activity15_notebook.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CMPSC-310-AI-Spring2023/activity15_transformers-jnormile/blob/main/activity15_notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7f697fec28a"
      },
      "source": [
        "##### Copyright 2022 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "EFwSaNB8jF7s"
      },
      "source": [
        "<style>\n",
        "td {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "th {\n",
        "  text-align: center;\n",
        "}\n",
        "</style>"
      ]
    },
    {
      "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": "TCg3ElKBUSBb"
      },
      "source": [
        "This tutorial demonstrates how to create and train a [sequence-to-sequence](https://developers.google.com/machine-learning/glossary#sequence-to-sequence-task) [Transformer](https://developers.google.com/machine-learning/glossary#Transformer) model to translate [Portuguese into English](https://www.tensorflow.org/datasets/catalog/ted_hrlr_translate#ted_hrlr_translatept_to_en). The Transformer was originally proposed in [\"Attention is all you need\"](https://arxiv.org/abs/1706.03762) by Vaswani et al. (2017).\n",
        "\n",
        "Transformers are deep neural networks that replace CNNs and RNNs with [self-attention](https://developers.google.com/machine-learning/glossary#self-attention). Self attention allows Transformers to easily transmit information across the input sequences.\n",
        "\n",
        "As explained in the [Google AI Blog post](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html):\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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fo1P7AN4lzdi"
      },
      "source": [
        "<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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RAxfGTJJYbQi"
      },
      "source": [
        "That's a lot to digest, the goal of this tutorial is to break it down into easy to understand parts. In this tutorial you will:\n",
        "\n",
        "- Prepare the data.\n",
        "- Implement necessary components:\n",
        "  - Positional embeddings.\n",
        "  - Attention layers.\n",
        "  - The encoder and decoder.\n",
        "- Build & train the Transformer.\n",
        "- Generate translations.\n",
        "- Export the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ddvOmAXhaDXt"
      },
      "source": [
        "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. \n",
        "\n",
        "A Transformer is a sequence-to-sequence encoder-decoder model similar to the model in the [NMT with attention tutorial](https://www.tensorflow.org/text/tutorials/nmt_with_attention).\n",
        "A single-layer Transformer takes a little more code to write, but is almost identical to that encoder-decoder RNN model. The only difference is that the RNN layers are replaced with self attention layers.\n",
        "This tutorial builds a 4-layer Transformer which is larger and more powerful, but not fundamentally more complex."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jk40oPm8OD51"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th>The <a href=https://www.tensorflow.org/text/tutorials/nmt_with_attention>RNN+Attention model</a></th>\n",
        "  <th>A 1-layer transformer</th>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=411 src=\"https://www.tensorflow.org/images/tutorials/transformer/RNN+attention-words.png\"/>\n",
        "  </td>\n",
        "  <td>\n",
        "   <img width=400 src=\"https://www.tensorflow.org/images/tutorials/transformer/Transformer-1layer-words.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "huJ97Eh-Ue4V"
      },
      "source": [
        "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), Transformers are parallelizable. This makes them efficient on hardware like GPUs and TPUs. The main reasons is that Transformers replaced recurrence with attention, and computations can happen simultaneously. Layer outputs can be computed 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)) or [convolutional neural networks (CNNs)](https://www.tensorflow.org/tutorials/images/cnn) (for example, [ByteNet](https://arxiv.org/abs/1610.10099)), 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://www.deepmind.com/blog/alphastar-mastering-the-real-time-strategy-game-starcraft-ii)).\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": 1,
      "metadata": {
        "id": "XFG0NDRu5mYQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff352ce8-6544-42af-bbfa-643ecff57415"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
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            "0 upgraded, 0 newly installed, 1 downgraded, 1 to remove and 22 not upgraded.\n",
            "Need to get 430 MB of archives.\n",
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            "Get:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64  libcudnn8 8.1.0.77-1+cuda11.2 [430 MB]\n",
            "Fetched 430 MB in 12s (34.5 MB/s)\n",
            "(Reading database ... 122349 files and directories currently installed.)\n",
            "Removing libcudnn8-dev (8.7.0.84-1+cuda11.8) ...\n",
            "update-alternatives: removing manually selected alternative - switching libcudnn to auto mode\n",
            "\u001b[1mdpkg:\u001b[0m \u001b[1;33mwarning:\u001b[0m downgrading libcudnn8 from 8.7.0.84-1+cuda11.8 to 8.1.0.77-1+cuda11.2\n",
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            "Preparing to unpack .../libcudnn8_8.1.0.77-1+cuda11.2_amd64.deb ...\n",
            "Unpacking libcudnn8 (8.1.0.77-1+cuda11.2) over (8.7.0.84-1+cuda11.8) ...\n",
            "Setting up libcudnn8 (8.1.0.77-1+cuda11.2) ...\n",
            "\u001b[33mWARNING: Skipping tensorflow-text as it is not installed.\u001b[0m\u001b[33m\n",
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# Install the most re version of TensorFlow to use the improved\n",
        "# masking support for `tf.keras.layers.MultiHeadAttention`.\n",
        "!apt install --allow-change-held-packages libcudnn8=8.1.0.77-1+cuda11.2\n",
        "!pip uninstall -y -q tensorflow keras tensorflow-estimator tensorflow-text\n",
        "!pip install protobuf~=3.20.3\n",
        "!pip install -q tensorflow_datasets\n",
        "!pip install -q -U tensorflow-text tensorflow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0GYpLBSjxJmG"
      },
      "source": [
        "Import the necessary modules:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "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": "Xf_WUi2HLhzf"
      },
      "source": [
        "## Data handling\n",
        "\n",
        "This section downloads the dataset and the subword tokenizer, from [this tutorial](https://www.tensorflow.org/text/guide/subwords_tokenizer), then wraps it all up in a `tf.data.Dataset` for training.\n",
        "\n",
        " <section class=\"expandable tfo-display-only-on-site\">\n",
        " <button type=\"button\" class=\"button-red button expand-control\">Toggle section</button>\n"
      ]
    },
    {
      "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)D Talks Open Translation Project. This dataset contains approximately 52,000 training, 1,200 validation and 1,800 test examples."
      ]
    },
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            "566e4b8732aa47ce96b23ca96c55f152",
            "98cf9fc1fbfa4ad1b46fa1e4c9eaaed0",
            "8933d46fc49b43f4a36ce9db44efe9dc",
            "9cc67cd346ff4e6889ce711d71306a05",
            "d4a2bfed37fd4f1f8d010296b3809044",
            "6d22eedd3a894f44a27697067a78c468",
            "b7b45556d33e410999732f0f5a10aa4c",
            "24e7c6fd26b74837a9c221595376a922",
            "9d1d111de9f6487898100bcddd4a88de",
            "a6a6eb466c374e9aab305a7123ac02df",
            "3fa34b2d75e049e0a1927548d6424e56",
            "d959e7d2d9194022afe62ad25f5c1a75",
            "a19b4919c55342e9b6473f8fbb837ea2",
            "42a4d678c20f4e76abad07557c932eb4",
            "1021c675cd594811a08d93ec68705b02",
            "779bcfea555c4cbf9caf55ed88721722",
            "8942c44a223741b7b284995b99d6a79b",
            "0c2d3db1422a4a3ca30fc0a93d1d80e2",
            "42471c1ff2f9483c863c48cec8c27186"
          ]
        },
        "outputId": "1616a2cc-bd8e-4d7d-bbe4-bda78ec3ec36"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ebc74d536d1c4db4b08b9907cf016241"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc39d2434c5a44bea23ddab5b2eb3e8f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f429a264cf1d406697427e3b049c5c87"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "723ada43f07f4203b6970ada9a02d779"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73ff0ebb84e04023944761c99eba7c4a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incomplete693SQ6/ted_hrlr_translate-trai…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffe7f23a358448bb8bf078f11f403465"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation examples...: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6f3190e3f1c475988a613ddb36bc580"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incomplete693SQ6/ted_hrlr_translate-vali…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "22372a7204554452beda8af3a45dd492"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test examples...: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "309272e6d9144dc28be0fe4c2793dd6a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incomplete693SQ6/ted_hrlr_translate-test…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d1d111de9f6487898100bcddd4a88de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset ted_hrlr_translate downloaded and prepared to /root/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0. Subsequent calls will reuse this data.\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": 4,
      "metadata": {
