpSA7n3qNVq6O3vZkWrU6+8+JsMfcjt+OoSeCsozOOmgTxv/8aHU96OZSclct4PjSNm6uWk1jYQG/GYZKKGrh3v4u+hgpqWjtp+PRdtp3I59T02dhs+Z5T02f/Lh1HjXNg6usRolhsqH15f04y/TW3KWnUMnA3j7cO53A34C02xN4gfopMx8cecxpGY0OwD010ZOX+7EcmDGpaOx9q/LsKg9v/6h/6a2OSZIihszc+Iy0xUHoRZmaHy8dnSJ39LAFJhfTXlXCrtp2SRi11u9WEXiglw22efDOUhcOwtUPBqzSlK4EpRdwNeIu8unaKGzvoba4Ra+eFpIq1mlG/XGuz5Xuxtu/ycXqbawhJK0GfsI95IalEjLVn+voYNKMshRY2blkg6c+4YauOpybQG48vL9F3NYG+pmp0+m70Cftw++Q8hRtf4ol10WhGWQphM25ZIP6GchbVvY//It9edv2UfPuYTo/++F6Sihoo+LMnT6yT02Z/Kqh2GMkZUUcmz8AzKoubq5Zz9FYtnTo9LVodiYXy2rUHr5IwdSam7hrBeHYYWWPi5sOxx5xYuT+b3Df+yNFbtXR9u4tWrY6EgnoGCtLFWkdNwjCmNbR22mo5QyUu78e1/bXFDBSkszrmCg2fvivWDrnDTNx88BlpybTVkeSt8ZAb9x3ZLWuodSUM5J8XeHfYekLgXSUpBN6nrY4kzMwO16Cz6L77VL7Vrb6M/twzFDd2PBTNuAadFTTTV182jGZC0kq46PqcwPsQzRg6exNubs+4ZYFyA72zxdQEynUYRQ0d9DVVC5px++Q8EWPtBd5/i2YK6jtkmmmsJPj8HfTH9/63aaavsVLQzB92nRM0M4T3n9PMEN77c5I5eqsWrU6PLm6PoJnH10Q9FM3E5clrh2gmf92LrI658ou1Q3iPn+Ik8B6XVyvwHp9fx0D+eVbHXCF+itMwmom0cHgkzHmOUm4L/zDj31UY/L+SJM3+V//Yn4/JkiGm7hry173I1vg8alo7uebxAg5bTwg/Z5JiDinFjRzOvUfTXg0TPUOELzF/3Yu0deq4d7/rF+tOTZ89bN2kl0OHrWvV/rhuyGz+6bqjt2p/d911z6XD1nUc+Og31+Wt8XiodfFTnB5q3ZRV4XQc+IikooZHtm7W+6fEurq2X65rjw74p+uGmsn9d9a1/GzdTmObYevi8n573YbYG7+6bqDixj9dl7Ny2UOvG7oGdGidiZvPb64bcuX8n66b6ZvIgzuZv7muLcqfgcqbxOX9GKD+6bqmDnldsIntr66Lz6976HWrvr78q+tUkrxuoCiDpg4dbx3O+c11yg+THsk6EzefRyYM6tu7Hmr8WwkDSW6PmidJUqEkSf2SJN0ZfJ8vSVLev/rHD7mJqgfNUUNnb7n74dliQtJKhCZVt1uNLm6PSLtce/AqAxU3uObxgjCpL1W2UNKoFWmJ6c+40dcoX6rtM9JSXMPYceAjkba3+VguX1+rFoHHcs1a8uraRUBz/Iog+q+fonDjS0x9PYKdxjbE59dR3y73lTd118iX63RpsVbF4TvaCsXbR7gb8Bb6xL8JE33l/mwSCxu4uWq5qEs4V9LE8i8yRZyh+0w0lX5v8pRXLH4GVuJWqSGz/NhjTrRqdRzOvccOIznds3jTq/RmxYmA99DFICWbXxMukticGmGKm7j50PXtLnT6bmzV8fiMtOTJDYfovV9P6uxnMV/oR/R4B5Z/kSlaGxgovbi5ajkPSrNFCqrLx2fQJ+wTqYm+o+U0zoCkQlLs5goXVX17l3CFTH09gv6a2/TnJDN+RRBhZnbMC0klr66dcHM5aHtpwXz6a4t5J+6WcEt1HPiIloj3cNQkiHTioUviDZReRFrI6aSuQWcJHSO7iAaKMhiouMHkV8JEIkB1ayd1u9WCxoLP3yH4/J3hNPbdpyg/TBJxiyEam+gZQugYO7KrWilu7CDSwgGLJQFcdH1uGI05ahJo69QJGlOPUAgaG0rFHaKxeSGpP9JYTjIFf/YUNJZQUC9ozMhFJdNYZzvhmeX4jrbCctMxagK9/ymNpZbKNBY9Xk6x7jl3gN779Ty54ZCgMZ3+RxqLn/IjjQ2lFJdsfo3erDgWfJYuanF+SmM7jKw5nHuPDbE3OPaYTGO6I7vRd/+SxrrPRP8+jd3JZOX+bJEqrk/8G3cD3sJy0zE0oywfCXN+WjmXxvauhxr/bsJA8XvjX/3jHcaY0dOlFVkNuu8+xdDZm9J33iBr8QKmrY4UV1su+vwiFYMtGxILG0RZe29rLQ5bT5A4bRZVLZ2YL/TjbsBb9Oeekcv3y69Rsvk1xi0L5HZdB7GTZjBn+2k2xN6QL6NPK8FaFSea47l9cp5IC/nSnUkvh3Jz1XJRFNRz7gDR4x0wcfOhrVNuKaB4+wjhmeX4jJQzmIYE1s17bYxfESS3Shhsd5A6+1lqd23GbL4vLRHvCd94X6Pc42bzsVxSS5tQSQrcPjkvWmMculHDE+uiyXCbR7dObkbW+U0Qurg9GDp709PRStbiBTLDzT1DmJkdC/deGLZf75+8LbewuF+Pw9YTJEydSceBjzBf6Ef1BxsFc31Qfo3Sd97AYkkABfUdHJks79dbh3N+sV+N7XIrhkgLB9ECIWflMrFf3WeixX61d+lRj1DwlFcsEVkVw/YrZoIj1+/K+1W48SWxX+ecXKjdtRlTdw3N+/6LJMUcbLZ8T19DxS/2q7ixg+VfZIpb2X6+X9qDO9Ef3yu3QGhv5vLSRUx9PYL4/DqxX0MML6Gg/sf9amvEUZMwbL8q/d78cb/KrhBsYiv2aygjbNPRm2hGWRJ8/g42W74XjQtN3TVEjLUftl8Ff/Zkgkcw3clRRI934JnAFNq79PiMtOQpr1gisyvxGWnJqq8v80xgCjETHEX7hoI/e9Jfc1tU8NbtVmPqrqG+vYtka6W8X4N3CGw6elNU1xc1dODx5aVh+3XR9Tkq/d4U+5UwdaZoGfHz/VrwWbrYr/j8Oqa+HiH2y9DZm4SpM9Ee3ImRi4pKvzdFfcPQDXMWSwJE8eXQfvkbWhN0thibLd+TbK0UrT4ixtqTUtz4yIRBU4fuoca/lTD4iVD4x8N89j89np7+GKXvvIHTtpNklMtNzIYqFyuataKXzVCrhzAzO2Jz5NYS1z2XEjtpBoEpRXh8eYk0pSt9VxOwWBLA1vg8VJKC/prbmLj58OCOrIH3aNvkQ3cmWuTcP7EumrYofwo3vsSc7adJKW4k2MSWex//BQOll2hGl1fXLvrKhJjaEp9fh5GLirw1HkSPdyAso4xFn18kw20ePenfMH5FEG8dzkElKUQjuYGKG6gkuQPluGWB9GYeRT1CrkSe+noEXd/uIveNP/JMYApxebWEjrGj0u9NDJReogncUBvgurYugoxtxAUhRW+vJNLCgSfWReP+1zSyFi+gOzmKiZ4hIud8oCAd84V+ootkX0MFYxf703/9FOoRcsXw5FfCODJ5Btc8XuAPu85x6EYN4eb2lL7zhqy9Dzabq2rpFHjaaWwj8FS29U+Em9vzlFcsrkFnue65FF3cHia/EobHl5fkeyoGG8UNtSbvr7mN+UI/0R567cGrTPAIJn6KE1mLF/B8aBqR2fI1iYUbX5ILpQbxVD/Yb6q4sQN/Q2vR4bX6g42EmNpirYrDeUcyeWs86PwmiKmvR7Do84tym5KMw4xfEST64zwov4apu4aBihskTpvFpqM3GbcskFPTZ3PR9TkWfX5RZKEN1b/E59cROsaOtk6dwJOfgRXFjR1YbjpG7a7NBJvYYv/uceZsP03hxpdoi/IXeBpqzDfRM4SeTrn9w0BRBiZuPvTX3CZ+ihPbTuRjsSSAFLu5pM5+Fo8vLxGQJF/pOeQmG8KTvrtb4EkzypKqlk6e3HCIpr2y795p20kcNQmUvvMGjXu28pRXLH/YdU50WJ38Shg6fbfAk7Grmr6GCnHWzBf6kaZ0JcVuLqtjrrA1Pk/gaag5XqSFAz2d7eLSI5+RltS3d4mz5m9ozZztp7FVx1Pp9yb3Pv4LlpuO4bwjWTQGnPp6hOjhFJ9fh/OOR5Na+vTcubRodQ81/l2FQc7P3o+SJKnwX/3jnQcb1eWt8RAXrQefvyMulB8oSMfYVY2tOp5uvV7MvVp9X8xti/KX5xZl/Orc7KpWcTH8/cjt8n3ERRks+CxdzD01ffbDzdV1cWr6bPLXvUh2VSsTPILF3KEGdT+dmzhtFvnrXuRSpXwZfEhaCS0R7+Ez0pIHdzLFnchDczWjLMXc0Aulvzq3p0v7i7kTPUP+j+YmWyvF3Bf2ZYi5Q5fXT/QMoXnff4m57n/95VztwZ2MXxHEpJdDuVXb/qtzE6bOxHe01a/OTbGbK5qwLYn4gf7rp34xN6/u9+d2fhPEB6cKxNzrnktJsZtLe9d/b67lpmO/Ofd2XcewuYq3j7D8i0xxefzQXCMXlZh71tGZtk7dL+Z2fbuL90/eHjZXJSnEXBM3n1+dW1DfIa6MHDb38nGCTWyJGGsv5jpqEsTcVu2vzx26WvKnc4s3vSrmRmZX/mKu07aTw+YOXVofmV1JkLGN3O+p8qaY29ve/PBzB4Vy8aZXxc11X12uIsjYBt13nz4S5qycO5e2Tt1DjX8rYSDJlzR3SpI0IEmSdvB1pyRJrZIkhfyrf7zljNmk2M3FZ6Ql1R9spLetkdALpRgovXhiXTTvn7zNNY8X2GFkLV/4cSeTpKIGlB//3WcEAAAgAElEQVQmMX5FEEsifqC0Sb4MRyXJt6CVNmlZEvEDpu4alB8mkVTUQMLUmahHKNhhZE23Xs/7J2/zxLpoLDcdI/RCKb1tjfgZWMlXItb8f+ydeZgkRZ3+38g76+y7Z5iL+xIRZxBhEQSBBRTBg2tZUBEUBBeBUQFdUVdZkFWRZQVBFFAUEFABRbnlEuQSF2T4DQwzzN1nnVl5Z/z+iMysrOqs6qzu6lkO43nqyczIs6oiPu8b34jMfJH+7Nl1dNdz7qSDH/kePfa6v9BNxSq9OLU9PQ1L6HV9O9O/ri/QfS95kOYOvIDufdH99KnXJ8O3R31b3T7s/Bv+2A/oTmf9lv7k6depvZH1gSwXtqEPLdub/uCxVXS7z91OpaWn0jNv+xutaDU6dvm59DQsobcOsZf9LLvwjzSz33J6wGUP0xWbS1S/51p6GpaETv3wqx6n/UdcTMU92Hsf3FVP0zPIEnqBtC198uAD6Ffvfoku+uTP2DDIe1+mRmmCrruQjaa5e8nuVLv5P8OXvSt7nUHXTVTCN8pd3bMTXXfhqXT/7z5E8wexRyUEQwSfPPgA+k1lO/r8MYdRd9XTdP6xV1Jxj5PZYzg2s9dLBi9d1++5lm5/xq+pvOdpNLPfclqoaHTyyvPorUO70psHd6Fjl59LK1qNZvZbTqWlp7IXqT90I31o2d700vQO7EapjSvpwJGXUnGPk9lby9Y8T1844YP02+r27LEGT/6GPvX6JJWWnkpzB15A973kQbrhW6fT6/p2pnfN341Wbvwm3VSsUnXvf6PKXmfQnc++g9Z+8/3Gsje5qaHsOX9/KCx7zx11SFj2xD1ODsvea8tPbFv2Sj/9Wlj2Nn/n82HZk5aeSrf+zK304gdX0kf2/qeGsveL5xrL3kuf+nBY9szHbgnLXvb9X6Z7X3Q/fWINe+l8u7J33657hmXPHFsXlr2tjr86LHtfV7ZrW/auzO8Ulr0145Ww7O1+3u8Tl70LpG3DsnfL3zbQ3b7EWh5H/fiJtmVvr2/eS8cuP7drYlCs1hJ93mxiQHzot02EkIsppRdMu+EWTpkFO9LCs38Ap5fw8z3/FSXbg+56AACV55ARCE5+5kZ4ah5eqhfvvfRpGDUblm5jw7P3gRMk8KKEb198BgZTEg5+4L9gTJShT5Rw2Y+fC8/zmSN2QGpAhdyTwZozL0dBtzFes/Gl866EY+rwHAvz3nUgZFXGU5ccCoVa4GoF/G73D6HqeKg6FBsNGwrHQeUJ/u3B74LvHYKn5nHAjZth1CwYmo2VD/wahONx7DmnYTCrYH6PgjM23gx9rABjooxLv30/XErhUuCEvbaC0qsg1a+C+95NKBrsmj517jVwLQNStheikoGYzuPR7x6JjMghxVM8umx/mCULZdvFc0UDEkegcARfvn05uN4hfHb1NhgtG6hUTPzllz8Pf4P9T/k0+jMSBrMKvpt9Gl5pAub4JC4661ewPAqXUnx4m14ovQqWPfIgyqaHqu3ioHNuhWNUYetVeI4FXlYhKhncc/lJyEgcMiKHVz50GMyyCb1g4O4NZUgcwQWXHAmlPw+xfwAXiYdgtGJitGzgD1f9BNRzAQBLP34C1IyEfEbCbe8pwy2MwSuO4jv/8kPorocdMxLyIo+0KuCgvz0AR8qgarnY57w/wtIqcIwqqiNrwEsKeEnFLy/7DAZSEnIyD+1zR0MvGLjhodcBADwBvnjuflD6c1D787jlXadhpGxgtGzi2kuuBPVcUM/FLoceDSUl4rHTdwCnlwCtgKv2OQ26S6G5HnICh4z/Ofb521nZVHux59cegFGzYNU0jK14Epwo4RsXnYmhjIxeVcTSW74GY6IEfaKKH978Uvi/fP6Ed0AdyELuyeK5476F8ZqF0YqJb33tSni2haF37AtJVSGrIp75jwPBGSVwtQJ+9e5j/LLJPqy+cPjskz8G0j3wlDz+6cqXYWg2/t99t4NwPAjH47SvnImhnIyBjIxjn7sK+kQJxkQJ37v8z3B9lJx88DZ+2UyjeMGPMV6zcOK518IxNHiOhczw1hDTeYhKCk9eejgyEgfeKOO+dx6Mqumg6nhYWbWg8hwUjuDcP34LfO8gPv5kGsWKCUOz8dztvwQAEI7HEWeeiqGcgqGsjPP1P8CamIAxUcIlX/kdLI/iiMV5HPS3ByD3DD5LKd1zNsxZunQpffixxxNtm0un2p6PEHIYgMvBIi3XUkovaVovA/gZgGVgBvw4Sukaf90FAE4B4AI4i1J6T+ffpul6EooBAfBRsPcRUACPUkp/O9uTzzZx6SHa+/4vQBtbB8+xsNWyQ/Hf574f964YxQ/+ScVTx5+CRfvtgOu+9zDKfqE/akkeh9zxXyg9eh82f/xrOO57j2D7nQfwwM9uQ3b+dhhf+TR2OuTjGF+9GhcuPwKfUVfC3X4frDz1ONxw28tYpIpYWTXxr/stxpqXxvGxn5+Nexcfib0WZPGRK57Ae98xjJ9f/RvohRG4lo4F7/kgfrR8f9zz8iguXUbwxDGfxaL9t8f1//04dNdDyfbw0R36cPCdl6P6+D1YfdgXsXOviKOufx6P/fI2ZIe3RmHNi9jl0I9idNXr+I/lH8Qnyd/g7fQ+rDj5X3DDHSuxSBWxpmbjXz+wNdb97yg+fNN5GHv4UeALl+GjV/wZe75jGDf96FaYlQJcS8fifY7AT5bvj/tWjuGbu5h45KOnYfH+2+D6q5/GdmkJT07qOHrXQbyyoYKTfngCXnzf57H7UAof/vHTeOKmW5GZtzVKa1dgtw9+HCOvbcDFyw/FcfqfgV33xwufOAmL3r8rfvr1u7HRcGB5FIcOp/Gh27+OiUcehvGZS3D05Y/jnbsM4TfX3gZbr8IxqtjmfUfihi/ujwdeHccFWxehzX8nNi8/Cdf99K/YOiXiuaKB4/YYxitrSjjxmk/j6aWfxh7z0jjyR3/BU7+6FemhRahsXIXdjzwGI6tH8N1zD8I9L43gRwf147kTPolF798Z1/3n/Ri3HOguxRHzszj0jotQePgBlD7xbRzzg0ex686D+N1Pb4PrWLC1ErY74COYWPMqvnT2kTh3eBOsrd+DNZ87Ftf/4kVsnZLwYtnA8e9dgFUrJ3HcdZ/DIzsdjz23yuCoK/+C/XYbxjU//E1YNue/+2Bc8cUP4A8vjeC/35fB08edjMUH7ISfXvpQbNkcOfpr2C7l4LhfrWwomzse9DFMvL4GXz33Qzg99Qrc7ffBK589Htf/asXUsnnDv2Hzw09C+cqV+OgPn8Q+7xjG9Vf9Or5s7snjiaNPjS2bL2/W8KnLjsHrh38JO+U5fPTnL+DhG29Fbv52mHj1Oexy6NEYW92+bFoexYGDKXzkl1/C2KN/biibN1/zaxil8Yay+ceXR/Ht3Ww8+vHTsXi/bXDdVX+ZUjZP/O/j8fIBX8BuQyqOuKaxbO562McwtmYjLl5+KH73vxvx/1aO428Xf6grYvBIQjHIthEDQggPYCWAQwCsB/A0gH+hlL4U2eYMALtTSk8nhBwP4KOU0uMIIbsCuAnAXgC2AnA/gB0ppe4svhqEhNv9EMD2/gUAwOmEkEMopWfO5uTdSITnUb7/28BLD+OcfZdj6YJDcOiBB+Cdl2Yxmj4UfYVd8dIjR+Hs954FAPjAtWfjXTeYGFs9H7mX7sfLp8goPHILvvbob5AXOXx73QNYdMadqIyswS8fXY1j1fuw9uvfxc13v4pL1t6DjfJ87HLk1/D84R/GgefPx64/PhuP3HkJNqQl3L76WTy+roSrRtbg7P9Yjg/tOozqAR/AK7eY+ODCHO665U4cZ+wEcj+P3//vE7APORgvjusAgD/QHfH7fA4/OeJcKPlBrL7r67jhvJOxTrfx2ZPeiR8uW4inFRHnfvlKHHnHhegvvIob7liJU47bFYsPXoZ/3rA/rrv3j8h9YEd8ZMdl6C9P4Jz+3XD/tScis9uhuHvxrrC1Eoa2W4In9n4V52y/JySOAC9ch0+/50sorngBC886CbvuOohLXrwC3/3+o9hKESF8dDk++LHvoTaxEYed9im8tH8Ja/90G/700ijOe/i7WFGi2Psj5+NXn/kkjhJN7FIwcPfF92KbtIgPvfoM7nt1HMeddREu5/fCgZ8+DE/vvBeWE4Jt3jmIPS+6Huee8S3wkopff+UD+Nvue8OuWnhi2XwsOWhnfOFd5+LB3X+G9OAibLzlTFw8tCfGTAeHPbcC3y28gtdeHseml/6K4oOXgF/5OL6w7Ef40sEW5h2/L95z418xtupV7PjgArzy3QtQevQ+rNNtXHbXlyAu+2csOuseyFfrWLD9Ufjd7Rdh74tvRkbgMLnm91h87p9Q2bQKPYNpfPgDh+KwH52F8257GVunRBy99jlcuerrMCuT+NyF56D/N19BdVMV15z4Q5yw/lQ8sb6Cp27+OZ4CcMdNF4M78nD8fVMVu5b/CnLewZB4DunDL8Jtv/gVtl+QRf6ypXjPcBqL9l2Inx77HRz1qe9DzvZh5Yk8+PI6bP+5E/DNj+6EJQdJ+NC7T8Tzv7sLufnb4fQlFr669eko2R4uvvJ4PLn7F3H7qgkYhc34wRWHY+lz9+LsD30HPAEuP+FxjK6t4PpHHsdWuy3FM/88gtGHHsN3vvNFHHTaD3HAQe/D8LH/g3n7nI9ttxvAlcsFXPKfD2CeIuCAZ+7HQk3AvI9cgP3NF/GRpQvw7zedj2XPbMAuWRl7vPAklh11AVxLh2Yfiue+cRlWPX8+XEpx+sa/4U+rC/jR6d8EJ0g4447v4Ld77IMVFQsH3XsoPnfFTbjz+Y3QCyN4/NeXYLc+Ht8ZeDfIs0P47H7b4diRz+P35F1Q/z6MTYWfQFz1Zzy9+ynY9p93wL4H7Yu9H9sGa2/8AfKLd8WqL26Fc865C5ZHcdlvvoB9nqLwHAv//j+PYKvth/HP+yzB37pDHHCEdONAe4G96Os1ACCE3AzgKLBh/EE6CsA3/PnbAPyPb8yPAnAzpdQEsJoQ8qp/vCdmdUWJOhaAlxF50QwADsCK/+sYlzK0XfiGr/t23ZPudNZv6Sd/8Sx11jwfDjPrOeTC8M1Xtw7tSvf8xj1srHORvdLypufX0+GP/YBe/8xaqv/+SnpFbkd60BWPskcQ+28pe2zf99Elp9xMz//d39mzcJTtwpeSB8/6v3vJ7vQdy+9iLzrZuJIuF7ahr551HO07/Nv0rpc20/INX6fX9e1M/+niB6i09FSq19hQyeeOOoRudfzV9AePraLGQzfSS9M70Oovv8WeIz9Roa+ceUx4Z3MwFPHf5W3phm+dTnMHXkCfen2SjvzXWfSOee+ge3zlbjZE0H/k9opTjqIDR15Klb3YfRZX9+xE9XuupfKep9FCRaPPH3MY/fOB+4exWeupO+gvBnahhasvoKl9z6Y9h1xIV3/5E/SPO7w7HH5or3uR/nGHd9PVX/4E7TnkQpra92xauPoC+ouBXcIXoyz65M/onw/cnz5/zGG0UNHYy+f9N4Zpt/0XVfY6gw4ceSldccpR9OG99qHmxAb2PP4VrLN45L/Ook+9PklzB15AN3zrdPrv8rbUffUvVNzjZLrd526nD+6xF33lzGPo6xOV8N6HS9M7UOOhG6m09FS61fFX0+eOOoSeQZZQvcZeAv9PFz9Ar+vbmZZv+Dq966XNtO/wb9NXzzqOLhe2ofZG9lL4dyy/i969ZPfwbuDMfsvp6bc+T7+pbEedF+6n5//u73TJKTfTx/Z9Hz0NbCixvOdp9KArHqVX5Hak+u+vpNc/s5YOf+wH9Kbn17N38hbH6Pu//ye65zfuobcO7UpPwxL6yKpx2nPIhfSb97K3gQWPCdnprN/S+3bdk56GJXTVWJnufPYd9F9ueIpenNqeflnchl784Eq68MTr6H0r2XDYilaje33zXvq+Sx+k1/buRP+NYy+4CV4xeTa/Nf1Bdkd6+FWP03dd8Hu6yX+72l/XF+jWn7mVnvPbF+i/y9vSryvb0bN+/b9029NuC19Is7HA7vE44uo/U2vza/Rsfmt65ROraf8RF9Nfv7CRnkGW0Gt7d6Lvu/RB+t5v3ReOIPrjyyN0wQnX0kv/9Aq75tT29Njr/kLVvf8tHKr8wDvfQ3f8/G/op296jjqr2ZDQC/+4guYP+ip9fHW9ri678I/0gMsepmaBvc707ycdQQc/8r3wkR/Bg/zkPU+j42VWVx/ffz+6+ORfsCcQPH8P/aayHR2/4ov0kVXjXYnhv3vpUlrT9UQfAGvgv6LX/3w2wtCjwUJDwfJJAP6nibuxb38E8D8ATozk/wTA0Vuqz+B3AM6klL7uLy/xL/zDs1KiWaaly5bRxx9v32RL8PXgJdgowWHgJdgoye+d+FjdOk7Sa0qwTbLfO8lxpt8oyfWwY02/zZYsA0DCa0pwxm793omP1aVrApL95m7CPznJsfZa0jfrMNGypUvp4489mmhbNZ1pFyY6GsBhlNJT/eWTALyXUvr5yDYv+tus95dXAXgvWGvhSUrpjX7+TwD8gVJ624y/GJKHibIAVhBCngKrE3sBeIYQcicAUEqPnM1FzFXqBgS6AYBEYJvuGFvqOhKcZ7rDdAP23ROeLfH/JjhGFyCa7Hed/bV0SzyTgHy647iJ6k73RCVJIrQrB9sA9g74IC308+K2WU8IEQDkwTqSk+zbcUoqBhfO9kRvxDQbUMwWvLOF/6zPP83xZwumuT//7CA/W8DPFqhzff5k1zANiKf5k2YL8tmevxvX0HmiQHfE4GkAOxBCtgED+fFgrwmIpjsBfBKsL+BoAA9SSqlvwn9JCPk+WAfyDmBvh5xVSiQGlNKH/dDQDpTS+wkhKthL7SuzvYC5Su3KQLsCNFMBaAe/2YB/xudsd762v83Mzje7c3b//5j2nG2OPPPfp/31zPycM4dmu33bgXKuzjn9edudc+bi4k7z33SUuiAwlFKHEPJ5APeADS39KaX074SQ/wDr27gTrC/g534H8SSYYMDf7ldgnc0OWAh/ViOJgIRiQAj5DIDPAugDsB1Ys+RHAA6a7QXMReo2eGYCyLb7tF7Vcr9ug7/bIrMlYd9tyM8U8DM7VxtgbUGoz825Wh+zHchnCvH2v2W3WwR+ol1rGYBSejeAu5vyLozMGwCOabHvRQAu6sqF+ClpmOhMsH6Cv/gX8gohZKibF7IlUqvCsyVEoNNztDx+60vqGHadnmMmwO/8e7c5R4ujdf69u3mOzuHeEtBdhPpMgD4TmLf+Lq2P1fq7zOD7z6JFNdPUpT6DN1xKKgYmpdQi/vhavzNjjqR3dqmTStsJqDqBfzfA3wmQ5+q47Niz/d1abNsBdDuBejdg3i2Iz9WxW/0e3QJ2/HXP3bEBwGtx/LjjdPw/dLWVQAHP6eLx3jgpqRg8TAj5CgCVEHIIgDMA3DV3l9XdlBRocyECSY8Zx4hOHO9sjtmJq08K/Dgod/Z9ZnvMZCCO3W6WcJ8rsMdfa/z+SYHeCcw7AXlH1zVHx2137Bkniq6Fid5oKakYnA/2HIwXAJwGFue6dq4uaqYpKeiac5LAdabwnymkZwr87v4GMcdq2jOpk5/y/RKCPQmAkwKmu8eakpUItkkhOxsxiNsuCVgTf/eEkO4E/HHft9t53UkU8N7GYkAp9QghvwXwW0rp2BxfU9fSTCA4ExGYiQDMBP4zAf9MoD8T4M9EOLoF55kcJwnguwX3bopEM4gTnX+OxaH5+3UT4m9EQXhb9hn4z8H4OoDPgz2CAoQQF8AVlNL/mPvL626arRB0vH/z+acRgM737wz8nUK/0/2TXGOnkJ7t/mybxuVmSEwH5m7AvXn9TGCcBOozAfpMADxX2ySFujPD/bqS3o5iAOAcAPsCeA+ldDUAEEK2BXAVIeQcSullc32BSVM7CHUC8XYC0Am8OwF/O+h3AvxOYN8J6DsBfCdw71wYoudMDvTpYN4JyDuF75TvMIv9OwXpll5uBnSiY3QoTPHQ99pu01VRoBTwZj2k/w2ZphODkwAcQikdDzIopa8RQk4EcC+AN4wYRFO7IWWN8GwNm/bQTQryNudquU9r+LcDeHt4x+/Tbr+ZAj8p7GcK+nbwnOl+QCOk255jhvs179sJ9OZq27hlp5NjzRLknUJ8utbAnIpAJL0tw0QAxKgQBIlSOkYIEefomrqaWsG0VWtgJiKQRACSuv/W1xvJbwnwNudIAP2k8G61T3uRiOR78cdi+7S6ts73aefCZwL4mcI9Kay7tW6mQO/s/MlBPhuIT7ctbTYAzevbGMOZJfq2DRNZM1y3RVPnYKVT86bZFqgDvVP4x11fN6A/G9gH+Z1CvlPAx8G0k22BZHB2Z7H9TKDbrXmgNbwb5mckPK2BnRTWMxWkKKTbAXpamEe+wpTWdZtzTjluN0XhbSoG7yKElGPyCQBlDq6na2m2QjATEYgTgOnEaTqgxwnFdOCfDuSdQH864Hfi5DuB/UxBnwTy0823gl6SY8wG7EmgPmtRSQj6VjBvBnLL7Zp4SVv8h83HaAfx5nXthGTqdaI7idK3pxhQSvktdSFzlZIKQaciMP12NPb40e1mA/924J/O7SeFfpxrjwP+dNslhX0nopAU0DMVgekAP1O4JwF7t6CeBOizhXkrkE/p+2oH/JbXhtbbTScMbURkNong7dtn8KZK7QHcCL1WIhDv7uO3aef+m51/APR24Z74bRqh3wz8drCPC+k0A7xdGKfdNkHFbOfO28G9HdiTgrpdXhxcY7fvSGC8luunA/h08O4E3K2gPRtgt9wmAaiTnCvp+dqds921sXVu2+XuJAq4b8/RRG+J1KkQdCoCSQWg1fq4Y04H/9br4a+PEYsp54iHfjPwY/dtIQpxTr4VqJPAvN027Rx7crGZHvAzEadYcCcEeydQnynQO4V50nBNkhbI1HO6sfNT1rntYb9FxIDi7RkmejOl1mBOLgTdFIHpBGA28J8O/DOFfqfAb4Z9dH620ySg7wTyiY7bZl0c8OpgjVkX49jjoBsH3OmgPp1Dn5uWQCuB6hzkbbfrEPBem31b5c02/SNM1GEihCwC8DMAw2DcvIZSejkhpA/ALQC2Bnth9LGU0oJ/t/PlAD4IoAbgU5TS56Y7T1MrEkC8EHQqAnECMFP4x4G/HfTbuf12wE8C+05A3wnAk8K9U7BPB/Wk19jOqTeva+fQmyEeB9j25wqz2sK73XGn+x5T1rcAZhJYJ5lvXvY6gX0X3D6NeVbQ3ISIALyNh5bOJjkAllNKnyOEZAE8Swi5D8CnADxAKb2EEHI+2EPwzgNwONjr23YAe+nzVf40cYrRhYZ+g/awbyUQNDx2FPRRhz+dAMS5/ukcf5ybj3P6ceCPA3sc9NuBeCbgd+LWtT3H3IA+KeSnA3w7CDfDvZ3rTwr26cNIU6HaLaC3gvlchm2aIZ7U2TeLTavrabX/rNM/xKCzRCndBGCTP18hhKwAsADAUQAO8De7AcCfwMTgKAA/o6xGPEkI6SGEzPePkzg1AD+Yb84Pr7FdfjIRSCIAUfc/U/jH5TeDP87tT+f0p4N8UuC3c/btYN8J6OcK8tMBPklIp5OWB8uLgXkMaKeDdzeB3ln4pnOQdwPi08F97loEwQko3q6Po+hKIoRsDeDdYG9KG44AfjNYGAlgQrEustt6P69BDAghnwV7BScWLVoEoNF5AU0OC/Guv1kEmsNBzQLQ2IJoFoWpkG8V9mkG/3TQbwZ7FPitXH4zzJPAPxbQtNX28YCfTlji4BsH9nZQnwnQZyoKseuaIJwU4NPltYJ3uG0bF956vj2wk8A6KajbQTgJoOcc4l1LFNSx5/wsrcLpTdvsARZByQFwAVxEKb3FX3c9gPcDKPmbf4pS+ny7c865GBBCMgBuB3A2pbQcvC0NACillBBCW+4ckyil1wC4BgCWLlsW7tt8EI82unqgvRA0twTaiUBzCyDO/ccJQDPoZwL/TsHfvNwO+q1COEmAH3XyncA+DtRxkE7i2pMLRTLAx7nmtmLgz3cC9m5DPQnQO3Xd/+dufBaJcF2+VYpiS7UMzkd8OD2aagA+QdlriLcCC8XfQykt+uu/RCm9LekJ51QM/OcX3Q7gF5TSX/vZI0H4hxAyH8Con78BwKLI7gv9vMQpaAE0C0Ej9KeKQKuWQLMANLv9oAXQuAx/G9rg/KPL7FppLOCb81oBvlV+M+ybhSMO9FP2aVpuFaphMI/8xhEIt4J7o1AgPG7cflPy2opMe6BPB/N2IJ+Z+28N8LadrQnAPZNRM28kYLcDdKt1cfnNeYRvXOYi6wnHd+X5ORQ0trU0B6lVOL1+LZSujMxvJISMAhgEUMQM0lyOJiIAfgJgBaX0+5FVdwL4JIBL/OkdkfzPE0JuBus4LnXaXxCXkgrBdCIw1e1PFYF2AhCFPxDZrwX82wlAnFsPwB/n8DuBfjNwmx19M7hbQTvq5JO0AOLcexzoZwv5JIBP4tATxernAOz/11DvFOSdQpxrXtcEdDblplkfyePreZWWV95BogCSv+lsgBDyTGT5Gj+ykSS1CqfHJkLIXgAkAKsi2RcRQi4E8ACA8ymlZrtjzGXLYF+wR2C/QAgJYlVfAROBXxFCTgHwOoBj/XV3gw0rfRWs+XNy0hPRYNrUKuiGENg+vW3Pm5UAtII/0B74za6/GfyN+3ux2zafoxX0m11+nMNvBn5zbD5ZiyAaJpoK4iSgnw7ySUI8bN6LyUvm3JMNe3zjgX1W7jshyOMgzea5luvDKc/H5nPN27WZxh1jyuOXZ5RoJ2GicUrpnq1WEkLuBzAvZtVXG844TTjdj7D8HMAnKQ2HOl0AJiISWFj9PABtX0g2l6OJHgN7lEdcOihmewrgzM5OUnfrXtNP1UoIXC9eBFwvviVge15bAbBdLzH8LdebMfSnA34c7KeCdyro46DdCvLNrYDWrQgXrVLfzw4AACAASURBVOA+G7C3FofkQE8Sr2+X1y5/LlJScLdbng7Y7Rx3ElC3g3QrQDfuT0A4hgpCCLhg3s/nSGQ9R/zt2bbsO5Ep2wbbcxwBIWz712N/yQ4T7V4HMqX04FbrCCGtwunN2+UA/B7AVymlT0aOHbQqTELIdQC+ON31vGXuQAamdhpPJwQB3JtbAtH+ANttFAE7EIKIIDiuFysAttca/pbTAuox8LccLzH446APoAH8zdCPC/3EtwKmAj8K7NhwTgvYdwP0M4H8/yXgZ+S8p4F6bGikyX3HAT3OdbeEeIv9ApCzZRKCvAHcETgHEAfQsG3sdj7Aw20i2/MxH6FhmWtcT9j0sdZ/TQeJbpGygtbh9DARQiQAvwEbkn9b07pASAiAjwB4cboTvmXEoGE4oA//VkLg0npIqD7PWgPRcBCbnyoCwXwU+u1aAFHwBx8zyKPNIuEmdv2u47V1+3Edtu22m9o5PNXhtwN+nLPvFPbtQD8d2JOCv1upU7jHgT2JU08aBuGaQN0O6NG8OOCGzjoG5t0AuSRwLSEuCVwI8Og2wT7NxwiAL/rn5wm7JpFnU56w7/Hfnfy5rRLFlhpNFBtOJ4TsCeB0Sumpft7+APoJIZ/y9wuGkP6CEDIIFp15HsDp053wLSEGYUvAnw/CQ81CEISCXC/aMqi7/6BVYHt15x/MBwIQbTkE81H4R8EfzDvBPI3me1OFIfpxvClu33W9tiBPAvtm0MeFcZpB3+zo4wCfFO6dDquMW+5WSgzuNhBPAvBWzjrOjbcDdytodwpsnudiYS00wZbnCOQwj6vDlzAQx0G6YZ7nQig3A1r0t4nOixxp2L4OcwJCAI7AFwlGOJ4j4MC+K8/BPwfAEz/PFwFCANK1MkQ76UCe+VkonUB8OP0ZAKf68zcCuLHF/h/o9JxvCTEAGoeRBq4/FAU0CkHQGqjD32toCdh+2Md2vSkiEK7zYR4Av1kEogJQX9cY8mloMTTB33Onun3P8dqC33P8IaNuAOR48LeDfrLQz1TgT+fktyTsO4V8EsBP57KD/Vu58pmCnePj1/MC1+DAOZ4Lt2t20AHQJYFvAHXjukagB+68Ac7+NYkcgRiAvg3IGax9eAcg9wHNEwbzID+AOM+REPSEUhDPYU6cev68A3j+vEvredQDcR2ARvI8l8X3PRfU7tKNYhRbamjpFk9vejGgqIeIAgEA6qIQxPujLYJACIKQEJuvtwCCTmE2XxcB2w/rNAtAMA0cvuV6sQIQ3b4OfS8W/kEroB4Sau/4WcgoHvpAo+tvBf3GbZM5/PgROnMD+25AvhPAx4VdOoE7c+Dtwc4JXBha4YX6MfgmSE8HdKlp+3DKcxGIc1OceR3sCNeLHMecdhhymerIg2UuXK7nB06cBJAO4Ow67ImfrjsV4p7L1nlOBOAW4Nigjs3KkGOzPM+F5+eH23keqGPBsx1Q1wunrm2Hy91JHY0melOlN70YBCkQBerH+N2IELi03iJwPRoKQSACzS0Bx59GWwGW4zXA3HK8WPhbjlvPD8JEjheC33VoGOP3HG+K449Cv1kQqEfhOk5bl98M+/gQkNeQB3QO+W4AflqYx0C9EeBcZL4O6Whes1uPbscJ0hSQ8wI31YH7YZdgXQD5aLiF47lYgLN5Nm0FbymyHAVyFNyiD/XoumZoB6GSKJijeZyfLzS57jqszcY815nitqljA44FatugjuUv26zc+MvsY8Gz7RDcnuXAsx24lg3qefAsB24E3J7lwPM8eJYdAbk/tVxW9i0X1GVTL7JMXQrXduG51N/eY+dwKTwryG85MrOz1MXRRG+09JYRAwBhpzGbJheCxml0fV0EomJgBsuhCDSJg+PFCoDrtwgCyLMppuS3cvuebU0L/jiHHxv/n6ZjN0izgX47wAfznYJ+OshzojTFrUcdeghygbQEfODQg7worBnI+Vi4N4NdCgHOpkLTcjPYRZ6BmiNomMaD3g+l+OAmrgVQD3AsBm6nDvnQeXsO4PqwjrpuH+5wrLrjjgDfs506sF03XHYtpy3IGYg9uLYXQtm160CPQrx5fQD18GZMvy5bEaPHpmhYH50CviFE/em5s0//aBm8QZMfAqJT+wxYS6HeURwVAsNhfQOG48W2BuyI29ctNwR8swgErYCoULiON0UAQvg7XhgOCloFQX4z/OPATz0XrmM1gB+Iun6vQQy8Fp290WnzfKepraOPOPs42HcC+ijkCceHMfMpMI+BfH3buoOPOvNmwNfzmj4xcFcEFjtXhDbO3Yd5HNjDzlXQENYNELddBvomqFPbbHTolsFCJaY+LdBd2w6deejYbSeEuWs5vnt34dpugyN3bVa+PctrCfooyC2vFcxbQzx8NAum5kUhH7cczZuTtOVGE23x9CYXA5aCEBHrJI4OE60P3WTgpzDdugAYjheKgOF4YUtAt9yGUJDleNBtF5bjNoqC4zXAP+r4PV8EgrBPkNfQT+CDPQp+z7Fauv1m4Eddfrdh3xLsMYBvjsM3Azy6XRCaCcI0vCDEhF4YzDl+KtSDPJ7nIERAHQBdlXhIfCPMVYlvgLks8D6s/XnOh7kPcNnvPBV4hB2nfCTEInKRcEvozG3AtRiwA6C7Dojpzzsmc+GmEcI7cOPUMkAdm5UDy2gAt2OYU1y4azvwbBuOboF6Hhzdhmt7IYQDgDcDe4oLDwY4+ICOgnuqA4+EXjHVfbeaD9JMAM2T6DwJ83h/hFEwH83ngIZ10SnKnV9Dc6KgiHuZzlshvSXEAIh2HPtP/ES9/8B2o+Efzwc/C/fYng/3SEtAt9y2ImBarg96r16p2ghAvWXgNMA/EIM4x98K/u3u5m2eT5LaQX86Rx9167wgNQI/EpMP4B1AnRNYKCaY5wiBIPFtQa9KQgj0VAB3H/SqyIeOXRa4KZBneRwkIQA7gcAh7ERtBjznslg3cS0WdnEbYU9cOx7upsFcuWUwyPvLnuXANSy4ts2mPtgdw2pw465hhyCfDuqu5YWO3fa8BphH3XezM48D+lzAPAB5AGuJqy9PB3GJa4S9xAXTpvUCB17iQXgCXuTBS2yZF3lwEsdagSLL4yQeuHVlzJV2mP7RMnjjpvB5RGGhZh/HRRgSMp2oADDQG25dBAIBsBwPNcv1BcCDbjlTBMCxmsHvwbEZ7B3bDUXAdTzm8pvA78a0BlpBP25YZ7CcJE2Jz7cAfRLIc4JUd/qCFEKb86fNrp0TOAgiFy7LPsCjYFdFPgS7KjW6esWfRqGeEvnQvQs8g7ngj4gJgC5wBAIBiGvVAe5E5k0bxLVAbRPU1AHHhueDnFrs4/r5rmn68GYQdwyrAeIM8E7ozD3LhW04IcCbwe7581F41wHeKiZOYcfAOg7anQC7GdZRxy1xjdAOQB3AeCqY2VTyO8AJTxiUJc4HMx9CWVCFENyCIjBQiwIEVQInihAUKVzmFQm8KLKpIoEIEoisgEj+J5iP5FNeAgQRlJf8jwgIbB639ib/gVolSll/y1swvenFAGgUhKgw1FsENFYIdNsNhaDmiwHLYyKgW+4UEXBst6EF0Ljs1UM9ttVSAFh+Mvh3Cv648e0A66BlTn0q9KPAD/KDEE44SiYG8IGbF0Tm/lUpCncBqsj78OfDjyLwoVuX/Zi7LHB12LcAveSHdTjqgrg2iGOAOBZgMbgTxwJxzBDyAdipaTDYR+Zdw4KjmyHMA9C7humHZiy4lgvHcOBanj91G/Ka4V4HOxqmzc7cQ6NbB2YH9ijU49x3MBXJ9CCPOu4otAO3zfKaoK6IDSDnFQm8JIBXJAiKzJZluQniaiPQ5RQDtyACPsCpIIEKCsCL8HgRjt/CtzwKx6/TjgdY/g2jNduv25YHU6+bQNNxYbgadKsLMSIAW+qms/+L9KYXg2jHcf0x0PWHzAWhoagQ1HznXzWcUAgCEahFWgmO7U4RAcf2GsJCju3CdZwprQDX8cXAjYaD4gVgtvBvGC7Z5PajgA/Az/mfIJQjSOIUly+IAfz5UAAEkQ+Br0pC6OSzilAHvi8ACs+cfwD7lD8vchwknoVpWAwfIewFAhDHrIdnLNOHvu/mzVro4D1dq0Ne10AdC07NCCHP4F6HfBCGsQ0ndPCO7sQC3jZduJRCd+thF8uLC8PUXXtcR2cnYI8Lq7SDegDuOKBLXD2Ewtx3dL6eJygCBDWAuBzCPJyqDOZcAG0lHQKcU9J1Ny4odZALcghyyovwCA/L9WD74HY8CsuHueXWoV0zXDbv10/btaFZeoNZq5s3JzRvluWyempH6mOLFnzX0j/CRG/MRBH0EfgV0G9uG35oqGYzIajZrJAZjhuKQMVwpoiAbjgM/C6FEylobgj+QBjq7j8O/p5/o8xswJ8E+JwgNrn5OuzreQHU+RD0gsiDEzhIMssXBA4ZRQhBn5GDeR4ZRYAi8EiJPFIiC92kxDrgJZ4BPviIgZMP4O4YILZWd/S6CWrpDOI+2KlRn3dNE45mwDFM2JrRAHXHsODoNmzNhhuBerOLt2w3dOZxMLe8ety8OTQDNM63S1GIx3VaiqQO7Thwq8GoI4m58UZg8z6wBYiKAF4RIaaV0HGLaSWEtpBiYObUNIN2AGw1XQe2KIfTANwuJ7L+MpdB2vQoKj6wjbDOsKluu6GZ0suRemQ60C0TuqWF9cixfVENTJXtNrSkm02Ua+lhKzpqqoJ61FyHgvqTxDxFQ6SzTnSLPahui6c3vRgEiTaFh0zfcdhupLnoC0HVYPCvmg50y2GiEIDf8mCbTtgacKxoYWb9AK6pN4pAJCTUqvAmKbjt4B+EeKKxe16QwEtqmCdIMjg/fMPCOHUREEQeosxD8gHP3DyDfuDsM4rgA599ZJ5DVubD8I0s1IEvBW4eHgO+Vas7edsEsXUf8jXm4n3YU12DU9Nha7rv5C3YNQOuD35Ht+H4gI+C3gmdPGvdNYK+8dMcimk1PLFVinZcAo0dms1wZ58mZy7yU0AegF5QBUjpekhFTKsQ0gqEyDwnKSBqGpyS8qc+1OUUc96iEpkq8EQlhDn7+L9PBOg1w0WtEpgiG4ZjTKkLVcMOYW6bDhzbazBEThAytV04lh1viGLqguvH2LtRF3jf4ETrQpwJitYFZoII/vbM1dNQJFn6x2iiN2CiQOQ+A1b5qX/HcNhP4DsZzW9iVg0nbBFUDRsVw4FpubCCwm/7FaFJBFxLDwu6Y+lN/QJefUjoDASgFfzjXD8vqeBlJgCCyIfgF2U+LPiCyEGSBcg+9DOK2AD9fEpsgH5GYg4/IwmQeAKZJ6HbVwQCMQC+Y4BYegh8mBpoAHqtDNeogeoaXL0GRzNgazpszZgCe1tj4Rq7ajcCX3dgex50tw72xlBNY1gm6uiB9pBvjq03d4iKMYBXeQ4SR6DwfgzdD61IaSmEu5QRISgCc+xp1Ye6AiGlMLinVAZyJQ0una2DXcmETt0TVcAHvE0EGC6FGYG74Xoh1Cs1N3TpNdtA1ayGZToAemBuokC3TAeeS1k5928GcyyzpbEJQe63djtp1TYPVuBEKWzNcoIYC/GwTEsigzfPQVT4BjMjSBxkWQhbsBlFRNZfzigCcrIAWWDlOSjbWZkPy/IOFydnS8tE2X0Ub8X0phYDwL+/IACEBxZ/tFmFqZoOaraLsumELqio22GFqRkOLNOBbdabtXUn5IaVxbX0MAwUrSxxFSVJJQlH6oSjc8SIo2GVIoA+LwgQZSGsDKIsQJIFCCKbz6dEpCQeeVXywS+EsM9IArISH86rIgdFIH7HLYO+SB0QWwexKmyqmyCWBk+rwKuV4WkVUEODq5Xh1AxYZY1BXTNgVTQ4hgVbs2BVbViaBVuzG1y9ZbvQXQrd9WB58KeNDj7OxbdKzaEY5sq5EOgq3xiCYVMmlqIPcVERIGZESGkRUlqCmJYhpFWIaQViWoGUTUNIK+DTGRZ2SeUYyNU0iJoFlVR4YgpUUkHFFDxR8fujKEyHQvPDLprlomrVy2LFcqHVHJTGbRRrtg/vAirGGHSDlUMGcBeWGZRB35BYeghuJ2JMmqEduHKgNbCbXXcA68CAcIIEQc00tDx5WQ1BLfrlT5IFCBJrcQaQ7klJUEUePSkR+RQzIRlJQEpkZiNoaSoCB5knDVPeNf2yqIPYNXCWDmpUmeGoVVhLs1aGq1VY+dushWbD0XRYZd03GRYsjbUwx6t2GDrsRqKUdvE5R2+s9KYXA6BxWGn4KAmXhRMMf+ho1XBQ8Z1TsWY3VL6gVRBURNswwjhm0Apwgzs7m+KanQgAJ0qRkTtixA2prNKJEkRFaQzryEwMRJmHqgh+JZOQ9YUgCv68zMI8eUWALDBHqwrscQkyB3CWxsI5ug5iaiyUUynCq1XgamV4WhmeUYNdrsGqaLDKNdiaDqtSiwW+5U8N14Puej70G118J7AP3LvK18MyEtfo2plbZ65d5QmUSAhGSktTIC/m0pCyKQb5XBpihjlzLtMDLs0gT9QsPDnN4ulyGlRKw+VE6I4Hw2GtS91mI1YqloNqJYC7jbIxjlKNmYuSbqNi2CjVbL9cuZEyFbQ+62APTEbdndfDKkkMRpy5EGQ1BDozExFjIakQRL4B4sxc8KGxyCoi8qqInpTI3LYiIivxPtR5ZCVWxlSxbirUKMhNDZxdA7Eq8KpFFiacLMAzaqBaGXZVg1X2y1bNgKbpKJR1WBoDuO2XMcdwWHlrYybsSOswKGPA3N+B/I+WwRs0BXccU/9JpMGIhKDPIGgRVEwHpZrVIASmwSptc0V19GrowAIRCMJASUQgTgDiKqigZiBIcui2ovCXVQHZlIisIiCvSqHT6lXrbisvCyH4VYGDKrDKKQVu39TA1aoglg6vWoRXLcLVKqC1MpxyCY5mwChWYJdrsGs6rHINlmbDLJthpQwqaBzwdddrqJBB6AaIr5BxNxFFYa/ywYeBXhJ5SBkRYlryAS9CzEiQczKkbAqSD3opl4aYTTG4B5DP9oAoGQZ5KQ0qpeBJaRguheFS6LYH3WHTkmmjVGShw5JpoKRXUKrZKOp2WGZqug3bdP2WZHyZaTYOMykzwSivVlBvVWYklbUYsykRPSkJGVnovMxYGoilg7OqIGaRmYNqEV6pXmasssbMQbkGo6ajXK7BLJsNIJ9JmWlnEPimYbFBmQnKSbTMBOYgKDNyTg7L0NU/6cqLL/8hBm/UFNxx7HqA6bphmKhiuSjpzKVNaBYmqiZKNRumbsPSWWsgmDq2C0evwrV02Ea1waklaQVMqciSGjr/wPXzsgpRSfkVmMFeVkWIMo9s2oe9KqE/IyGfEtGjiuj1Y6K9ioi0xCpuSmRuX/QsEKPC3L6mAVoBbqUIr1KEWynA1aowixWYhSqsSg1WWYNR1GFXLZhliwHfcGBWLOiuh6rTWGkNf+x8812szSlaWSWOICNwsRU1I3CQUmJYQcUMmwZgl3uykHszLESTyzGg5/vBZ3tAUnl4chqenAWVM7B5mYHc8TBpe9AsH+iGg7LpoGjYmFzHID6paSjWiqjWbFiGA9OHOps6cCwz/O+bxd9tumGw1f8eJ/pSNgNBUiEoGfbfyxITeoWHrIiQ/P8/nxLRn5HRo4roy0joTUnISjxyihiCm/3n7H9PCYT952YVnFkBMTX2n5dGQWtleJUirFIF5voKE/eKBrOghcA2yyZGyiZr0RlOw/8euG6j4Wa49uIeHeIqcQRKpAWXEdi0T62LupyTIPlTpTcNKZuGmEtB6clCzKXA5/vBZXrAZ3vAZXtA1RwLySlZeFIaJuVQsz3oDmutlUwbFdNFwbAxajgoGzYmqxYmNCsUckO3YeoOYt4c2XGilML7x/sM3ngp6ECOjiIKxi1rlhN2rJVqFqoGc3SW7rAWgeG3DAxjCgxcy5hWBFqBoA5+BgEplQ7j/JIqQEmJkFUR/RkJfREI9Kcl5GUBvaqIvCwiI3NICxxUkQkAb1YZ/KtlQC/DqxTglSZgV4twyiUYEyWYxSqssgazWIVZNkPom2UTVtVm4TKnDv4o9OOGVwapMT4fVHquwZFl/JvLAifGQM9cvNyTCWEv92Qh9PQxB5/tAZ/vZxVezsJTcvCkFKvstgfN8VA2XJRMB6VJGwXdRkEfDyv7RNVEsWrB9P9TS3dCyNtGDa5ZF3fX0qeIu+dMvZO0+X8VZBW8pITCLigZ352LIdQlWYCSFpH2Rb0vLaMnJWIwJ6NHEZGTBfSpzKEHgp6ROMjEA2dWwBkM6qiNwS1NwBsrsv+2XIZZqMAsVmFUNJQKGvSCAUuLCHrVhua4U6BuN90n0b6l1ui4ByQm3sH/qigCE/C0CDnPBFzpUSHl0pB7MlD78yHIA5hzuT54ShZUSsNTcnAFBVXLhea3yMqmg3W6zf5bw8F41cRE1cJk2cLERtMXbxOGtgGW6cLSbWbc9Coco9pQVwPhjj7eJe5/7Vb6x2iiN2gKOpCjISLDv3uYxXEtVAwndAemwaa26cCqaWHzPggNuZbRtmBFWwHBMLcAFqIPClFJQVJF5v4VEUpahOILwGBW8acyen3336eKyCk8VIFDRuSY+zMr4PQSSKnCXH9pAm5pAlZpIgS/Wayy+bIJvWDU4e+7/ZLthXAwvLrzmw76QcerwjXCPgp8OSdBzstQexUoPXVnr/TnIeZ8V5/vB5fvB9I9IextQUXF9qDZHmo2xaRuo1R1MDFqYaJWwGR1BKMVE6WahfGyCcNvzZm6EwLB1kpTgNA8rLE5RR+pIaoZcILoQ70OeFGWIPv/m6SKUFIi8hkJ/RkZQ1k5FO1eRUReETCQkpDxW2wZiYPkmsyxGyVwZgVe8TUG9/IE7EIBxkQJVqUGfaKEQkGDWbZgFIxQsA3DCf+zquOFYj0d1JvvX1A49l8tUiNC7btxOcf+MzknQ+5NQ+nLh/9b6Mrz/eDyA/CULAO6nIVOeWg2Rc32oNku1tVslAwm0COVOshH1xuo6TYMrQxTn4iYL2a6ouKctK6Fwiw1CrOSG4SUzvotbTE0W6k0a2H3pWXfdEnoUyV8Ytm1s+bNlhpNRAjpA3ALgK0BrAFwLKW0ELOdC+AFf3EtpfRIP38bADcD6AfwLICTKKVtn6PxphcD9o5jP0TkDyMtGzYrnJqJ0bKJmmZB9x2koVmwdD0smKxVYDSM0GgnAFGnKCoZCEoGUjobwkNWRagZCX05Gf0ZGfPzCgZzMvoUEUMZGQMpEWmRR1Zm4BcdHZxeBKcVGPQLzB2ak2PQRwswi1XURgswijqMggG9YMAoGLBqNkq2G8JDaxp73wz7qAtU+TroMwKHvMimcpbBIjWghpBX+vNQh3qYo+8bBN87CL53CEj3wkv1wlNyMDgZVctD0XZRNlxM6jYmahbGahbGVpsYrZjYVNyEivY6DM2GUav/FwHYbb91Nh0ggjuqhaDzXUlDzvaBl1SI6TyUtALZ/y8kVUA2K2Mop2AoK2MwJ2Mow0R4KC2hxw/B5CQOKnFB9BI4owRSnYRXmoBbGIVXmoA+Ngl9RRFmsQJ9vBL+DyNFA2tLFjSH/Q+BOzciw2NbiW60Q1zh6mKbFzkMZwUoPrDVXgXqABPb1FAPE9ueHvC9Q+B7hxi01Ty8VC9cOYOK6aJqe6haHsZrFgq6jdGajZGygdGyibGKEYqsoVkwSjbMETs0RraxEY6+clqBjYprEBZlRogJq5RKQ0lJUNIieoczkFUBAzkZ8/Iq+jMShnMKBvz+jMG0hIzIISPxyEgceL0IzqiAqxXgFVl9cAujsItF1EZHYEyU/f+iBn0tE1OjYEDTWdiL1Ym6AVrjUazqUqfyFhxNdD6AByillxBCzveXz4vZTqeU7hGT/x0Al1FKbyaE/AjAKQCuanfCN7cY0OCtZvWWQc32bywzHRRrNswgNBQ4S12HrZUahcDvI2iGT5wIBAVeTOchqSqUtIRUhhX6nA+e+XkF83oUDKQkDKQkDKWZg8zLPNI8Zc6xUgCpTsItjMItjMIYH4ExUYI+WoQ+UYI+oaE2XheAqlkv6FXHC+O67cAfxGybYZPyIaP0Kkj1q1AHslD68kgN9ULuy4Pvnwe+fz64/ADcFIO+LagoWh7KFgP+iGZhdMzEhDaOTUUDm0s6Rssm9KrFIOND39IqsGulBtg7ph4bpokCRlb7IChpiCoTXAZ6CUpKhJKWkM4x1zcvr2J+j4KBtIShjBxCPuuDRfFMcLUCOL0EWtoAd2Iz3DWjsCYmmchOlLB+tITaRP23NivWFKGNjmJphns0zKJwBL0ij0Wq/3v7IRa1V0FqQIXSq0Ad6kVq0Af74HBdYLP9cNVeUDWPqsej6v/eGzUf6pqFzSWDietrOooVE3qlAKM2CkOzYOombK0Eq1aCa+pwDC0crdRJ2U71LwjLtpJmwprNypjfo2IoK2Nej4LhjOyXbxF5mY3tz4oEnM5+a06bhDOxGV5hHZzCGCvbrxZDY1Mbr0Ef12GWTbxUs/3wJWsRNRubaIr2U6g8QZrnoPKsXOdFDvN6FGzt/9ZqrwK1n7V6lP4crvjC97uCHW/LdCAfBeAAf/4GAH9CvBhMSYQQAuADAE6I7P8NvKXFAEG/Qb2vIAwP1SxUa3YknmzHVpZWYAruZoxWFCmVD92nmpFCKM3vUTCYVbCwT8VgWsb8LINSNhAAzmVAmpgELY3CndgMa2Iz9JEx6GNF6KMFaKMV6AUD+rgOvWCg5IcMgsrRCkbNLjMdcfs5kQ8BlBpQkR7KQunPIzWvD+qw7/IHF4Dkh+CleuCl+6FTHiXTQ8l0MV6zsHmTic3VAjYXN2FTScemogG9aqFWMf3Wlg2rMgmrVpoirq1gH/+bpqBmJSgpKWxZLexNYWGfGkJ+flZGRuKRl3lkBSaqvDYBlNbCLYzBWbce5ug49IkSapsnMDlRRW2cgccoGCjX7PA3bYZOnJgG4bI0z2FAEn3g8Ej1qSHcU0Mswwd7GAAAIABJREFUZq4O9SA1rx987xCEwQXgegbhpvvhpfthcDIqpouS6WG0ZjEh1UxsLhpYX9Cxaa0OrWJBr25ArbqaldVKEY5RhaWVpoV6HNDV3nmQ05nQrKhZCT0+0Bf2qhjOslbS/IyMnMJ+07zMM1euTTCjMrEJ7sRm2BPj0DZPQP9/BRgTFVRH60bl1SoTzqpDUbLd0KTo/itjoyl6/4fC1VukeZHD4uE05JyM9FCagdwXzNT8fgi9g+D754Pvn8dao+l+WGI6LKdFw8amionNvlhuKhlYP1mDplnQyiYzJmMWgC6IQWdDSwcIIc9Elq+hlF6TcN9hSukmf34zgOEW2yn+ORwAl1BKfwsWGipSSoMmzHoAC6Y74ZteDFwP9RaB5WCyamFSszBRZaGhAFxGuQS7VgorVxyw4iqWnOmDmM5DzapI52SoGQn9vSoW9qYwv0fB4t5UCP8ev1KpjgauuhHc5CSckXVwRtejNjqO6oYxVqnGa6hsYqCqVi1MWsyFapGheM2OKKhEeZFHznf5fRKPrB+3Tw+lkZmfRWqoF5kFg5CHBiAMLYQwvAheug9eZhC6kEbJdLHBcLGpYmJUs7BhUsfrr9SwqbgZE4XV0KusAukV1oIyq5OxLaggBaIpqhnwkoJU/1YQU3kouTxSWfZ7ZXoULOxLYX5eweL+FOZlZAxnJAym2G+WFSj46hj46ji8wgickVdgj4+g9soEqhvGUBstozqiYXykhvUFAwXLwaQ1VSjjRDIQyMA5LshI7PcaTiM9lEJqKI/0/H6kFwxC6J8HYXgxSO88eOk+uJlBlC0PBdPFRI0BZ23VxNrxGjaVdGyc1FEtGjBqNrRXDJjPlf0y9mziMiam85BSeUjZPqQyMtI5GUMLc+jNL8D8HhVL+lNYkFcxP8t+s16ZR17hIVsVcNUxkOoknM1r4Y5tgD4yhtrmTew3G9ehrdBiy1jB8TDS5LqjAqjyHHI+pPskHqmcjFS/isxwGqmhDLZ673bILBiE1N8HYd5iCIML4GUG4GYGoXMKypaHouFiQ9nAqGZhfVHH+oKO1ZM1FAoG9KqJWtWCXtEbjIRtaPBGLXgbgzJWAVABJ2wEJ6yAIKsQUzlIaWYg5HQG6bwM1f/dFvalsLBXxbIlvThit3kYStdbLlmRQG3rixOmzvoMximle7ZaSQi5H8C8mFVfbTwlpYSQVoGuJZTSDYSQbQE8SAh5AUAp6QVG05taDCjYS2xMv+O4arLRQ8Wa5Xc6BkNJ9TBUEVTS4MFYQYpCTVDSkNJ5yPlBqFkVKR9oA74IbDuUxsKcgvlZGYMpCX0qjwzngquMgC9MwNm8BtbmtTBHx1FZO4LqhnFURzVoIxq0kVoIs8BJNcOMJwgrpcIxAeiTOPRKAtLDKaSH08gMpZFZMID0ggGo84dZpRxeDDczCC8zgCoVUTBcjGoWNk2aWL+mjNdGN2FzScfmiRqqRSOskGZpLBRJ2+9IbwYYL6kQ1QzEVA6imvF/m3QI+0AglwyksCCnYEFOwbyshLzMo0cE+MooeG0C7thKOK+uhjk6huqGMUxsGMPaEQ2VTVVoIxpKhoNJf9RT2YkHfSCMzLHz6JMk9Ek8MoOpEPKZhQNIz+tDeqsh8MOLIQwtAO2ZDzc7DJ1ImDRcjGo2XtMsrC3pWDtew/rRGjauqKFaHIFeWYda1YRVmYRZnYStlSNDT6fCPVpuMvO2CUGVzinI5GQsGUhj28E05ucULMormJeRkZd59Kk8hMooOG0S3thaOCOvwhrZhOr6MdSenUB1Uzk0Di9VzLDcaP5w4Fa/TTPQt90qi9SAiuxWGaTn9SA9vx+ZRcMQwt9mHrzsMEwxjUndxXjNwaaqiQ1lAxuLOl4bq2LjpA6tbKJaNKA/Z8KsFGFWX4et/S9sQ2v52wiyykJ+6TzkTB/kbA7pnIKewQGkcwuwZCCNJf0M5AuyCrbKyeiVefQoPCSjAL4yBjq5Cc7YBtib16Gy9u/QxwqorC+g8lwVtQkdlZIZCt56x8UrbnyYqRvM6dZoIkrpwa3WEUJGCCHzKaWbCCHzAYy2OMYGf/oaIeRPAN4N4HYAPYQQwW8dLASwYbrreVOLAdDYMijVbIxVDBQrJmpl9tErFRilMb+jsg67IAU39YiRwqr29CGdl5HJqxgeTGFJfxrbDqWxdU8KC3MyBlQB/SkBQmUUfHkVnFfWwNm8FuW1G1DdMI7y2nGU15VRm9BRLBoYM1khLTvuFNcfVN5ekUde5DEo8+hNi8jMzyC7VQbZhb3ILhpGZskC8IMLIC7cDm5uHtzsMCYMDxt0BxvKBtYUdaxeoeH1iQlsHFuLml9pa8UizOokrMpkbIWNfn85P4Dcgh2R6ulBKicj06Ngq8E0th3MYHF/Clv3qFicV9Gj8OhXOPDlzeArI3A2rYG9/lVoq8dQeWSEVdKNVby2qYqCZvvf3/VdfPz3zwl8CK7tB1JID6WQW5RDbvEAE73FCyBstQ2EoUVwc/NgZ4YwqTsY1x2sLujYUDXxykgVr41VMT6ho1rSob1qQn9mEmZ1I2xtRcvvH4BcTOeh5AeRyueQzskYXJTDcO9i7DCcxbaDaSzIKliYU9Cf8gWuvBlccSOczWthb1iFytoRaBteROn1ArQVGiobq5jUbUxaHgq2i3HHw0aP4omm758RWOulT2L/fzYvI+v//z3bD2Pxwe+COn8Y4sLtwQ1vDTczACvVjwkf2mtLOlYXanh9vMa+/3jN//91mJUijPIYbK3M+m1W6qAvuwA0AK+BlzaFjlvO9rEWSj6DTI+CTF7BkqEMlvSncPAuw6GIDag88pwNvjoGUtgAe/0qOGMbUF5toLK2iOqmCsrry9A21TBuMnEvh0OavQbTE95VLnAwRR4VmcfIQArV4RTKC3PILh5CZsEglEWLIAwvgrjTnpDz82FKWXA1B4Zmo1o2sHqyhtfGNLw+rqEwqaMyqUOvmtALo7BqJdhaGXioCw8nohSetUU6kO8E8EkAl/jTKTdJEEJ6AdQopSYhZADAvgAu9VsSD+H/s/feUW2f9x7/C5CEhPZCQiAQew9jY2PjvR3H2c1q0iad6cjtSnu7R26atje9vU3apmnTJM1sVuNsJ97bxgZszN4CIYEQktBAAjH0+0Og2E3a5nfS5nf7O33O8Tkc/JWe9fm83p/P83yfB7iO2BtF7/n5vyz/+mIQjcZPHAcWzhVMhxf/xfYIZhcygvcSgsVoN1muIVmpR6aSItdIUGsk5OhlFJnkZColWFQS9CkC1CIQ+BwkOIaYcVgJ2QbwD4wQGBrDN+wnOBLE4w7/VQFYjPplgkTUwqQYAFKEMfCnyVBm65Cl65Flm2POn5rJnDqDYJIMz9Qcdv80VluY/vFBepwB7K5J/AvLFeEJzwL4vfEN28XsZzG6jzm8GrFCT4pKhUwlRqFNIUOXQr5BTrZWikUlIV0hQisRIJn2IphwMOvsYLalj8CAjcCQk7ZBb8zhnSGcUzHgv1e0ejHsLSki9MlJqHQpyE0yFBlyFJY05JkGks3ZCDMLYkKnNOGemscVmuG8N8yQL0znSID+c0F8Hj8Bj5PgRIBpn4vpgIdIyP8uyCeJJHHIiZV6ZIZsZCoJCo0EjUZCvkFOoVFOukJMtkqCPiUJpWCepIlhGLcxO2JlashKoNeJb98ovkE/wbFJmr1TcXHz/xW4LS7l6ZOT0MlEZBmkKDLkKLNTkWemIrVkIUzPJUGXwZwqnUBiCuPhWez+afo9Ic67Q/Q4A4y6Q/jcIYLtU0yd8jLl7yQSOPWuzDYhMWlhr0CKSK6JC5omTY6sSE+OsZp8g4xMlYQcTQppslg2mxxyI/A5mB21MjPUjd/aQXDIia/Li384QNAVYnQhU+uanadxYW4Xy6KQxTKQJIziJJSpUmQmGaaadBTZsbkVpuciNFmYU6UzIzfiCc/inJylzxti0BuixxmkfjRAcGIKvzfMpNdHJOBhyuFipn+SuekJ5mfHgMZLMrHFuRUrtchUEuRqCTqthDXlRgoMMsxKCVkqMRpxbG7Fsn+EGMD8h3PO4KfA8wkJCZ8EBoHrARISEpYBd0Sj0U8BxcDvEhIS5oFEYnsG7Quf/0/g2YSEhHuAc8Ajf6/Cf2kxWPwbx1OzsSWi4PQsofDiZvHMO28NXbR+u1gWlz0WwSjVaONgzDHKyTfIyNdJyddK0UoE6ETzJHmt4LIxY+sm2DdAYMiJt9eFf9hPYHQy7jjemXcLwKLTLEZ/Wr0UhVmOMlOFKi8debYZQUYuSaY85pRpBJJkWCdnsfmm6OmYpNMxSr8riHtsEp87RMjnJ+wdfU/wL0b7Ur0ZsVJPslKPQpOCQivBpJdSlKagMFWGRS0hTSbCKBMi8I+S6LExO9zL1Lk+fL12bENjePsn8A8H8IRnGJ1a7Fsswl8sF4tbgUyETiZCkaFAnaNCnpmKMjed5Ky8WFajNjMl1eMKzWLzTXPKG4voOob8jDZM4vd0EvA0MO1zMeVzvQv0i6ImkioQyTUoMgqRqeTINRL0qVKK0xTkpsrI1cSyuNQUAbJZP0m+Uebs3cwMncNvHcF3xo6330twJEirOxzv219GrxcDTyNKwiQRkpGjQp4Wi9pVuemIzWZEliLmdRbmFEbGp6KMh2fpdodocgXpcQaxOgP4xkMEu6YInXIx5TvPTOhofO7+ll2q9FLkBTryTUspSpOTrU4hV5NCqlSAOmmWpIlhoiN9zI5YCfYN4Os7ja/fi++QLw501/QczbNznH4Pu1zMSoxiASpdCqosBea6zBjMc7MQmLJJMuUxqzYTTBDjnJyl3xvGOhGi0xHgzIifgCeMzx1i0uNlyu8i0usl0uxjftZGdN4at8uLIS5Rp8YEWivBbJCxulBPYaqMfK2UVKmQVKkAwYSdxAkHM9YOpu1D+HrtePuG8dv8+PsCjE5G4pnXYiDiB84lQFvipUHXP4Q5fDjnDKLRqBvY9B6/bwA+tfDzSaD8r3y+H1j+/6bOf2kxgNgSUXhmDk8wwph/inBgYQN0whODSdB7SUZwsUFK1EZkulTkGglag4yiNAXl6QoKdTIyFCLSpAIEHitYB4n0t+Lu6MNvHcHT48E36MMZiDA6Nfsu+Meiw0QUgiTSJQL0OgmKDAWaPC3q4iykOTmIckqZ11mYlhkYCc7S5AnR5w1xod5H32gXHmcQvztE2DtG2DvKdMB7CfAXoZGs1KHMLEahU6PSSzHoU6g0qyhIlVGkk2KSCVEmTCNwW5kd7CAy1IP38CCebgf+4QAtQ37eCM3EU/mLQbi4YW1IFqBPTmJZvgJNvgZldirqAjPi3GKEmYXMajKZSJDiDM3SPT5J8/gkF2wTDI0E8blD+I/5CL1sJxLoJDLpi8PvYrBL1EZStGnINRIspUZy0wqoMCsp0snIUolJkwlJmXSSNGEn0nuBYG8vvr4zeHtceE9N4HGHsYdjc9E5O8+5ub8U4ljkalIkx4XKsqUMZU4GwpxSEkz5zKkycE4nMDgxTb83RJvDT8eIn7HRIBOuSSY97pgAB73M2IPMD0bgwBiJggmEkgFEMnUsC7nIpirNKlbnaflkbRZZqmRSJUkIPFaijh5mhrrxdQfxdriYGPThG/Th8E/jmn53QLFoUwiTCIsFOHUSItkqAllq1MVZyPLyEOVVoKq9ApHUQHRyljFPCPtEmHNDE3TbffjGQ/jdIULuEaZ8rr9uU0IdKdPpyEblqKMyDL4UKoNiSo1T5GoSMcmE5IkTSJL4mJ1uY2qiA1/YjndkFE+Ph8DoJEOhGcYvOvG+WBZfcdaJkshMESI3SlHnqNAUmFDOpCNOLkagKmYuKYOJaSmOOS19MxLaklLpFPoZUgXxZgQJygKEUu1M+8YvEdR3MuDYXCzalFKbAndt+ODAiUL039dR/N8si+cLAlOzsVdJpxY2jUO+d2UECYlJcYCKFXqUhlTUBhnpBilLstQU6mUU66Wky4WIg04SuruJ9F4g0DeIu9WKu8eDb9CPLTSDc3oW34LDQgw6SmEsCkmXCDDqU1BmKdEWpqItzUGSk48wp5wZXQ7eaDI2X4R2W5A2Rz/NtgnGRwKxyMrliDvq7FQw3m6BREaK1kSKNh15qgGlNoU0k5xKs4pio5zSVBnpMiGymQmSxvqYsR4hcLADT8cgAwNePD0eHP7pS6L7uejislWs3cVyEUapCHWOCn1JLKJXlBQhyqtgVpNFIFmDPTDDmfFJ2kb8NHV7cR2fwOscJjC2AJigl9lw8BLAJMvVSNRGFAU5qPRSdGlyStOVVGYoKUuVY5QJ0SeGEYz3MzPYSajrKN76ITxPOHH3ejnriUXu45HYWYuLxUotjEW0RrGAmjI9uiItytx01GUFCLOKiZoKCcmMjE7O0DUeomnET+eIn/5hPx5nkGC7h5C7hUjw6CVBQ0yklCQrdaRo01GlyknLyaPYvIwqs4oinRSLSowxeY6kiWHmB9uY6m5lovsU7g47E8d9jNkD2MOzDM7McWFm/hKwa0RJqIVJZKYIUC+IU+b6QtaV5CC0FJNgLmZWnYljcpYed5gezyTnBic4bJvA7w4zMTZByG1numOcSIOP+dmzwNnYvUhSJSK5Gqk+E7lGgdogJTddScWyDEpT5eRqJBilAsS+YaLDHcxYO/G09uDu6Mc36MPd6WMoFBPWRRt3AOOJCTQugDxdIkCrl6LNV6MuTENdYMZ42TaSLKXMqjNxRpIY8E7R457kvM1Hh92HxxkT1bDbTtjrfCcw6IfEIRFJxyYRK6ykaGeQ6YZR6aVkZygoTVdSla7k2rI00uUCpKExEsetRLqdTHQE8fXZcXe68fZP4AjPXBKgLfqmJCmRo/8Q4kT/f3sdRUI0+s+87/WfW8qrqqP3v/g250f8XLD56BmawOMMEhhzEXLbmfKNxyOfRaBK1AZStOkodGq0aTLKLRrKM5SUG2RkKpJJTZpC4Ool0nsBf2sb7rYBPD1e3H1ehuIi8I5jXwwkszIZZZYSfUkquoo8UvILEeQtYSY1H2c4So8nzIXRd9rqHgkQHB//m22VqIwoDTp06XIK0pUszVJTaZRjViTHAOrsItLfhr+1jfEL/Xj7J/5mW5XCJEwLbVXlqNCXGNBV5CHJK0KQW8ms1sLodBI9njCd40Hq+9xxcAbGXEy6hoiE/HHgJyQmXQJNZaoGlT6F4iw1yy1qCnUyctRi9AmTCNxWIl2N+Frb8XbbcLW5GB+YwB6e/Zvjmi4ToclXoy8xoC7KQlZSHm+rYyqRbnesrQ0DHnqHfEy4JvGPjhL2jr5rXEVSJSKZGmlqJspUDRqDlJIsNUszVZSkyshWidHyTlsDXd242wZwtbnwDfqxTkbeV1t1hXo0pdnIyqoQZBUzm5qPIwwDE1O0OAPU97kZGgniHg1c0ta/DADECh3S1My4vZZkqanIUFJplJOtEqNJnEY41s10ZyPBnl7GL/Thah9nYsgfj849kbl3tVWfLCBTLkJXpEWTp43Za3E5Aksps/pc7GGwTkxd4lvvx17/L/rWYoASafpD49961fN9MSdVE919/V99CeiSkv+bFz5wfR9m+adlBgkJCY8ClwNj0Wi0bOF373nfxsKJufuBy4AQcFs0Gm16P/WEZubxhWYY808RCkwT8gWZ8i9EqAuOlSgQkSzXIFEbkBszSTUryclQsCpPxzKTkiylCPW0iwRbI1Otpxlu6GS8YxRX2zgD/ulLjDSWAcSWTjJTBKTmaTBUpcUMtHwpiVllBBVmrL4ILc4gJxvctPSdiTv9pGsonp4DCMQyxEodusIalKkaUjMUVC6AdEmanHSZEIm7l9muBgLtrbj29DB63slxe4ChUAyii2n4IuzNEgF5uhRWlugwVFvQlOWTXL6SubRiXPMSOsZDNAxPcHbAw+DgBO6GAJN7hgi7nyQy6bsEnGK1AUVaDmqDjJLyMury11NhVFCklZCaECTJ2cN0yynGmzpwdxzEecyF3RvGFp7lQmSOhgXHXsyasqVC0rJV6Aq1FN9Ui7yiGkF2GdOphQxMRGhzBTk94KF50MvYsB+vfTQOn9kTQTjhJklUj1jZh0RtRGlKJzVDQaFZxeZiA9/YlI9ZLkTmtxG1dTDZfAbXOQeu9jGcXW4GJmOA9M3MxTMjjSiJGbEQr1xEUpGW6epMdBW5iEuWo1q2g2iyHo93CrszSH2Pix7rBF5nEJ9jkJDbQWTSx/xs5B0oSg2khNPROLSYBCqqwxKWhycpT5VSlyZmfeIgn/GeYcLezpi9H1fbOCPOIEOhWcYjs/GIVpKUgE4Us7M0gwx9qY7USA4qcQnJqSuYERbiCAtpiVg4k6SkSbIER9oEnqibUGos+l4U7cWMIUVrQmnKQm2QkW9RsTpfT7lBhlItJnHaRdTeycyJV4meaUfa7qSw040kEME8FVtKXBwzpTAJnSg2n4ZCLfqSVPRLJEgri0gw5xNUmOnzRmgdC/DHMza6bBOMDfvxOewLwjfLXEQH6BBYYj6Qok1HnW6M+0BttoZKowyzXETyWBezA60ELjQxcqqN8T8eZWRggoHJmYUMJuYDisQEtohiPpCulmAo16Ndko62IhfZzXzgEo1Gmftw3ib60Ms/LTNISEhYCwSBJy4Sg/8GPBfdt6GORqP/mZCQcBlwJzExWAHcH41GV/y9OsqqlkR/9OQbnOj3cG7Aw9iQD699OA6PRQcVSZVI9WZkBjP6DCXleVpqczTUpCvJUycjcvUw03YKf2sbI2e6GT3vZHg8hC08G4+qFqFhSBaQqxZjrDKgr8xCW1OFqHQlM4ZCrIE5LjiDnOhz09Q7zrgjwMTwEJMu2yXASJZrSNGaUGVY0KbJqcjTsiZXS6VRQa5KiHCsm5m2k7jPnsd1wYqzxUX/WAjn9Cyu6dlLIGaWCMnUSDBWpWJYloe6sgxhSaw9/b4ZGh1+6gc8nO914xyaIDhmZ9JlYzrgiUNisT1KkxmdSUFVnpbVeVoqDHJylEJEjhYiXY2M1zcxeraf8S4PA+5w7NbJBaguLn2YJQIydCkYyvWkrShAWVaCsHQVYX0Bg74ZGhw+jvaM02n1Mmbz4XMMMOV1vmd7VBmZGDKVVFo01OVqqU6TkyFNRORoYar5OJ4LXYye7cfZ4mLAP41javY925OVJkdfqiNteQHK6qUI8pcQ0ubR7Z6m2ennaPc4Hb1u3KMBfPY+Qm7HJRAVK3VI9Zmo0tJIzVRSm69jWaYqJtaiGYSjHUw1n8BZ34Kr1c7YBRfWyci72rO4Tp6ZLsdYZUBXkYNy2XKSCpczqcig2z1FvX2C+j43nX2emOD8RXsWl91khmzU6UZ0Jjl1hXrqsjUU6VJIF06TZGtmuu0Mo6dbcLWO4Gx10Rt8J6OBd4QmWyokLSPWHuPyEqRlVSTk1zCRkkafd4r64Vh7ega8uGxeJl1DhNwOZiZj55qSRJJ4RqA1p5FuUVGTrWGVRUNZqhR9wiSJg+eZbj+D80w7I2dtuPu89AZj47MYyCxu9GZLYxv0qeWpsfYsqYXMcrziVPonpjk55OVol4thm49x+wQBRy9T/vF3tUduykNtVGHKVFGbq6U6Q8WuEuMHjtTL9Kroi1euf1/PFj/yyr9UZvBPXSZKSEiwAK9fJAZdwPqLDlIcjkajhQkJCb9b+PlPf/nc3/r+ssol0W//8TUO94zT1u9h1OrFZ+8h7HXGjUMglpGiM6EyF6AzKago0LGxUE+VUU62NIpg6BzTLacYOdmM87wDW8c4vcGZeIS2GAVZUoRkG6WklqdiWlmEsnY1CdlVuCVGmkcnaRie4GCbk7FhPx6bnaDTynTAw/xsJA4UmSEbXVYapkwVG4pTWWPRkK8Ro/QPMttyDO+5Zkbre3C2uOgdD2ELz8TXyBc33QrlIkzlqaRWZmBcXY2och0zxmJ6JiKcHJrgeI+Lzj4P43Y//uEuQm5HPAtJlmuQaE0oTdkYLSoqc7RsKNCxzKQgTTBFYn8DU+1nGDl+AftZO45BH73BCOMXCaI+WYBZIiA7Q4FpuQl9VT6KFWuI5i1nLCrjvHOS01YPxzvHGLVO4HPYLhGfRWeVGbPRm/Xk5WpYU6hndaaaAo2Y5LEuIi3H8TReYKS+l9HzY/QFIzimZuIgUwoTMYmF5MpEGKtSSVuRh2ZpBaLy1QS0+Vh9EY4PeTnW5aK3z4PL5iI4OkDY62QuEo4LslRvRmkyY7SoWF2USq1FQ5VBimHOA9bz+OuP4Trfg+OMg4FhP7bwO2K8CPc8mQhTlpL0mnTSVlcgLlnOfM4yRmbFNDj8HOoep7nfzah1gonh3rjwQQxcKVoTioxCdOkKSvN1bCjUU21SkK8SIRztINJ8hNHjTYw1D+NoGaMrEIlvzC5ujpslQvIWBNi4Ih/NylUk5VfjU2TR45nimNXDoY4xHEMTjA+OEHQOxIOlRfGVGSxozLEsa2OpgdUWDYU6CdrwKNG+RnxnTuE41clYyxgDo5NYQ+8EAovCkicTYi7WYagykbaqEvHSDcwYixmYTOD8aICDXS4udI8z7vAzYesmNO6IZ+9CqRKJ2oDKXIAhU0Vpjob1+TqWZygxS+Yv8VPHGRvO/omFsXjHTzWiJExiIXlpUtKqjRiWFaBcvpKE3KX4pWnoFNIPLgY6VfSFXWvf17Mlf3zt32IQ//J3i8FENBpVLfycAHij0agqISHhdWLvyB5f+L8DwH8uvEb1l9/5GeAzAGnp5qV3v3SM1y+MYO334BoYJjDSFwePcCEt1loKSM/VsK44lc35OipSU5CMdTF9di/2g2cYaXLQ0+O5xNllgkQsKULyDVJMS42YN1QhX7WJ2cwl9IUEHLF62N/upLfbjWtgMA7/6PwcArEMmdGCOiMHU66GbZVp1GVpqDCkIHe2MXXuMI5DZ7CdGGRwyE/fZATXdGzDdRF0hfoUMusyMCz68wJvAAAgAElEQVQvQrlmC3O5KxiYTOCI1cuelr/fX6NFxcYyI1sL9BRpxchdnUyfeZvR0y0MHRugr9+LNfRO6r/Y31y1mIyV6Zg3VCFbWsdcQR29wUTq7RO8cWGE3m43btsIfnv3JfVK9eZ4f9cWp7I5X0+VMYUUVzfTDftwHDqD4+ww/b1e+iYj8XoX+1tklGKuTSdtZSnyVRuZt1TTPyXiiNXLwQ4nXd3jjPUNMumyMeVzxcdZmmqO93dLRRqb8nTx/kaaj+A4VI/txCA26wTdwci7+punSyGj1kTGuspL+nvK5mVP6yj9fR7G+m3vOc6azPx393fBrpz1LdiO9dHfGxvni+3KLIn111hlIHNDJfJVG5nLXkZfSMCxwVh/OzvHcdtGCIz0XdJfmdGC0pRNep6GLRVprLZoqDJKkTvbmL5w7K/292K7yqg1kbayBGXdJubyV2INJXDM6v2rfnTxOBsylWyqSGNzvo4irQSlu5vps3sZPXme4ZPWv9rfAk3MrjLWViBbtor5wtX0hQScHp7g7dbReH8vtqu/9KO6Qj1bCvRUGaXIPL1EGvfjOFSP44yNvm7Pe9pVoT6FtKVG0pYXYPj6Ax9cDLSq6PM717yvZ0uffP1fSgz+P3ub6O/ct/G3Pvd74PcAJZVLopMzcwQWLqSLLFw5EV9ukMXSaZ1JwepCPetytDEhGGogdOYAtv2NDB4dotszhTUUITwXRZSYgCVFSKFcROaqdLI2L0Feu57ZwjWcd89wpNnNa412bN3jTNi6mRyzMRcJkygQITNYUGUWkZGvZWuViQ25WqoMUpIHTjN55EkG9zYwdHyYrokpbOGZeH1miZC6dAVpS41Yti1FtmIDM7mrOD8W5tV+N3v22xn+7X4mhjrjkX6SSII01Uxa+UrSctTsrE5nW76eArUIQfcxgvVvM/jwOWzHh3nYP40tPENkPookKVZfaZqMFTVpZG5eimzt5UyZKmgcmeRQ3zj3nncw3ODG//J5Jl2vMT8bIUkkQWa0oLUUkF+Tz6471rMtT0+eIoGkzqN4Du3FfvwtbC86aPdP88LULE8v1GdJEVGSLiO9xkT1f2xHsupyQoZizjqCvN7m5IVOF8M9Y0xYW5h6dJz53z+LQPw60lQzupxC0nM1fGpXMZtzVpOdMk9i+2E8Rw4wfKyDofoDtOydZjwyh20+ym7hAmwzFGSsTCdzywosX/0WAW0+9fYgb7Y7aeoYw9HrxDfUTtjrJNo+h3AwjHRPF6m5UfKKdOwoT+Pey4vJTo5Ae4jxA+dwnOzH2jhKq386DjulMBFjioiZTAVjG3JIW11Ncu0OFNu/yKQ9QH3rKOc6XTh6R/ANdcTBnizXIJ0zox/Io0SiYst0gPXZGj6Rk8DtkX7GBg5gH+vF2uqkIxCJ16cRxbLUglw1WfY8jKurEYq3M6IuprEgg9cjG2hUjDJmHcVn64iDdTH6T83NobREz7ZSI2vNKjImx7G0HSPl4BEKD3Ri6/XSEZiOBycaURK5UiH58xoyi/IxFC5HWJaOIzmZQ9YJXmuZ53xrGeNjWvySbqYyXQCIlXoU6QXostKoKjOiL06lNEuFfHqE2ZajjP74K4wf7kHY5yU3EEG98Afr9cmxsyqWfA2WTUWkbtSRWFrJcJKOs44AvzsxQFu7i/HBYfx2O9OBFKAQcbkepbmYVIuRpeVG6sqMLE2TYwgNMdtyDNfx+n8Mt4h+WLeWfujlwxaDv3bfhh0wX/Tc+7pLIxqFcGQufj317EWvBgolsat4NQYZVXla1uVqqTSkkDLchHf/qwwfbaX/6BAtvtgG8aKjFctF5FWkkrmuiNTtO5gv20S7b57XTtnZ02DH3ufG238+BpH5OcRKPbqCGgw5adRVmdhenMoqswKZrYHggSfofvMsAyeGaffHljoW66lWiSlYYsS8rgj9th3MlW6i0zvDL1pHOfz2KIMdrzJhbbmkHkV6ATkralhTZeKyEgO16TIk/ScJnHybgf9toPuUnZf97/RHn5xEsTyZuqVGsreXo996GXMFdbROwO7WEY60jmI7MYb3yZeZ8j0ch4bSXEx6YSY7t+3imso0atPliG2N+A68Rv+bv2boOQctvmkevKieMkUymWV6VnxlA9vXrCdavplz7jkO9I7zQoMdW9dIDL6/sBH9+a8RK/UL9WSwotTAj68rZ3n6lYisZ/HufZnhI60MnDhA894YeFuj4FwARcESI9nbKqj4/p2Ulm7CMDbF/p5x3m60Y+u04x/uio1b4xzi3glUey5gyHaypsrEdZUm7tmWR4q1Hs/bZxk+MkjfKfslgDeKBchlInxLjbjXFaPfehmar/2CkY9P09I+yt5GOyP943j7mwl7R4EY/FS+MtI6jKwTJ7K9eJI1mQq2TDcTcB2kv76R3lY7XYHIJfNTpkgmd1ka5vUlaIU7mSleT2vWNl5aXsGxFCc28wgT1ta4iEjURhTpBaQXZrBhiYntRaks18owdBym7sQBTG820dcwQsfCGRh4px5LsoFsXQWayp3M5RVxbnyG314IcaItA9vweiaSdUxnei6pJ6PYzFSpgYLyNIxpKQgH6vG+8BCjb54jes6JxT+NeKEeo1gQs4MlBrK3KdFsy2UmewVnR0Ls73Hxqz1d2Lsd+Ib6CHuTSEgsJblMg8pShrkonbpSAzvKjVSnihH2n8b91quc/8ljWJt/GhdERRSuFgtidlCThuW6KlRrtjBTvJ4z9iD7ul3sb7Sz57Xz+O3d8flJlmv+EfyKnTP4B9939H+lfNjLRPcB7os2kDXRaPQbCQkJO4Ev8s4G8gPRaPTvnp4rKq+KXnHv0xw4Y2Oke4CJoQ5mp4IIxDJUmcWYS/Ooq0zjo9UZlMumiJ7by/Arb9LzehctzkmsoUjc+atVYrLWmMm/di2itddhExp5tcvFiycHsXWN4eltYsrnimUARgvGwjJKy1K5dUUW67MUSKz1jL3yAtZ9rbScc9IdjBCcnUcmSKRMkUxBmZ78q5ai3XUDfmMFe/snePL0IL0dLpwd55l02eJOqM6pxFJq5JY6CxtzNGRODRE++ALWPWfoOjhIsy92JYIoMYFcaSzqzr+iDNNVVxAt20Sje46XW0Y5cMaGvb0Lv6OP2akgQqkSZXoB6SW5XFabyeXFBioVM8yf/DP2t4/Qu6ebC47gu8Ylb0s2WTtWkLz+BvqSDOzrc/PccSuDbcNxUP3luHx0eSYbLcr4uPS+ep6OTve7xqWkJo3sbZVod17HuKGKY0M+nqwfoqttjLGuFoJOa3xcdAVLMRfquGmVhZ35WoxhG+EDz9H70kn6Ttnfc1zydpaQfvUuouWbafREeabRzukLIwy3duIb7mYuEkYoVaLOKiOtwMy25WZuqDJRKJlm/vTLON4+TM8bnZyzB7CFY2JuEguoWhiX7Gs2IlxxOX1JBna3OXmjYZjBtmF8Qx2EvaMkCkRI9WbSSipZVm3imioTGyxKJB0HcR94i+7dTTS3j2NduNNfKUykWB4bF8vmcnRXXH/JuHQ0jzLe1/aucckuM/CRGjM787Wk+boJHXuV3pdOYj1tp2ninXGxpAhZkqfGsqmA9Kt3Mb9kJ01jUzzTaOd44zDOnn4mhjr++rikzDB/6iUGX3oL62Hru8ZlqS6FrLVmcj+yGUHdtVijav7cNsqeJjsDLTa8/c1MBzwkCkTITbmk5hZRsyydW2rMrEiXIek4yOirLzOwt53WDjfdwWnCc1GUwkQKZCLKlqZRcM0KlJt2MW6o4u0+L39uGqajeZSxrnOE3A6i83NI9WY0ORVklxm4dlkGW3K15KcqPvCyTYlaGX1mY+37enbJS3v/pZaJ/plvE/2J2B9n0AFO4AfAy8DzQCYL921Eo1HPwv