        "id": "CZFAMZJyDrFn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3816f7b7-c1c5-4dc0-bf8d-896ab4b72ea5"
      },
      "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": [
        "### Set up 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) or token ID (a numeric representation).\n",
        "\n",
        "Tokenization is the process of breaking up text, into \"tokens\". Depending on the tokenizer, these tokens can represent sentence-pieces, words, subwords, or characters. To learn more about tokenization, visit [this guide](https://www.tensorflow.org/text/guide/tokenizers)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJr_8Jz9FKgu"
      },
      "source": [
        "This tutorial uses the tokenizers built in the [subword tokenizer](https://www.tensorflow.org/text/guide/subwords_tokenizer) tutorial. That tutorial optimizes two `text.BertTokenizer` objects (one for English, one for Portuguese) for **this dataset** and exports them in a TensorFlow `saved_model` format.\n",
        "\n",
        "> Note: This is different from the [original paper](https://arxiv.org/pdf/1706.03762.pdf), section 5.1, where they used a single byte-pair tokenizer for both the source and target with a vocabulary-size of 37000.\n",
        "\n",
        "Download, extract, and import the `saved_model`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "QToMl0NanZPr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "a8c9f486-7d71-4d1b-de6e-04fcbedc51b8"
      },
      "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": 5
        }
      ],
      "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": 6,
      "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": 7,
      "metadata": {
        "id": "s-PCJijfcZ9_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00876da6-baa1-4ff6-e2b1-25b8d4654be0"
      },
      "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": 7
        }
      ],
      "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": 8,
      "metadata": {
        "id": "z_gPC5iwFXfU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7622c0f1-8b73-4845-a7dc-9eae34d7494d"
      },
      "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": 9,
      "metadata": {
        "id": "uSkM7z8JFaVO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a40789a1-aaf8-4092-f526-ce37d9e1f002"
      },
      "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": 10,
      "metadata": {
        "id": "-CFS5aAxFdpP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f371d2c6-1180-4c6e-f925-d9502ac1c12d"
      },
      "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": 11,
      "metadata": {
        "id": "XaCeOnswVAhI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eecff160-cb9a-4fc2-eb94-24df5053e83c"
      },
      "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": 11
        }
      ],
      "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": 12,
      "metadata": {
        "id": "KRbke-iaaHFI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "432eb7ed-cafb-495a-cd47-76f072f3130f"
      },
      "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.pt.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": 13,
      "metadata": {
        "id": "9ucA1q3GaK_n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "9ea36e7e-bd83-42ff-c15a-b88a157d95f2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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": "-Yb35sTJcZq9"
      },
      "source": [
        "### Set up a data pipeline with `tf.data`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZHsns5obJhN"
      },
      "source": [
        "The following function takes batches of text as input, and converts them to a format suitable for training. \n",
        "\n",
        "1. It tokenizes them into ragged batches.\n",
        "2. It trims each to be no longer than `MAX_TOKENS`.\n",
        "3. It splits the target (English) tokens into inputs and labels. These are shifted by one step so that at each input location the `label` is the id of the next token.\n",
        "4. It converts the `RaggedTensor`s to padded dense `Tensor`s.\n",
        "5. It returns an `(inputs, labels)` pair.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "6shgzEck3FiV"
      },
      "outputs": [],
      "source": [
        "MAX_TOKENS=128\n",
        "def prepare_batch(pt, en):\n",
        "    pt = tokenizers.pt.tokenize(pt)      # Output is ragged.\n",
        "    pt = pt[:, :MAX_TOKENS]    # Trim to MAX_TOKENS.\n",
        "    pt = pt.to_tensor()  # Convert to 0-padded dense Tensor\n",
        "\n",
        "    en = tokenizers.en.tokenize(en)\n",
        "    en = en[:, :(MAX_TOKENS+1)]\n",
        "    en_inputs = en[:, :-1].to_tensor()  # Drop the [END] tokens\n",
        "    en_labels = en[:, 1:].to_tensor()   # Drop the [START] tokens\n",
        "\n",
        "    return (pt, en_inputs), en_labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dAroQ6xelzdx"
      },
      "source": [
        "The function below converts a dataset of text examples into data of batches for training. \n",
        "\n",
        "1. It tokenizes the text, and filters out the sequences that are too long.\n",
        "   (The `batch`/`unbatch` is included because the tokenizer is much more efficient on large batches).\n",
        "2. The `cache` method ensures that that work is only executed once.\n",
        "3. Then `shuffle` and, `dense_to_ragged_batch` randomize the order and assemble batches of examples. \n",
        "4. Finally `prefetch` runs the dataset in parallel with the model to ensure that data is available when needed. See [Better performance with the `tf.data`](https://www.tensorflow.org/guide/data_performance.ipynb) for details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "bcRp7VcQ5m6g"
      },
      "outputs": [],
      "source": [
        "BUFFER_SIZE = 20000\n",
        "BATCH_SIZE = 64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "BUN_jLBTwNxk"
      },
      "outputs": [],
      "source": [
        "def make_batches(ds):\n",
        "  return (\n",
        "      ds\n",
        "      .shuffle(BUFFER_SIZE)\n",
        "      .batch(BATCH_SIZE)\n",
        "      .map(prepare_batch, tf.data.AUTOTUNE)\n",
        "      .prefetch(buffer_size=tf.data.AUTOTUNE))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FX_h3tCnwgR4"
      },
      "source": [
        " </section>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itSWqk-ivrRg"
      },
      "source": [
        "## Test the Dataset "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "BSswr5TKvoNM"
      },
      "outputs": [],
      "source": [
        "# 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": "PSufllC7wooA"
      },
      "source": [
        "The resulting `tf.data.Dataset` objects are setup for training with Keras.\n",
        "Keras `Model.fit` training expects `(inputs, labels)` pairs.\n",
        "The `inputs` are pairs of tokenized Portuguese and English sequences, `(pt, en)`.\n",
        "The `labels` are the same English sequences shifted by 1.\n",
        "This shift is so that at each location input `en` sequence, the `label` in the next token.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJdJttsF751"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th>Inputs at the bottom, labels at the top.</th>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=400 src=\"https://www.tensorflow.org/images/tutorials/transformer/Transformer-1layer-words.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsF751JJdJt"
      },
      "source": [
        "This is the same as the [text generation tutorial](text_generation.ipynb),\n",
        "except here you have additional input \"context\" (the Portuguese sequence) that the model is \"conditioned\" on.\n",
        "\n",
        "This setup is called \"teacher forcing\" because regardless of the model's output at each timestep, it gets the true value as input for the next timestep.\n",
        "This is a simple and efficient way to train a text generation model.\n",
        "It's efficient because you don't need to run the model sequentially, the outputs at the different sequence locations can be computed in parallel.\n",
        "\n",
        "You might have expected the `input, output`, pairs to simply be the `Portuguese, English` sequences.\n",
        "Given the Portuguese sequence, the model would try to generate the English sequence.\n",
        "\n",
        "It's possible to train a model that way. You'd need to write out the inference loop and pass the model's output back to the input.\n",
        "It's slower (time steps can't run in parallel), and a harder task to learn (the model can't get the end of a sentence right until it gets the beginning right),\n",
        "but it can give a more stable model because the model has to learn to correct its own errors during training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "CAw2XjRwLFWr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fb18918-7f55-4a7c-befb-5fda71c732dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 77)\n",
            "(64, 76)\n",
            "(64, 76)\n"
          ]
        }
      ],
      "source": [
        "for (pt, en), en_labels in train_batches.take(1):\n",
        "  break\n",
        "\n",
        "print(pt.shape)\n",
        "print(en.shape)\n",
        "print(en_labels.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tzo3JKaqx46g"
      },
      "source": [
        "The `en` and `en_labels` are the same, just shifted by 1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "apFeC-WWxzR4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f33d7731-7eca-4fa1-eb5e-0db3ee983e4b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor([   2   83 1559   92   71   38 2973 6484   88   78], shape=(10,), dtype=int64)\n",
            "tf.Tensor([  83 1559   92   71   38 2973 6484   88   78   89], shape=(10,), dtype=int64)\n"
          ]
        }
      ],
      "source": [
        "print(en[0][:10])\n",
        "print(en_labels[0][:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7e7hKcxn6-zd"
      },
      "source": [
        "## Define the components"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVE5j6JlcAps"
      },
      "source": [
        "There's a lot going on inside a Transformer. The important things to remember are:\n",
        "\n",
        "1. It follows the same general pattern as a standard sequence-to-sequence model with an encoder and a decoder.\n",
        "2. If you work through it step by step it will all make sense."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O0R4bYJ0DiFR"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The original Transformer diagram</th>\n",
        "  <th colspan=1>A representation of a 4-layer Transformer</th>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=400 src=\"https://www.tensorflow.org/images/tutorials/transformer/transformer.png\"/>\n",
        "  </td>\n",
        "  <td>\n",
        "   <img width=307 src=\"https://www.tensorflow.org/images/tutorials/transformer/Transformer-4layer-compact.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>\n",
        "\n",
        "Each of the components in these two diagrams will be explained as you progress through the tutorial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YS75Y-9-lkzn"
      },
      "source": [
        "### The embedding and positional encoding layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "26l90xiq3Nis"
      },
      "source": [
        "The inputs to both the encoder and decoder use the same embedding and positional encoding logic. \n",
        "\n",
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The embedding and positional encoding layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/PositionalEmbedding.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "279u2DiDlmdS"
      },
      "source": [
        "Given a sequence of tokens, both the input tokens (Portuguese) and target tokens (English) have to be converted to vectors using a `tf.keras.layers.Embedding` layer.\n",
        "\n",
        "The attention layers used throughout the model see their input as a set of vectors, with no order. Since the model doesn't contain any recurrent or convolutional layers. It needs some way to identify word order, otherwise it would see the input sequence as a [bag of words](https://developers.google.com/machine-learning/glossary#bag-of-words) instance, `how are you`, `how you are`, `you how are`, and so on, are indistinguishable.\n",
        "\n",
        "A Transformer adds a \"Positional Encoding\" to the embedding vectors. It uses a set of sines and cosines at different frequencies (across the sequence). By definition nearby elements will have similar position encodings."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4gcCNZP7lzdy"
      },
      "source": [
        "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": 20,
      "metadata": {
        "id": "1Rz82wEs5biZ"
      },
      "outputs": [],
      "source": [
        "def positional_encoding(length, depth):\n",
        "  depth = depth/2\n",
        "\n",
        "  positions = np.arange(length)[:, np.newaxis]     # (seq, 1)\n",
        "  depths = np.arange(depth)[np.newaxis, :]/depth   # (1, depth)\n",
        "  \n",
        "  angle_rates = 1 / (10000**depths)         # (1, depth)\n",
        "  angle_rads = positions * angle_rates      # (pos, depth)\n",
        "\n",
        "  pos_encoding = np.concatenate(\n",
        "      [np.sin(angle_rads), np.cos(angle_rads)],\n",
        "      axis=-1) \n",
        "\n",
        "  return tf.cast(pos_encoding, dtype=tf.float32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ra1IcbzFhnmF"
      },
      "source": [
        "The position encoding function is a stack of sines and cosines that vibrate at different frequencies depending on their location along the depth of the embedding vector. They vibrate across the position axis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "AKf4Ky2dhg0L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "4e7fe727-1652-45b3-f6ce-939562ed4f55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2048, 512)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@title\n",
        "pos_encoding = positional_encoding(length=2048, depth=512)\n",
        "\n",
        "# Check the shape.\n",
        "print(pos_encoding.shape)\n",
        "\n",
        "# Plot the dimensions.\n",
        "plt.pcolormesh(pos_encoding.numpy().T, cmap='RdBu')\n",
        "plt.ylabel('Depth')\n",
        "plt.xlabel('Position')\n",
        "plt.colorbar()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eKqVkl9Jlzg6"
      },
      "source": [
        "By definition these vectors align well with nearby vectors along the position axis. Below the position encoding vectors are normalized and the vector from position `1000` is compared, by dot-product, to all the others:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "CXY-8_uEhcRD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "81575784-7a36-4a18-9e2e-5d8dbebcbd40"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@title\n",
        "pos_encoding/=tf.norm(pos_encoding, axis=1, keepdims=True)\n",
        "p = pos_encoding[1000]\n",
        "dots = tf.einsum('pd,d -> p', pos_encoding, p)\n",
        "plt.subplot(2,1,1)\n",
        "plt.plot(dots)\n",
        "plt.ylim([0,1])\n",
        "plt.plot([950, 950, float('nan'), 1050, 1050],\n",
        "         [0,1,float('nan'),0,1], color='k', label='Zoom')\n",
        "plt.legend()\n",
        "plt.subplot(2,1,2)\n",
        "plt.plot(dots)\n",
        "plt.xlim([950, 1050])\n",
        "plt.ylim([0,1])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LUknPLlVm99o"
      },
      "source": [
        "So use this to create a `PositionEmbedding` layer that looks-up a token's embedding vector and adds the position vector:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "838tmM1cm9cB"
      },
      "outputs": [],
      "source": [
        "class PositionalEmbedding(tf.keras.layers.Layer):\n",
        "  def __init__(self, vocab_size, d_model):\n",
        "    super().__init__()\n",
        "    self.d_model = d_model\n",
        "    self.embedding = tf.keras.layers.Embedding(vocab_size, d_model, mask_zero=True) \n",
        "    self.pos_encoding = positional_encoding(length=2048, depth=d_model)\n",
        "\n",
        "  def compute_mask(self, *args, **kwargs):\n",
        "    return self.embedding.compute_mask(*args, **kwargs)\n",
        "\n",
        "  def call(self, x):\n",
        "    length = tf.shape(x)[1]\n",
        "    x = self.embedding(x)\n",
        "    # This factor sets the relative scale of the embedding and positonal_encoding.\n",
        "    x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n",
        "    x = x + self.pos_encoding[tf.newaxis, :length, :]\n",
        "    return x\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpWnjwygmw-x"
      },
      "source": [
        "> Note: The [original paper](https://arxiv.org/pdf/1706.03762.pdf), section 3.4 and 5.1, uses a single tokenizer and weight matrix for both the source and target languages. This tutorial uses two separate tokenizers and weight matrices."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "tfz-EaCEDfUJ"
      },
      "outputs": [],
      "source": [
        "embed_pt = PositionalEmbedding(vocab_size=tokenizers.pt.get_vocab_size(), d_model=512)\n",
        "embed_en = PositionalEmbedding(vocab_size=tokenizers.en.get_vocab_size(), d_model=512)\n",
        "\n",
        "pt_emb = embed_pt(pt)\n",
        "en_emb = embed_en(en)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "3fJZ_ArLELhJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "349a60ee-1320-44bc-8aa5-487086c22de1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(64, 76), dtype=bool, numpy=\n",
              "array([[ True,  True,  True, ..., False, False, False],\n",
              "       [ True,  True,  True, ..., False, False, False],\n",
              "       [ True,  True,  True, ..., False, False, False],\n",
              "       ...,\n",
              "       [ True,  True,  True, ..., False, False, False],\n",
              "       [ True,  True,  True, ..., False, False, False],\n",
              "       [ True,  True,  True, ..., False, False, False]])>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "en_emb._keras_mask"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mE9cEBWCMKOP"
      },
      "source": [
        "### Add and normalize\n",
        "\n",
        "<table>\n",
        "<tr>\n",
        "  <th colspan=2>Add and normalize</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/Add+Norm.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lfz3WjFLTEk_"
      },
      "source": [
        "These \"Add & Norm\" blocks are scattered throughout the model. Each one joins a residual connection and runs the result through a `LayerNormalization` layer.\n",
        "\n",
        "The easiest way to organize the code is around these residual blocks. The following sections will define custom layer classes for each. \n",
        "\n",
        "The residual \"Add & Norm\" blocks are included so that training is efficient. The residual connection provides a direct path for the gradient (and ensures that vectors are **updated** by the attention layers instead of **replaced**), while the normalization maintains a reasonable scale for the outputs.\n",
        "\n",
        "Note: The implementations, below, use the `Add` layer to ensure that Keras masks are propagated (the `+` operator does not).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJAJ2_VlPXrZ"
      },
      "source": [
        "### The base attention layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1tMGOGki35KI"
      },
      "source": [