7Br4HtxF4tvf299gv+shSWV0U3/uBxjp224ezpwD/cDYBEbcBYWkNNTTofqc5ga7aShPqXGNmzl67dLdTb/DinY9FMgSyZcouSwmuXkHrZTkIlW9jT6+HZszZaGuyMdzfGnVqVWYyxMJ+tK7P4+DIz+clBZg89Q89zB+g/OsRZ7xSeyBySpIQY6KoMFN9Yh3zHTdil2ezuGOPZIwMMd9jwDDQzM+lDKFWiya4kp8rCNbWZXF2cSvrkAP7Xn6bj2ZN0tIzR6o85hEaURI1aTNGOXLIuX4tgw800BcS8dGGEvacGGW65QHDUyvxsBInaiL5oGWXVJm6sMbMjT0NK+z6cr79G9+5mzvR5sYVnALCkiKgwySi8uhzT5TuYq7mKff0TPNtgo/GsHWdHA2GvEwBFRgGG/GLW1Jr5xPJMKlRRosefo//ZN+nfb6XBG2Z0ahZRYgLF8mRKCzUUfaQG3TW3MqYt5vWucR4/0s9Qxwie3iamAx6SRBI0OZVYKvO4rCaDGyrSsMyOEt73NF3PHqP77AhNE1NxWFYqxZRuyCL3ypUkb76VznkNu1tHefW49RLIJ8s1aAtqyKtK5+baTC7P16K1nWb8zZfp+nMjDQsQjsxHMUuEVGklFF5ZROYN1xCt2s6x0RmeP2fnxGkbI+3nmHTZAJAZLOjzy6ipSedTKy2sMElJqH+Joed207e3jzOOwHval/EjNxHIXM6bPR4eOTaAtX0MV+eZuJiqMosxVxSzeVkGt1RnkC/0M3voGbqfPUDv8WGaJmL2tSik5asyyN1Vg+yyj9InMvNGl4sXjlkZbO6OB0aL9pVbnc11tZlcVajH5GnF+9af6XqhnqbzTvomY0ukRrGASmUyRTtyyb7pShJrdtLkF/H8eQcHTg1hb3vHvqR6M9q8SsqqTXyqLpt1WQrELW8z/MKf6dvTSYN1AmtohqSEd+yr+NpKTNddR6RkE2/3eXn0pJXuFifOjgZCbkdcIDLKyqirTuf25ZmUS4LMn3ktbl/1nhCu6ZiPFciSqao2kLerGs1VtzKsKOCtXjdPHe1n4Hx/fJlMIJahMOXifOnLH1wMVIro0+vfnxhUv7Lv32LwYZWCsqrosm88zNn6YVxdjYTcjnhWkFNdxCc25bIjT0vq8GlsTz5B7+vt1A9MYAvPIEqMAXvpajMFH6lDvP02uuc0/P70IAdODDLS3kxgpC9uoFlVFdywMZerig3kTPYw/sJjdO9u4myri77J2NJUgSyZygINpR+tRXPNbQxJc3imeYSXDvdjb+9iYqgjHrWkVyxlxxoLN1enU5rkJvTGY3S9cJLG0454NGSWCFmWIafk+iWYbrkNj2kpL3W4+OOBXmxtVty9TcxFwkjURoyly1m5MpOPLTezWhdl/vTL9Dz+Muf3DdDqn8Y3Mx+P9EuuLSHzozcwt2Qnr3V7+O3BXgZaR3B1nmZm0heHaG6Fic+uz2VXgYakc28w+MSf6Hi5k7Pe2EV8i2Cu2JpN3q1XkrjyGo6ORXnqrI1Tp4YYbTtD2DtKkkiCNq+arPJsPrYhl+tK9KhsZ3A88yRtf2qicSSILTyDJCkmINUr08m/diUpO2/nwoyWp5uG2XdiEPuFRiZdNhISk1BlFpNeUsg163O4pcpERrAP9wuP0vb0ac4tbFID5EpF1JTpKbx2Gdrrbqc/JZc/t43y4uF+Bs9fIODoIzo/h9yUi7GwjK1rLHxqRSb5CW7Cbz5Gz0unaDxuo9U/HReNFdkqCq4oI/2jtzCaXsuennEeO9jHwLmuOIQlaiOpxTXUrjRzS00m69MEzB98nL4X9nNh70Bc3PTJSdTqpRRcXkDWLdcTrtrF/n4vDx7qo7/VyXjnaaYDHoRSJbqCGkqXW/jYyiyuKNCSdOp5hl95k7bnWzntCeOJxOakRi2hcGMW+bfuInHl1Rx3J/G74wOcb3RcMieanErylhVwY52Fa4v1aHoPM/rqy7T9qZF6mx/H1GwcuktrTZR+fCPJ62+gPZrKb09aOdlgx3a+gaDTSqJAhCKjgOwl5excmcmtS0xk+TrwvPEcrU+cpLnbQ3dwGoiJw4pSHaW31qG+7Ab65YU82WTnzdNDDJ5vxbfgJ/K0XMxVS1i9NIPPrsqiSOBjas+jtD95hNbGEZp9sTkxiQWstCgpuXEZhquuZdxSx4vtYzx33EpfQ2d8TmbOP/YPEYMn1/zdt94BWPb6/n+LwYdV8koro7lfeJC2E514+puZnQqiyCjAXFHJrdsL+HhVGsruQ/T97hEad3fGHTBXGjPGys9sRHL152nwJ/Pf+3toabAz2nqcmUkfKVoT5uqV3Lgtn5sq08gaa8T6u4fofKmdo84gvpl5TGIBq/M1VH5mHaorP8Z5zPyhfpD9h/oZbTnBdMCDRG0ko2ola1dl8uW12eT72xl99nEuPHaao8N+PJE59MlJrM1WUXRtFaZPfoFBeT6/PjHImwf7GG07S8jtQKzUoy9azpq12XxtQy7FjOF99kGa/3A8LnAaURK1GgnlH12C+aYbGc1ex0P1Nt44bmWw8SxBpxWBWIa+uJbatXl8ui6bdaowwRcfpP3pExw7N4o1FINyrUZC+fZc8j53G56S7ezucPHIni4Gzr7j/LqCGvKW5vC1bYVsNUaZPfgULQ+9QdPZEVr9UyQlxAR3xZZsiu64iflV1/N8m4vf7ulisDk2Z4tgz6+t4M5thVyeryHhwCP0Pv0GZ97up9k3zVw0SrE8mZVrMyn+2BaSdnyW3X1BHj7ST+epDty9TUTn51BkFJC3opqPbsjhlnID0ra99D70KA2v9dDsmyY4Ox8Tm1IdS764DdHOOzg+nsDP9/fQcqIbd29T3IYyyiv46LYCbig3Yho8zsDv/0DHy52ccIfwzczHNv1LdFR9ZgOyyz9Ow4ye+w70cP7M8CU2lFFVy6a6LO5cbSHbfZ6RPz1B86P1nBibxBOZwygWsDpHTclNyzDcegdtQgu/PzXIvsP9cXCLlXpSS1awcX0OX16bQ8GsDfczD3H+Dyfi0NYnJ7FCk0LFx5eR8anPMags5renhnj7xCBDTfVMumwIxDIM5WtZvdbCZ1ZlU5syge/5B2l/+hTHWlzYwjPIBInUaSVU3lSJ+eYbGcvbxIvtTp54q5uBs2cIOq0kiSSkltaRX53J17cUsEEbYfbAk5x/cA8nGkexhiKIEmM2VLolm4I7biWy9EqebhnjD291YbvQgdfaSqJAhNpSRkFtCV/dVshWi5zo3t/T8tBrNJ52XGJDyzdmUXjrdhK23cELHW4eOdJH18kWPP3NAKgtZeQtr+C2DTncWJaK4OiT9D75CvV7+uKZ9e8Y/OBioFREn6ireV/P1uw5+G8x+LBKTmll1PyJ++k43oRvqINEgYjU0joqarP54Y5iquasjP3pYZoePBYHeK5UxLrVGZR9cjuJl9/J4xfGeGxfDz0n6wmM9CEQyzAtWc/KVZl8Z0sB+d5m7I8/SuNjZzg6HiI8F6VMkcyabTkU3nYFU2s/zkNn7bxwsI/++lNMumwkyzVkLF3L1rXZfHVtNqb+gww+/hSnnm3hrHeKuWiUapWYVVcWkPuJm3GX7+KBE4O8fmSAoYYThL2jSNRGLMtXc9WGHL5Qa0bZ+Gf6/vgCx1/rodkXc5JajYQVN1eQddttDJnruO9wP4eOWbGfO8Z0wIPMYCFnxQpu3pzHp5eaEOx/mM5HX+fowUE6AtNIkhLYoJey5JMrSL/t03RIS/j1iQH27u9lpPlIHIyFdcv55OY8PlqsZvbV+7nw8D6O1DuwhmZQChNZb5Kz9Ivr0d7wKc7Op/PAkT6OH+xkrO0E0fk51JYyilZXcefmfHZlJDL57C9p+eNRDl8Yi4NsXa6GZV/agvTKT3NwQsr9h3ppPtLKePdZAHQFNVSuK+NLG/LYqAzgeeKXtDx+miN9HlzTc5jEAjZUGVj65csRbPskLw/N8uCBHjqPn8fT3xwHWN36fP5jXS412HA8/ACtTzVw2BGI28balelUfOk6EjbexlOt4zyyv5eeU2fxD3cjEMtIq1zH1s15fLEum+JACwO/+RUXnmvh6HiIyHyUAlky67ZYKLz9CiIbPsEjTQ6e2d9L36mTcdtIX7KG67YX8ImaDDIHj9Lz699x7vVeTnti50HKFMms3pVP7iduxLvkKn59cohXDvVfYhuZy+r42M5CbqlMQ3P+ZTp+9QSNhwZpmphClJhAjVrC8hvKsNz+cWxZa/mfIwPsPzoQtw2p3kzOipV8blcx15XoER99nOb/fZ6zZxy0+qdRChOp1UhYevtyMm7/FJ2Kcn6yv5tTJ4dwnDsct438lTV85fJirshXM//qL2n85eucbolly/rkJFYZZVR/fi26Gz5FU0Imd7/VyYXTA4y1nWB+NoIys5ji1dV8c2cxm9KSCL/4Sxrv38vxHg+OqVlMYgGrctQs/eImZFd/miMBBT9+o4Oesz1x29DmVVO2ppQfXFZETUoA7xP/S9Nvj3LC7o/bxkqLkq2dTR8YzsVKRfTxle/vK1a8fejfYvBhFUtxRVR3/X30njhGyO1AZrBQsmENX7mskCv0YUZ/fS/nHqln/9gkSQlQq5FQ97k6DF/4Dge9Kdy9u5WOw8cJOq2kaE1Ylq/iK9eWc1OOkNALD3D6Z3vYP+QjODtPrUbCutuXkXXHFzgjLOCbL7XEQSNW6jFXr+Yz15byiSVpJL7yP5y655U4qCqVYlZtzab0O1+hI3UFP9nXzZE95xjvjl0slrFsM7deVcIdyzOQH/sjF+5/kf0nhrGFZzBLhGxbn0nFdz6Lu3Qnd+/v5c032+KgTVuymXXrc/jRtgKMzS/R8Zun2ff2AH2TEYxiAVuXm6j64uVMbf8i9x21sntvD0NnDjAXCaPNq2b5liX8dFcJ+d5muu79b0693sNZ7xQaURLrspSs/NblCG/6Nk9fcHL/i61YTx9iOuBBZSmjYtNyvr29kHUCOwM/v5czz7Vywh1ClJjAplQZtd/civLGO/nziJD7XmyJR5bytFzyapfznevKuUwdwP3kA5z8xUEOuUIArNWlsPTWasxf/ib7gjp+tqeT1oOn8A93I1bqya1bz1evLef6AjmhJ3/C6Z/t4bAjEJ+n5dcUk//Nb9Mmyeeu3a00H2jAa21FIJGRXbuJm3YU8KWVZth9H00PvMW+Zieu6TkKZCK2XpFP8be+yoBhOd95o4NTh7twthwlSSTBUL6Wq3cW8a0NOSgaXqTpnkfYd8qOY2oWS4qQLRstlN55I+PLbuA3p4Z4/rUOHOcOMj8bIbWkjq07K/nGxlxyBg7S+pMH46JsEgtYW6pnxXevJ7TpszzcYOexl9pwNB9nOuBBV1BDyZpyfn51GeW+8ww8+CAHn75A08QUSmEim3PV1H5rF6Kr/oNH2/385sVWbE0nCXtHkaflUrJhFfdeW8FK8TjD99/LsUfOcNY7RVICbE6Ts+Szdeju+A4v2uCBNzrpOnqMSZcNqd5M4do1fOGyIm7IhPGHfkzzH06yd9jPXBRq1GLWfHI5GV/6NqemdHx3dwutB04QGIldFZJXt5ZP7SziE6UqZl59gFP3vML+Pi++mXmqVWI23FhGzp1fpEVZxV27W2k/1sJ491mS5RpMlau5/ZrS2Dy99Vvq73meo20uHFOzFMuTWb0uk4rvfoH+7I38z+F+3nqzBWfLURIFIkxLNnL9rmK+sDITXcNztP3qWY4fGeLLge4PLgYKefTRZdXv69lVh47+Www+rJJZXB5V7vop/Sf3MzPpw1C2liuvquYbG3LQn3yc0997goMd43gic9RpU1j58aVkfP1unhkW8MDLbfQci4FNm1fNpl01fH9rIZmtu2n4/sMcaBzBMTVLjVrM2hvKyPn2D3jVp+O/X22n4+AhQm4HKksZK3as5H+vLiPLeohz3/slB4/ZsIZmKFMks/7yPEru/gEH57P5r1fbaNl3NA7Diq1reOCGSkp8F2j5zj289VY/1tAMBTIR69ZnUXnP1zmnruG7r7fTuOcY/uFupHozpZvW8/MbK6mZt9J99w/Z82In3cEIZomQnTtyKPnqJ+nN3c6P3urkyGun8PQ3I1EbKVq/gbtvqGST3Mvgz37A3sfP0+ybwigWsGO1mcqv34Kj+np+vL+Xt145i6vzNMlyDbmrN/LtG6u4yjSP85c/4MBvT3LWG0YpTGJHtZGld12Nb9Pn+NabXRzc2xYHZ/bKLXzt5ko+Wqhg4rff4/DPD3DCHUaUmMBlxTpq7rqMuWu+wQ/29fH6293YG/eRKBBhXraRz99YwaerTUz/8Yccu/t1DrkmAdiWpaLmK5uRfvJufnJ0kOfe7GLw9F4AjJUb+Ni1ZXxrbRazL93H8e/v5pDNR3guyuZUKcu+sAbdnf/Fb1v9PLy7nf4Te2OQLq3j6iur+NGWXJIP/oFT332KNzvGCc/Ns1aXworba0j/2o94ciiR37zSTveR/UQmfWjzqtl65Qp+clkhmobnOfO9P7CnaRRPZI5ajYTVN5Vj+daPeMWj5L5X2uk4eJCwdxSVpYxVO1fxi6tKMfcdoOm797PnuI3RqVkqlWK2fryK7C/dxb6ZTO55tY3WfYeZdNlQZBRQuaWOX11fSaGniZbv/YQ39vRjC8dsZsd1RRR87UuclS/hrmeb6TxeH7eZsi3r+cUNVSyd7aPzhz/ird1ddAcjWFKEbN+eQ8ldn6HLspkvv3CBtiNNePqbkRks5K9ayY9vqmJ9yjjWn/yAt55sptU/jVEs4LJ1mVTedSu2yuv4yu5Wmg614Oo8TYrWRPaKOr5/UxVXKMexP/gL9v3u1IJwJbGzxkT1XdfgWfcZvvlGB0f2d+BsOUqyXEPmsjV8/aZKbs6YZfzRn3PoF4c44Q4hEySyvVRPzV07iVxxF9/f28sbb3czcm4/SckSMpas486bK/lMsZTAU/dx4sd7eNsZRJSYwPYcNcu/shXpzXeRrNJ/YDgXyeXRR6uXvK9n644e+5cSg3/pK6zn5yESnmFuOnYIS5WmZ0OBjrSIk/5XD9DQ4zy6rwQAACAASURBVMU1PUexPJnaW6owf/mb/Mku5KdPNTF4ej/R+Tny1l3B5z5SxmdzE3D+5qs8/sAxmiamMEuEfPHmUorvvZdDERM3/r6RzkOPMhsOYlq6je//4HY+v9SI5xd3sbfoMD/3hDGJBWxbbWbpz7/FCVk1n3uphQuffpkpnwtdQQ23fe4afrwtj5k/3cuBb32Oh746iUyQyJUrM/ja4f+hx7yezz3dxL2HT+O/7Vl0BX2s2bGU1qfuRPZ6LNt47dtPsPt7CQTztay+7xPs+tnTfPP1dv78ylF+1N+B4qf9rL6ynZ/uKsFiPM/Jb7Ty9vHDhI/8CZ5SE/7uFcjvfoRjS9s5succrs7T/EqYS0lzDg+VR/ldvoOzzqd5rX0YT2SObd172GC5kvlbv8evVnyVZwZW4eo8jVip5+i2LdxdWca64dN8/okfk3NkKC6g27dPoytay0PNbh7oX8+weQpBgYyCdVvZdGs1KdIxuj5/A6kvdrIlPEO1SsyOL9RhvLOSPwwksPx7++k7FiDBspHM6zfxrY9Vs9kYxPa/9/InYxVO/zSflIrYdWMJeXf/jNc8Cr7/RBP3/fBB5mcjmLd/n9uuKeW/lmkY/enXOfDLw5z43jIsKUIe32hhyeM/5M25PO75cwuP3f8UD93jwVC+ltvve4IfrzISeOy/OHDPHu677wia+zexa30Wp753B+13fJcvPNlEx+HjPPP/sPfuYVVXef/3QuQoKOLZrM0ZxEM7JKLwEHkguinNKTPHxjBsO1vhwSiUwUEZmSgM0x9FcYeSJKkjoYQCioIRIioigghxRs5n2LA5O6/fH1/93tOc7p5reua+7nmm61pXKNu192e9v+vzfn8Oa+39n4hLaUvFqv9Qiv0XMsT8Y78TeR+mibR6jbgTky+88jYLr4+2C6etL4mt5kYi/+xl0VN7R+SeE2KP4Xjxu+eXi4XbakR3dby4eK9XlPcPC/usajFrcaaYu1wt7B41ExWm5kLbXi/+OCoVxPV1dcT97jahaegT3aP3ha6OEPZmhuKxVS5iwOoZcTS1XFQXlMlEYL90idi/7nGhbP5O3Ar7VKRcrBHtw2PCbYqR8Ni1Sphs+1Dsz70n/jP4nGi+dVEYmJqLx9esFwfeWCTcqBJFgRvFxw8iTVdzI7EzYIl4JHi/OFTUL36VVCJqg94TugZGwtJ1ufg4KEz8YmKbqP4wTKQ8XSS29Y+IuaYGwsv7CfHmu8HiZMdk4RNfIO59lCX+GB4oLJ7xEAFb3ITabpm4FxYoLh9JFlcjh0Snib5Y6WUjXrvxtRivmS4+PFUkPsk6L8YO1IvZ36UK9S+VovSgp+g+lCXOf5Qu8q6cFl2x48XdF6zF/N07xOQru8T1r26Kiu+zxaedTWJatpVYNqHm53E6CMH9/70C+u/997+aDMb++Ecx0NMj/jg2IkxnWYtFylniBVtz0fNZkPju1F1RpR0RTmaGYk2wh9B5O1z4nC0TZ48fE5qGcjFj/lKx4+0lws9qRFSFBojwr4pE09CY8JplKt74ylc0u/5KbIrLF4VvSDd52ix7SSTG7hIe42tE3luBIum5BhEE4hXXOeKNK/8p5ug6CP+Ya+KDKxeEzrY0ofQaL/7PL53EvAUV4tKWs+J84UUx8YyuqF3vKOx+93vR6OAtUmJyRMvtLBE7wVp4lM4SB+x0xTd6KeLStTSR1T4gHq/JFc8/+qQwW/e52Kb7okhbOE10GN4Q0x3dxOi6p8TSZ6YLEeQt3I4ViWn9I+KFmSbiWb85ovul+WL7N8UiJ7FRDOg9Lma/6SH2qp4SK82aRXHQHpH49kIxY+yP4ktbc7HsP98TV2evEO8eKxBPrt0t+ON94bBil/gs7inh3HFVfO/zOxHy6+PCaPsJsW6FpQj/7ID4ptdTBMdcEzf+8LV4+fw04bzGUxw7kSLePf9/RKrfMZHVrhXtEZliXc+vhTrofdH18jzxRXeb6Ci/IRrvlor00jlisYeNmGQ9W5iMf9AOrKsjTB+bIbQTZojzd4pEa4XU6z57kYd446W5YsMjI6J0R4D4NrlCNA2NipdtzcWS8A2i1u1t4faf10RppqTYHVa+JPZseEK8OHpbFARtFb+5VCt0dYT4jydmitcvHRIpugvE5mO3xA+//EroT5gkHFesFHkngoXF95+L7F0HREp8t/jYSE94rbETa4vPib66cSLsP/PEhzfPC5N9ZcJ9nUKk+j4tML8ozgRdFXmFmWJusoEYDHhWmLx7UJybvkH84dhF0VN7Rxy3dRWqUWex0/CPIvqHKHHqu5uidmBUeDRfEx4bxkS37lzxQu0icfPRFjFs1iUsnvkPsfhtF2Fg2ibaNq4Wj2bViXUj98XaJ2aKp99dIm7ZzhOvfPy9qMi+KhjnLBb89kUR8csnhGv9eXH57UiR/sZhMXX8OHH+ZXsx75vfiS+7pouIr26JZWt3CuMps8VCj2Dx1VFnMen0B+K73WdEyHvJYu7v0sW6Xy4Q7338uXgvc4H4JvG6uH3mhHjtbpXw+sXTIio+Ucz4P7vENx9cFHldg+L+kRtijUWk2Lhxr7hV1y3afpgt+ltrRV+XRrRph4WYrCvGhkaEZuyPQn+cjrAzNxSP/MdKUW1gIT49f0Pcu5El7g8PClv3F8Wht13EspESkftSsEi60SR0dXTEJneFeOqL/eL8qEKsO3RNVGd/KvQnTBJOq18Sn/7SSdjmHxWXXg8VAS19YobBePHaGwvFxr3h4uMKXfFibI5o3fwHMXFOoVj9Sw+R/s5vxNhnO8Wl/QdF9omBn8nr/OueQBbA/9oxzcqRGWsPoqf05tFN8XyZf4+x4ovkui/Fd5wFoYbWFG94geHuVracvMW0NZHoKb2x236a+Jv1jBakccHBCd9xFoQZ2XD3zRcZ6u1kU8JN+bX2fmc4XtjAyPVk0m2fwHecBeHGNtTtepOhvh5eP3ods5Uh6Dv54LjjWxKLGhnOTSTNSolaR0G4sQ31IT4MavtZeziPScuD0XfyYV5ACtfruhjOPk6qYiEqoSBigi2N+7YyMDjI6i+uMtE9CH0nHxbuPMethm6GLh0lZdZ8VEJBpIktLR9up6GrH8/PrmC6LBBDFzWPB52jqKmHwfOxJM+ch0ooOGhqR+t+P+o6+1ge9T2mywIxcvVF+ZtUSpp70RzdQ9IMR1RCQdREO9oPvUN1u4bnDmVjsiQAYzd/FoWkU9baS++R33JquvTahKlz6YreSUWbBpfQCxi7+WPs5o/z3vNUtGnojgkiYepcVELBiWlz6YkNpqy1l0Uh6Ri7+WOyJIBnP/6O6nYNHVHvEjXRDpVQcGq6I5qjeyhp7sVpdxpGrr6YLAlgedT31HX20XbAn4Om0muTZ85j8HwsRU09PB50DgNnFabLAvH87AoNXf20fLidSBNbVEJByqz5DGUc4VZDNwt3nkPfyYeJ7kF4xeSiHRikcd9WIk1sUesoODtnAUNZx7he18X8987Kr117OI/BgQEa9r5NxATptamKhQznnCSpuAnHHd+i7+TDpOXBvH70OkP9Gu4Fbybc2Aa1joJ02ycYyTvNyduNOPgno6f0ZupLEWz86gZDmm7uvvkiYUY2+I6z4IKDE6P5Z0koqMfe7wx6Sm+mrYlk8/EChnvaKXnDi1BDa3zHWXBl6RJGC8/zZf49HvNOQE/pzYy1B9l6qpDhrmaK1nuyx9Aaf10L8lY8y1hJFl9cq2XOxjj0lN7MfCWK8MxyhtvrubV2FYF6lvjrWnDNw537P+QQfbWGRzbEoqf0Zta6aCKzKxlpraFg9UoCxlsSMN6SfK8VjDRXEpVbzez1MegpvZm9Poao3GpGmiu54fmc/NqC1SsZaa0hMruSWeui0VN688iGWD7Pq+X+Dzlc83DHX9eCPYbWFL76PMPt9YRnljPzlSh5vx++UcdYSRa57kvx15X2e9F6T4a7mtl6qlD2DY95JxBxuQIhRP4/6nPsjSfwncvTP2n8I+8nhDAXQmQIISoe/H/yX3mNuxCi8E/GkBBizYPffSmEqPmT3yn/2/f8n3bo/8gwt5yLuWcYBs4qFu48R1lrL20H/Ikxs0clFGQtcmW08DxRudXyg2G3/TRXajoYyjjCiWn/5aiGLh0lu6oDG3WSvDmir9YwWpBG1iJXVEJBjJk97YfeobSll4U7Jcdj7hlGcOpdRtrquObhTsB4S/YYWlO36020A4M8dygbI1dfjN38WfVpDoPafqoDNrLbwIpAPWkDDXc18/rR60z2CMXAWcXjQeeoaNPQut+P6En2qHUUfOfyNGMlWRzMqZJtsfc7w/W6LgbTYmSne2q6I0NZx7hc2Y71r7+RN+/nebWM5p8lU+mCSiiInWxPR9S7lDT3Mi8gBQNnFVO8wtl8vICR1hryVjxLwHhLQg2tuRe8mT7tAMsOXJZt8fzsCkP9Gqr8X5dtKVi9kuHuVvySipjsEYqhixrlb1KpatfQ9L6aqIl2qHUU5LgtZqw0m4jLFTLpOvgnk3+vm8Fz0cRPcUAlFCTNcGQ4+zgZ5W1YqRJl53L4Rh0jeae5tOBJVEJBnLkD3TFBFDX1yM546ksRbD1VyEhz5Y+cRcPet+npH2DpR1kYuqgxWRKAV0wuQ5puKra9SpC+FUH6Vtxau4qh3k62Jd7GbGUIhi5qnHanUdvRR+O+rRw0lWy5snQJ9yuuEp5ZztSXItBTeuO441vSy1oZSD5EnLmDTFrDuYmkl7ViseWUbMuX+fcYzk0kw9FZtqUnNpjbjT0sCkmXbdmWeJvRpnJy3BbjryuJl8Z9W+nu07I4IlO2ZfUXVxnq7aR86y8I0rfCX9eCwlefZ0jTzdZThUxaHoyhixrnveep6+yjYe/bsi257ku5X3mN0Atlsi3zAlLIKG9j4PQB2ZaUWfMZyTtNyt0WFG+dkB15QkE9w9nHueDghEooiJ/iQO+R35J/rxvlb1LRd/Jh2ppI/JKKGG24S7brM/jrSuKq6X01HRotbh9ItpguC2Rd3DWGu1sp2/IygXoSiRSt92SoX8Pm4wVMWh6MkasvLqEXaOjq517wZiJNbPEd94Dwqm8Skl4qk83PQQZ2xhPIWuT6k8Y/SAYRQohdD37eJYT48CeQR5cQwvhPyOCV/1fv+T/t0P+RMfExe0yWBGC2MoQtJ28xVn2Ts3MWoBKSshurvknYxR8wWxmCyZIAVn2aw0hbHVmLXFHrSMp25HoyX+bfY9qaSPSdfHD9/UUGBwYofWs1/roWRE20ozsmiIzyNllFLdx5jqbufupDfAjStyLMSFL/d5okktBTemOjTuJSRRvdMUGEGdngr2tB2ZaXaerux/X3F9F38mHOxjjib9YzkHxIdpT5XisYbq9n1ac5GLqoMVsZQtjFHxjJOy3blrXIlbHqm2w+XoDJkgBMlgSw5eQt7lfdkInroW0PN7ahixrPz64wODDADc/nUOtIUcBAyid8mX+PRzbEou/kw+NB52ju6af0rdWybT2xwdxp6pXJxUadRElzL/eCNxNqaE2QvhUNe9+moatfVtFzNsZxqaKNgeRDHDS1w1/XgnyvFQwMDuL6+4sYOKuYtiaS+Jv1DOcmEj/FQSa94fZ6Nh8vwNjNH7OVIWw9Vcj9qhuy/amKhYzV3CL0QhmTlgdjsiQAz8+uMNJaQ6bSRcZ28Fw0h2/UMfWlCPSdfHgm/BKD2n7uvvkivuMkbHtig0kva5VV70P77wVvJlDPUratqKmH+e+dle2/XNlOV/ROQh+o7bItL9PQ1c9T+zJkBZ9QUM/A6QOybfleKxjubGR51PcYOKswWxkiqfHcRDni+87lacZqbrEp4aZs29ZThYy0VMtEfmLaXEbzzxKSXipj6xWTy6C2n2se7qiEguhJ9gyei5Zte4hta49kf6CeJeHGNvQe+S1FTT0y2dptP01pSy91u96UsS3f+gvqO/tk+x/dFM/lynYGTh+QsS1YvRLtwCBP7cvAwFnFjLUHJXLIOSmT+3cuTzPc2cimhJsYu/kz2SOUbYm3uV95TbY/zUrJWG0hIemlsv1eMbmMtFTLtp2YNpfBtBi+uFYrY+v2QSZD/RpK3vDCd5wF0ZPs6T3yW1JLW2TbfhYyMDLm0oInf9L4B8ngByHErAc/zxJC/PDfvP5tIUTCn/z5/19kYDLHDkMXNbPWRZNQUE/vkd+yx9CaQD1LWvf7kVHexmPeCeg7+fDcoWyG+nr4zuVp2aGMNpXz3KFsDF3UzHwlisM36uj/eh+hhtYEjLekcd9Wsqs6sNhyCn0nH5YduMzgwAA5botlhTRafwfPz65g5OrLtDWRRF+tQZu4n3BjGwLGW3IveDPX67pkR+r2QSZ92gFy3ZfK6nes+iarv7iKsZs/U1+KIDK7ksFz0URMsMVf14LqgI3cbuzBbvtp9JTeuP7+Ip0aLTc8n5M3x/2Kq7x+9DomSwIw9wwj7OIPDGUc4aCpHb7jLKjY9iolzb1yasJ573lae/opWL1SVnFjJVmEXfwB02WBTPYIJTj1LsPZx2WiKtvyMhVtGtkpOO1Oo6Grn6L1nqh1pGhjtCCNyOxKJroHMWl5MDvOFDOSd5oYMynCKXnDi9qOPh4POic73+p2aROrdaToayTvNNFXazBbGcJE9yC2nipktCCN2MlSxFf46vM0dPXjtDtNVq/lrRrKtrwsO/mhrGMcvlHHZI9QTJcFsvl4AWMlWbJjKli9krZeLS6hF+TIpKS5l0q/1/AdZ0GkiS2D5yW1a+4ZhsmSAF4/ep37ldfkiPKahzvdfVpcf39RdqS3G3uoCfwVQfpWREywZSDlExKLGpn6UgTGbv6s/uIqYzW35LRcrvtS+rQDLI7IRE/pjfWvv+FqbSf3gjez28CKcGMbtIn7SbnbwrQ1kRi5+rLq0xxG6+/IDjTHbTGDA1Lkpu/kg8WWU1yubKdx31ZCDa0JNbSm/+t9ZJS3MfOVKAxd1Dx3KJvRpnI5RZm1yJUhTTfLo75H38mHx7wTSC9rpXW/H2FGNuwxtKb3yG+5UtPB7PUxGDirWPpRFiNtdaTbPoFKKLi04EmGu1vx/OwKBs4q5myMI7mkmY6odwk3tmG3gRVd0TvJv9fNnI1xGDircPsgk+GuZjkyuuDgxHB7PWsP52HgrGL2+hhO3m6kJzaYSBNbgvStaDvgT1FTD49uikffyYen9mUw1NspC6E0KyUjzZVs/OoGhi5qzD3DCL1Q9rOQga2hMRmOzj9p/INk0PMnP+v86Z//xuszhRBef0YGPwghioQQHwshDP6lyWDCbDs5XVLVruHW2lWyox/uamZRSDp6Sm/mv3f2R6op130pQ/0aWcUrf5PKcE+77OTzvVbQpx3Acce36Cm9cQm9wEhbnbz5itZ7Utbai406CX0nH9YezmO0sUzOpZe+tZrbjT1YbDmFgbOKTQk3Gau5RfwUBwLGW1Ll/zpXajqYtiYSQxc12xJvM3TpKDFm9gTqSQSSXtbKFK9wjFx9CU69y0DKJ0RNtGO3gRX9X+8jsaiRyR6hmK0MIeJyBdoT77PbwIpQQ2sGkg/xZf49Ji0PxtwzjOirNfQe+a2sBocyjuCXVITpskCmrYkku6qD9kPvEDDekkgTW4ZzE2VlPvOVKG41dNO4byv+uhaSKi08z7q4a+g7+fDIhlhKW3op3vACvuMsSJ45j/sVV1n1aY6cr61u11CweuV/5de7W1l24LKMTVN3P3krnkUlFGQ4OjPUr+GZ8Esy4XRqtDI2WYtc6dMOyKT21L4MOSJQCQXFG16grVcrp/sWR2Qy2nBXdlhlW16mvrNPxua5Q9mMVd8kzlxS71X+r5Nd1cGjm+JlxT1Wmk2MmT2+4yy4F7yZ1NIWZq+PwcjVl5D0Ukbzz3LQVMKm5cPtnLzdyIy1BzFZEkDE5QqGs48TMcGWUENrOqLe5fCNOia6B2G6LJDP82rpiQ0mzMhGwubSUaJyqzFdFsik5cEkFNTTdsCfPYbWMjZhF3+QsUkuaZaxiZpox2jhedYezsPI1ZdHNsRyqaKNmsBf4TvOgtjJ9jI2Bs4qHvNOoLajj4ptr6LWeaDK7xXJ2FipEml+EEk8xGakuVLGxm77abr7tGS7PiMrf412AOe952VsBgYH5XTelaVLaOvVykJgcUQmQ5puGZsbns9R39knp54eYnN2zgLUOpIIqGjTMGdjHPpOPjI2STMc8R1nQX2ID0VNPTI2G7+6wcj1ZBKmzkWto/jZyOCCg9NPGkL6at/8Pxlv/5kDvyiEuPNXxuo/d/5C+u6Xv0UEs4QQ7UIIvT/7Ox0hhIEQ4qgQIuRfmgyMZtqgp/Rm9RdXuf9DDhETbDloasdwbiLPHcpGT+nNopB0xmoLOWhqR5C+FUMZR1j9xVW5MDvSXEnKrPkEjJeiiYjLFRg4q3h0UzydGi1pVkpUQkF9iA+f59Uy0T2IWeuiaejqJ99rBSohOZDrdV1M9ghl0vJgLle2U7zhBVRCQd6KZylp7mXOxjhMlwWSXNJMy4fb5ZSIdmAQxVsnMHbzJzj1Ll3ROwnSt5Jy/5punHanoe/kw7bE2wznnGSPoTXxUxy4X3lNTkks/SiL0cLzhBnZEGZkw2jheTZ+dUPekMPt9cRPcWDPA4W4LfE2+k4+2KiTGNJ0c2q6tJm6oncSf7MeYzd/FG+doLpdw3cuT6PWUVC+9RcklzRjuiyQKV7hlDT3yg68eMMLXK5sZ9qaSCZ7hHK8sIEq/9dltdjQ1c+sddEYufryeV4t9SE++OtakGalpFOjxcE/GQNnFRGXKxg4fYCA8ZZShNBcKRP26i+uMpRxRMZ4rLZQxnh51PcyxhETbP8qxskz5xEw3pKB0wcIzyzHwFmFg3+yjLG/roWMsZGrL7PXx/xNjKetieRyZTtF6z1ljO809TLFK/wvMM52fYY+7YCMcUh6KV3RO/EdZyFh3Ncji4ptibfp/3ofuw2s/jrGBWk/wnjpR1lypHi/4ipx5hLGwzkn/ybG3TFBBKfelTHWDgzKGLd8uJ2k4iZMlwUyZ2McJc29cgRbvOEFLlW0yRhfr+uSMc73WiFjPGl5sIyxSkgF806NFnu/MzLG2sT9BIy3lGoPLdVyanHt4TwGz8cSpG9F1EQ7xu4VyYJuedT3jNXc+guMH6ZtR5vKiTGzlzBOPsSmhJsyxt19Wq4sXfLzkIGBEem2T/yk8c9KEwkh/h8hxH/+nd8/K6Tbo/91ycBgujX6Tj4czKmiK3qnHP6XtvRitjKEORvjqO3oI81KSaCeJf1f72Nd3DVZlQxnHydgvCUJU+eiHRjEbvtpjN38yShvo9LvNVRCQU3gr0guacbI1RcH/2SG+jXEmTsQbmzDyPVkOf+/KeEmPbHBBIy3JMPRmfJWDbPXx2DuGUZVu4ZrHu6odRRoju4hPLNczmGP3Ssi3NiG+CkOjDaWYe93Bn0nHyKzK6kO2CjnmlNLWzBbGcIjG2IZGBwk3fYJAvUsGc45Kdv0+tHrDOecJFBPsmlgcJBHNsRi7OZPelmr7NiqAzYSmV0p2zTacPe/bMo7Ldu08asb9B75LWodKXyvaNMw2SOUyR6hVLRpyHB0Rq2joPfIb9mUcFOuuYzW3yHc2IY4cwdGG+7i4J+MkasvkdmV1AT+SnYg6WWtGLv588iGWLQDgyRMnSvZlH2c149eR0/pLRURH9iUbvsEgwMDPLIhFrOVIaSWtshpruqAjRzMqULfyQd7vzOMNpYRP0Wyaay2kGfCL6Hv5EPYxR/QHN2DWkdK81S1azD3DGPWumjKWyWbAsZb0hMbLNv01L4MRq4nEzHBljhzB4b6NTju+BYjV1+Sipuo2/UmKqGg0u81MsrbMHbzZ/b6GPq0AyRMnUvAeEuGso7JqaC1h/Po/3ofgXqWpFkpqevsY87GOMxWhlDS3CvbVOX/OlG51eg7+WC3/TSjTeVEmtgSZmTDWM0t5gWkoKf0JvRCGfUhPqh1JGLKruqQbSpr7eXSgicJGG9Jd0wQm48XyCJhNP8sQfpWxE62Z6hPSrs8tKnw1edRCQUV217lUkUbJksCsFEnodEOcGLaXEINrRm6dFQmpNVfXEV74n18x1mQqlhIfWcfU7zCmbQ8mJLmXjlq74h6l+irNRg4q3Dee56R5koiTWyJMbNnrPom8987i6GLmpD0Uhr2vi1H8ldqOpjiFc7MV6Lo7tNyacGT+OtaMHg+li0nb6Gn9MbzsyuMFqQRpG8ldYNpunnMOwEjV1/CLv7w85CBvhFpVsqfNP5BMtj/ZwXkiL/z2jwhhPuf/d1DItERQhwU0jdJ/uuSgf5UK4zd/LnV0M0Nz+fw17WQowJ9Jx/ib9ZTHbARlZAc1o4zxegpvfFLKqL3yG/lDRd/sx59Jx8pXZSbiL+uBem2T3C7sUdWUKONZewxtCbO3IEOjRZzzzCmvhRBn3aAGDN7woxsGGmrw3HHt5guC6S0pZcrS5cQMN6S0cLzLI7IxMBZRVJxE+Vbf4FKKND+4UP5QQ5JL6Uj6l05DfV5Xq2c2x/KOILvOAsylS5cqenAyNUX619/w1jNLXYbWHFi2lwauvoxWxnCzFeiGOrXcGq6IxETbBnuacdu+2kmugdR3a4ha5ErgXqW3K+4ylP7MjB0UZNe1krxhhdQ6yhoO+Avk8vGr27Q9L5aTn1FXK5AT+nNM+GXGEj5RFa+6WWtGLqocfBP5v4POYQb25A8cx5V7RqmrYlk9voYhrtbiZ/iQNREOwa1/VipEjFbGUJTdz8Zjs7sNrBirLZQbiU9fKOOW2tXyWrW87Mr6Cm92ZZ4m3vBm2XnG5x6Fz2lN8sOXEZ74n1ZqScWNWKyJID5751ltCCNUENrUhULKWmWFPyjm+IZaa0hdrI9MWb2aLQDPOadgLlnmBwthBpaM5p/loU7z2Hs5s/J241yy2P/1/t49uPv0HfyYdfZe+1PWgAAIABJREFUElkl1+16E7+kIvSdfFj1aQ49scH4jpMKrF/m38PI1Ren3WkM55xkt4EVFxycyL/XzWSPUCy2nGK04S5RE+2In+LAcFczj2yIZdqaSCraNCTPnCeRW2k2Dv7JmC4LJLW0hRy3xbIidvtAes7CM8sp2/IyKqGgcd9WmQTWHs6j/dA7qHUktR+VW42hixqX0AuyKs9UunC5sp1Jy4OlyKK3k0gTW6nrrq+Hma9EMfOVKOo7+zgxbS6RJraMVd/ERp0kR8aZSheC9K0YPB8r12WicqvliLn90DusPZyHntKbTQk3ady3VU7jPYzc3D7IZCD5EAHjLclxW0xZay+mywJx8E+WWk0fPGfagUGmrYnkkQ2xtPVq5edstOEuFltOYezmT0JB/c9CBjb6RqQqFv6k8Q+SwRQhxCUhtZZeFNJ3vwghhLMQIvZPXmchpC8CG/dn/z5TCFH8IO10TAhh8i9NBuMnP8qMtQcZbSwjzMiGbNdnZCXll1RE4avPo9ZRMFp4nse8E5jsEcqQppvdBlakWSlJKm6SVc3DSGAo4wj2fmcwXRZIp0ZLuLENJ6bN5XpdF/pOPiyOyKTlw+2ohIK+Y6E47U5j0vJgqto1RE+yJ3qSPVXtGrkVse9YKCohheCLIzIxdvPnel0XJ6bNJX6KA50aLabLAnkm/BJDGUdkJ7f6i6sYuqhJKm4izUopFUU1ktNYuPMcY8UXUesoaNj7tux8Lle2k+36jJRGaCxjxtqD2KiTGK2/I3e8RGZXoqf05nhhAwWrVxKkb8VwVzOPbopnzsY4hrtbCdK3omD1So4XNqCn9CbicgXlW3+Bv64FY/eKsFEnMW1NJKNN5YQZ2fCdy9NEX62RUx2N+7bK6/540DnMVoYwpOkmaqIdaVZKkkuaMXRRs/qLq7ITHTwfi4N/MiZLAujQaEmYOpcT0+aSf68bkyUBLI7IpHW/HyohRVdOu9MwdFFT0aYhxkxa9+p2jXx+ov/rfaiEgqb31Sz9KAt9Jx+u1nZyaroj4cY2dPdJ627vd+ZH6/6wcJlY1Ei67RPsNrBiqLeTyR6hPOadIK974avPy+IiKreabNdnJPJ4sO4zX4mS1z3HbTEHc6rQU3rz3rd3uLV2FUH6VoyVZP3Xuve0E6RvRb7XCk7ebpSjooptr+Kva8FQ1jHstp9m2ppIevoH5HW/UtOBvpMPW08V/te6F6TJ617b0UfURDtSFQspb9XIdZCHYmgwLQa3DzL/5rq7fZDJYFqMvO5eMbkYuqgpb9WQqlhI9CR7ajv6MFsZgvI3qWhPvI9a58frfqWmg+9cnibc2Iae/gGmrYnEbvtphi4dxV9Xam54KEBO3pbWPUjfiuGediZ7hPLopnjGSrII1LPk1tpV8rofzKmSW21HG+4y85UoyR803P2LdX/uUPbPQwZ6hqTMmv+Txs/xfv/M8T/+Af6RoTtxNta//oaB0wckpZ24H+tff4O5Zxhj94pQ60gbfOlHWUxaHkxbr5Y9htaUvOFFSHop+k4+1Hb0ET/FgXTbJ8iu6pAdZY7bYg6a2lHU1IORqy+bEm7Kh2aG+nowWRKA0+40Wj7cju84C0YLzzPzlShpY+ecRCUUdEXvZP57Z5n5ShTDXc3461rQuG+r3Dk0qO0nYoIt1zzcZeV2u7GHlFnzOTXdkcSiRjlldMHBSWph7ezDwFnF8qjvufvmi+w2sGKkpRqzlSG4fZApp0CGMo7wmHcCVqpE7ldcldfnqX0ZmHuGMVp/h0A9Syq2vSr33Lf1aokxsydT6ULohTL0lN6klraQMHUuMWb2XKmR1mfHmWJ5fTTaAblYV7frTQLGW3K/8hpTX4rAaXea7EhGC9Kw9zvDIxtiGc5NRK0jpQwW7jzHRPcgeX0KX32etYfz0HfyobxVQ8QEW87OWcAX16RI6fCNOs7OWcCp6Y6Utfai7+RDxOUKbq1dRaihNfWdfZguC2R51Pe0H3oH33EWjDRX8siGWOz9zsjkPHg+FsVbJ5i2JlJen3vBm3lqXwaTPULp0w4QpG/FlaVL8EsqwtBFzdXaTnl9Uu62oKf0JuVuC5lKF2LM7Lla24mhixq/pCL5zEKfdoDJHqE8tS9DjmjuV1xl2ppIFG+dYPB8rCwsHq7PSHMlvuMsKH1rtXxIsL6zj1BDa26tXUXE5Qr0nXwoa+3l1HRHzs5ZwOEbdegpvfniWi1n5ywgYoIt5a0ajN38WXs4j8JXn5ci565mJroHsXDnOTqi3kWto2A4N1Fen9GCNJkknHanMfWlCIY03QSMt6Ru15ts/OoGRq6+aLQDHDS1I8dtseycr9R0EGNmT8LUuaSWtsgprEylC3sMrWnt6cfQRSKJim2vEqhnyWj9Hcw9w3hqXwbaxP3y+lipEnnMO4GhjCNyetXtg0zMVoYw0lLNbgMr7r75otymW9/ZR5iRDRccnIjMrkTfyYfEokZOTXeU2sP7tD+Lc7Yeb0jyzHk/afybDP6JY5zJDJ47lE2O22IiJtiScrcFfScfSpp7CdK3ov3QOzjtTkPx1gk5NB4YHETfyYfg1Lucmu5IjttiglPvYrIkgMGBAWlzZB/nMe8EnHanUfKGF0H6VpQ098qbP2KCLZV+r/HcoWymrYlkIOUTyeE1lWPuGcbaw3lcWvCkFO7frMfAWUVtRx8B4y3pOxbKvIAUbNRJ1If44DvOgg6NVlY6ceYOpCoWsuXkLSYtD2a4uxWVUDBWfJHZ62N4JvwS+V4rCDW0lp3z1dpO+UDVM+GXmL0+htHC86iEguHuViYtD2brqUJSFQuJM3fgYE4VRq6+dGi0+I6zYCDlE2zUScwLSKHvWCgB4y2p7ZBIJ/5mPVET7bi04EnWHs6TiKSpXPpM1dJJ7T/HQE/pLWNQ8oaXjMFw9nEZA5MlATIGSTMc/wKD9kPv8Jh3AotC0mk/9I6Mgb6Tj4zBlaVLeO5QNjPWHmSs+iYqoWCkuVLGoOQNL6In2RN/sx49pTd1nRIGlX6vyRgMpHwiY2Dk6vsXGBg4q2QM+o6FyhjUh/j8VQzyvVbwTPglJi0PljEYK74oY5DvtULGQE/pLWPQsPftv8BguLtVxiBVsVDGwMjVV8ZgIOUTGYNKv9f+AoOoiXYyBuaeYTIGY9U3ZQxy3BbLGOg7+fwFBg/3T5C+lbx/UktbODXdkStLl/Dsx99hsiSA+1U3pGcu56SMQfGGF4ieZM+dpl4Zg4f7Z15ACjPWHmQg+ZBM2g8xuLTgyR9j0NUs75/Z62Pk/RNqaE1br1bGIM7c4a9i0Hcs9Gcjg6QZjj9p/JsM/olDx3gaIemlRE+yJ2/Fsyh/k8q8gBSa3lcTaWIrdwaN3SuSnbW+kw+3GrqJmGBL2wGpPc9573ly3BYTO9meHWeKmeIVzlhJFr7jLKhq16Cn9KaqXYPvOAv6joUyxSuc9769Q4ajMzlui3HeK0UFbQf88de14FZDN/pOPrT1aiVncK+IScuDibhcQaSJLQWrVzIvIAXlb1K5F7yZ6En2hKSXYuii5n7lNWlDtdfLdY+HB79s1Em4/v4iWYtciZ/iIF+xMZp/Fn9dC5mwGrr6pY17+gBmK0N47lA2aVZKrnm4s/qLq8xeH0Pjvq0E6lmSXdWBgbMKjXZASpNlHZNbHpNmOFK84QXs/c6geOsElX6vETvZnqTiJoxcfRkrvij9m95O9JTeJBY1ssfQmkq/17DYcoqlH2VRvOEFTk135PWj1zFdFigX7TXaAdkZBupZMpgWw6TlwXjF5JIyaz6pioUsj/qeRzfFM3D6AEH6VqSXtaKn9GZQ2y85/utSWimhoJ6EqXMp2/Iy1r/+Butff0PpW6sJNbQm/mY9JksCGO5slBTvwIBcJwnSt6Ju15s8uime5VHfU7B6JSmz5uMVk8uk5cFyVPOQrPPvSQp5OPs4M1+J4vWj1zk13ZEMR2eWfpSFxZZT9H+9jz2G1lQ/eG6Ge9rxHWfBWEkWRq6+7DhTTOxke6r8X8d573ns/c5QtN6TcGMbPs+rxXRZICPNldJzU1uIgbOKKzUdHDS1o3HfVmavj+HxoHNc83CX6hoXypjsEcrA6QNS2rCrXyaCh/WWaWsi2XLylnwy1vX3F5n6UoR8ILK0RSLZ4fZ61DoK7ldek4u4f7q35r939v/zvWXuGfajvbUoJP0n7a3I7Mq/2Ft1u94kepI9GeVtP9uhM6vxhpya7viTxr/J4J9KBlO5VNGG7zipq8DI1Zc7TZIiHS2QWjKTS6T0w1htoZyvfxgaGzirqGrXSKmNiquYLguU5xvKOPLj+QrP/6T5EgrqfzTf5cp2eT5DF7WsmB/Ol3K3Rc7FG7v5k39Pqmk8nO94YYOcejFZEvA35xsrvvij+QZSPvnRfCN5p//+fJeOYuii5otrtQTqWTJWfJGJ7kF/Md+thr8/X3ZVh9xpYuii5vCNuh/Nl1r69+c7ebuRgPGWaP/w4d+fryRLns93nAWD56TWVXm+68mYrQz52/NlHftJ80XlVsvzTVoeTGLRj+e7UtPBnj+Z78v8ewTqWaI5ugfTZYGkl7USZmTzd+fz17VAm7j/R/MNZx9nskfo354vLUaeL0jfitH8s391vvDM8h/NF3+z/kfzZZS3/Wi+6Ks1P5ovqbiJiAm2aBP3Y+zm/zfn6zsW+nfn64re+aP5Bk4f+PF8OSf//nznY5niFf7fzhdxueJH8yUU1HPQ1I6yLS//bGRwYtrcnzT+TQb/TDIwmiIXPBMK6qVTk7ZPULTeE7OVIZS3aqSOoXYNk5YHS8fTFQtRvHWCtYfzJKfbKRXrxkqz8de1YMeZYhbuPMd3Lk/TuG8rRq6+XK3tlA6zlbZgsiSAKv/Xuebhjr3fGcIzyyXld68IPaU3ow13CTW05tmPv8NGnUTSDEcGkg9huiyQ1p5+fMdZkH+vm6kvRXDNw52sRa7MWhdNammL1BEyOCgpypyTxE62Z12c1Gt+0FS6OsHAWSWr3C/z76F46wTFG16geMMLTFoezJf596TIorMRA2cVPbHBHDS146l9GayLu0bsZHuGc05KCntggIDxlmxLvM2sddFkLXLlmoc7U1+KIP9eN77jLGjt6cd0WSADyYdImuGIjTqJZz/+TiqWNkidPGP3iqRDU5nl2PudkVo2/V/HZEkAqaUtqISCq7WdGLn60rhvK9+5PM3CnefYcaZYIsJS6bzAUNYxdhtYsfZwHoq3TpCqWIjm6B65QK8SCspbNZitDKFovSfptk/wmHcCCQX1UsHxQVplrPgiB03t2JZ4m8eDzhFjZk/Lh9sxdFEz1C/Nk1zSzOz1MVT6vcYNz+eY4hVOZHalVPzsltS1NnE/YUY2LDtwmeVR33NquiOD56J/lN55eN1HrvtSagJ/xcxXosgob8Nf14K+B/WUoaxjxJk7MP+9s7j+/iKRJrbcr5DOQYy01bHbwIovrtViseUUZ+cs4O6bL8oHzlRCQWlLLwbOKrqid3LBwQnnvdI5kkA9S0bypFPpowVpBIy3ZMvJWzjtTiNT6ULbAX+meIVzu7FHKqZ3S1iWb/0FyTPnYf3rbziYUyVh+UDZj9XcItzYhsURmTj4Swe2agJ/hbGbP919khrPrpIOTNaH+JDjtpgZaw+y62wJ/roW9PRLEd9QxhHipzjgFZOL2weZnJ2zgL5joXKdTq2jIPpqDZM9Qrm1dhVlW15mzsY4jhc2yNGmgbOK9kPvEDXRjkUh6TjtTiN6kj2j+VL76ZCmm0A9SxKLGnlkQywZjs4UrF6JuWcYd5p6UesoqO/sk+36OZyzha4h8VMcftL4Nxn8M8nAeCrD2cdJs1LiuONbTt6WnGT+Pam/OM7cgb5jofLGjjGzx2LLKbKrOqTrJO62YLf9NBmOzgyej8XAWcX9H3I4MW0uznvPczCnSuphb9cw85UoUmbNp+XD7bKzjphgi+OObzle2IDvOCmEnegeRK77UjRH98i53ehJ9iw7cJkdZ4pRCQXdfVome4RyacGTVPq9Jr1vxVV2G1jhvPc8yw5clvP2Rq6+FK33pOl9NRPdgxjSdBNubMO6uGusPZyH7zgLhnvaMV0WSN6KZyl89XmM3fypfhDxPPvxdywKSWePoTU9/QMYOKuoDthIxbZX5TbK3QZWvPftHZZ+lEWMmT2jDXcxcvWl71goV5YuYaJ7EPn3ulHrKHj96HUc/KWe++FuKW3Tut+PW2tXMfOVKNlpR+VW47Q7jVPTHRkrkbpKhjKOcMHBiSle4SSXNKMSCnadLUHx1gnpKov6OxJWJ94ndrI9irdOcL2uC5VQkFgk3fKZbvsEQ5eOSiRUfJGkGY7MWhdN9NUa6U6eqzXMWhdN0gxH2g+9IzmmS0eJNLHFwT+ZpOIm1DoSOSneOkGO22K0J96XHGr9Hfl9d50tQSUUtPVqmeIVzgUHJ2oCfyU5lpIs9hha47Q7jajcagLGW1LRpmHWumhurV1F2wF/KSXW3UrEBFtWfyFdFaLWUTDU18NE9yCuLF1CyRte0jPScJdAPUuWfpTFe9/eYbeBFZ0aLQbOKiq2vUp1wEYme4Si0Q6wx9CabYm3efbj7wgYb8lIcyXGbv4Uvvo8eSueld73QWpq7eE85gWkSCebNd3oKb1pel9N8YYXmLH2oFzHirhcgUvoBXYbWHG/4ioGzioq/V7j0oInmewRSnqZVDPxSyrCSpUoOeSmcvSU3miO7iHXfSmPbornVoMkII4XNjAvIEV6Rh4Ij9H8s6TMmi9f+6ISCiKzpcL+iWlzuf+DdGJ9MC2GDEdn7LZLF+E9JB+LLaekQ5qJ+yXsa24RZ+7AY94JhF4oQyUUnLwtXfuRZqVkOPs4+k4+clvx5uMFPxsZxJk7/KTxbzL4Z5LBhOnU7XqT9kPvYOCsYvBcNIWvPo/ZyhBG6++QqljIo5viaevVctDUDue958kobyNgvCWbjxfIG/54YQOLIzIJM7Khql2DjTqJU9MdGe5sZIpXOHkrnmU4NxEjV1/qQ3xo3e8nPbjnYylYvVJ66JrKOTtnAYq3TtDc00+kiS0uoRdkxb/1VKFMBolFjbh9kIlaR0FdZx9WqkT2GFoz0laHuWcY1zzcGc4+jqGLmsZ9W2l6Xy07tXyvFZKibKkmZdZ8Ji0PpqFL6kp6eBeMv64FfklFbEu8Latg199flAqVXf1YbDlFqKE1Iy3VUig9da6chmn5cDsNe9+WCC/7ONc83DFwVjHSVkfSDEcmLQ+mtqOPcGMbHt0UL6clFoWks/VUoRxBuYRewHecBc09/SjeOiG3u5qtDJEU9gPyzXVfSn2ID3pKb+4FbybXfakc/Zya7ojZyhCq2jWEGdnwmHeCTLwuoRfYfLwAlVCw5eQtnPeex3ecBSdvN/LopnjCjW0Yrb+D2coQKTpL+QQDZxXXPNyp2/WmVFvZ+zZXli6RVOaDW2wne4Qy0lxJqKE1FltO8WX+PXzHWeD6+4ts/EoqkG5LvI3T7jT8dS1IKm5izsY4IibYUt/Zx6TlwSTPnMfA6QPoO/lww/M5qgM2yo442/UZ6f2yjhE/xQFzzzBGWmvYY2iNlSqRuk5JNT8Tfol1cVL9aMeZYh4POoe/rgWppS08siGWSBNbmnv6megexNk5C2RlX7B6JYPnY2WS/s7laYxcfRnOTSTO3IEpXuEMdzay28AK619/Q3W7BrWOgsURmbIq33W2hM3HC6QDlOVtzF4fw0FTO9p6pXbcVMVCRhukYnPhq8/L0VL7oXeo2/UmRq6+jFxP5srSJUx9KYLh7lZ2G1hht/00FW3S+y07cJn4m1LkE5JeKkc6lyvbJZKdaEenRsucjXGk2z4hR97FG15gIPkQekpvuqJ3Uh2wEWM3f0YL0shxW8y0NZEM9XaSMHUu9n5nKG3pRSUUfJ5X+7M4Z4WuAbGT7X/S+DcZ/BPHeLM5JEydS1GTdKRfJRQs/SiLqNxqQg2tMVsZwnB7PfleK9BTelMT+CvGSrKYvT6GGDN7LlW0yQ7MJfQCxwsbiJhgi+myQMbuFXH3zRfRd/Kh9K3VjN0rQvHWCSIm2HLydqOsRua/d5ahvh6iJ9lLRdWSLGoCf4Whi1q+gdTe7wyhhtZE5VbzxbVaVEKBjTqJkZZqEqbOlWoX/q/Tut8PYzd/6eIxbT+PB50jSN+K4NS7JBU34TvOQup1f3Bfy8PNoTm6h4nuQYQb29ChkS5O89e1YPPxAq7UdOCva8GsddGMFqSR4eiMntKbax7uDKR8wmSPUPYYWlP14PsL1DoKPD+7QmmLVIuY4hXO0KWjXFm6BD2lt3TzZG4i09ZEEqhnye3GHtbFXcN3nAWLIzJp6Oon1NCaScuD0f7hQ26tXYWe0ps0KyX3f8jhkQ2x+I6zIKO8Db+kIgLGW7IoJB2NdoBMpQsmSwLoit5J2ZaX0XfyIWmGo3x4SK2jIKFAutd+t4EV8wJSGOrtJG/Fsxi5+tKw9205hRBnLh3acvBPRiUUHMyp4vCNOsKMbLD+9TeMNFdStN5TVt/3q25g7CZ9T0LK3RaUv0lFJRS89+0dUu62cNDUjkc3xXO/6gYV215FT+lN0XpPRpqlS/nCjGw4fKOOZ8IvoRIKNn51g+t1XcSZO0hnDvLPyiSbt+JZhno7MfcMky6jyyxnedT3qHUUWGw5RXmrhqQZjph7hjF4Ppau6J3oKb3JVLqg0Q4wY+1BAsZb4pdUxMavbuA7zoJHNsTS3NNPuu0TcnSl/cOHUnuwYiENXf3M2RiH7zgL1sVdY8eZYgL1LJm2JlK+kuKhQx+6dJQpXuGcmu5IaUsvViqpFfi5Q9lEZleyx9CayR6hDHe3cs3DXSby0cLzzFoXTexkqQ35IVm7/v4i8TelQ2IT3YMYbSyjeMMLUvvw1l8wVnOLx7wTiDSxJam4ieDUu6iEgseDzjGo7Sdqoh3Gbv7cr5DOpRg4q7i1dhUjrTXYqJMIM7Lhi2u1ROVWoxIK7P3OyFewGLqoGck7/Q9fKf1wPKZrQIyZ/U8a/yaDf+LQnyrdo7/swGUuV7bLG+Gahzu1HX3sOluCWkfBFK9wWj7cjuboHma+EiVv1k6NluINL6Cn9CZphiPagUHWHs5jj6E1s9fHoD3xPmOl2Uz2CMV3nAVOu9Mob9VwZekSDJxVXFrwJNfruvg8r5aA8ZZSv3xuotzDHqgnpWmGe9pJs1Kip/Qmxsyeus4+dpwpJtLElile4bTu92O0qVz+bJsSbtKh0VK03hM9pTfJM+eRWNSIV0wuuw2sMHL1RZu4n7Hiizju+BbfcZIyH62/Q7brMxg4q8hUunCroZuo3Gr8dS2Y6B5Elf/rDKbF8Jh3Aiohbe4hTbesXGMn29PQ1Y9fUhERE2yZ+lIE7YfekQ9SqYQCB/9kWnv6KXz1eYxcfUmZNZ/kkmaSS5rlzzZw+gCjhedx8E/Gd5wFznvPM9pwl+9cnpbOBUyw5XZjDwdzqoidbM+k5cFUB2xkSNPNo5viUQkFy6O+Z6ivhyr/19FTehNn7kBUbjXbEm8TbmyDvpMPHVHvMlp/B+e9Uvug445vGSu+yK21qzBy9eXsnAWk3G0hqbiJIH3ptHrRek9G889i73cGlVBI1zI0ldO6308602FiK2GTXUmMmfTZagJ/xXBPu/zZFG+dYFDbT6Xfa0x0DyLO3IHP82rJvyel8B7m98dqC1kUko5aR8G8gBTGSrLI91qBkasvewytSS9r5eTtRpJmOGLs5k/xhhfo1Gix235adqIjzZW0fLhd/mzvfXuH8Ewp9ain9KZu15vyxX8qIZHJUMYRyrf+AtNlgcRPceCLa7Vcre0kzEj6bFeWLmGs5hbK36TiO86C+e+d5f4POfR/vQ9DFzV7DK1ZF3eN44UNnJruiMmSAEre8KKnf0AWXbPWRTPSWkPT+9KtoAdN7QhOvSsfvtRTeks3kHY2svSjLFRCgZUqkaGsY5RteRnTZYEEjLfk8I06rtR0cMHBCQNnFXkrnqWqXcPjQedQ6ygw9wzjfsVV+o5J348RamjN60evE3+znhPT5qKn9Obumy+i0Q7IkdTs9TH0f72Phr1vM9kjlKiJdpS3an42Mnh4uPS/G/8mg3/iMJ4lfYPVw+LwwOCgXBDTU3oTMcGW+Jv1tPVqad3vh8mSAFRCIR0Cy01ktP4OG7+6gb+uBQbOKsq2vMztxh6u13WRMHUu+k4+ckpptKmc4ezjslM0XRZI+6F36NRoOXyjjnBjG1m9RV+tQaMdINv1GSa6B8mRx1DGEUZaqrH3O0OgniX6Tj4UrffkSk0HJc29JM1wxMBZRZy5A2sP5zFWc4tKv9eYvT4GtY7khJreV9PU3c+yA5c5aCrd2ppu+wThmeUMavvJdV+K2coQ9hhao/xNKgPJh9CeeJ/570n30Jh7hsl3HQWn3uXsnAUYuqiJnmTPqk9zqG7XULfrTeZsjEOto2DOxjjqdr3J/R9yWPVpDtGT7DF0UXN2zgJ2nS2R7zwy9wwjSF9S68NdzQykfCKr60nLg8l1XyodsrtcwQUHJ+lchakdyw5cpqm7n6b31bL6n7UumtH8s4zV3GJd3DUZn6QZjtxp6uVKTYesLAPGW2Lvd4aRlmqGLh3FJfSCjE93TBAa7QDRV2sIM5LwCTeWVGR3n5aOqHcxXRaISiiYsfYgw9nHGW0ql1Mk+k4+lLzhxfW6Lm439nBquoSPv64Frx+9zmj9HUbyTsskbrIkgNb9frT29LM4IpNIE1v0lN5kODoTmV3JwOAgV5YuYdLyYFRCwaKQdAbTYhhur8dxx7cE6lmip/Tm1tpVXKpoY8eZYlJmzcfQRU2MmT1eMbncr7xGdcBGHtkQi1pHwWPeCdSH+FDX2cdzh7KJmmgnR2KhF8pILmnmhudzTPYIZbeBFQt3nkObuB9t4n4eDzrHbgMrzFaGcM3DneSSZkIvlJFmpZT0zSqRAAAgAElEQVQ6lSba8dyhbOoffAnOY94JqHUUPLIhluqAjdyvuoFXTC6xk6VnInnmPHacKeZyZTuFrz7PFK9wAvUscfBPZqStjsG0GBaFpKMSCia6B0n1moFB+VzBwz37f9u78rCqqvX9LmQSEQRBcEBmBBxCJNJwwjG85GwOVzMrwyi9KIqRZnrzp0XhxbhRljkVqYkazvOAqDijgCCToAwyzzP4/v7Y5+yLpWVlqbXf51nP2Wefb52997e/tb41fIPHh8fkNmvjE/GzbXaLqROvZJXw4q0SeSbvr2kted5nJ8lt1q+F1SPpnC00dBhq4PBQRVEGf2Ixs3NmuIkTTUYG0QeW1HaVomF29d/NC16DpKQYuVIjvF0khep9fuVRHu3+LFv2nk1fYUktlxkM1rdnxylreeejt/lFTAbrY3ayMTGKW69ms2RNIC1f28Iwwy5sM3QJfWBJHTcfBmrbsNeSA4ybMkJOQ1hXnMvp4Zd4bZIXXd7dxwAta+q4+dAHljQavkzewC5b9x4jrmWz6UY066K3MuysNMLqOGUtg1pJHYivsGTL3rN5zMWdHh8eY+qcicwqrmRFVTUbspPo9flpnhvuSee5u+jXwopaLjPoA0uajAziFlMnvrMngdWRq3kitYCnbxayevd/ueRAIre1c6bp6GBZYfq1sKJ+P3+e8ezPwaGn2HA7nuVV1cwtlTKy9Q06Jq87q/kV1MqeHSat4Z5O3RkSnca6qM2MzyljZEIuK75dRhufCK416kKj4cvk9xKgZc1nAvcydsILnPrNBdYVZUvZxfLLef2VF+m29CAP2PeU+WU4eBHDDLuw84xwFoctZPjl22yMO8KGi3v49YVM5q/yY6ep6xnS2kFWuLruvlysY8PnPjjM5FnjmFFYwZqqStbfSefYr2N40XsIuy3YI3f0PpBmjZvaOtLOdwertqzggaQ8Nt68wtrD67jyWDIjzbvSfHyorExma1jJS3kD/3OStxa9ysLyKhaWV7Hx5hUO/M9JRnv0pZ6HH2drSO9leUs7Kdy0eVdJaR9cy4u3SnggKY9V33/EuT/EcVNbaT1/sY6N3KF1W7CHF72HcOzXMay/k86aqkpmFFYwedY4PvfBYR52dqOuu6/8XkJaO8hr7F9fyJQUatwRhl++zeKwhew8I5xhhl1kZaSWY7elB3n9lRc5a1ssa8tLWFeUzficMsZOeIHPBO6VBy5qOV5r1IU2PhEs3/g+IxNy2ZR6jnVRmxkSncaspW+ww6Q1DGplT/1+/rIcH+/Vm32DjjHdfypzSytZXlXNhtvxHPZZNGOGDKSjX+Q9cmw6Opjb2jnTee4uVu/+L0/fLGRUWiGrI1dz0b7r3GIqtfv3m8lx6wEBPDfck16fn2bOCl9WVFUzq7iSaX6TaTo6+JF0zp00dBisb/9QRVEGf2Kx69pDDlbWvHE6z90lK4M9nbrLnZDF9E08YN+TbksPyh3zMRd3uUNo2Xs2l+na0nR0MGdrSJ2keklCvQykbkTqUZy6E/KBpSyU6oaj389f7hCW6drKHap6rVg9Im8zdAkDtKzlzu2ZwL3c3b4b278UJnf429o5yx2Nne8Ops6ZyOdXHuUxF3fO2hYrd6jFYQul6KVnIvhFTAbvfPS2lD/58DoGnUhhQ9Z1KUtV5GrmlVayKe0Cd1+/w5rqamYWVTAht4x1xbmsKy1gw+X9TCsoZ21lOXfE5bAp5SxzSipZFfExDyfnszEjlsuP3GDN/jU8n1nM7A9mMfRMOi+PGioHRHNdvF9WmIed3eSO2vbN7Tzp3of9Pzkub/ad8ewvd5qtBwTIStpf05qGgxcx1MBB7sCMhi+TOxp1h6DuqNXvQs179TtSd5qB2jb3KB21clDvF/m1sJIVQJc5P8gDjpPufWRFp+6MO88IZ9yUEXRdvJ/7LHvISkfdMQ4OPcWavWFcdiiJ6f5TeTg5X1YATTeiGXEtm2Xr3mNaQTkbLu+X0mAW5zI+p4yZRRWsrqlhfW4qG9MvMb+sSsofkFbIhqzrrKiqZu3RjbyaXcr6/Ex5IJOUV8bisIXcHJvFxNdGyaGx1YOK+7WXc8M9f9Je1IqlzdAlcntZrGPzm9qL+p38+PuvaS+Wr23hmjZSezlg3/Oh28s7exLYlHbhkXTOHTV0GNTK/qGKogz+xNLDpScbM2LZlHqOmUUVLAydz24L9jBmyEB5tqDj5kNfIS0DrNSzo8u7+1i27j3O2XFNqptylplFFSxYPY9fxGQwZshAtvVeybVGXeS66uUFl3f38YB9T6nurWs8m1HEjEKpbtjZmzzj2Z8DVp2Q6/prWssjaQPPQB5ydOXcH+Lk2PjpBeWsKy1g6Jl0nu7fj/0/Oc61RtJoWF1XPaU+5OhKq5nbmO4/lRdvlbD2+Ldy3ZqDa2WzUHVd9fKHgWcgA7Ss5bqDVkexLmoz60ryWFtZzpqDa9k36BiTZ42jxfRN3G/jItfVcpnBAC1r2vhIaSkHh55iXfRWfn0hkzVVlazZv4YnUgvkuvsse1C/n788ig7Qsqauu69c94LXIG64eIs1VZXMKank0ZR8Js0cw5lbr/ykrq+QOvBwEyeajg7mRe8h3HTpNkvXLmJuaSUPJ+ezKfUcX918mXs6dWe3BXvk2U7zzt9sbAgveg+R33tuaaUsM69uvszC0Pns6r+by3Rt2dZ75U/q/kRmbl1jU8pZ+b139d/NM5795brNZSaolT1dF++X33vD7XiezShiekE581f5yTJzP3n7NTKzps395e2ws9s9MqN+7yHRafJ7by4zzeVNXfdmwMuSzERvZV1xriQz+9fcIzPq2aVauapl5mbAyz+Vmb1h8ntvLjMPlLczEZLMVFf/RGb2dOp+j8wEatvIMjPss+h7ZOatiKuPTBms1LN7qKIogz+xuDracubWK/Io/Wj3Z5lXWslrOaXcYeZMbVcpdklwVCq3Xs1mylsT5PXXXksOsKZKyrDVYdIaztawYlvvlaze/V9WbVnBgf85SV9hSYvpm6RE76UF7LFwL0MNHNiy92zeDHiZu6/f4bJDSfLIdE+n7hwceoqZRRU8YN+TRsOXMVBbsuFvjDvCrKVvyCNX57m7WJ+fycb0S7Saue2evY+kvDKO/TpGnjZHe/RlRVU1o9IKuamto5QkpJU9N1y8xS9iMpgwzZvarq8z3MSJz688ypKKKtYe3SivlZqNDWHt8W9ZsiaQvf/viLyZlzDNW97DUE/rN7V15OmbhXwr4iqjej8vbwS+tP4cE++UMdK8Kw0HL+L7urZceSyZjemXmOY3WZ4duby7j9kfzGJj3BF2nLJW3qeo3rmKt1Xr2uoO74LXINaWl3D39TvyXkSYoRTuorasSLYC8xWWHPifk8wqrmR15Goaey1ngJY1O05Zy4bL+5mzQooQq97gTp0zkY0ZsQw6kcJlurY08AyU9xsmbzwvW1SddO/DWdti5T0i9cj2i5gM1uem8vorL9L2ze3yRnNh6HzWRW+l+fhQ+mtKlji1h9exSJXIXb0kFjdlBMPO3uSmS7cZrG8v7wOdSC1gVXWNbJk1W8OKo746y+S8ctm57n1dW1q+tkVaeinKZlf/3fSBJfU8/Hh7yetsTIziO3sS5NnRfhsXpheUc9hn0dxn2UO2Vlq07zoPJOXx1qJXpXg/rR3YbcEe1pXk8crYYfL6/4BVJ1j53Qes2RvGtt4rOVvDih0mrWH9+UjWVEtZy9SKKnnWOG6OzWJIdBqXt5RmANvaOfNqdinzy6p4zMWdWi4z5H22K1klLFg9Tx5c2PnuYEPWdTZkJ8kb0a0HBDB/lR/rY3Zy5tYr9Ne0psnIILkt9//kOLe1k9ry8pZ2DIlO4+bYLKa8NYE6bj5ca6Rqy9VSKll1W/b6/DRr9oax8rsPOGDViUcWK6iD0JETDP1SUZTBn1h6dDCl+fhQhhl24axtsZyz45q8GVhbWc61Rl1ks7e8j+eww6Q1UhL0ymrZx2DqNxdYW1bETW0dqeUyg+n+U1mwep5sy11YXsUlBxLpA0uO/TqGdcW5DDdxorHXcqa8NYElawLl0VhuaSVXHpMCiHmvOcP6/Exua+dMo+HLmDRzDMs3vi+vIWeqUi+qLWdCz6Qz0ryrlOBkmjertqyQlVlaQbls3jdg1Qk5MqWBZyAbb12TbdpjJ7wgp+NU245vunRbHu03pl9izd4w6rj58KL3EMbnSDmRF+vY8PmVR7n1ajYPObpKQcdSpMxiatv8K1kl7Oq/m4HaNnRfdkj2Z9Dz8GNjwnEec3Gnrrsvz3j259mMIvZYKK0191pygAeS8ujXwkpysLq8X96DiOr9POvPR9Ll3X3017Tm0ZR8nkgtoL+mtWwSeLp/P+p5+PF4r95siD3IXksOyPb25zOLGaAlra3XHv+WMUMGylZeTTei+dwHhzlbw4oR17J5NbuUgdo2dPSLZM3BtbzgNYg6bj485OjKprQLbD0ggLM1rBh++TY9Pjwm28VX7/4vr4wdRm3X1+Vk7QaegfQVlvz6Qib7fyI5oNm+uZ0p+eW8NsmL2q6vS7b/WddpOHiR7G07aHUUl7e0o9XMbcworOD1V16klssMVnwr+TaofSKCo1Lp9flprtSTfCuyiiuZNHMMtVxmsGzde7I/yhZTJ648lsxRX0lRV9U+NeqN6+KwhawryZOXdZYcSOTkjefpA2kTuKSiSjZCyF/lx8x3XqHp6GApgU9FqWwa2v6lMFZUVctWQjkrfHl7yes0GxvCNW26sKa6WjbRnrUtljXVUo4Ps7EhvL3kdeaskPKUhxo4sKKqWva3mR5+ibUVpVxv7EiTkUG8GfAy81f5yfsfJRVV7DhFiuo6eeN51pXkMdzEiW29VzLNb7JschvUyp55pZWytdeor85y5bFkbjGV2mnyrHHMKal8ZMpAnVf6l4qiDP7E0kWvFbVcZrApVYr7bjh4ERPvlHGLqRO7LdjDmVuv0K+FFZcfuUHbN7fTV1gyt7SSeh5+DDVwkL0jL48ayoRp3rIHpNo6oryqWrbtDzt7U/bq7LXkANcbO/J8ZjGNvZYzYZq37D172NmNOSt8qefhx4LV87jfxkW2aw/UtuHMrVd4IrVADjPg9flpLm9px6bUc+w4ZS1PuvdhTbXkKVy+8X25o1fbdKvz3gbr27P/J8dls70dcTnyiLWutICOfpGMNO8qx6C5GfCyPNJvSjnL5S3tZH6p/SXU/Fp2KEnmV05JM36diZACtnkPYcI0bxoNX8aa/Wu43tiRrov3y/yymL7pJ/xaaySFeDb2Ws64KSPkUBZ10Vt556O3qefhx9zSSu63caGNT4TMr1c3X5Y9xhNy7+VXp6nredK9DzPfeYW67r4sW/ceI827SvwqzuW54Z40GxvCyIRchrR2YN+gY7IJZsS17Hv4pfZpqPh2meylLfNLlbDHwDOQSXkSv7r67+asbbEM1LaR+bXPsofMr5DWDnKoiIveQ3j9lRclfu0Nk/lVoeJX6Jl0fhGTQV8heaerR+P38Cs7SXYEVPMrT+VXYOMTIftevLr5Mnss3EsfWDI+p4zea85I/Eq7wE5T1/N4r94yv0rXLuLu9t1kfgVoWdNsbIjs+ftjfrV/KUwK4V5WdF9+RXv0ZYdJa2R+DQ49JYeEac6vJQcSaee7Q/Z/0O/nz6ylb8j8asyIlfl1JauE640dpVwJ1TWcrWHFkOg0fnXuXn6tadOFtYfX0dhrOa9N8pL9W+qiNsv+O4+ic24vdPi+ru1DFUUZ/InFXGhz0OooGngGMnbCC1yma8vcUinjlzomfEp+uTTCPpQkp2h0nruLYWdv8oB9T25q60jbN7fTYvomRpp35Q4zZ269mk0bnwg5NLU669iVrBL6wJJNaRdoMjJIjrd/xrM/jYYvY+//O8Jlura8PGooJ288zwGrTshx24d9Fs3WAwJYW17C93VtWVheJY1+8zM5W8OKOSt8qePmI/tGlKwJZLcFe+RcBmuNutDh7Z1yPoAtpk6MTMiVFZgPpCieWi4z5FhKTTeiaTY2hAm5ZXLseWOv5XRbepDZH8ziBa9BnB5+iS17z2aa32QGaFnTe80ZKZR0VSUX69iwtFKKr6PON5C19A3quPlw7g9xPN2/HwtWz+MzgXtpOjqYR7s/yzVtutDRL1IKo31xD8NNnORoo7VHN9IHlrISPpFawPXGjmyMO0Lz8aHs6r+boQYOPOneR86HcOejtxkzZCBnbYulrruvnDOhtqyIeh5+rKquYaC2Dauqa6jnIWVpU8fe13X35axtsYwZMpB3Pnpbjs9/0r0PQw0ceDVbytqljr56IrWAWi4z/hcfRxX24kCSlKWt4eIedpi0ho5+kXJeg+VHbvCZwL1y7oS5P8RJ8fVVeZ7riqWUqaWV1VysY8OaKklZjPrqLP01rWWLrVc3X+YFr0FcqWdHt6UHaey1XM4ZkZBbRrOxIWy6EU0fWMphs9VxqNR+LZEJudxi6sQ9nbqz45S1dHh7J9cadZFNW7st2MPisIX0FZKXsY6bDzOLKqTw0fmZNBy8iIXlUs6PuCkjqN/Pn8M+i5bzXgxYdYKTN57n5VFDubylnZz7oSE7icH69nJ+CXUoa3UomC9iJEfL6t3/pY1PhOxbEWnelRbTN8k5SQ7Y92TomXR5BuQrLNl48wq1XV+XI5uqZ1C5pZJzY+yEF9h6QAAHrY5i0swxTJo5hoNWR7H1gADGTniB7+va0uPDY5y59dHEJjIX2gzUtnmooiiDP7GYQJvVNTW0fXM7B6w6wcaE4wxqZS+F5j1yg7ruvjyfWSznc1XT6rr7yrRBrey57FCS5DF8q+T+tHFHGNRKMhdT09r57pBpr03yeiCtjpvPPbRxU0bItCuPJcu0jYlR96W18YmQaVfq2UlBzm5EU8fNhw5v75TDOcdNGcHTNwup6+7LoBMp96VVB2n7MW1h6PwH0iZM874/bcpZ9g06JtPubt+NCdO8GZUm0QZHpbJg9TyZ1uNDFW1F6T20RsOXybSzNaxk2i5zfmBtRSkjzbv+Iq2WywyZ1l9TCmnwE9rUcw+kbTN0CU1GBjF/lWQOesC+J59feVSiLS95aNrBoacemnbYZ9HcYeYsx+hX08bnSKGnDzm63pe28rsP2GboEpqODr6HtqSiSqatPx/58LQxO2XaBbviaTo6mAm5ZbzgNeiBtFVbVnDuD3E/oS0qr6Lla1vo9flp1sfs5PKWdvelPezsxqLyKnaeES7TLtO1lWm1XV9n4p0ynhvuSR9Y3kNbdybiZ2kLVbR6Hn7/o/3+IxoOXvRIOmczoc0ALeuHKk+bMhAk8bTCSlOX81tZ4hlnE7TYvAvfXMzCmYvZSDl5AEZW3WDR1QYn++Yha+dupOy5gdPZ5XjGUBeOQ6xwfu7nCD+TibRrOSi7dR0m9i5IDHRA9eGtSNlxFhv3pcJKTws97YxgtOcgvrucjaPnb+P6od1o09kJHZ3tcXZEDXJ/iETyrngcTinCM4a6eCH9Ig6ll2DT2UwcWbMWLY3MYeLQC0lLXdBw8nukbI/ClxGJsGiphV6dDdDx+HFExOVi79lbuLp7Oww6OSD5m1nQT4tG/u6d+Pd7+6GvqYFuBjoYf/syTmSW45tzmdi5+gvotDaGsZ0rUoL6o+nMDtyMOIxPv4lDB11NvJsSiVu6nfFDYj7emfN/EBot0LqDLeK/nY12BXEo2r0V783biZYtBBz0dfBq9gWczq2F97QP0FhbCa1Whjj87WL0MhZgzE4EeC1H/V3CXFcT713/HrmGDtibUoQ5vssBAK1MLRD73Tx0LE/FB07jcae2EdoaAkF7A6HRZywul2pg8D8/QENVGTR19bFr03vo21EPGpd2Y6FnIGqaCGPtFlgetwmFZi5wnhqKyjsZuNtYj/8LeRejncxg3ZCFj+xH4nZNA1oI4JMd/4KmxzgkNRrCY/IK1JYVoIV2S2xd/x48rQyx3cIV8eV1qGy8i/eWDofZyDGochgA+6mfozwrGXcb6xH4YQBe6tEBDi2KEWIzDBnV9QCAoPDXoTNwIrosu4bC5EuoKbkDz5mvYXofSwy3M8YRWzdcLatDcX0T3gkYgA6jR6Kp5z9gM20tSm8lwnGINzzdLfBP104oHzkcV1JLkFHdgJeH28B+XB/oD5sI56A05N+4guqiHDw/bRr++bwlvOxNcN7JHZdLa+HR0QB2XvawGOuNgTEdcTshAyWZ8bB5fgj6uHXEP3t1gsbUUYhPLEJyZR0meFjAYdyzMP7HRHT/LA93kq5Br21HWHazxIS+lnhh43yk7k/FpcJquBq1hN0IW1iNHY7hiU64GXcbJelX0alXf/Ts2R7T3DvDePYkxF/KRU0T0cOxLexH98S/7d/AmYvZyElMgLaeISy62uDFPp0xec9SpOxNwsWsCnRprY0ugyxhO3YgXsztg/T4PJSkx8K0Sy907dkem1rsQ8qOs7h+NgsFdU1wsW4D+5HdEfrcPBw9fxvZ128AADp1c8RQdwu8Gf3xPW3MyaMTUv69Ad/EZCIxNhcFSRdhbOcK2x7m2GV6Cinbo5By8hZSK+vRs2NrWJ46gecmf4jy4ysvkXT7PX2OudDhVM2OD0Ub3Hjzd1/vz8QTpQyEEC8AWA2gBaSkzx/+HH03kzb8R7kxTLQ1cbumAfV3iRYC6Gagi5zaBpQ13MXkAZ1RlFSMjKp6vBwyEQWxyciLzYH7lq9xN+MaquMu4Zb3QlgaamPkF+fgYtMWz1kZIeCTg2jT3gymnQxwbmsEdA1N0Mq0M/LiowAAmrr60Gqpj7qKYtxtrIfl8y+i4s4tBM57EefSipCWUYods59HanENLuWU4V+mOWgy74L8u3rA6rlo29MZOs7uODpqDkydTdCupw3WrziCznqaOF1UAwDQ19SAgaYGShqaUNNEjLE3RlZhNW7XNOJVv34oiM9CwfVCDNnxEeqTLiH/chJ03w2DcUMJ3jiUj+dsjNGzvQGmfxIFk44GcLQywva126HXtiMMzDsgPSoSLbRbQqe1EeoqStBUXwN7zzEozc1FdVE2Jr0xFvE3i1GYU4Fv5/XHldwynM8owWcD26BUtx0ySuvgdCoUus7uYCdnXJn2Cto9Y4ENn55GB11NdDZuiR/SS6CtIWCs3QKVjXdR2XgXANDfRA+3qhtQWN+E6eMdUZBQiOycCozf8DZKYuPR8ObHMNeqh1Z+MoJut4VbpzZwaNsS40LPoLOFITzsTfDhqkgYtLdCW/PWuLp7O7RbGUKvbQeUZMSDd5tg/ownqvJvoaYkD30mT0BhTgWKs/MRvGAYYm4W48rNYux+41lkljXg6p1yjK+MhoZtT9S06Yw7/tNg2tMBG+dvh3UrLZg4GCP81C0YarVAB11NJFbUoYlACwHYttJGXl0jKhvvYtJzHZGXXooXNy9ERfxVFFxJgdXn30M77wYaUy5jj9lw9DBrDQt9DYzaEItnrY3hbmmE2Z8cg3F7I7S3MMTJb7ehpZEZKnLSwLtNEBotYNDJAbUleaivKkN37/Eoyq1AZd5NzH57JGLSipB7uwzb5/bF9YJqXLhVgiUO1Wgw64LUmS+hXU87GPTogcipwTDrbAiz7qbYEB4PE21NWLfSwpH8KrQQkrxpawiUNdxF/V3Cu33re9pNflwu3L5bB96MRc31K8j0WgBLQ220KrsFv7N1eM7aGK7tDTAxOAomHVvD1aYtNny+46HaTU1JHv7xyhik3ypDYU45vvbvj0s5ZTiXVoRvRls9sN2Y9rDCho+OobOeJtqb6SPiegH0NTVgpNUChfWNcrsZ8sMqaHb1/N2ds5nQ4ZQWHR6KNqQp4zdfTwgxAcBSAE4A3ElefADdfftMIYQ1gC0A2gK4BGAayfqfveaTogyEEC0AJAMYCiALwAUAk0lef1Adt25deHTuBGR6LUAXQw2M+SYOJ7/dBoP2tihKvQyn4eNRcDMT//YfgeniKu526YvEGZOxMTIZFi21kFHdgH8OskLG1TyM/m4B8k9EQ8xbjXH/PQtX53bY8uUO1JYVoqm+Bp37eONr//44kJSP5d0acGrcLHTuZ431n59D/V2irOEuxjubIiW7AlM/nYSkgf9Ct3Yt4f3lBZzdvA365lYou5UI5xfGoiAjByv9h2NizRnAuT/iXp6GjftSYdFSS1Zqw81a4R/b3kPhyZOomxWE8atPw6WrGSK+3IaGmko01lbCuu9IbJzfH4eSC7DYtgInRvvCwqMz1q+7AttWWrhQUouJLmZIySjD1C+m49Kzb8DFXA8vrjmP899vR6t2FijPSkaPkRNwJzUHwQuGYV/8HXw11ARXpr0CiwFOWPfBIblRebdvjeE7P0DJqeMoe3k5JoScgrOjKfasi0BTYz0aqspgO3A0ijJSscBvJPzb56HWohduzZ6E9ZuuwUpPG/HltZj4bAekp5Zg4tdv4HTXqXA1b4WRYedwaUcE9Np2QFXBbdxtrEf7nkMQMs8Th5Ly8WlffVyc8ios+jlgXdBxlKsUyyhLQwzd+RFKTx1G/kvvY+KqU3BwNMGhDdvQur0tCpMvwGHwWBTeTMPi+SPxZut0NNj0RurMl7Dh+0RY6WkhsaIOU563QHpSISZsmoMj1mPh3kEfoz6LQZ+uZtjw+Q5UF+XgbmM9Oj47AmHz+uFAYj6C3TVxdvzrsBzogHUhp2RlN8beGEl3qjD9k3G45b0QXQyAMd8m3Fc23583Aq9pxqPJwQNJr03Ghh1JsmzW3yU8TfVk2dTw/xRjQ8/g2a5mCF+z/R7Z/HJePxy+UfAT2bRtpY2Y4hqMdzZFUlY5podORtLAf6G7qS68v76Es1u2o5WphSyb+em3sWK+FybXnwec+iFu2j/ReVA3bHhv731ls+HNjzHu09N4xqkdtq/djvqqclk21/v3w5GUQiy2rUCFWVcULJyOdV9deqBs9jTXg/ePZPcCgvAAAApjSURBVLO793jkpefi4/lDcSAhD18Oa4fYqS/DYoATNi4/hLy6/8nmsO1LUXLqBCpmrMCEkGh0czLFrnU70Lq9LcxsOuLycq9HogwmajycMgi9+7uUgROAuwDWAJh/P2Xwc32mEOJ7ADtIbhFCfAHgKsnPf/aij3udSl0A9AFwsNn3QACBP1envX1XWr62hb2WHODu63e4Us+O6f5TWX8nXc7aVZ8vxU5vTL/EfYlSjuTaSsnpZ7GODa9klbD/J8fZ/qUwTg+/xP02Ljza/VlWR66m6ehgDvssmkl5ZQzQsmbp2kVy8o7TNwvZmCBZ8tSWFTE4Soor323BHibNHCNnV+q2YA/tfHcwOCqV640deXnUUDmr1twf4lheJeWbrT3+LRPvSGkrTUcHszpyNf1aWHG/jQunh1+i0fBl7P/JcZ7x7M9t7ZyZv8qPnWeE023pQe5LlJ79ZsDLrM/PpK67r7Q5fUeK4tg8P7Tla1uY9/Ecvq9ry5ghA9k36Bg7TFrDVzdf5j7LHnKWL5ORQdRymcHkvHIGaFlLKRRV68cGnoFybtlNbR0ZdCKFeh5+7LFwLxNfG8VgfXsmzxrHrv675Zy2a426SO8hUdrc03KZwQrVszfPMW02NoTVO1fxsLMbD9j35NRvLtB8fCgHrDrB0/37cbGOjZybV9v1dTkTlg+kVKG67r5SCOXcVKb5TWZQKykSpsu7+2g1cxtzVkhOSeeGe9Ljw2M0HLxIdmCarWHFmv1r5GdPyZec8mZrWLGovIpvRVyV8upe3s8LXoMYbuLE5Udu0MYngs8E7uX1V15ksL6U37chO0nO9FVXLOVuaEo5y6Mp+dR2fZ1V1TUsDJ3PQG0bxueUceB/TtJsbAirIj7mIUdXHnJ0ZVXExzQbG8KB/zkpZ9wrDJ0v5yE+mpLPphTJrLSuOJdhZ29K5rvZSUydM5HB+va8/sqLfCZwL218Irj8yA2GmzjxgtcgNlzeT8PBkjNWkSpRT83BtUzJl1JRphdIzmWzNay4p1N3OSe3x4fHeG64J8NNnOR4Ui7v7pMzjqX5TZZzGX8Rk8G6AilM9fKWdjycLD175xnhLFg9j4t1bHi6fz8OWHWC5uNDOfWbCzxg35OHnd1YvXMVzcaGcNDqKDmbn6+wZIUqXWrrAQFsTIyiDyy51qiLnNe7q/9uJs8ax2B9eya+Noo9Fu6lnocftV1ffyRr+CbQpg8sH6o8iusBOAHA7df0mQAEgEIAmveje+L3DIQQ4wG8QPJ11fdpAJ4j+faP6N4A8IbqazcA8X/qjT55MIH04v/uUPig8ECNB/HBkqTp7/ljIcQB1f8/DHQB1Db7/iXJL3/l9U7gwTOD+/aZkJaXYkjaqc5bANhPstvPXUvz19zYkwAVM78EACHExadpg+aPgMIDCQofFB6o8UfygeQLj+q/hBBHAJjf56dFJCMf1XUeFk+SMsgGYNHseyfVOQUKFCj4y4HkkN/5Fw/qM4sAtBFCaJJsxEP2pRq/82YeJS4AsBdCWAshtAFMArDrMd+TAgUKFDypuG+fSWnt/ziA8Sq66QB+cabxxCgDlQZ7G8BBAIkAvieZ8AvVftX6218UCg8kKHxQeKDGU88HIcQYIUQWpM3fvUKIg6rzHYQQ+4Bf7DMXApgnhEiFZF769S9e80nZQFagQIECBY8PT8zMQIECBQoUPD4oykCBAgUKFDy9ykAI8YIQ4oYQIlUI8c7jvp8/EkKIDCFEnBAiVghxUXXOWAhxWAiRovo0Up0XQohPVXy5JoRwfbx3/9sghFgnhMgXQsQ3O/ern1kIMV1FnyKEmP44nuX34AF8WCqEyFbJQ6wQYkSz3wJVfLghhBje7PxT216EEBZCiONCiOtCiAQhxL9U5/928vCH4nF7Hv9Gr7wWANIA2ADQBnAVgPPjvq8/8HkzAJj86FwQgHdUx+8A+Eh1PALAfkheiL0BnHvc9/8bn7k/AFcA8b/1mQEYA0hXfRqpjo0e97M9Aj4sheSI9GNaZ1Vb0AFgrWojLZ729gKgPQBX1XFrSCEYnP+O8vBHlqd1ZuAOIJVkOqXgS1sAjHrM9/RnYxSAjarjjQBGNzu/iRJiINkbt38cN/h7QDIKQPGPTv/aZx4O4DDJYpIlAA4DeGROQ38GHsCHB2EUgC0k60jeBJAKqa081e2FZC7Jy6rjCkiWMx3xN5SHPxJPqzLoCOB2s+9ZqnN/VRDAISHEJVU4DgAwI5mrOr4DwEx1/Ffmza995r8yL95WLYGsUy+P4G/AByGEFYCeAM5BkYdHiqdVGfzd0JekKwAvAG8JIfo3/5HSHPhvZSP8d3zmZvgcgC0AFwC5AIIf7+38ORBC6APYDsCPZHnz3/7m8vBI8LQqg79V6AqS2arPfAA7IU3789TLP6rPfBX5X5k3v/aZ/5K8IJlHsonkXQBfQZIH4C/MByGEFiRFEE5yh+q0Ig+PEE+rMvjbhK4QQrQSQrRWHwMYBilS6y5IbubAve7muwC8rLKo6A2grNlU+mnHr33mgwCGCSGMVEspw1Tnnmr8aA9oDP4XuXcXgElCCB0hJTexB3AeT3l7EUIISB60iSRXNftJkYdHice9g/1bCySLgWRIVhKLHvf9/IHPaQPJ+uMqgAT1s0JyMT8KIAXAEQDGqvMCwGcqvsThAbHQn/QCYDOkJZAGSGu7r/2WZwbwKqSN1FQAMx73cz0iPnyjes5rkDq+9s3oF6n4cAOAV7PzT217AdAX0hLQNQCxqjLi7ygPf2RRwlEoUKBAgYKndplIgQIFChQ8QijKQIECBQoUKMpAgQIFChQoykCBAgUKFEBRBgoUKFCgAIoyUPAEQQjRpIrCGS+E2CaE0PuV9TsIISJUxy4/iuY58mmL1qlAwZ8JxbRUwRMDIUQlSX3VcTiAS7zXyejX/NcrkOzL336Et6hAwV8WysxAwZOKUwDsVDHrf1AFZYsRQvQAACHEgGbx/K8IIVoLIaxUswptAP8GMFH1+0QhxCtCiP+q6loJIY6p/vOoEKKz6vwGVRz8M0KIdCHE+AfenQIFfzEoykDBEwchhCakoHxxAJYBuEKyB4B3AWxSkc0H8BZJFwD9ANSo61MK07wEwFaSLiS3/ugSoQA2qv4zHMCnzX5rD8nj1RvAh4/62RQoeFKhKAMFTxJaCiFiAVwEcAtSPJq+kMIvgOQxAG2FEAYATgNYJYSYA6ANycZfcZ0+AL5THX+juoYaP5C8S/I6/hcSWYGCvzw0H/cNKFDQDDWqkb4MKUbZT0HyQyHEXkgxak6rUjzWPoJ7qGt++UfwfwoUPBVQZgYKnnScAvBPABBCDARQSLJcCGFLMo7kR5Cicjr+qF4FpBSJ98MZSJE7ofrvU4/8rhUoeMqgKAMFTzqWAuglhLgGaQ1fHbLYT7VZfA1SRM/9P6p3HICzegP5R7/NBjBDVXcagH/9YXevQMFTAsW0VIECBQoUKDMDBQoUKFCgKAMFChQoUABFGShQoECBAijKQIECBQoUQFEGChQoUKAAijJQoECBAgVQlIECBQoUKADw/9ZYxTwu02NEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Set the inner-layer dimensionality and the input/output dimensionality.\n",