        "Attention layers are used throughout the model. These are all identical except for how the attention is configured. Each one contains a `layers.MultiHeadAttention`, a `layers.LayerNormalization` and a `layers.Add`. \n",
        "\n",
        "<table>\n",
        "<tr>\n",
        "  <th colspan=2>The base attention layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/BaseAttention.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z6chjIrOVSYp"
      },
      "source": [
        "To implement these attention layers, start with a simple base class that just contains the component layers. Each use-case will be implemented as a subclass. It's a little more code to write this way, but it keeps the intention clear."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "5VLa5QcdPpv5"
      },
      "outputs": [],
      "source": [
        "class BaseAttention(tf.keras.layers.Layer):\n",
        "  def __init__(self, **kwargs):\n",
        "    super().__init__()\n",
        "    self.mha = tf.keras.layers.MultiHeadAttention(**kwargs)\n",
        "    self.layernorm = tf.keras.layers.LayerNormalization()\n",
        "    self.add = tf.keras.layers.Add()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBY06TCqV2lv"
      },
      "source": [
        "#### Attention refresher\n",
        "\n",
        "Before you get into the specifics of each usage, here is a quick refresher on how attention works:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BsRsq4TV5FY"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The base attention layer</th>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=430 src=\"https://www.tensorflow.org/images/tutorials/transformer/BaseAttention-new.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtGTy7vZ5aaT"
      },
      "source": [
        "There are two inputs:\n",
        "\n",
        "1. The query sequence; the sequence being processed; the sequence doing the attending (bottom).\n",
        "2. The context sequence; the sequence being attended to (left).\n",
        "\n",
        "The output has the same shape as the query-sequence.\n",
        "\n",
        "The common comparison is that this operation is like a dictionary lookup.\n",
        "A **fuzzy**, **differentiable**, **vectorized** dictionary lookup.\n",
        "\n",
        "Here's a regular python dictionary, with 3 keys and 3 values being passed a single query.\n",
        "\n",
        "```\n",
        "d = {'color': 'blue', 'age': 22, 'type': 'pickup'}\n",
        "result = d['color']\n",
        "```\n",
        "\n",
        "- The `query`s is what you're trying to find.\n",
        "- The `key`s what sort of information the dictionary has.\n",
        "- The `value` is that information.\n",
        "\n",
        "When you look up a `query` in a regular dictionary, the dictionary finds the matching `key`, and returns its associated `value`.\n",
        "The `query` either has a matching `key` or it doesn't.\n",
        "You can imagine a **fuzzy** dictionary where the keys don't have to match perfectly.\n",
        "If you looked up `d[\"species\"]` in the dictionary above, maybe you'd want it to return `\"pickup\"` since that's the best match for the query.\n",
        "\n",
        "An attention layer does a fuzzy lookup like this, but it's not just looking for the best key.\n",
        "It combines the `values` based on how well the `query` matches each `key`.\n",
        "\n",
        "How does that work? In an attention layer the `query`, `key`, and `value` are each vectors.\n",
        "Instead of doing a hash lookup the attention layer combines the `query` and `key` vectors to determine how well they match, the \"attention score\".\n",
        "The layer returns the average across all the `values`, weighted by the \"attention scores\".\n",
        "\n",
        "Each location the query-sequence provides a `query` vector.\n",
        "The context sequence acts as the dictionary. At each location in the context sequence provides a `key` and `value` vector.\n",
        "The input vectors are not used directly, the `layers.MultiHeadAttention` layer includes `layers.Dense` layers to project the input vectors before using them.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7QcPJvmv6ix"
      },
      "source": [
        "### The cross attention layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8VJZqds37QC"
      },
      "source": [
        "At the literal center of the Transformer is the cross-attention layer. This layer connects the encoder and decoder. This layer is the most straight-forward use of attention in the model, it performs the same task as the attention block in the [NMT with attention tutorial](https://www.tensorflow.org/text/tutorials/nmt_with_attention).\n",
        "\n",
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The cross attention layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/CrossAttention.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jhscgMUNUFWP"
      },
      "source": [
        "To implement this you pass the target sequence `x` as the `query` and the `context` sequence as the `key/value` when calling the `mha` layer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "kfHVbJUWv8qp"
      },
      "outputs": [],
      "source": [
        "class CrossAttention(BaseAttention):\n",
        "  def call(self, x, context):\n",
        "    attn_output, attn_scores = self.mha(\n",
        "        query=x,\n",
        "        key=context,\n",
        "        value=context,\n",
        "        return_attention_scores=True)\n",
        "   \n",
        "    # Cache the attention scores for plotting later.\n",
        "    self.last_attn_scores = attn_scores\n",
        "\n",
        "    x = self.add([x, attn_output])\n",
        "    x = self.layernorm(x)\n",
        "\n",
        "    return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3tJU6aTYY1X"
      },
      "source": [
        "The caricature below shows how information flows through this layer. The columns represent the weighted sum over the context sequence.\n",
        "\n",
        "For simplicity the residual connections are not shown."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBE5JNB26OjJ"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th>The cross attention layer</th>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=430 src=\"https://www.tensorflow.org/images/tutorials/transformer/CrossAttention-new-full.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sU9MeSvzZSA-"
      },
      "source": [
        "The output length is the length of the `query` sequence, and not the length of the context `key/value` sequence.\n",
        "\n",
        "The diagram is further simplified, below. There's no need to draw the entire \"Attention weights\" matrix.\n",
        "The point is that each `query` location can see all the `key/value` pairs in the context, but no information is exchanged between the queries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GRrB_GcyKv-4"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th>Each query sees the whole context.</th>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=430 src=\"https://www.tensorflow.org/images/tutorials/transformer/CrossAttention-new.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCQsj7ljKv-4"
      },
      "source": [
        "Test run it on sample inputs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "Qw1FJV5qRk79",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8786933e-b65e-40b1-c1b1-fea112a8d936"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 77, 512)\n",
            "(64, 76, 512)\n"
          ]
        }
      ],
      "source": [
        "sample_ca = CrossAttention(num_heads=2, key_dim=512)\n",
        "\n",
        "print(pt_emb.shape)\n",
        "print(en_emb.shape)\n",
        "#print(sample_ca(en_emb, pt_emb).shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J6qrQxSpv34R"
      },
      "source": [
        "### The global self attention layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-LbLRTkaTh5"
      },
      "source": [
        "This layer is responsible for processing the context sequence, and propagating information along its length:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YlYBQX3E388Y"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The global self attention layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/SelfAttention.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9j9LPJFbEkF"
      },
      "source": [
        "Since the context sequence is fixed while the translation is being generated, information is allowed to flow in both directions. \n",
        "\n",
        "Before Transformers and self attention, models commonly used RNNs or CNNs to do this task:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87Rlu8N_avBF"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>Bidirectional RNNs and CNNs</th>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=500 src=\"https://www.tensorflow.org/images/tutorials/transformer/RNN-bidirectional.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=500 src=\"https://www.tensorflow.org/images/tutorials/transformer/CNN.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPyXM4vabhup"
      },
      "source": [
        "RNNs and CNNs have their limitations.\n",
        "\n",
        "- The RNN allows information to flow all the way across the sequence, but it passes through many processing steps to get there (limiting gradient flow). These RNN steps have to be run sequentially and so the RNN is less able to take advantage of modern parallel devices.\n",
        "- In the CNN each location can be processed in parallel, but it only provides a limited receptive field. The receptive field only grows linearly with the number of CNN layers,  You need to stack a number of Convolution layers to transmit information across the sequence ([Wavenet](https://arxiv.org/abs/1609.03499) reduces this problem by using dilated convolutions).\n",