        "n, d = 2048, 512\n",
        "\n",
        "pos_encoding = positional_encoding(position=n, d_model=d)\n",
        "\n",
        "# Check the shape.\n",
        "print(pos_encoding.shape)\n",
        "\n",
        "pos_encoding = pos_encoding[0]\n",
        "\n",
        "# Juggle the dimensions for the plot.\n",
        "pos_encoding = tf.reshape(pos_encoding, (n, d//2, 2))\n",
        "pos_encoding = tf.transpose(pos_encoding, (2, 1, 0))\n",
        "pos_encoding = tf.reshape(pos_encoding, (d, n))\n",
        "\n",
        "# Plot the dimensions.\n",
        "plt.pcolormesh(pos_encoding, cmap='RdBu')\n",
        "plt.ylabel('Depth')\n",
        "plt.xlabel('Position')\n",
        "plt.colorbar()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pz0HBopX_VdU"
      },
      "source": [
        "Before you start putting together the Transformer, define a function for the point-wise feed-forward network that you'll be reusing later."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PtzTFKOk_4lk"
      },
      "source": [
        "## Create a point-wise feed-forward network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Yb-IV0Nlzd0"
      },
      "source": [
        "A point-wise feed-forward network consists of two linear layers (`tf.keras.layers.Dense`) with a ReLU activation in-between:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "ET7xLt0yCT6Z"
      },
      "outputs": [],
      "source": [
        "def point_wise_feed_forward_network(\n",
        "  d_model, # Input/output dimensionality.\n",
        "  dff # Inner-layer dimensionality.\n",
        "  ):\n",
        "\n",
        "  return tf.keras.Sequential([\n",
        "      tf.keras.layers.Dense(dff, activation='relu'),  # Shape `(batch_size, seq_len, dff)`.\n",
        "      tf.keras.layers.Dense(d_model)  # Shape `(batch_size, seq_len, d_model)`.\n",
        "  ])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MiMx547skJBW"
      },
      "source": [
        "Test the function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "mytb1lPyOHLB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "986f6c3a-3c8d-4c8f-8181-b93bdce27f9d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 50, 512)\n"
          ]
        }
      ],
      "source": [
        "sample_ffn = point_wise_feed_forward_network(512, 2048)\n",
        "\n",
        "# Print the shape.\n",
        "print(sample_ffn(tf.random.uniform((64, 50, 512))).shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QlzsUK1QAWMs"
      },
      "source": [
        "Having loaded the data, it's time to create the Transformer model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7e7hKcxn6-zd"
      },
      "source": [
        "## Build the Transformer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ub5j7x6slzd1"
      },
      "source": [
        "The Transformer model in this tutorial follows the same general pattern as a standard [sequence-to-sequence](https://www.tensorflow.org/text/tutorials/nmt_with_attention) model with an [encoder](https://developers.google.com/machine-learning/glossary#encoder){:.external} and a [decoder](https://developers.google.com/machine-learning/glossary#decoder){:.external}.\n",
        "\n",
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/standard_transformer_architecture.png\" alt=\"Standard transformer architecture\">\n",
        "\n",
        "Figure 4: The standard Transformer architecture. The image is from Google Research's [\"Efficient Transformers: a survey\"](https://arxiv.org/abs/2009.06732){:.external} (Tay et al., 2022).\n",
        "\n",
        "The [\"Attention is all you need\"](https://arxiv.org/abs/1706.03762){:.external} paper's authors demonstrated in 2017 that a model made of [self-attention](https://developers.google.com/machine-learning/glossary#self-attention){:.external} layers and feed-forward networks can achieve high translation quality, outperforming [recurrent](https://www.tensorflow.org/text/tutorials/text_classification_rnn) and [convolutional](https://www.tensorflow.org/tutorials/images/cnn) neural networks.\n",
        "\n",
        "Main similarities and differences between the Transformer and [RNNs with attention](./nmt_with_attention.ipynb):\n",
        "\n",
        "- In both cases, the model _transforms_ sequences of input embeddings into sequences of output embeddings (which are covered later in this tutorial).\n",
        "- The decoders generating the output sequences use attention layers to select relevant information from the input sequence.\n",
        "- To process each sequence—to learn the mapping between inputs and outputs—the [RNNs with attention use RNNs](https://ai.googleblog.com/2016/09/a-neural-network-for-machine.html){:.external}. The Transformer, however, doesn't need RNNs, and relies on the attention layers in both the encoder and the decoder. Self-attention layers with a [residual/skip connection](https://en.wikipedia.org/wiki/Residual_neural_network){:.external} update each location with information selected from the rest of the sequence. Self-attention performs sequence processing by replacing an element with a weighted average of the rest of that sequence.\n",
        "\n",
        "Read the [Google AI blog post](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html){:.external} for more details.\n",
        "\n",
        "### Architecture\n",
        "\n",
        "- The input [embeddings](https://developers.google.com/machine-learning/glossary#embeddings){:.external}, added to positional encoding, are passed through the encoder (with N layers) that generates an output for each token in the sequence. This makes sure the model can recognize the word order and helps avoid a [bag-of-words](https://developers.google.com/machine-learning/glossary#bag-of-words){:.external} representation.\n",
        "  - You will use `tf.keras.layers.Embedding` for the embeddings layers, as well as the `positional_encoding()` function inside the encoder/decoder later in this section.\n",
        "- The Transformer's encoder and decoder consist of N layers (`num_layers`) each, containing [multi-head attention](https://developers.google.com/machine-learning/glossary#multi-head-self-attention){:.external} (`tf.keras.layers.MultiHeadAttention`) layers with M heads (`num_attention_heads`), and point-wise feed-forward networks.\n",
        "  - The encoder leverages the self-attention mechanism.\n",
        "  - The decoder (with N decoder layers) attends to the encoder's output (with cross-attention to utilize the information from the encoder) and its own input (with masked self-attention) to predict the next word. The masked self-attention is causal—it is there to make sure the model can only rely on the preceding tokens in the decoding stage.\n",
        "  - You will create these Transformer building blocks in this section.\n",
        "\n",
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/transformer.png\" width=\"600\" alt=\"Transformer\">\n",
        "\n",
        "Figure 5: The Transformer architecture from Google Research's [\"Attention is all you need\"](https://arxiv.org/abs/1706.03762){:.external} (Vaswani et al., 2017).\n",
        "\n",
        "Before creating the encoder and the decoder, start with defining the encoder and decoder layers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QFv-FNYUmvpn"
      },
      "source": [
        "### Define the encoder layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ruDp-wNqlzd1"
      },
      "source": [
        "An encoder consists of N layers.\n",
        "\n",
        "Each of these encoder layer consists of sublayers:\n",
        "\n",
        "- A multi-head attention layer (with a padding mask), implemented with `tf.keras.layers.MultiHeadAttention`.\n",
        "- A point-wise feed-forward network with `tf.keras.layers.Dense`.\n",
        "- Each of these sublayers has a residual/skip connection around it, followed by layer normalization (`tf.keras.layers.LayerNormalization`). Residual connections help in avoiding the vanishing gradient problem in deep networks.\n",
        "\n",
        "Therefore, the output of each sublayer is `LayerNorm(x + Sublayer(x))`. The normalization is done on the `d_model` (last) axis (the dimensionality of the input/output). There are N encoder layers in a Transformer.\n",
        "\n",
        "Note: Each multi-head attention block gets three inputs; Q (query), K (key), V (value). These are put through linear (`tf.keras.layers.Dense`) layers before the multi-head attention function (`tf.keras.layers.MultiHeadAttention`). Instead of one single attention head, Q, K, and V are split into multiple heads because it allows the model to [\"jointly attend to information from different representation subspaces at different positions\"](https://arxiv.org/abs/1706.03762){:.external}. The equation used to calculate the self-attention weights is as follows: $$\\Large{Attention(Q, K, V) = softmax_k\\left(\\frac{QK^T}{\\sqrt{d_k}}\\right) V} $$\n",
        "\n",
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/multi_head_attention.png\" width=\"500\" alt=\"multi-head attention\">\n",
        "\n",
        "Figure 6: Multi-head attention from Google Research's [\"Attention is all you need\"](https://arxiv.org/abs/1706.03762){:.external} (Vaswani et al., 2017).\n",
        "\n",
        "Define the encoder layer by subclassing `tf.keras.layers.Layer`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "ncyS-Ms3i2x_"
      },
      "outputs": [],
      "source": [
        "class EncoderLayer(tf.keras.layers.Layer):\n",
        "  def __init__(self,*,\n",
        "               d_model, # Input/output dimensionality.\n",
        "               num_attention_heads,\n",
        "               dff, # Inner-layer dimensionality.\n",
        "               dropout_rate=0.1\n",
        "               ):\n",
        "    super(EncoderLayer, self).__init__()\n",
        "\n",
        "\n",
        "    # Multi-head self-attention.\n",
        "    self.mha = tf.keras.layers.MultiHeadAttention(\n",
        "        num_heads=num_attention_heads,\n",
        "        key_dim=d_model, # Size of each attention head for query Q and key K.\n",
        "        dropout=dropout_rate,\n",
        "        )\n",
        "    # Point-wise feed-forward network.\n",
        "    self.ffn = point_wise_feed_forward_network(d_model, dff)\n",
        "\n",
        "    # Layer normalization.\n",
        "    self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "    self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "\n",
        "    # Dropout for the point-wise feed-forward network.\n",
        "    self.dropout1 = tf.keras.layers.Dropout(dropout_rate)\n",
        "\n",
        "  def call(self, x, training, mask):\n",
        "\n",
        "    # A boolean mask.\n",
        "    if mask is not None:\n",
        "      mask1 = mask[:, :, None]\n",
        "      mask2 = mask[:, None, :]\n",
        "      attention_mask = mask1 & mask2\n",
        "    else:\n",
        "      attention_mask = None\n",
        "\n",
        "    # Multi-head self-attention output (`tf.keras.layers.MultiHeadAttention `).\n",
        "    attn_output = self.mha(\n",
        "        query=x,  # Query Q tensor.\n",
        "        value=x,  # Value V tensor.\n",
        "        key=x,  # Key K tensor.\n",
        "        attention_mask=attention_mask, # A boolean mask that prevents attention to certain positions.\n",
        "        training=training, # A boolean indicating whether the layer should behave in training mode.\n",
        "        )\n",
        "\n",
        "    # Multi-head self-attention output after layer normalization and a residual/skip connection.\n",
        "    out1 = self.layernorm1(x + attn_output)  # Shape `(batch_size, input_seq_len, d_model)`\n",
        "\n",
        "    # Point-wise feed-forward network output.\n",
        "    ffn_output = self.ffn(out1)  # Shape `(batch_size, input_seq_len, d_model)`\n",
        "    ffn_output = self.dropout1(ffn_output, training=training)\n",
        "    # Point-wise feed-forward network output after layer normalization and a residual skip connection.\n",
        "    out2 = self.layernorm2(out1 + ffn_output)  # Shape `(batch_size, input_seq_len, d_model)`.\n",
        "\n",
        "    return out2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QeXHMUlb6q6F"
      },
      "source": [
        "Test the encoder layer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "AzZRXdO0mI48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db56ef47-7c52-42db-c183-f5755f429f1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 3, 512)\n"
          ]
        }
      ],
      "source": [
        "sample_encoder_layer = EncoderLayer(d_model=512, num_attention_heads=8, dff=2048)\n",
        "\n",
        "sample_encoder_layer_output = sample_encoder_layer(\n",
        "    tf.random.uniform((2, 3, 512)), training=False, mask=None)\n",
        "\n",
        "# Print the shape.\n",
        "print(sample_encoder_layer_output.shape)  # Shape `(batch_size, input_seq_len, d_model)`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SE1H51Ajm0q1"
      },
      "source": [
        "### Create the encoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DA6sVo5rlzd3"
      },
      "source": [
        "The Transformer encoder consists of:\n",
        "\n",
        "- Input embeddings (with `tf.keras.layers.Embedding`)\n",
        "- Positional encoding (with `positional_encoding()`)\n",
        "- N encoder layers (with `EncoderLayer()`)\n",
        "\n",
        "As mentioned before, the input (Portuguese) is turned into embeddings, which are added to the positional encoding. The output of this summation is the input to the encoder layers. The output of the encoder is the input to the decoder.\n",
        "\n",
        "Define the encoder by extending `tf.keras.layers.Layer`:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "jpEox7gJ8FCI"
      },
      "outputs": [],
      "source": [
        "class Encoder(tf.keras.layers.Layer):\n",
        "  def __init__(self,\n",
        "               *,\n",
        "               num_layers,\n",
        "               d_model, # Input/output dimensionality.\n",
        "               num_attention_heads,\n",
        "               dff, # Inner-layer dimensionality.\n",
        "               input_vocab_size, # Input (Portuguese) vocabulary size.\n",
        "               dropout_rate=0.1\n",
        "               ):\n",
        "    super(Encoder, self).__init__()\n",
        "\n",
        "    self.d_model = d_model\n",
        "    self.num_layers = num_layers\n",
        "\n",
        "    # Embeddings.\n",
        "    self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model, mask_zero=True)\n",
        "    # Positional encoding.\n",
        "    self.pos_encoding = positional_encoding(MAX_TOKENS, self.d_model)\n",
        "\n",
        "    # Encoder layers.\n",
        "    self.enc_layers = [\n",
        "        EncoderLayer(\n",
        "          d_model=d_model,\n",
        "          num_attention_heads=num_attention_heads,\n",
        "          dff=dff,\n",
        "          dropout_rate=dropout_rate)\n",
        "        for _ in range(num_layers)]\n",
        "    # Dropout.\n",
        "    self.dropout = tf.keras.layers.Dropout(dropout_rate)\n",
        "\n",
        "  # Masking.\n",
        "  def compute_mask(self, x, previous_mask=None):\n",
        "    return self.embedding.compute_mask(x, previous_mask)\n",
        "\n",
        "  def call(self, x, training):\n",
        "\n",
        "    seq_len = tf.shape(x)[1]\n",
        "\n",
        "    # Sum up embeddings and positional encoding.\n",
        "    mask = self.compute_mask(x)\n",
        "    x = self.embedding(x)  # Shape `(batch_size, input_seq_len, d_model)`.\n",
        "    x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n",
        "    x += self.pos_encoding[:, :seq_len, :]\n",
        "    # Add dropout.\n",
        "    x = self.dropout(x, training=training)\n",
        "\n",
        "    # N encoder layers.\n",
        "    for i in range(self.num_layers):\n",
        "      x = self.enc_layers[i](x, training, mask)\n",
        "\n",
        "    return x  # Shape `(batch_size, input_seq_len, d_model)`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "texobMBHLBEU"
      },
      "source": [
        "Test the encoder:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "SDPXTvYgJH8s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd5c7141-50f4-4f99-b932-629315e77275"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 62, 512)\n"
          ]
        }
      ],
      "source": [
        "# Instantiate the encoder.\n",
        "sample_encoder = Encoder(\n",
        "    num_layers=2,\n",
        "    d_model=512,\n",
        "    num_attention_heads=8,\n",
        "    dff=2048,\n",
        "    input_vocab_size=8500\n",
        "    )\n",
        "\n",
        "# Set the test input.\n",
        "temp_input = tf.random.uniform((64, 62), dtype=tf.int64, minval=0, maxval=200)\n",
        "\n",
        "sample_encoder_output = sample_encoder(temp_input,\n",
        "                                       training=False)\n",
        "\n",
        "# Print the shape.\n",
        "print(sample_encoder_output.shape)  # Shape `(batch_size, input_seq_len, d_model)`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6LO_48Owmx_o"
      },
      "source": [
        "### Define the decoder layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nn1G64wqlzd2"
      },
      "source": [
        "A decoder also consists of N layers. Each of these decoder layer consists of sublayers:\n",
        "\n",
        "- A masked multi-head attention layer (with a look-ahead mask and a padding mask), implemented with `tf.keras.layers.MultiHeadAttention`.\n",
        "  - Masked self-attention in the decoder treats only the preceding tokens, but not the future ones, as its context.\n",
        "  - In `tf.keras.layers.MultiHeadAttention`, set the `use_causal_mask` argument to `True` to apply a causal mask to prevent tokens from attending to future tokens.\n",
        "- A multi-head attention layer (with a padding mask) (also implemented with `tf.keras.layers.MultiHeadAttention`). V (value) and K (key) receive the encoder output as inputs. Q (query) receives the output from the masked multi-head attention sublayer.\n",
        "- A point-wise feed-forward network with `tf.keras.layers.Dense`.\n",
        "- Each of these sublayers has a residual/skip connection around it, followed by layer normalization (`tf.keras.layers.LayerNormalization`).\n",
        "\n",
        "Therefore, the output of each sublayer is `LayerNorm(x + Sublayer(x))`. The normalization is done on the `d_model` (last) axis.\n",
        "\n",
        "Note: As demonstrated in Figure 4, as query (Q) receives the output from the decoder's first masked self-attention block, and key (K) receives the encoder output, the attention weights in cross-attention represent the importance given to the decoder's input based on the encoder's output. In other words, the decoder predicts the next token by looking at the encoder output (via cross-attention) and self-attending to its own output (targets).\n",
        "\n",
        "Define the decoder layer by subclassing `tf.keras.layers.Layer`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "9SoX0-vd1hue"
      },
      "outputs": [],
      "source": [
        "class DecoderLayer(tf.keras.layers.Layer):\n",
        "  def __init__(self,\n",
        "               *,\n",
        "               d_model, # Input/output dimensionality.\n",
        "               num_attention_heads,\n",
        "               dff, # Inner-layer dimensionality.\n",
        "               dropout_rate=0.1\n",
        "               ):\n",
        "    super(DecoderLayer, self).__init__()\n",
        "\n",
        "    # Masked multi-head self-attention.\n",
        "    self.mha_masked = tf.keras.layers.MultiHeadAttention(\n",
        "        num_heads=num_attention_heads,\n",
        "        key_dim=d_model, # Size of each attention head for query Q and key K.\n",
        "        dropout=dropout_rate\n",
        "    )\n",
        "    # Multi-head cross-attention.\n",
        "    self.mha_cross = tf.keras.layers.MultiHeadAttention(\n",
        "        num_heads=num_attention_heads,\n",
        "        key_dim=d_model, # Size of each attention head for query Q and key K.\n",
        "        dropout=dropout_rate\n",
        "    )\n",
        "\n",
        "    # Point-wise feed-forward network.\n",
        "    self.ffn = point_wise_feed_forward_network(d_model, dff)\n",
        "\n",
        "    # Layer normalization.\n",
        "    self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "    self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "    self.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "\n",
        "    # Dropout for the point-wise feed-forward network.\n",
        "    self.dropout1 = tf.keras.layers.Dropout(dropout_rate)\n",
        "\n",
        "  def call(self, x, mask, enc_output, enc_mask, training):\n",
        "    # The encoder output shape is `(batch_size, input_seq_len, d_model)`.\n",
        "\n",
        "    # A boolean mask.\n",
        "    self_attention_mask = None\n",
        "    if mask is not None:\n",
        "      mask1 = mask[:, :, None]\n",
        "      mask2 = mask[:, None, :]\n",
        "      self_attention_mask = mask1 & mask2\n",
        "\n",
        "    # Masked multi-head self-attention output (`tf.keras.layers.MultiHeadAttention`).\n",
        "    attn_masked, attn_weights_masked = self.mha_masked(\n",
        "        query=x,\n",
        "        value=x,\n",
        "        key=x,\n",
        "        attention_mask=self_attention_mask,  # A boolean mask that prevents attention to certain positions.\n",
        "        use_causal_mask=True,  # A boolean to indicate whether to apply a causal mask to prevent tokens from attending to future tokens.\n",
        "        return_attention_scores=True,  # Shape `(batch_size, target_seq_len, d_model)`.\n",
        "        training=training  # A boolean indicating whether the layer should behave in training mode.\n",
        "        )\n",
        "\n",
        "    # Masked multi-head self-attention output after layer normalization and a residual/skip connection.\n",
        "    out1 = self.layernorm1(attn_masked + x)\n",
        "\n",
        "    # A boolean mask.\n",
        "    attention_mask = None\n",
        "    if mask is not None and enc_mask is not None:\n",
        "      mask1 = mask[:, :, None]\n",
        "      mask2 = enc_mask[:, None, :]\n",
        "      attention_mask = mask1 & mask2\n",
        "\n",
        "    # Multi-head cross-attention output (`tf.keras.layers.MultiHeadAttention `).\n",
        "    attn_cross, attn_weights_cross = self.mha_cross(\n",
        "        query=out1,\n",
        "        value=enc_output,\n",
        "        key=enc_output,\n",
        "        attention_mask=attention_mask,  # A boolean mask that prevents attention to certain positions.\n",
        "        return_attention_scores=True,  # Shape `(batch_size, target_seq_len, d_model)`.\n",
        "        training=training  # A boolean indicating whether the layer should behave in training mode.\n",
        "    )\n",
        "\n",
        "    # Multi-head cross-attention output after layer normalization and a residual/skip connection.\n",
        "    out2 = self.layernorm2(attn_cross + out1)  # (batch_size, target_seq_len, d_model)\n",
        "\n",
        "    # Point-wise feed-forward network output.\n",
        "    ffn_output = self.ffn(out2)  # Shape `(batch_size, target_seq_len, d_model)`.\n",
        "    ffn_output = self.dropout1(ffn_output, training=training)\n",
        "    out3 = self.layernorm3(ffn_output + out2)  # Shape `(batch_size, target_seq_len, d_model)`.\n",
        "\n",
        "    return out3, attn_weights_masked, attn_weights_cross"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6T3RSR_6nJX"
      },
      "source": [
        "Test the decoder layer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "Ne2Bqx8k71l0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46cd41fe-2758-4425-8229-02cd1eec6ecb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 5, 512)\n"
          ]
        }
      ],
      "source": [
        "sample_decoder_layer = DecoderLayer(d_model=512, num_attention_heads=8, dff=2048)\n",
        "\n",
        "sample_decoder_layer_output, att1, att2 = sample_decoder_layer(\n",
        "    x=tf.random.uniform((2, 5, 512)),\n",
        "    mask=None,\n",
        "    enc_output=sample_encoder_layer_output,\n",
        "    enc_mask=None,\n",
        "    training=False)\n",
        "\n",
        "# Print the shape.\n",
        "print(sample_decoder_layer_output.shape)  # `(batch_size, target_seq_len, d_model)`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vBlek6K4aGK"
      },
      "source": [
        "Having defined the encoder and decoder layers, you can now create the Transformer encoder and decoder, and then build the Transformer model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-uO6ls8m2O5"
      },
      "source": [
        "### Create the decoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2q49vtv5lzd3"
      },
      "source": [
        "The Transformer decoder consists of:\n",
        "\n",
        "- Output embeddings (with `tf.keras.layers.Embedding`)\n",
        "- Positional encoding (with `positional_encoding()`)\n",
        "- N decoder layers (with `DecoderLayer`)\n",
        "\n",
        "The target (English) is turned into embeddings, which are added to the positional encoding. The output of this summation is the input to the decoder layers. The output of the decoder is the input to the final linear layer, where the prediction is made.\n",
        "\n",
        "Define the decoder by extending `tf.keras.layers.Layer`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "d5_d5-PLQXwY"
      },
      "outputs": [],
      "source": [
        "class Decoder(tf.keras.layers.Layer):\n",
        "  def __init__(self,\n",
        "               *,\n",
        "               num_layers,\n",
        "               d_model, # Input/output dimensionality.\n",
        "               num_attention_heads,\n",
        "               dff, # Inner-layer dimensionality.\n",
        "               target_vocab_size,\n",
        "               dropout_rate=0.1\n",
        "               ):\n",
        "    super(Decoder, self).__init__()\n",
        "\n",
        "    self.d_model = d_model\n",
        "    self.num_layers = num_layers\n",
        "\n",
        "    self.embedding = tf.keras.layers.Embedding(\n",
        "      target_vocab_size,\n",
        "      d_model,\n",
        "      mask_zero=True\n",
        "      )\n",
        "    self.pos_encoding = positional_encoding(MAX_TOKENS, d_model)\n",
        "\n",
        "    self.dec_layers = [\n",
        "        DecoderLayer(\n",
        "          d_model=d_model,\n",
        "          num_attention_heads=num_attention_heads,\n",
        "          dff=dff,\n",
        "          dropout_rate=dropout_rate)\n",
        "        for _ in range(num_layers)\n",
        "        ]\n",
        "    self.dropout = tf.keras.layers.Dropout(dropout_rate)\n",
        "\n",
        "  def call(self, x, enc_output, enc_mask, training):\n",
        "\n",
        "    seq_len = tf.shape(x)[1]\n",
        "    attention_weights = {}\n",
        "\n",
        "    # Sum up embeddings and positional encoding.\n",
        "    mask = self.embedding.compute_mask(x)\n",
        "    x = self.embedding(x)  # Shape: `(batch_size, target_seq_len, d_model)`.\n",
        "    x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n",
        "    x += self.pos_encoding[:, :seq_len, :]\n",
        "\n",
        "    x = self.dropout(x, training=training)\n",
        "\n",
        "    for i in range(self.num_layers):\n",
        "      x, block1, block2  = self.dec_layers[i](x, mask, enc_output, enc_mask, training)\n",
        "\n",
        "      attention_weights[f'decoder_layer{i+1}_block1'] = block1\n",
        "      attention_weights[f'decoder_layer{i+1}_block2'] = block2\n",
        "\n",
        "    # The shape of x is `(batch_size, target_seq_len, d_model)`.\n",
        "    return x, attention_weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eALcB--YMmLf"
      },
      "source": [
        "Test the decoder:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "xyHdG_jWPgKu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2555913c-9f7f-4602-8040-653e2ba6abee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorShape([64, 26, 512]), TensorShape([64, 8, 26, 62]))"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# Instantiate the decoder.\n",
        "sample_decoder = Decoder(\n",
        "    num_layers=2,\n",
        "    d_model=512,\n",
        "    num_attention_heads=8,\n",
        "    dff=2048,\n",
        "    target_vocab_size=8000\n",
        "    )\n",
        "\n",
        "# Set the test input.\n",
        "temp_input = tf.random.uniform((64, 26), dtype=tf.int64, minval=0, maxval=200)\n",
        "\n",
        "output, attn = sample_decoder(\n",
        "    x=temp_input,\n",
        "    enc_output=sample_encoder_output,\n",
        "    enc_mask=None,\n",
        "    training=False)\n",
        "\n",
        "# Print the shapes.\n",
        "output.shape, attn['decoder_layer2_block2'].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3uvMP5vNuOV"
      },
      "source": [
        "Having created the Transformer encoder and decoder, it's time to build the Transformer model and train it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y54xnJnuYgJ7"
      },
      "source": [
        "## Set up the Transformer architecture"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PSi8vBN1lzd4"
      },
      "source": [
        "You now have `Encoder` and `Decoder`. To complete the Transformer model, you need to put them together and add a final linear (`Dense`) layer. The output of the decoder is the input to the final linear layer.\n",
        "\n",
        "To recap the architecture:\n",
        "\n",
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/transformer.png\" width=\"600\" alt=\"transformer\">\n",
        "\n",
        "Figure 7: The Transformer architecture from Google Research's [\"Attention is all you need\"](https://arxiv.org/abs/1706.03762){:.external} (Vaswani et al., 2017).\n",
        "\n",
        "Create the `Transformer` by extending `tf.keras.Model`:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "PED3bIpOYkBu"
      },
      "outputs": [],
      "source": [
        "class Transformer(tf.keras.Model):\n",
        "  def __init__(self,\n",
        "               *,\n",
        "               num_layers, # Number of decoder layers.\n",
        "               d_model, # Input/output dimensionality.\n",
        "               num_attention_heads,\n",
        "               dff, # Inner-layer dimensionality.\n",
        "               input_vocab_size, # Input (Portuguese) vocabulary size.\n",
        "               target_vocab_size, # Target (English) vocabulary size.\n",
        "               dropout_rate=0.1\n",
        "               ):\n",
        "    super().__init__()\n",
        "    # The encoder.\n",
        "    self.encoder = Encoder(\n",
        "      num_layers=num_layers,\n",
        "      d_model=d_model,\n",
        "      num_attention_heads=num_attention_heads,\n",
        "      dff=dff,\n",
        "      input_vocab_size=input_vocab_size,\n",
        "      dropout_rate=dropout_rate\n",
        "      )\n",
        "\n",
        "    # The decoder.\n",
        "    self.decoder = Decoder(\n",
        "      num_layers=num_layers,\n",
        "      d_model=d_model,\n",
        "      num_attention_heads=num_attention_heads,\n",
        "      dff=dff,\n",
        "      target_vocab_size=target_vocab_size,\n",
        "      dropout_rate=dropout_rate\n",
        "      )\n",
        "\n",
        "    # The final linear layer.\n",
        "    self.final_layer = tf.keras.layers.Dense(target_vocab_size)\n",
        "\n",
        "  def call(self, inputs, training):\n",
        "    # Keras models prefer if you pass all your inputs in the first argument.\n",
        "    # Portuguese is used as the input (`inp`) language.\n",
        "    # English is the target (`tar`) language.\n",
        "    inp, tar = inputs\n",
        "\n",
        "    # The encoder output.\n",
        "    enc_output = self.encoder(inp, training)  # `(batch_size, inp_seq_len, d_model)`\n",
        "    enc_mask = self.encoder.compute_mask(inp)\n",
        "\n",
        "    # The decoder output.\n",
        "    dec_output, attention_weights = self.decoder(\n",
        "        tar, enc_output, enc_mask, training)  # `(batch_size, tar_seq_len, d_model)`\n",
        "\n",
        "    # The final linear layer output.\n",
        "    final_output = self.final_layer(dec_output)  # Shape `(batch_size, tar_seq_len, target_vocab_size)`.\n",
        "\n",
        "    # Return the final output and the attention weights.\n",
        "    return final_output, attention_weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsINyf1VEQLC"
      },
      "source": [
        "### Set hyperparameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IjwMq_ixlzd5"
      },
      "source": [
        "To keep this example small and relatively fast, the values for the stack of identical encoder/decoder layers (`num_layers`), the dimensionality of the input/output (`d_model`), and the dimensionality of the inner-layer (`dff`) have been reduced.\n",
        "\n",
        "The base model described in the original Transformer paper used `num_layers=6`, `d_model=512`, and `dff=2048`.\n",
        "\n",
        "The number of self-attention heads remains the same (`num_attention_heads=8`).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "mzyo6KDfVyhl"
      },
      "outputs": [],
      "source": [
        "num_layers = 4\n",
        "d_model = 128\n",
        "dff = 512\n",
        "num_attention_heads = 8\n",
        "dropout_rate = 0.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g08YOE-zHRqY"
      },
      "source": [
        "### Try it out"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYbXDEhhlzd6"
      },
      "source": [
        "Instantiate the `Transformer` model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "UiysUa--4tOU"
      },
      "outputs": [],
      "source": [
        "transformer = Transformer(\n",
        "    num_layers=num_layers,\n",
        "    d_model=d_model,\n",
        "    num_attention_heads=num_attention_heads,\n",
        "    dff=dff,\n",
        "    input_vocab_size=tokenizers.pt.get_vocab_size().numpy(),\n",
        "    target_vocab_size=tokenizers.en.get_vocab_size().numpy(),\n",
        "    dropout_rate=dropout_rate)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qbw3CYn2tQQx"
      },
      "source": [
        "Test the `Transformer`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "c8eO85hpFHmE"
      },
      "outputs": [],
      "source": [
        "input = tf.constant([[1,2,3, 4, 0, 0, 0]])\n",
        "target = tf.constant([[1,2,3, 0]])\n",
        "\n",
        "x, attention = transformer((input, target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "hOxodbqH5DuQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2001ae99-ed3c-4f1f-adfb-69ccea1bc6c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 4, 7010)\n",
            "(1, 8, 4, 4)\n",
            "(1, 8, 4, 7)\n"
          ]
        }
      ],
      "source": [
        "print(x.shape)\n",
        "print(attention['decoder_layer1_block1'].shape)\n",
        "print(attention['decoder_layer4_block2'].shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_jTvJsXquaHW"
      },
      "source": [
        "Print the summary of the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "IsUPhlfEtOjn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a473bb9-c79a-4548-ab66-950c06ee608c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"transformer\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " encoder_1 (Encoder)         multiple                  3632768   \n",
            "                                                                 \n",
            " decoder_1 (Decoder)         multiple                  5647104   \n",
            "                                                                 \n",
            " dense_30 (Dense)            multiple                  904290    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 10,184,162\n",
            "Trainable params: 10,184,162\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "transformer.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfoBfC2oQtEy"
      },
      "source": [
        "## Training\n",
        "\n",
        "It's time to prepare the model and start training it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xYEGhEOtzn5W"
      },
      "source": [
        "### Define the optimizer with a custom learning rate scheduler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SL4G5bS6lzd5"
      },
      "source": [
        "Use the Adam optimizer with a custom learning rate scheduler according to the formula in the original Transformer [paper](https://arxiv.org/abs/1706.03762){:.external}.\n",
        "\n",
        "$$\\Large{lrate = d_{model}^{-0.5} * \\min(step{\\_}num^{-0.5}, step{\\_}num \\cdot warmup{\\_}steps^{-1.5})}$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "iYQdOO1axwEI"
      },
      "outputs": [],
      "source": [
        "class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
        "  def __init__(self, d_model, warmup_steps=4000):\n",
        "    super(CustomSchedule, self).__init__()\n",
        "\n",
        "    self.d_model = d_model\n",
        "    self.d_model = tf.cast(self.d_model, tf.float32)\n",
        "\n",
        "    self.warmup_steps = warmup_steps\n",
        "\n",
        "  def __call__(self, step):\n",
        "    arg1 = tf.math.rsqrt(step)\n",
        "    arg2 = step * (self.warmup_steps ** -1.5)\n",
        "\n",
        "    return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzXq5LWgRN63"
      },
      "source": [
        "Instantiate the optimizer (in this example it's `tf.keras.optimizers.Adam`):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "7r4scdulztRx"
      },
      "outputs": [],
      "source": [
        "learning_rate = CustomSchedule(d_model)\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98,\n",
        "                                     epsilon=1e-9)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fTb2S4RnQ8DU"
      },
      "source": [