        "\n",
        "The global self attention layer on the other hand lets every sequence element directly access every other sequence element, with only a few operations, and all the outputs can be computed in parallel. \n",
        "\n",
        "To implement this layer you just need to pass the target sequence, `x`, as both the `query`, and `value` arguments to the `mha` layer: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "RNqoTpn1wB3i"
      },
      "outputs": [],
      "source": [
        "class GlobalSelfAttention(BaseAttention):\n",
        "  def call(self, x):\n",
        "    attn_output = self.mha(\n",
        "        query=x,\n",
        "        value=x,\n",
        "        key=x)\n",
        "    x = self.add([x, attn_output])\n",
        "    x = self.layernorm(x)\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "jPn2D07-Jcmj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c24f3d4d-003c-491f-a024-028b3bae8b2b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 77, 512)\n"
          ]
        }
      ],
      "source": [
        "sample_gsa = GlobalSelfAttention(num_heads=2, key_dim=512)\n",
        "\n",
        "print(pt_emb.shape)\n",
        "#print(sample_gsa(pt_emb).shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nd-ga2tQfzhE"
      },
      "source": [
        "Sticking with the same style as before you could draw it like this:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1bcv9Zc6--k"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The global self attention layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=330 src=\"https://www.tensorflow.org/images/tutorials/transformer/SelfAttention-new-full.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ze7D0WHOe-d8"
      },
      "source": [
        "Again, the residual connections are omitted for clarity.\n",
        "\n",
        "It's more compact, and just as accurate to draw it like this:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "imlyNt2K7RnA"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The global self attention layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=500 src=\"https://www.tensorflow.org/images/tutorials/transformer/SelfAttention-new.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yq4NtLymD99-"
      },
      "source": [
        "### The causal self attention layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VufkgF7caLze"
      },
      "source": [
        "This layer does a similar job as the global self attention layer, for the output sequence:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KMEDiP63-hQ"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The causal self attention layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/CausalSelfAttention.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0AtF1HYFEOYf"
      },
      "source": [
        "This needs to be handled differently from the encoder's global self attention layer.  \n",
        "\n",
        "Like the [text generation tutorial](https://www.tensorflow.org/text/tutorials/text_generation), and the [NMT with attention](https://www.tensorflow.org/text/tutorials/nmt_with_attention) tutorial, Transformers are an \"autoregressive\" model: They generate the text one token at a time and feed that output back to the input. To make this _efficient_, these models ensure that the output for each sequence element only depends on the previous sequence elements; the models are \"causal\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDyn29oahHiL"
      },
      "source": [
        "A single-direction RNN is causal by definition. To make a causal convolution you just need to pad the input and shift the output so that it aligns correctly (use `layers.Conv1D(padding='causal')`) ."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_1yd-LjhM3b"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>Causal RNNs and CNNs</th>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=500 src=\"https://www.tensorflow.org/images/tutorials/transformer/RNN.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=500 src=\"https://www.tensorflow.org/images/tutorials/transformer/CNN-causal.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4St1AWq9bIZg"
      },
      "source": [
        "A causal model is efficient in two ways: \n",
        "\n",
        "1. In training, it lets you compute loss for every location in the output sequence while executing the model just once.\n",
        "2. During inference, for each new token generated you only need to calculate its outputs, the outputs for the previous sequence elements can be reused.\n",
        "  - For an RNN you just need the RNN-state to account for previous computations (pass `return_state=True` to the RNN layer's constructor).\n",
        "  - For a CNN you would need to follow the approach of [Fast Wavenet](https://arxiv.org/abs/1611.09482)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLYfIa8eiYgk"
      },
      "source": [
        "To build a causal self attention layer, you need to use an appropriate mask when computing the attention scores and summing the attention `value`s.\n",
        "\n",
        "This is taken care of automatically if you pass `use_causal_mask = True` to the `MultiHeadAttention` layer when you call it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "4MMQ-AfKD99_"
      },
      "outputs": [],
      "source": [
        "class CausalSelfAttention(BaseAttention):\n",
        "  def call(self, x):\n",
        "    attn_output = self.mha(\n",
        "        query=x,\n",
        "        value=x,\n",
        "        key=x,\n",
        "        use_causal_mask = True)\n",
        "    x = self.add([x, attn_output])\n",
        "    x = self.layernorm(x)\n",
        "    return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5oumNdAjI-D"
      },
      "source": [
        "The causal mask ensures that each location only has access to the locations that come before it: "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aFJy5L1U8TBt"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The causal self attention layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=330 src=\"https://www.tensorflow.org/images/tutorials/transformer/CausalSelfAttention-new-full.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWX0RNnFjaCj"
      },
      "source": [
        "Again, the residual connections are omitted for simplicity.\n",
        "\n",
        "The more compact representation of this layer would be:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3C9qVfvh8-jp"
      },
      "source": [
        "<table>\n",
        "</tr>\n",
        "  <th colspan=1>The causal self attention layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=430 src=\"https://www.tensorflow.org/images/tutorials/transformer/CausalSelfAttention-new.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQBhYEZ2jfrX"
      },
      "source": [
        "Test out the layer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "x4dQuzvlD99_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8eca6168-6b49-42db-cb7a-99b0b86a84ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 76, 512)\n",
            "(64, 76, 512)\n"
          ]
        }
      ],
      "source": [
        "sample_csa = CausalSelfAttention(num_heads=2, key_dim=512)\n",
        "\n",
        "print(en_emb.shape)\n",
        "print(sample_csa(en_emb).shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-IPCEkajleb"
      },
      "source": [
        "The output for early sequence elements doesn't depend on later elements, so it shouldn't matter if you trim elements before or after applying the layer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "bwKlheQ-WVxl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c39957bf-149d-42ce-c07f-e2ca2855c818"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.7683716e-07"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "out1 = sample_csa(embed_en(en[:, :3])) \n",
        "out2 = sample_csa(embed_en(en))[:, :3]\n",
        "\n",
        "tf.reduce_max(abs(out1 - out2)).numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOVv38ynuBW-"
      },
      "source": [
        "Note: When using Keras masks, the output values at invalid locations are not well defined. So the above may not hold for masked regions. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLjScSWQv9M5"
      },
      "source": [
        "### The feed forward network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pz0HBopX_VdU"
      },
      "source": [
        "The transformer also includes this point-wise feed-forward network in both the encoder and decoder:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDHMWoZ94AUU"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The feed forward network</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/FeedForward.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Yb-IV0Nlzd0"
      },
      "source": [
        "The network consists of two linear layers (`tf.keras.layers.Dense`) with a ReLU activation in-between, and a dropout layer. As with the attention layers the code here also includes the residual connection and normalization:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "rAYLeu0uwXYK"
      },
      "outputs": [],
      "source": [
        "class FeedForward(tf.keras.layers.Layer):\n",
        "  def __init__(self, d_model, dff, dropout_rate=0.1):\n",
        "    super().__init__()\n",
        "    self.seq = tf.keras.Sequential([\n",
        "      tf.keras.layers.Dense(dff, activation='relu'),\n",
        "      tf.keras.layers.Dense(d_model),\n",
        "      tf.keras.layers.Dropout(dropout_rate)\n",
        "    ])\n",
        "    self.add = tf.keras.layers.Add()\n",
        "    self.layer_norm = tf.keras.layers.LayerNormalization()\n",
        "\n",
        "  def call(self, x):\n",