        "Test the custom learning rate scheduler:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "Xij3MwYVRAAS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "e069b614-4e01-4ced-d14f-398b66414370"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Train Step')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "temp_learning_rate_schedule = CustomSchedule(d_model)\n",
        "\n",
        "plt.plot(temp_learning_rate_schedule(tf.range(40000, dtype=tf.float32)))\n",
        "plt.ylabel('Learning Rate')\n",
        "plt.xlabel('Train Step')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YgkDE7hzo8r5"
      },
      "source": [
        "### Define the loss function and metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6y7rNP5lzd6"
      },
      "source": [
        "Since the target sequences are padded, it is important to apply a padding mask when calculating the loss. Use the cross-entropy loss function (`tf.keras.losses.SparseCategoricalCrossentropy`):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "MlhsJMm0TW_B"
      },
      "outputs": [],
      "source": [
        "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n",
        "    from_logits=True, reduction='none')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "67oqVHiT0Eiu"
      },
      "outputs": [],
      "source": [
        "def loss_function(real, pred):\n",
        "  mask = tf.math.logical_not(tf.math.equal(real, 0))\n",
        "  loss_ = loss_object(real, pred)\n",
        "\n",
        "  mask = tf.cast(mask, dtype=loss_.dtype)\n",
        "  loss_ *= mask\n",
        "\n",
        "  return tf.reduce_sum(loss_)/tf.reduce_sum(mask)\n",
        "\n",
        "\n",
        "def accuracy_function(real, pred):\n",
        "  accuracies = tf.equal(real, tf.argmax(pred, axis=2))\n",
        "\n",
        "  mask = tf.math.logical_not(tf.math.equal(real, 0))\n",
        "  accuracies = tf.math.logical_and(mask, accuracies)\n",
        "\n",
        "  accuracies = tf.cast(accuracies, dtype=tf.float32)\n",
        "  mask = tf.cast(mask, dtype=tf.float32)\n",
        "  return tf.reduce_sum(accuracies)/tf.reduce_sum(mask)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ckx_S6kORpIa"
      },
      "source": [
        "Set up the metrics:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "phlyxMnm-Tpx"
      },
      "outputs": [],
      "source": [
        "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
        "train_accuracy = tf.keras.metrics.Mean(name='train_accuracy')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aeHumfr7zmMa"
      },
      "source": [
        "### Checkpointing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j-FN6QJ2lzd6"
      },
      "source": [
        "Create the checkpoint path and the checkpoint manager. This will be used to save checkpoints every `n` epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "hNhuYfllndLZ"
      },
      "outputs": [],
      "source": [
        "checkpoint_path = './checkpoints/train'\n",
        "\n",
        "ckpt = tf.train.Checkpoint(transformer=transformer,\n",
        "                           optimizer=optimizer)\n",
        "\n",
        "ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)\n",
        "\n",
        "# If a checkpoint exists, restore the latest checkpoint.\n",
        "if ckpt_manager.latest_checkpoint:\n",
        "  ckpt.restore(ckpt_manager.latest_checkpoint)\n",
        "  print('Latest checkpoint restored!!')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAwlRFvUlzd7"
      },
      "source": [
        "With the Portuguese-English dataset, Portuguese is used as the input (`inp`) language and English is the target (`tar`) language.\n",
        "\n",
        "The target is divided into target input (`tar_inp`) and real target (`tar_real`).\n",
        "\n",
        "- `tar_inp` is passed as an input to the decoder.\n",
        "- `tar_real` is that same input shifted by `1`: at each location in `tar_input`, `tar_real` contains the next token that should be predicted.\n",
        "\n",
        "For example, `sentence = 'SOS A lion in the jungle is sleeping EOS'` becomes:\n",
        "\n",
        "* `tar_inp =  'SOS A lion in the jungle is sleeping'`\n",
        "* `tar_real = 'A lion in the jungle is sleeping EOS'`\n",
        "\n",
        "A Transformer is an auto-regressive model: it makes predictions one part at a time, and uses its output so far to decide what to do next.\n",
        "\n",
        "During training this example uses teacher-forcing (like in the [text generation with RNNs](https://www.tensorflow.org/text/tutorials/text_generation) tutorial). Teacher forcing is passing the true output to the next time step regardless of what the model predicts at the current time step.\n",
        "\n",
        "As the model predicts each token, the self-attention mechanism allows it to look at the previous tokens in the input sequence to better predict the next token. As mentioned before, to prevent the model from peeking at the expected output the model uses a look-ahead mask.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8XxYEnb9QMyS"
      },
      "source": [
        "### Train the Transformer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y67SKiyAQfBb"
      },
      "source": [
        "Define the training step:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "iJwmp9OE29oj"
      },
      "outputs": [],
      "source": [
        "train_step_signature = [\n",
        "    tf.TensorSpec(shape=(None, None), dtype=tf.int64),\n",
        "    tf.TensorSpec(shape=(None, None), dtype=tf.int64),\n",
        "]\n",
        "\n",
        "# The `@tf.function` trace-compiles train_step into a TF graph for faster\n",
        "# execution. The function specializes to the precise shape of the argument\n",
        "# tensors. To avoid re-tracing due to the variable sequence lengths or variable\n",
        "# batch sizes (the last batch is smaller), use input_signature to specify\n",
        "# more generic shapes.\n",
        "\n",
        "@tf.function(input_signature=train_step_signature)\n",
        "def train_step(inp, tar):\n",
        "  tar_inp = tar[:, :-1]\n",
        "  tar_real = tar[:, 1:]\n",
        "\n",
        "  with tf.GradientTape() as tape:\n",
        "    predictions, _ = transformer([inp, tar_inp],\n",
        "                                 training = True)\n",
        "    loss = loss_function(tar_real, predictions)\n",
        "\n",
        "  gradients = tape.gradient(loss, transformer.trainable_variables)\n",
        "  optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))\n",
        "\n",
        "  train_loss(loss)\n",
        "  train_accuracy(accuracy_function(tar_real, predictions))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bp2wiMMRQRDe"
      },
      "source": [
        "You can now train the Transformer.\n",
        "\n",
        "Note: This example model is trained for a few epochs (20) to keep training time reasonable for this tutorial. In Colab with a GPU, it may take around 150 seconds per epoch, or about 50 minutes in total to train the model. To speed things up, you can try reducing the number of `EPOCHS`, which may affect your accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "LKpoA6q1sJFj"
      },
      "outputs": [],
      "source": [
        "EPOCHS = 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "bbvmaKNiznHZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84446421-0849-453f-e197-b7a7d092e18c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1 Batch 0 Loss 8.8768 Accuracy 0.0007\n",
            "Epoch 1 Batch 50 Loss 8.8099 Accuracy 0.0027\n",
            "Epoch 1 Batch 100 Loss 8.6988 Accuracy 0.0193\n",
            "Epoch 1 Batch 150 Loss 8.5779 Accuracy 0.0299\n",
            "Epoch 1 Batch 200 Loss 8.4285 Accuracy 0.0399\n",
            "Epoch 1 Batch 250 Loss 8.2529 Accuracy 0.0479\n",
            "Epoch 1 Batch 300 Loss 8.0576 Accuracy 0.0557\n",
            "Epoch 1 Batch 350 Loss 7.8600 Accuracy 0.0631\n",
            "Epoch 1 Batch 400 Loss 7.6723 Accuracy 0.0721\n",
            "Epoch 1 Batch 450 Loss 7.5011 Accuracy 0.0821\n",
            "Epoch 1 Batch 500 Loss 7.3464 Accuracy 0.0915\n",
            "Epoch 1 Batch 550 Loss 7.2035 Accuracy 0.1002\n",
            "Epoch 1 Batch 600 Loss 7.0705 Accuracy 0.1079\n",
            "Epoch 1 Batch 650 Loss 6.9476 Accuracy 0.1151\n",
            "Epoch 1 Batch 700 Loss 6.8325 Accuracy 0.1223\n",
            "Epoch 1 Loss 6.8280 Accuracy 0.1226\n",
            "Time taken for 1 epoch: 159.08 secs\n",
            "\n",
            "Epoch 2 Batch 0 Loss 5.2365 Accuracy 0.2292\n",
            "Epoch 2 Batch 50 Loss 5.2550 Accuracy 0.2213\n",
            "Epoch 2 Batch 100 Loss 5.1868 Accuracy 0.2294\n",
            "Epoch 2 Batch 150 Loss 5.1353 Accuracy 0.2349\n",
            "Epoch 2 Batch 200 Loss 5.0897 Accuracy 0.2395\n",
            "Epoch 2 Batch 250 Loss 5.0492 Accuracy 0.2444\n",
            "Epoch 2 Batch 300 Loss 5.0119 Accuracy 0.2484\n",
            "Epoch 2 Batch 350 Loss 4.9754 Accuracy 0.2526\n",
            "Epoch 2 Batch 400 Loss 4.9437 Accuracy 0.2558\n",
            "Epoch 2 Batch 450 Loss 4.9122 Accuracy 0.2593\n",
            "Epoch 2 Batch 500 Loss 4.8817 Accuracy 0.2629\n",
            "Epoch 2 Batch 550 Loss 4.8480 Accuracy 0.2669\n",
            "Epoch 2 Batch 600 Loss 4.8185 Accuracy 0.2702\n",
            "Epoch 2 Batch 650 Loss 4.7891 Accuracy 0.2735\n",
            "Epoch 2 Loss 4.7632 Accuracy 0.2766\n",
            "Time taken for 1 epoch: 149.06 secs\n",
            "\n",
            "Epoch 3 Batch 0 Loss 4.5656 Accuracy 0.3075\n",
            "Epoch 3 Batch 50 Loss 4.3250 Accuracy 0.3261\n",
            "Epoch 3 Batch 100 Loss 4.2899 Accuracy 0.3289\n",
            "Epoch 3 Batch 150 Loss 4.2662 Accuracy 0.3317\n",
            "Epoch 3 Batch 200 Loss 4.2429 Accuracy 0.3347\n",
            "Epoch 3 Batch 250 Loss 4.2220 Accuracy 0.3373\n",
            "Epoch 3 Batch 300 Loss 4.1995 Accuracy 0.3398\n",
            "Epoch 3 Batch 350 Loss 4.1792 Accuracy 0.3419\n",
            "Epoch 3 Batch 400 Loss 4.1570 Accuracy 0.3444\n",
            "Epoch 3 Batch 450 Loss 4.1345 Accuracy 0.3465\n",
            "Epoch 3 Batch 500 Loss 4.1174 Accuracy 0.3483\n",
            "Epoch 3 Batch 550 Loss 4.0977 Accuracy 0.3506\n",
            "Epoch 3 Batch 600 Loss 4.0797 Accuracy 0.3527\n",
            "Epoch 3 Batch 650 Loss 4.0589 Accuracy 0.3550\n",
            "Epoch 3 Loss 4.0431 Accuracy 0.3565\n",
            "Time taken for 1 epoch: 148.72 secs\n",
            "\n",
            "Epoch 4 Batch 0 Loss 4.0039 Accuracy 0.3457\n",
            "Epoch 4 Batch 50 Loss 3.7310 Accuracy 0.3901\n",
            "Epoch 4 Batch 100 Loss 3.7112 Accuracy 0.3919\n",
            "Epoch 4 Batch 150 Loss 3.6886 Accuracy 0.3942\n",
            "Epoch 4 Batch 200 Loss 3.6713 Accuracy 0.3957\n",
            "Epoch 4 Batch 250 Loss 3.6545 Accuracy 0.3975\n",
            "Epoch 4 Batch 300 Loss 3.6365 Accuracy 0.3997\n",
            "Epoch 4 Batch 350 Loss 3.6201 Accuracy 0.4012\n",
            "Epoch 4 Batch 400 Loss 3.6069 Accuracy 0.4027\n",
            "Epoch 4 Batch 450 Loss 3.5926 Accuracy 0.4042\n",
            "Epoch 4 Batch 500 Loss 3.5797 Accuracy 0.4055\n",
            "Epoch 4 Batch 550 Loss 3.5668 Accuracy 0.4067\n",
            "Epoch 4 Batch 600 Loss 3.5554 Accuracy 0.4081\n",
            "Epoch 4 Batch 650 Loss 3.5397 Accuracy 0.4099\n",
            "Epoch 4 Loss 3.5281 Accuracy 0.4111\n",
            "Time taken for 1 epoch: 150.24 secs\n",
            "\n",
            "Epoch 5 Batch 0 Loss 3.0870 Accuracy 0.4495\n",
            "Epoch 5 Batch 50 Loss 3.2204 Accuracy 0.4425\n",
            "Epoch 5 Batch 100 Loss 3.2249 Accuracy 0.4437\n",
            "Epoch 5 Batch 150 Loss 3.2167 Accuracy 0.4444\n",
            "Epoch 5 Batch 200 Loss 3.2114 Accuracy 0.4448\n",
            "Epoch 5 Batch 250 Loss 3.1972 Accuracy 0.4463\n",
            "Epoch 5 Batch 300 Loss 3.1894 Accuracy 0.4473\n",
            "Epoch 5 Batch 350 Loss 3.1806 Accuracy 0.4482\n",
            "Epoch 5 Batch 400 Loss 3.1743 Accuracy 0.4488\n",
            "Epoch 5 Batch 450 Loss 3.1646 Accuracy 0.4500\n",
            "Epoch 5 Batch 500 Loss 3.1524 Accuracy 0.4517\n",
            "Epoch 5 Batch 550 Loss 3.1443 Accuracy 0.4526\n",
            "Epoch 5 Batch 600 Loss 3.1354 Accuracy 0.4539\n",
            "Epoch 5 Batch 650 Loss 3.1223 Accuracy 0.4557\n",
            "Saving checkpoint for epoch 5 at ./checkpoints/train/ckpt-1\n",
            "Epoch 5 Loss 3.1131 Accuracy 0.4569\n",
            "Time taken for 1 epoch: 149.82 secs\n",
            "\n",
            "Epoch 6 Batch 0 Loss 2.8165 Accuracy 0.4842\n",
            "Epoch 6 Batch 50 Loss 2.8863 Accuracy 0.4829\n",
            "Epoch 6 Batch 100 Loss 2.8763 Accuracy 0.4840\n",
            "Epoch 6 Batch 150 Loss 2.8634 Accuracy 0.4845\n",
            "Epoch 6 Batch 200 Loss 2.8571 Accuracy 0.4852\n",
            "Epoch 6 Batch 250 Loss 2.8467 Accuracy 0.4868\n",
            "Epoch 6 Batch 300 Loss 2.8460 Accuracy 0.4867\n",
            "Epoch 6 Batch 350 Loss 2.8399 Accuracy 0.4876\n",
            "Epoch 6 Batch 400 Loss 2.8327 Accuracy 0.4889\n",
            "Epoch 6 Batch 450 Loss 2.8280 Accuracy 0.4893\n",
            "Epoch 6 Batch 500 Loss 2.8247 Accuracy 0.4900\n",
            "Epoch 6 Batch 550 Loss 2.8218 Accuracy 0.4904\n",
            "Epoch 6 Batch 600 Loss 2.8168 Accuracy 0.4911\n",
            "Epoch 6 Batch 650 Loss 2.8132 Accuracy 0.4914\n",
            "Epoch 6 Loss 2.8096 Accuracy 0.4921\n",
            "Time taken for 1 epoch: 148.66 secs\n",
            "\n",
            "Epoch 7 Batch 0 Loss 2.6879 Accuracy 0.4821\n",
            "Epoch 7 Batch 50 Loss 2.5852 Accuracy 0.5158\n",
            "Epoch 7 Batch 100 Loss 2.5647 Accuracy 0.5192\n",
            "Epoch 7 Batch 150 Loss 2.5641 Accuracy 0.5201\n",
            "Epoch 7 Batch 200 Loss 2.5668 Accuracy 0.5200\n",
            "Epoch 7 Batch 250 Loss 2.5642 Accuracy 0.5206\n",
            "Epoch 7 Batch 300 Loss 2.5592 Accuracy 0.5213\n",
            "Epoch 7 Batch 350 Loss 2.5560 Accuracy 0.5220\n",
            "Epoch 7 Batch 400 Loss 2.5500 Accuracy 0.5227\n",
            "Epoch 7 Batch 450 Loss 2.5440 Accuracy 0.5237\n",
            "Epoch 7 Batch 500 Loss 2.5427 Accuracy 0.5241\n",
            "Epoch 7 Batch 550 Loss 2.5344 Accuracy 0.5254\n",
            "Epoch 7 Batch 600 Loss 2.5310 Accuracy 0.5261\n",
            "Epoch 7 Batch 650 Loss 2.5278 Accuracy 0.5266\n",
            "Epoch 7 Batch 700 Loss 2.5269 Accuracy 0.5268\n",
            "Epoch 7 Loss 2.5259 Accuracy 0.5270\n",
            "Time taken for 1 epoch: 150.95 secs\n",
            "\n",
            "Epoch 8 Batch 0 Loss 2.3512 Accuracy 0.5438\n",
            "Epoch 8 Batch 50 Loss 2.3451 Accuracy 0.5488\n",
            "Epoch 8 Batch 100 Loss 2.3158 Accuracy 0.5528\n",
            "Epoch 8 Batch 150 Loss 2.3085 Accuracy 0.5543\n",
            "Epoch 8 Batch 200 Loss 2.3028 Accuracy 0.5551\n",
            "Epoch 8 Batch 250 Loss 2.3032 Accuracy 0.5550\n",
            "Epoch 8 Batch 300 Loss 2.2965 Accuracy 0.5561\n",
            "Epoch 8 Batch 350 Loss 2.3014 Accuracy 0.5556\n",
            "Epoch 8 Batch 400 Loss 2.2970 Accuracy 0.5563\n",
            "Epoch 8 Batch 450 Loss 2.2950 Accuracy 0.5566\n",
            "Epoch 8 Batch 500 Loss 2.2908 Accuracy 0.5571\n",
            "Epoch 8 Batch 550 Loss 2.2948 Accuracy 0.5565\n",
            "Epoch 8 Batch 600 Loss 2.2926 Accuracy 0.5570\n",
            "Epoch 8 Batch 650 Loss 2.2902 Accuracy 0.5576\n",
            "Epoch 8 Batch 700 Loss 2.2873 Accuracy 0.5582\n",
            "Epoch 8 Loss 2.2871 Accuracy 0.5582\n",
            "Time taken for 1 epoch: 149.54 secs\n",
            "\n",
            "Epoch 9 Batch 0 Loss 2.1029 Accuracy 0.5671\n",
            "Epoch 9 Batch 50 Loss 2.0971 Accuracy 0.5821\n",
            "Epoch 9 Batch 100 Loss 2.1110 Accuracy 0.5798\n",
            "Epoch 9 Batch 150 Loss 2.1134 Accuracy 0.5798\n",
            "Epoch 9 Batch 200 Loss 2.1134 Accuracy 0.5806\n",
            "Epoch 9 Batch 250 Loss 2.1097 Accuracy 0.5817\n",
            "Epoch 9 Batch 300 Loss 2.1122 Accuracy 0.5817\n",
            "Epoch 9 Batch 350 Loss 2.1139 Accuracy 0.5814\n",
            "Epoch 9 Batch 400 Loss 2.1154 Accuracy 0.5813\n",
            "Epoch 9 Batch 450 Loss 2.1160 Accuracy 0.5811\n",
            "Epoch 9 Batch 500 Loss 2.1182 Accuracy 0.5808\n",
            "Epoch 9 Batch 550 Loss 2.1216 Accuracy 0.5805\n",
            "Epoch 9 Batch 600 Loss 2.1196 Accuracy 0.5810\n",
            "Epoch 9 Batch 650 Loss 2.1186 Accuracy 0.5813\n",
            "Epoch 9 Batch 700 Loss 2.1174 Accuracy 0.5817\n",
            "Epoch 9 Loss 2.1174 Accuracy 0.5817\n",
            "Time taken for 1 epoch: 149.53 secs\n",
            "\n",
            "Epoch 10 Batch 0 Loss 1.9866 Accuracy 0.5910\n",
            "Epoch 10 Batch 50 Loss 1.9843 Accuracy 0.6001\n",
            "Epoch 10 Batch 100 Loss 1.9659 Accuracy 0.6036\n",
            "Epoch 10 Batch 150 Loss 1.9707 Accuracy 0.6023\n",
            "Epoch 10 Batch 200 Loss 1.9713 Accuracy 0.6023\n",
            "Epoch 10 Batch 250 Loss 1.9749 Accuracy 0.6015\n",
            "Epoch 10 Batch 300 Loss 1.9796 Accuracy 0.6005\n",
            "Epoch 10 Batch 350 Loss 1.9794 Accuracy 0.6006\n",
            "Epoch 10 Batch 400 Loss 1.9818 Accuracy 0.6001\n",
            "Epoch 10 Batch 450 Loss 1.9795 Accuracy 0.6005\n",
            "Epoch 10 Batch 500 Loss 1.9778 Accuracy 0.6009\n",
            "Epoch 10 Batch 550 Loss 1.9778 Accuracy 0.6010\n",
            "Epoch 10 Batch 600 Loss 1.9795 Accuracy 0.6007\n",
            "Epoch 10 Batch 650 Loss 1.9826 Accuracy 0.6003\n",
            "Saving checkpoint for epoch 10 at ./checkpoints/train/ckpt-2\n",
            "Epoch 10 Loss 1.9839 Accuracy 0.6002\n",
            "Time taken for 1 epoch: 150.75 secs\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for epoch in range(EPOCHS):\n",
        "  start = time.time()\n",
        "\n",
        "  train_loss.reset_states()\n",
        "  train_accuracy.reset_states()\n",
        "\n",
        "  # inp -> portuguese, tar -> english\n",
        "  for (batch, (inp, tar)) in enumerate(train_batches):\n",
        "    train_step(inp, tar)\n",
        "\n",
        "    if batch % 50 == 0:\n",
        "      print(f'Epoch {epoch + 1} Batch {batch} Loss {train_loss.result():.4f} Accuracy {train_accuracy.result():.4f}')\n",
        "\n",
        "  if (epoch + 1) % 5 == 0:\n",
        "    ckpt_save_path = ckpt_manager.save()\n",
        "    print(f'Saving checkpoint for epoch {epoch+1} at {ckpt_save_path}')\n",
        "\n",
        "  print(f'Epoch {epoch + 1} Loss {train_loss.result():.4f} Accuracy {train_accuracy.result():.4f}')\n",
        "\n",
        "  print(f'Time taken for 1 epoch: {time.time() - start:.2f} secs\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cxKpqCbzSW6z"
      },
      "source": [
        "## Run inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mk8vwuN1SafK"
      },
      "source": [
        "You can now test the model by performing a translation. The following steps are used for inference:\n",
        "\n",
        "* Encode the input sentence using the Portuguese tokenizer (`tokenizers.pt`). This is the encoder input.\n",
        "* The decoder input is initialized to the `[START]` token.\n",
        "* Calculate the padding masks and the look ahead masks.\n",
        "* The `decoder` then outputs the predictions by looking at the `encoder output` and its own output (self-attention).\n",
        "* Concatenate the predicted token to the decoder input and pass it to the decoder.\n",
        "* In this approach, the decoder predicts the next token based on the previous tokens it predicted.\n",
        "\n",
        "Note: The model is optimized for _efficient training_ and makes a next-token prediction for each token in the output simultaneously. This is redundant during inference, and only the last prediction is used.  This model can be made more efficient for inference if you only calculate the last prediction when running in inference mode (`training=False`).\n",
        "\n",
        "Define the `Translator` class by subclassing `tf.Module`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "eY_uXsOhSmbb"
      },
      "outputs": [],
      "source": [
        "class Translator(tf.Module):\n",
        "  def __init__(self, tokenizers, transformer):\n",
        "    self.tokenizers = tokenizers\n",
        "    self.transformer = transformer\n",
        "\n",
        "  def __call__(self, sentence, max_length=MAX_TOKENS):\n",
        "    # The input sentence is Portuguese, hence adding the `[START]` and `[END]` tokens.\n",