        "    x = self.add([x, self.seq(x)])\n",
        "    x = self.layer_norm(x) \n",
        "    return x\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQBlOVQU_hUt"
      },
      "source": [
        "Test the layer, the output is the same shape as the input:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "r-Y8Yqi1_hUt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e56c894f-6484-4aff-dac6-a9ae6c871915"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 76, 512)\n",
            "(64, 76, 512)\n"
          ]
        }
      ],
      "source": [
        "sample_ffn = FeedForward(512, 2048)\n",
        "\n",
        "print(en_emb.shape)\n",
        "print(sample_ffn(en_emb).shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QFv-FNYUmvpn"
      },
      "source": [
        "### The encoder layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zk-DAL2xv4PZ"
      },
      "source": [
        "The encoder contains a stack of `N` encoder layers. Where each `EncoderLayer` contains a `GlobalSelfAttention` and `FeedForward` layer:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RgPaE3f44Cgh"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The encoder layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/EncoderLayer.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8kRUT__Ly9HH"
      },
      "source": [
        "Here is the definition of the `EncoderLayer`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "ncyS-Ms3i2x_"
      },
      "outputs": [],
      "source": [
        "class EncoderLayer(tf.keras.layers.Layer):\n",
        "  def __init__(self,*, d_model, num_heads, dff, dropout_rate=0.1):\n",
        "    super().__init__()\n",
        "\n",
        "    self.self_attention = GlobalSelfAttention(\n",
        "        num_heads=num_heads,\n",
        "        key_dim=d_model,\n",
        "        dropout=dropout_rate)\n",
        "\n",
        "    self.ffn = FeedForward(d_model, dff)\n",
        "\n",
        "  def call(self, x):\n",
        "    x = self.self_attention(x)\n",
        "    x = self.ffn(x)\n",
        "    return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QeXHMUlb6q6F"
      },
      "source": [
        "And a quick test, the output will have the same shape as the input:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "AzZRXdO0mI48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66b8c135-3d35-4e3b-90bf-55500d793484"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 77, 512)\n",
            "(64, 77, 512)\n"
          ]
        }
      ],
      "source": [
        "sample_encoder_layer = EncoderLayer(d_model=512, num_heads=8, dff=2048)\n",
        "\n",
        "print(pt_emb.shape)\n",
        "print(sample_encoder_layer(pt_emb).shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SE1H51Ajm0q1"
      },
      "source": [
        "### The encoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fym9ah11ykMd"
      },
      "source": [
        "Next build the encoder."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXI2B-Ad4ETO"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The encoder</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/Encoder.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DA6sVo5rlzd3"
      },
      "source": [
        "The encoder consists of:\n",
        "\n",
        "- A `PositionalEmbedding` layer at the input.\n",
        "- A stack of `EncoderLayer` layers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "jpEox7gJ8FCI"
      },
      "outputs": [],
      "source": [
        "class Encoder(tf.keras.layers.Layer):\n",
        "  def __init__(self, *, num_layers, d_model, num_heads,\n",
        "               dff, vocab_size, dropout_rate=0.1):\n",
        "    super().__init__()\n",
        "\n",
        "    self.d_model = d_model\n",
        "    self.num_layers = num_layers\n",
        "\n",
        "    self.pos_embedding = PositionalEmbedding(\n",
        "        vocab_size=vocab_size, d_model=d_model)\n",
        "\n",
        "    self.enc_layers = [\n",
        "        EncoderLayer(d_model=d_model,\n",
        "                     num_heads=num_heads,\n",
        "                     dff=dff,\n",
        "                     dropout_rate=dropout_rate)\n",
        "        for _ in range(num_layers)]\n",
        "    self.dropout = tf.keras.layers.Dropout(dropout_rate)\n",
        "\n",
        "  def call(self, x):\n",
        "    # `x` is token-IDs shape: (batch, seq_len)\n",
        "    x = self.pos_embedding(x)  # Shape `(batch_size, seq_len, d_model)`.\n",
        "    \n",
        "    # Add dropout.\n",
        "    x = self.dropout(x)\n",
        "\n",
        "    for i in range(self.num_layers):\n",
        "      x = self.enc_layers[i](x)\n",
        "\n",
        "    return x  # Shape `(batch_size, seq_len, d_model)`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "texobMBHLBEU"
      },
      "source": [
        "Test the encoder:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "SDPXTvYgJH8s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf367447-48b6-440e-8933-394ea58adaea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 77)\n",
            "(64, 77, 512)\n"
          ]
        }
      ],
      "source": [
        "# Instantiate the encoder.\n",
        "sample_encoder = Encoder(num_layers=4,\n",
        "                         d_model=512,\n",
        "                         num_heads=8,\n",
        "                         dff=2048,\n",
        "                         vocab_size=8500)\n",
        "\n",
        "sample_encoder_output = sample_encoder(pt, training=False)\n",
        "\n",
        "# Print the shape.\n",
        "print(pt.shape)\n",
        "print(sample_encoder_output.shape)  # Shape `(batch_size, input_seq_len, d_model)`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6LO_48Owmx_o"
      },
      "source": [
        "### The decoder layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGxm57u6E4g2"
      },
      "source": [
        "The decoder's stack is slightly more complex, with each `DecoderLayer` containing a `CausalSelfAttention`, a `CrossAttention`, and a `FeedForward` layer:  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ZYER7rC4FmI"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The decoder layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/DecoderLayer.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "9SoX0-vd1hue"
      },
      "outputs": [],
      "source": [
        "class DecoderLayer(tf.keras.layers.Layer):\n",
        "  def __init__(self,\n",
        "               *,\n",
        "               d_model,\n",
        "               num_heads,\n",
        "               dff,\n",
        "               dropout_rate=0.1):\n",
        "    super(DecoderLayer, self).__init__()\n",
        "\n",
        "    self.causal_self_attention = CausalSelfAttention(\n",
        "        num_heads=num_heads,\n",
        "        key_dim=d_model,\n",
        "        dropout=dropout_rate)\n",
        "    \n",
        "    self.cross_attention = CrossAttention(\n",
        "        num_heads=num_heads,\n",
        "        key_dim=d_model,\n",
        "        dropout=dropout_rate)\n",
        "\n",
        "    self.ffn = FeedForward(d_model, dff)\n",
        "\n",
        "  def call(self, x, context):\n",
        "    x = self.causal_self_attention(x=x)\n",
        "    x = self.cross_attention(x=x, context=context)\n",
        "\n",
        "    # Cache the last attention scores for plotting later\n",
        "    self.last_attn_scores = self.cross_attention.last_attn_scores\n",
        "\n",
        "    x = self.ffn(x)  # Shape `(batch_size, seq_len, d_model)`.\n",
        "    return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6T3RSR_6nJX"
      },
      "source": [
        "Test the decoder layer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "Ne2Bqx8k71l0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cbca07dc-b219-4434-b1d8-c236f61485cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 76, 512)\n",
            "(64, 77, 512)\n",
            "(64, 76, 512)\n"
          ]
        }
      ],
      "source": [
        "sample_decoder_layer = DecoderLayer(d_model=512, num_heads=8, dff=2048)\n",
        "\n",
        "sample_decoder_layer_output = sample_decoder_layer(\n",
        "    x=en_emb, context=pt_emb)\n",
        "\n",
        "print(en_emb.shape)\n",
        "print(pt_emb.shape)\n",
        "print(sample_decoder_layer_output.shape)  # `(batch_size, seq_len, d_model)`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-uO6ls8m2O5"
      },
      "source": [
        "### The decoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fgj3c0TVF3Pb"
      },
      "source": [
        "Similar to the `Encoder`, the `Decoder` consists of a `PositionalEmbedding`, and a stack of `DecoderLayer`s:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ADGss2nT4Gt-"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The embedding and positional encoding layer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/Decoder.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2q49vtv5lzd3"
      },
      "source": [
        "\n",
        "Define the decoder by extending `tf.keras.layers.Layer`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "d5_d5-PLQXwY"
      },
      "outputs": [],
      "source": [
        "class Decoder(tf.keras.layers.Layer):\n",
        "  def __init__(self, *, num_layers, d_model, num_heads, dff, vocab_size,\n",
        "               dropout_rate=0.1):\n",
        "    super(Decoder, self).__init__()\n",
        "\n",
        "    self.d_model = d_model\n",
        "    self.num_layers = num_layers\n",
        "\n",
        "    self.pos_embedding = PositionalEmbedding(vocab_size=vocab_size,\n",
        "                                             d_model=d_model)\n",
        "    self.dropout = tf.keras.layers.Dropout(dropout_rate)\n",
        "    self.dec_layers = [\n",
        "        DecoderLayer(d_model=d_model, num_heads=num_heads,\n",
        "                     dff=dff, dropout_rate=dropout_rate)\n",
        "        for _ in range(num_layers)]\n",
        "\n",
        "    self.last_attn_scores = None\n",
        "\n",
        "  def call(self, x, context):\n",
        "    # `x` is token-IDs shape (batch, target_seq_len)\n",
        "    x = self.pos_embedding(x)  # (batch_size, target_seq_len, d_model)\n",
        "\n",
        "    x = self.dropout(x)\n",
        "\n",
        "    for i in range(self.num_layers):\n",
        "      x  = self.dec_layers[i](x, context)\n",
        "\n",
        "    self.last_attn_scores = self.dec_layers[-1].last_attn_scores\n",
        "\n",
        "    # The shape of x is (batch_size, target_seq_len, d_model).\n",
        "    return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eALcB--YMmLf"
      },
      "source": [
        "Test the decoder:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "xyHdG_jWPgKu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df4f8721-b713-4f0c-9b1a-d89836959693"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 76)\n",
            "(64, 77, 512)\n",
            "(64, 76, 512)\n"
          ]
        }
      ],
      "source": [
        "# Instantiate the decoder.\n",
        "sample_decoder = Decoder(num_layers=4,\n",
        "                         d_model=512,\n",
        "                         num_heads=8,\n",
        "                         dff=2048,\n",
        "                         vocab_size=8000)\n",
        "\n",
        "output = sample_decoder(\n",
        "    x=en,\n",
        "    context=pt_emb)\n",
        "\n",
        "# Print the shapes.\n",
        "print(en.shape)\n",
        "print(pt_emb.shape)\n",
        "print(output.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "ioJ4XJAUAReI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d079405-4a5f-48dd-919a-f81092190fad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([64, 8, 76, 77])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "sample_decoder.last_attn_scores.shape  # (batch, heads, target_seq, input_seq)"
      ]
    },
    {
      "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": [
        "## The Transformer"
      ]
    },
    {
      "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 which converts the resulting vector at each location into output token probabilities. \n",
        "\n",
        "The output of the decoder is the input to this final linear layer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46nL2X_84Iud"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>The transformer</th>\n",
        "<tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img src=\"https://www.tensorflow.org/images/tutorials/transformer/transformer.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trHHo2z_LC-u"
      },
      "source": [
        "A `Transformer` with one layer in both the `Encoder` and `Decoder` looks almost exactly like the model from the [RNN+attention tutorial](https://www.tensorflow.org/text/tutorials/nmt_with_attention). A multi-layer Transformer has more layers, but is fundamentally doing the same thing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09kxrwiaBB36"
      },
      "source": [
        "<table>\n",
        "<tr>\n",
        "  <th colspan=1>A 1-layer transformer</th>\n",
        "  <th colspan=1>A 4-layer transformer</th>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=400 src=\"https://www.tensorflow.org/images/tutorials/transformer/Transformer-1layer-compact.png\"/>\n",
        "  </td>\n",
        "  <td rowspan=3>\n",
        "   <img width=330 src=\"https://www.tensorflow.org/images/tutorials/transformer/Transformer-4layer-compact.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th colspan=1>The RNN+Attention model</th>\n",
        "</tr>\n",
        "<tr>\n",
        "  <td>\n",
        "   <img width=400 src=\"https://www.tensorflow.org/images/tutorials/transformer/RNN+attention-compact.png\"/>\n",
        "  </td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I5vSbJ_gKx7C"
      },
      "source": [
        "Create the `Transformer` by extending `tf.keras.Model`:\n",
        "\n",
        "> Note: The [original paper](https://arxiv.org/pdf/1706.03762.pdf), section 3.4, shares the weight matrix between the embedding layer and the final linear layer. To keep things simple, this tutorial uses two separate weight matrices."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "PED3bIpOYkBu"
      },
      "outputs": [],
      "source": [
        "class Transformer(tf.keras.Model):\n",
        "  def __init__(self, *, num_layers, d_model, num_heads, dff,\n",
        "               input_vocab_size, target_vocab_size, dropout_rate=0.1):\n",
        "    super().__init__()\n",
        "    self.encoder = Encoder(num_layers=num_layers, d_model=d_model,\n",
        "                           num_heads=num_heads, dff=dff,\n",
        "                           vocab_size=input_vocab_size,\n",
        "                           dropout_rate=dropout_rate)\n",
        "\n",
        "    self.decoder = Decoder(num_layers=num_layers, d_model=d_model,\n",
        "                           num_heads=num_heads, dff=dff,\n",
        "                           vocab_size=target_vocab_size,\n",
        "                           dropout_rate=dropout_rate)\n",
        "\n",
        "    self.final_layer = tf.keras.layers.Dense(target_vocab_size)\n",
        "\n",
        "  def call(self, inputs):\n",
        "    # To use a Keras model with `.fit` you must pass all your inputs in the\n",
        "    # first argument.\n",
        "    context, x  = inputs\n",
        "\n",
        "    context = self.encoder(context)  # (batch_size, context_len, d_model)\n",
        "\n",
        "    x = self.decoder(x, context)  # (batch_size, target_len, d_model)\n",
        "\n",
        "    # Final linear layer output.\n",
        "    logits = self.final_layer(x)  # (batch_size, target_len, target_vocab_size)\n",
        "\n",
        "    try:\n",
        "      # Drop the keras mask, so it doesn't scale the losses/metrics.\n",
        "      # b/250038731\n",
        "      del logits._keras_mask\n",
        "    except AttributeError:\n",
        "      pass\n",
        "\n",
        "    # Return the final output and the attention weights.\n",
        "    return logits"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsINyf1VEQLC"
      },
      "source": [
        "### Hyperparameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IjwMq_ixlzd5"
      },
      "source": [
        "To keep this example small and relatively fast, the number of layers (`num_layers`), the dimensionality of the embeddings (`d_model`), and the internal dimensionality of the `FeedForward` 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_heads=8`).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "mzyo6KDfVyhl"
      },
      "outputs": [],
      "source": [
        "num_layers = 4\n",
        "d_model = 128\n",
        "dff = 512\n",
        "num_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": 47,
      "metadata": {
        "id": "UiysUa--4tOU"
      },
      "outputs": [],
      "source": [
        "transformer = Transformer(\n",
        "    num_layers=num_layers,\n",
        "    d_model=d_model,\n",
        "    num_heads=num_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 it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "c8eO85hpFHmE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee940cf9-ad81-4e80-ae46-135d0aa5d0c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 76)\n",
            "(64, 77)\n",
            "(64, 76, 7010)\n"
          ]
        }
      ],
      "source": [
        "output = transformer((pt, en))\n",
        "\n",
        "print(en.shape)\n",
        "print(pt.shape)\n",
        "print(output.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "olTLrK8pAcLd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03903df0-bee9-4ebd-f3c4-70795d086078"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 8, 76, 77)\n"
          ]
        }
      ],
      "source": [
        "attn_scores = transformer.decoder.dec_layers[-1].last_attn_scores\n",
        "print(attn_scores.shape)  # (batch, heads, target_seq, input_seq)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_jTvJsXquaHW"
      },
      "source": [
        "Print the summary of the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "IsUPhlfEtOjn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4011d9f7-386a-4aee-d7ab-276df9913d3f"
      },
      "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_38 (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": [
        "### Set up the optimizer"
      ]
    },
    {
      "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).\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": 51,
      "metadata": {
        "id": "iYQdOO1axwEI"
      },
      "outputs": [],
      "source": [
        "class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
        "  def __init__(self, d_model, warmup_steps=4000):\n",
        "    super().__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",
        "    step = tf.cast(step, dtype=tf.float32)\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": 52,
      "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": 53,
      "metadata": {