        "    assert isinstance(sentence, tf.Tensor)\n",
        "    if len(sentence.shape) == 0:\n",
        "      sentence = sentence[tf.newaxis]\n",
        "\n",
        "    sentence = self.tokenizers.pt.tokenize(sentence).to_tensor()\n",
        "\n",
        "    encoder_input = sentence\n",
        "\n",
        "    # As the output language is English, initialize the output with the\n",
        "    # English `[START]` token.\n",
        "    start_end = self.tokenizers.en.tokenize([''])[0]\n",
        "    start = start_end[0][tf.newaxis]\n",
        "    end = start_end[1][tf.newaxis]\n",
        "\n",
        "    # `tf.TensorArray` is required here (instead of a Python list), so that the\n",
        "    # dynamic-loop can be traced by `tf.function`.\n",
        "    output_array = tf.TensorArray(dtype=tf.int64, size=0, dynamic_size=True)\n",
        "    output_array = output_array.write(0, start)\n",
        "\n",
        "    for i in tf.range(max_length):\n",
        "      output = tf.transpose(output_array.stack())\n",
        "      predictions, _ = self.transformer([encoder_input, output], training=False)\n",
        "\n",
        "      # Select the last token from the `seq_len` dimension.\n",
        "      predictions = predictions[:, -1:, :]  # Shape `(batch_size, 1, vocab_size)`.\n",
        "\n",
        "      predicted_id = tf.argmax(predictions, axis=-1)\n",
        "\n",
        "      # Concatenate the `predicted_id` to the output which is given to the\n",
        "      # decoder as its input.\n",
        "      output_array = output_array.write(i+1, predicted_id[0])\n",
        "\n",
        "      if predicted_id == end:\n",
        "        break\n",
        "\n",
        "    output = tf.transpose(output_array.stack())\n",
        "    # The output shape is `(1, tokens)`.\n",
        "    text = tokenizers.en.detokenize(output)[0]  # Shape: `()`.\n",
        "\n",
        "    tokens = tokenizers.en.lookup(output)[0]\n",
        "\n",
        "    # `tf.function` prevents us from using the attention_weights that were\n",
        "    # calculated on the last iteration of the loop.\n",
        "    # Therefore, recalculate them outside the loop.\n",
        "    _, attention_weights = self.transformer([encoder_input, output[:,:-1]], training=False)\n",
        "\n",
        "    return text, tokens, attention_weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJ3o-65iS6CN"
      },
      "source": [
        "Note: This function uses an unrolled loop, not a dynamic loop. It generates `MAX_TOKENS` on every call. Refer to the [NMT with attention](nmt_with_attention.ipynb) tutorial for an example implementation with a dynamic loop, which can be much more efficient."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TeUJafisS435"
      },
      "source": [
        "Create an instance of this `Translator` class, and try it out a few times:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "-NjbvpHUTEia"
      },
      "outputs": [],
      "source": [
        "translator = Translator(tokenizers, transformer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "QfHSRdejTFsC"
      },
      "outputs": [],
      "source": [
        "def print_translation(sentence, tokens, ground_truth):\n",
        "  print(f'{\"Input:\":15s}: {sentence}')\n",
        "  print(f'{\"Prediction\":15s}: {tokens.numpy().decode(\"utf-8\")}')\n",
        "  print(f'{\"Ground truth\":15s}: {ground_truth}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "buUeDo58TIoD"
      },
      "source": [
        "Example 1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "o9CEm4cuTGtw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42c540b9-0254-4d34-f422-67d8d9a05369"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input:         : este é um problema que temos que resolver.\n",
            "Prediction     : this is a problem that we have to solve .\n",
            "Ground truth   : this is a problem we have to solve .\n"
          ]
        }
      ],
      "source": [
        "sentence = 'este é um problema que temos que resolver.'\n",
        "ground_truth = 'this is a problem we have to solve .'\n",
        "\n",
        "translated_text, translated_tokens, attention_weights = translator(\n",
        "    tf.constant(sentence))\n",
        "print_translation(sentence, translated_text, ground_truth)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfJrFBZ6TJxc"
      },
      "source": [
        "Example 2:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "elmz_Ly7THuJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8e52c78-d929-4893-ef2c-8072d7044866"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input:         : os meus vizinhos ouviram sobre esta ideia.\n",
            "Prediction     : my neighbors have heard about this idea .\n",
            "Ground truth   : and my neighboring homes heard about this idea .\n"
          ]
        }
      ],
      "source": [
        "sentence = 'os meus vizinhos ouviram sobre esta ideia.'\n",
        "ground_truth = 'and my neighboring homes heard about this idea .'\n",
        "\n",
        "translated_text, translated_tokens, attention_weights = translator(\n",
        "    tf.constant(sentence))\n",
        "print_translation(sentence, translated_text, ground_truth)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EY7NfEjrTOCr"
      },
      "source": [
        "Example 3:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "bmmtPo3vTOwj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4af9dda4-cbed-40f8-ac9c-651e57110a00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input:         : vou então muito rapidamente partilhar convosco algumas histórias de algumas coisas mágicas que aconteceram.\n",
            "Prediction     : so i ' ll be very quickly share with you a little bit of a bad news stories that happen .\n",
            "Ground truth   : so i'll just share with you some stories very quickly of some magical things that have happened.\n"
          ]
        }
      ],
      "source": [
        "sentence = 'vou então muito rapidamente partilhar convosco algumas histórias de algumas coisas mágicas que aconteceram.'\n",
        "ground_truth = \"so i'll just share with you some stories very quickly of some magical things that have happened.\"\n",
        "\n",
        "translated_text, translated_tokens, attention_weights = translator(\n",
        "    tf.constant(sentence))\n",
        "print_translation(sentence, translated_text, ground_truth)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aB_03k0kTQLb"
      },
      "source": [
        "## Create attention plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "miZXl9i-TSs6"
      },
      "source": [
        "The `Translator` class you created in the previous section returns a dictionary of attention heatmaps you can use to visualize the internal working of the model.\n",
        "\n",
        "For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "V3m2wcNLTU8K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d5afa62-a98e-4791-fefc-ccd7ac8e5d71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input:         : este é o primeiro livro que eu fiz.\n",
            "Prediction     : this is the first book i did .\n",
            "Ground truth   : this is the first book i've ever done.\n"
          ]
        }
      ],
      "source": [
        "sentence = 'este é o primeiro livro que eu fiz.'\n",
        "ground_truth = \"this is the first book i've ever done.\"\n",
        "\n",
        "translated_text, translated_tokens, attention_weights = translator(\n",
        "    tf.constant(sentence))\n",
        "print_translation(sentence, translated_text, ground_truth)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-rhE_LW7TZ40"
      },
      "source": [
        "Create a function that plots the attention when a token is generated:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "gKlxYO0JTXzD"
      },
      "outputs": [],
      "source": [
        "def plot_attention_head(in_tokens, translated_tokens, attention):\n",
        "  # The model didn't generate `<START>` in the output. Skip it.\n",
        "  translated_tokens = translated_tokens[1:]\n",
        "\n",
        "  ax = plt.gca()\n",
        "  ax.matshow(attention)\n",
        "  ax.set_xticks(range(len(in_tokens)))\n",
        "  ax.set_yticks(range(len(translated_tokens)))\n",
        "\n",
        "  labels = [label.decode('utf-8') for label in in_tokens.numpy()]\n",
        "  ax.set_xticklabels(\n",
        "      labels, rotation=90)\n",
        "\n",
        "  labels = [label.decode('utf-8') for label in translated_tokens.numpy()]\n",
        "  ax.set_yticklabels(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "yI4YWU2uXDeW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81249224-418e-4c54-f275-33d2b294901f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([9, 11])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "head = 0\n",
        "# Shape: `(batch=1, num_attention_heads, seq_len_q, seq_len_k)`.\n",
        "attention_heads = tf.squeeze(\n",
        "  attention_weights['decoder_layer4_block2'], 0)\n",
        "attention = attention_heads[head]\n",
        "attention.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "facNouzOXMSu"
      },
      "source": [
        "These are the input (Portuguese) tokens:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "SMEpyioWTmSN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57f118fc-c96a-43f4-c686-b7def2488bac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(11,), dtype=string, numpy=\n",
              "array([b'[START]', b'este', b'e', b'o', b'primeiro', b'livro', b'que',\n",
              "       b'eu', b'fiz', b'.', b'[END]'], dtype=object)>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "in_tokens = tf.convert_to_tensor([sentence])\n",
        "in_tokens = tokenizers.pt.tokenize(in_tokens).to_tensor()\n",
        "in_tokens = tokenizers.pt.lookup(in_tokens)[0]\n",
        "in_tokens"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JLg9HTKCXPKz"
      },
      "source": [
        "And these are the output (English translation) tokens:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "GzvIo5uYTnHG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6d227b4-4460-463b-a708-0e619aacf82e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=string, numpy=\n",
              "array([b'[START]', b'this', b'is', b'the', b'first', b'book', b'i',\n",
              "       b'did', b'.', b'[END]'], dtype=object)>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "translated_tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "lrNh47D1ToBD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "d03a4933-f5ca-4bb3-9308-912c4c5e00c9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_attention_head(in_tokens, translated_tokens, attention)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "iMZr-rI_TrGh"
      },
      "outputs": [],
      "source": [
        "def plot_attention_weights(sentence, translated_tokens, attention_heads):\n",
        "  in_tokens = tf.convert_to_tensor([sentence])\n",
        "  in_tokens = tokenizers.pt.tokenize(in_tokens).to_tensor()\n",
        "  in_tokens = tokenizers.pt.lookup(in_tokens)[0]\n",
        "\n",
        "  fig = plt.figure(figsize=(16, 8))\n",
        "\n",
        "  for h, head in enumerate(attention_heads):\n",
        "    ax = fig.add_subplot(2, 4, h+1)\n",
        "\n",
        "    plot_attention_head(in_tokens, translated_tokens, head)\n",
        "\n",
        "    ax.set_xlabel(f'Head {h+1}')\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "lBMujUb1Tr4C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "outputId": "c96d5177-76a6-43a4-e0e5-d90922fd1f5e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_attention_weights(sentence,\n",
        "                       translated_tokens,\n",
        "                       attention_weights['decoder_layer4_block2'][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9N5S5IptTtHI"
      },
      "source": [
        "The model does okay on unfamiliar words. Neither `'triceratops'` nor `'encyclopédia'` are in the input dataset, and the model almost learns to transliterate them even without a shared vocabulary. For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "w0-5gjfWT0CS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "outputId": "d633f5e9-663f-4ec4-eaf6-8a320f033e5f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input:         : Eu li sobre triceratops na enciclopédia.\n",
            "Prediction     : i read about triburophords in encycloodia .\n",
            "Ground truth   : I read about triceratops in the encyclopedia.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sentence = 'Eu li sobre triceratops na enciclopédia.'\n",
        "ground_truth = 'I read about triceratops in the encyclopedia.'\n",
        "\n",
        "translated_text, translated_tokens, attention_weights = translator(\n",
        "    tf.constant(sentence))\n",
        "print_translation(sentence, translated_text, ground_truth)\n",
        "\n",
        "plot_attention_weights(sentence, translated_tokens,\n",
        "                       attention_weights['decoder_layer4_block2'][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9zz4uIDbT1OU"
      },
      "source": [
        "## Export the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zunHPJJzT4Cz"
      },
      "source": [
        "You have tested the model and the inference is working. Next, you can export it as a `tf.saved_model`. To learn about saving and loading a model in the SavedModel format, use [this guide](https://www.tensorflow.org/guide/saved_model).\n",
        "\n",
        "Create a class called `ExportTranslator` by subclassing the `tf.Module` subclass with a `tf.function` on the `__call__` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "NZhv5h4AT_n5"
      },
      "outputs": [],
      "source": [
        "class ExportTranslator(tf.Module):\n",
        "  def __init__(self, translator):\n",
        "    self.translator = translator\n",
        "\n",
        "  @tf.function(input_signature=[tf.TensorSpec(shape=[], dtype=tf.string)])\n",
        "  def __call__(self, sentence):\n",
        "    (result,\n",
        "     tokens,\n",
        "     attention_weights) = self.translator(sentence, max_length=MAX_TOKENS)\n",
        "\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wad7lUtPUAnf"
      },
      "source": [
        "In the above `tf.function` only the output sentence is returned. Thanks to the [non-strict execution](https://tensorflow.org/guide/intro_to_graphs) in `tf.function` any unnecessary values are never computed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7KJEFWI5v84"
      },
      "source": [
        "Wrap `translator` in the newly created `ExportTranslator`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "wm1_eRPvUCUm"
      },
      "outputs": [],
      "source": [
        "translator = ExportTranslator(translator)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7VPH4T5XUDnc"
      },
      "source": [
        "Since the model is decoding the predictions using `tf.argmax` the predictions are deterministic. The original model and one reloaded from its `SavedModel` should give identical predictions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "GITRCiAYUE5w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5cc8b84-e38c-40a6-e0d6-fd80544132eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "b'this is the first book i did .'"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "translator('este é o primeiro livro que eu fiz.').numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "_v--e1XmUFw3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88344d2c-a2f0-4004-f6db-ec7dad29c407"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as embedding_2_layer_call_fn, embedding_2_layer_call_and_return_conditional_losses, dropout_21_layer_call_fn, dropout_21_layer_call_and_return_conditional_losses, embedding_3_layer_call_fn while saving (showing 5 of 248). These functions will not be directly callable after loading.\n"
          ]
        }
      ],
      "source": [
        "tf.saved_model.save(translator, export_dir='translator')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "5KJSQEzlUGo-"
      },
      "outputs": [],
      "source": [
        "reloaded = tf.saved_model.load('translator')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "lIVpKWBNUHhr",
        "colab": {
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        "## Conclusion\n",
        "\n",
        "In this tutorial you learned about:\n",
        "\n",
        "* The Transformers and their significance in machine learning\n",
        "* Attention, self-attention and multi-head attention\n",
        "* Positional encoding with embeddings\n",
        "* The encoder-decoder architecture of the original Transformer\n",
        "* Masking in self-attention\n",
        "* How to put it all together to translate text\n",
        "\n",
        "The downsides of this architecture are:\n",
        "\n",
        "- For a time-series, the output for a time-step is calculated from the *entire history* instead of only the inputs and current hidden-state. This _may_ be less efficient.\n",
        "- If the input *does* have a  temporal/spatial relationship, like text, some positional encoding must be added or the model will effectively see a bag of words.\n",
        "\n",
        "If you want to practice, there are many things you could try with it. For example:\n",
        "\n",
        "* Use a different dataset to train the Transformer.\n",
        "* Create the \"Base Transformer\" or \"Transformer XL\" configurations from the original paper by changing the hyperparameters.\n",
        "* Use the layers defined here to create an implementation of [BERT](https://arxiv.org/abs/1810.04805){:.external}.\n",
        "* Implement beam search to get better predictions.\n",
        "\n",
        "There are a wide variety of Transformer-based models, many of which improve upon the 2017 version of the original Transformer with encoder-decoder, encoder-only and decoder-only architectures.\n",
        "\n",
        "Some of these models are covered in the following research publications:\n",
        "\n",
        "* [\"Efficient Transformers: a survey\"](https://arxiv.org/abs/2009.06732){:.external} (Tay et al., 2022)\n",
        "* [\"Formal algorithms for Transformers\"](https://arxiv.org/abs/2207.09238){:.external} (Phuong and Hutter, 2022)\n",
        "* [T5 (\"Exploring the limits of transfer learning with a unified text-to-text Transformer\")](https://arxiv.org/abs/1910.10683){:.external} (Raffel et al., 2019)\n",
        "\n",
        "You can learn more about other models in the following Google blog posts:\n",
        "\n",
        "* [PaLM](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html){:.external}\n",
        "* [LaMDA](https://ai.googleblog.com/2022/01/lamda-towards-safe-grounded-and-high.html){:.external}\n",
        "* [MUM](https://blog.google/products/search/introducing-mum/){:.external}\n",
        "* [Reformer](https://ai.googleblog.com/2020/01/reformer-efficient-transformer.html){:.external}\n",
        "* [BERT](https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html){:.external}\n",
        "\n",
        "If you're interested in studying how attention-based models have been applied in tasks outside of natural language processing, check out the following resources:\n",
        "\n",
        "- Vision Transformer (ViT): [Transformers for image recognition at scale](https://ai.googleblog.com/2020/12/transformers-for-image-recognition-at.html){:.external}\n",
        "- [Multi-task multitrack music transcription (MT3)](https://magenta.tensorflow.org/transcription-with-transformers){:.external} with a Transformer\n",
        "- [Code generation with AlphaCode](https://www.deepmind.com/blog/competitive-programming-with-alphacode){:.external}\n",
        "- [Reinforcement learning with multi-game decision Transformers](https://ai.googleblog.com/2022/07/training-generalist-agents-with-multi.html){:.external}\n",
        "- [Protein structure prediction with AlphaFold](https://www.nature.com/articles/s41586-021-03819-2){:.external}\n",
        "- [OptFormer: Towards universal hyperparameter optimization with Transformers](http://ai.googleblog.com/2022/08/optformer-towards-universal.html){:.external}\n"
      ]
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