        "id": "Xij3MwYVRAAS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "b2bd2752-dde5-46fe-dedd-9f5f3716570d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Train Step')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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MZGZmory8HEDDEN4dd9yBu+66C3PmzIHBYIDBYEBBQUHb/XIcjBDCbnOiGkUGeOKuy8sd/Pvns3Z5DSIiouZylfPFJ06ciIKCAiQlJcFgMGDgwIHYsmWLNDE8JycHSuVvOW/48OH44osvsHDhQixYsABRUVHYsGED+vXrJ7V59tlnUVFRgZkzZ6K0tBQjRozAli1boNFopDZJSUn45JNPpJ8HDRoEANi2bRtuv/12fPXVVygoKMBnn32Gzz77TGoXHh6Oc+fO2evX4dBKKutQXlMPoGFtJ3uZeVs3/HAkDxsyLuLpu3oi0Edz452IiIjsQNZ1opxdc9eZcAaZuaVIXPkr9D4a7F4w2q6v9ad3d2Ffdgkevb0b5o3pZdfXIiKijqfdrxNFziW7qAKA/YbyrjTztq4AGu6n13j2i4iIqK0xRJFN5F6eDxXWBiEqvrcOXTt7oqy6Hmv25Nj99YiIiJrCEEU2kV1k3yvzrqRUKjBzZMPZqI9+yUKd2WL31yQiIvo9hiiyCXtfmfd7iYNC0NlbjYvGanyz/0KbvCYREdGVGKLIJtpyOA8ANG4ueOTy3Ki3t51GPc9GERFRG2OIoptWU2/GJVM1gLYZzmv059gu6OSpQk5xJf6bebHNXpeIiAhgiCIbOF9SBSEAD5ULOnmq2ux1PVSueJhno4iISCYMUXTTrpwPpVAo2vS1HxoWDj8PN2QVVmDjwUtt+tpERNSxMUTRTbPnjYdvxFPtiv+9fKXeW1tPwWzh2rFERNQ2GKLoprX1lXm/NzUuHFp3N5wpqMDGg5wbRUREbYMhim6aFKLacFL5lbw1bnh4ZCQA4PXkk1w3ioiI2gRDFN00OYfzGk2/NRIBXiqcK6rEun25stVBREQdB0MU3RQhhOzDeUDD3KjH/xAFAHjzp1OoqjXLVgsREXUMDFF0UwrLa1FVZ4ZCAYT6yReiAGDy0C4I9XNHflkNVu86J2stRETk/Bii6KbkFFcAAIK17lC5yvvHSeWqxJw7ewAA3t1+GsbKOlnrISIi58YQRTclR7rdi7vMlTSYMDAEPXXeMFXX490dZ+Quh4iInBhDFN2U7MuTysP9PWWupIGLUoFnEnoCAD7+NQsXSqtkroiIiJwVQxTdFLmXN2jK6N6BiI30R029BS9vPi53OURE5KQYouim5ErDee0nRCkUCiy6pw8UCuDbAxeRnl0sd0lEROSEGKLopvw2nNd+QhQA9AvR4sGYMADAi98dhYW3gyEiIhtjiKJWq6o1I7+sBoC8a0Rdy9yEHvBUueDAeSM2ZF6QuxwiInIyDFHUaudLGs5Ceatd4evhJnM1Vwv01mDWH7oDAF7echyVtfUyV0RERM6EIYparXEor0snDygUCpmradpfb41EmL878kw1eHvrabnLISIiJ8IQRa3WHm73ciMaNxcsHNcHAPDvn8/idH6ZzBUREZGzYIiiVnOEEAUAd/XR4Q+9AlFnFli44TCE4CRzIiK6eQxR1GrtcY2opigUCrxwb19o3JTYfbaYk8yJiMgmGKKo1RzlTBTQsI7V43+IAgC89P0x3lePiIhuGkMUtYrFIqSFNtvLLV9u5OGRXdGtsycKy2vxyo9cyZyIiG4OQxS1Sn5ZDWrqLXBRKhDkq5G7nGZRuSrxjwn9AACfp+UgPbtE5oqIiMiRMURRqzQO5QX7auDm4jh/jIZ3D8AfbwmBEMCzXx1AdZ1Z7pKIiMhBOc6nH7UrOQ42lHelpHv6IMBLjTMFFViRckrucoiIyEExRFGr5BRVAGhfNx5uLl8PFZYkNgzrvbfzLA6dN8pcEREROSKGKGoVR7oyrylj+ulxT/8gmC0Cz3x1ALX1FrlLIiIiB8MQRa2S3Tic187XiLqeF+7tC39PFY4byvDOdt4ShoiIWoYhilol18HPRAFAJy81nr+3LwDg7a2ncfB8qbwFERGRQ2GIoharqKlHYXktAMecE3Wl8f2DMC46CPUWgdlrMlFZWy93SURE5CAYoqjFGudD+Xq4QevuJnM1N0ehUOCl+/pB76PB2cIKvPT9MblLIiIiByF7iFq5ciUiIiKg0WgQGxuLPXv2XLf9+vXr0atXL2g0GkRHR2PTpk1WzwshkJSUhKCgILi7uyM+Ph6nTllfxv7SSy9h+PDh8PDwgK+vb5Ovk5OTg3HjxsHDwwOBgYF45plnUF/PsxSA408q/z1fDxVee3AAgIZFOFOO5clcEREROQJZQ9TatWsxZ84cLF68GPv378eAAQOQkJCA/Pz8Jtvv2rULkydPxowZM5CRkYHExEQkJibi8OHDUpvly5djxYoVWLVqFdLS0uDp6YmEhARUV1dLbWpra/HAAw/g0UcfbfJ1zGYzxo0bh9raWuzatQuffPIJVq9ejaSkJNv+AhxU43woRx/Ku9Kt3QPwvyMiAQDPfnUQBWU1MldERETtnpDR0KFDxaxZs6SfzWazCA4OFkuXLm2y/YMPPijGjRtntS02NlY88sgjQgghLBaL0Ov14pVXXpGeLy0tFWq1Wnz55ZdXHe/jjz8WWq32qu2bNm0SSqVSGAwGadu7774rfHx8RE1NTbP7ZzQaBQBhNBqbvY8jWPjNIRE+b6N4efMxuUuxqaraepHw+g4RPm+j+MtHacJstshdEhERyaC5n9+ynYmqra1Feno64uPjpW1KpRLx8fFITU1tcp/U1FSr9gCQkJAgtc/KyoLBYLBqo9VqERsbe81jXut1oqOjodPprF7HZDLhyJEj19yvpqYGJpPJ6uGMnG04r5HGzQVvThoElasS204U4N8/n5W7JCIiasdkC1GFhYUwm81WQQUAdDodDAZDk/sYDIbrtm/82pJjtuR1rnyNpixduhRarVZ6hIWFNfs1HYm0vIEDrxF1LT313nh+fMOyB8t/OIF954plroiIiNor2SeWO5P58+fDaDRKj9zcXLlLsjmzRSC3xDnPRDWaPDQMEwYGw2wReOyLDBRX1MpdEhERtUOyhaiAgAC4uLggL8/6Sqi8vDzo9fom99Hr9ddt3/i1Jcdsyetc+RpNUavV8PHxsXo4G4OpGnVmATcXBYK07nKXYxcNyx5Eo2uAJwymajy1NhMWi5C7LCIiamdkC1EqlQoxMTFISUmRtlksFqSkpCAuLq7JfeLi4qzaA0BycrLUPjIyEnq93qqNyWRCWlraNY95rdc5dOiQ1VWCycnJ8PHxQZ8+fZp9HGeUU9RwFirUzwMuSoXM1diPl9oVK6fcArWrEjtOFuDdHWfkLomIiNoZWYfz5syZg3//+9/45JNPcOzYMTz66KOoqKjA9OnTAQBTp07F/PnzpfZPPvkktmzZgtdeew3Hjx/H888/j3379uGxxx4D0HAGYfbs2ViyZAm+/fZbHDp0CFOnTkVwcDASExOl4+Tk5CAzMxM5OTkwm83IzMxEZmYmysvLAQB33XUX+vTpg4ceeggHDhzADz/8gIULF2LWrFlQq9Vt9wtqh3KKKwA41/IG19I7yAcvTmiYH/Xajyew42SBzBUREVF74irni0+cOBEFBQVISkqCwWDAwIEDsWXLFmkSd05ODpTK33Le8OHD8cUXX2DhwoVYsGABoqKisGHDBvTr109q8+yzz6KiogIzZ85EaWkpRowYgS1btkCj0UhtkpKS8Mknn0g/Dxo0CACwbds23H777XBxccHGjRvx6KOPIi4uDp6enpg2bRpefPFFe/9K2r3frsxzzqG833twcBj2Z5di7b5cPP7Ffnz72AhEBHjKXRYREbUDCiEEJ3vYiclkglarhdFodJr5UY99sR8bD17C38f2xsO3dZW7nDZRU2/GpPd3IyOnFFGBXvhm1q3wUsv6/w8iIrKj5n5+8+o8ahFnXK38RtSuLlj1PzEI9FbjVH455nCiORERgSGKWshZF9q8EZ2PBqseioHKRYkfj+bhra2n5S6JiIhkxhBFzWaqrkNJZR0A51xo80Zu6eKHJYkN8+9e/+kkvj94SeaKiIhITgxR1GyNyxt08lR12DlBDw4Jw1+GRwAAnlqXifTsEnkLIiIi2TBEUbN1xPlQTVl0Tx/E9w5Ebb0FD3+6D9lFFXKXREREMmCIombLvhyiwjvgUN6VXJQKvDlpEPqF+KC4ohbTV+9FaSVvDUNE1NEwRFGzddRJ5U3xVLviw2lDEKzV4GxBBWb+Xzpq6s1yl0VERG2IIYqajcN51nQ+Gnw0fQi81K7Yk1WMOesOwMylD4iIOgyGKGq27MsTy8MZoiS99D54939ugZuLAt8fvISk/x4G168lIuoYGKKoWerNFlworQLQMZc3uJ6RUZ3xrwcHQqEAPk/LwevJJ+UuiYiI2gBDFDXLJWM1zBYBlasSOm/NjXfoYMYPCMaLExrWkFqx9TQ+/jVL5oqIiMjeGKKoWRqH8sL83KFUKmSupn16aFg45t7ZAwDwwndH8U3GeZkrIiIie2KIombhlXnN89gfumP6rREAgKfXH8TmQ1zVnIjIWTFEUbNkFzcsKBneyVPmSto3hUKBReP64P5bQmG2CDz+ZQZ+OGKQuywiIrIDhihqFi5v0HxKpQLL/9QfEwYGo94i8NgX+/HT0Ty5yyIiIhtjiKJm4XBey7goFXjtgQG4p38Q6swC/+/z/dh2PF/usoiIyIYYouiGhBC/rRHF5Q2azdVFiTcmDsTYaD1qzRY88lk6tp9gkCIichYMUXRDxqo6lFXXAwDC/BiiWsLVRYk3Jw1CQl+ddMPiLYc5R4qIyBncVIiqrq62VR3UjjUO5XX2VsNd5SJzNY7HzUWJtybfgrHRetSZBWZ9sR8bMi7IXRYREd2kFocoi8WCf/zjHwgJCYGXlxfOnj0LAFi0aBE+/PBDmxdI8uPtXm6eylWJFZMGSVftPbUuE1+k5chdFhER3YQWh6glS5Zg9erVWL58OVQqlbS9X79++OCDD2xaHLUPnFRuG64uSrzyp/54aFg4hAAWfHMIH/x8Vu6yiIiolVocoj799FO8//77mDJlClxcfhvaGTBgAI4fP27T4qh94PIGtqNUKvDihL54ZFRXAMCS74/htR9P8KbFREQOqMUh6sKFC+jevftV2y0WC+rq6mxSFLUvvDLPthQKBZ4b00u6RcxbW09j3n8Oos5skbkyIiJqiRaHqD59+uDnn3++avtXX32FQYMG2aQoal84nGd7CoUCj4+Owj/vi4ZSAazbdx4zP92Hytp6uUsjIqJmcm3pDklJSZg2bRouXLgAi8WCr7/+GidOnMCnn36KjRs32qNGklFtvQWXjFUAgC48E2Vzf47tgs7eajz+5X5sO1GAye/vxkd/GYJOXmq5SyMiohto8ZmoCRMm4LvvvsNPP/0ET09PJCUl4dixY/juu+9w55132qNGktGF0ipYBKBxU6IzP9jt4s4+Onz+v8Pg5+GGA+eNuP/dXThXWCF3WUREdAMtPhMFACNHjkRycrKta6F26MqhPIVCIXM1zism3A9fPTocUz/cg3NFlUh851es+p8YDOvaSe7SiIjoGlp8Jqpr164oKiq6antpaSm6du1qk6Ko/fgtRHnKXInz69bZC9/8v+EYEKpFaWUd/ueDNKzdy7WkiIjaqxaHqHPnzsFsNl+1vaamBhcucBVmZ5NT1DCsxEnlbSPQR4O1j8Thnv5BqLcIzPvPIbz0/VGYLVwCgYiovWn2cN63334rff/DDz9Aq9VKP5vNZqSkpCAiIsKmxZH8fjsT5S5zJR2Hxs0Fb00ehO6BXnjjp1P4989ZOFNQgTcnDYS3xk3u8oiI6LJmh6jExEQADZdmT5s2zeo5Nzc3RERE4LXXXrNpcSS/39aI4nBeW1IoFJgd3wPdOnvh6fUHsPV4Pu57ZxfeeygG3Tp7yV0eERGhBcN5FosFFosFXbp0QX5+vvSzxWJBTU0NTpw4gXvuuceetVIbE0JwtXKZjR8QjHWPxEHno8bp/HJMePtXbDlskLssIiJCK+ZEZWVlISAgwB61UDtTXFGLilozFAog1I/DeXIZEOaLjY+PRGykP8pr6vG3z9Lx8pbjqOcK50REsmrVEgcVFRXYsWMHcnJyUFtba/XcE088YZPCSH7Zl89C6X000Li53KA12VNnbzU++99YvLz5OD74JQvvbj+Dg+dLsWLSIC7MSUQkkxaHqIyMDIwdOxaVlZWoqKiAv78/CgsL4eHhgcDAQIYoJ8KhvPbFzUWJhff0wYAwX8z7z0H8eroI49/6BW/9+RbEhPvJXR4RUYfT4uG8p556CuPHj0dJSQnc3d2xe/duZGdnIyYmBq+++qo9aiSZ5BTxnnnt0fgBwdgw61ZEBnjiorEaD76Xine2n4aFyyAQEbWpFoeozMxMzJ07F0qlEi4uLqipqUFYWBiWL1+OBQsW2KNGkknjcF44Q1S700PnjW8fuxXjBwTDbBFYvuUEpn60B/ll1XKXRkTUYbQ4RLm5uUGpbNgtMDAQOTkNKyprtVrk5ua2uICVK1ciIiICGo0GsbGx2LNnz3Xbr1+/Hr169YJGo0F0dDQ2bdpk9bwQAklJSQgKCoK7uzvi4+Nx6tQpqzbFxcWYMmUKfHx84OvrixkzZqC8vNyqzQ8//IBhw4bB29sbnTt3xv33349z5861uH+OTFojijcebpe8NW5YMWkglt/fHxo3JX45XYixb/6MHScL5C6NiKhDaHGIGjRoEPbu3QsAGDVqFJKSkvD5559j9uzZ6NevX4uOtXbtWsyZMweLFy/G/v37MWDAACQkJCA/P7/J9rt27cLkyZMxY8YMZGRkIDExEYmJiTh8+LDUZvny5VixYgVWrVqFtLQ0eHp6IiEhAdXVv/0PfcqUKThy5AiSk5OxceNG7Ny5EzNnzpSez8rKwoQJE/CHP/wBmZmZ+OGHH1BYWIg//vGPLeqfo8st5nBee6dQKPDgkDBsfHwEeum9UVhei2kf7cHSTcdQW8+r94iI7Eq00N69e8XWrVuFEELk5eWJhIQE4e3tLW655RaRkZHRomMNHTpUzJo1S/rZbDaL4OBgsXTp0ibbP/jgg2LcuHFW22JjY8UjjzwihBDCYrEIvV4vXnnlFen50tJSoVarxZdffimEEOLo0aMCgNi7d6/UZvPmzUKhUIgLFy4IIYRYv369cHV1FWazWWrz7bffCoVCIWpra5vdP6PRKAAIo9HY7H3ai6raehHx3EYRPm+jKCyrlrscaoaq2nqx8JtDInxew/t29xs7xfFLJrnLIiJyOM39/G7xmajBgwfjjjvuANAwnLdlyxaYTCakp6dj4MCBzT5ObW0t0tPTER8fL21TKpWIj49Hampqk/ukpqZatQeAhIQEqX1WVhYMBoNVG61Wi9jYWKlNamoqfH19MXjwYKlNfHw8lEol0tLSAAAxMTFQKpX4+OOPYTabYTQa8X//93+Ij4+Hm9u1b7tRU1MDk8lk9XBU50uqIATgqXKBv6dK7nKoGTRuLvhHYj+s+p8Y+Hm44eglE8a/9Qve33mG994jIrKDFoeoa9m/f3+LViwvLCyE2WyGTqez2q7T6WAwNL0is8FguG77xq83ahMYGGj1vKurK/z9/aU2kZGR+PHHH7FgwQKo1Wr4+vri/PnzWLdu3XX7tHTpUmi1WukRFhZ23fbtmTSU18kTCoVC5mqoJcb00+OHp27D6F6BqDVb8M9NxzH5/d3Se0pERLbRohD1ww8/4Omnn8aCBQtw9uxZAMDx48eRmJiIIUOGwGJxjjkYBoMBDz/8MKZNm4a9e/dix44dUKlU+NOf/gQhrv0/+vnz58NoNEqP1ky0by+yiyoA8MbDjirQW4MPpg3Gy/dHw1Plgj3nijHmjZ34ck/Odf8MExFR8zV7sc0PP/wQDz/8MPz9/VFSUoIPPvgA//rXv/D4449j4sSJOHz4MHr37t3sFw4ICICLiwvy8vKstufl5UGv1ze5j16vv277xq95eXkICgqyatM41KjX66+auF5fX4/i4mJp/5UrV0Kr1WL58uVSm88++wxhYWFIS0vDsGHDmqxPrVZDrXaO1aNziqsAcFK5I1MoFJg4pAuGdwvA3HUHsOdcMeZ/fQjfHbiIpX+M5k2liYhuUrPPRL355pt4+eWXUVhYiHXr1qGwsBDvvPMODh06hFWrVrUoQAGASqVCTEwMUlJSpG0WiwUpKSmIi4trcp+4uDir9gCQnJwstY+MjIRer7dqYzKZkJaWJrWJi4tDaWkp0tPTpTZbt26FxWJBbGwsAKCyslJaxqGRi4uLVGNHkFN8+UwUP2gdXpi/B76cOQwLx/WGxk2JXWeKkPDGTry/8wzvv0dEdDOaO1Pdw8NDZGVlCSEaroJzc3MTv/zyy03MfRdizZo1Qq1Wi9WrV4ujR4+KmTNnCl9fX2EwGIQQQjz00EPiueeek9r/+uuvwtXVVbz66qvi2LFjYvHixcLNzU0cOnRIarNs2TLh6+sr/vvf/4qDBw+KCRMmiMjISFFVVSW1GTNmjBg0aJBIS0sTv/zyi4iKihKTJ0+Wnk9JSREKhUK88MIL4uTJkyI9PV0kJCSI8PBwUVlZ2ez+OfLVeXf+a7sIn7dRbD+RL3cpZEPnCsvF5PdTpSv47lnxszh8oVTusoiI2pXmfn43O0QpFAqRl5cn/ezl5SXOnDnT+gove+utt0SXLl2ESqUSQ4cOFbt375aeGzVqlJg2bZpV+3Xr1okePXoIlUol+vbtK77//nur5y0Wi1i0aJHQ6XRCrVaL0aNHixMnTli1KSoqEpMnTxZeXl7Cx8dHTJ8+XZSVlVm1+fLLL8WgQYOEp6en6Ny5s7j33nvFsWPHWtQ3Rw1RFotF9Fy4SYTP2yjOFpTLXQ7ZmMViEWv35IjoxVtE+LyNouv878XLm4+Jqtp6uUsjImoXmvv5rRCiebNMlUollixZAi8vLwDAvHnz8MwzzyAgIMCqHW9A/BuTyQStVguj0QgfHx+5y2m2fFM1hv4zBUoFcPwfd0PlarOLOKkdyS+rxvPfHsGmQw1XpUZ08sDz9/bF7T0Db7AnEZFza+7nd7NDVERExA0vdVcoFNJVe+S4IWrfuWL8aVUqQnzd8etzf5C7HLKzH44YkPTfw8gz1QAAEvrqsOiePgj140UFRNQxNffzu9lX53W0+8Z1ZDm83UuHktBXj+HdOuHNn07h413n8MORPOw4WYDH/xCF/x0ZCbWri9wlEhG1SxynoatkFzWEqHDeeLjD8Na4YeE9fbDpiZEYGumP6joLXvnhBMa8wRsaExFdC0MUXaVxZeswnonqcHrqvbF25jC8MXEgOnurkVVYgWkf7cHDn+7D2YJyucsjImpXGKLoKo3DeTwT1TEpFAokDgpBytxR+OutkXBRKpB8NA93vb4TL353FKWVtXKXSETULjBE0VWyOSeKAPho3JA0vg+2PDkSd/TsjHqLwEe/ZmHUK9vx0S9ZqK3nQp1E1LExRJGVqlozCsoartJiiCIAiNJ54+PpQ/HpX4eip84bxqo6vLjxKBLe2Ikfjxh4Lz4i6rCafXVeI5PJ1OR2hUIBtVoNlUp100WRfHJLGs5C+Whc4evB95J+c1uPzhjerRPWp5/Haz+eQFZhBWb+XzoGh/vhmYSeiO3aSe4SiYjaVIvPRPn6+sLPz++qh6+vL9zd3REeHo7Fixd3mHvMOZvGK/O6cD4UNcHVRYnJQ7tg29O34//d3g1qVyX2ZZdg4vu7Me2jPTh8wSh3iUREbabFZ6JWr16Nv//97/jLX/6CoUOHAgD27NmDTz75BAsXLkRBQQFeffVVqNVqLFiwwOYFk31xjShqDm+NG54d0wvThkdgRcoprN2bix0nC7DjZAHu6R+EuXf1RGQAb15NRM6txSHqk08+wWuvvYYHH3xQ2jZ+/HhER0fjvffeQ0pKCrp06YKXXnqJIcoB5RRVAAC6+PMDkG5M56PBS/dF4+GRXfH6Tyfx7YGL2HjwEjYfNuDBwaF4YnQUgrTucpdJRGQXLR7O27VrFwYNGnTV9kGDBiE1NRUAMGLECOTk5Nx8ddTmeCaKWiMiwBNvThqETU+MxOhegTBbBL7ck4tRy7fj798cwoXSKrlLJCKyuRaHqLCwMHz44YdXbf/www8RFhYGACgqKoKfn9/NV0dtjiGKbkbvIB98+Jch+OpvcRjW1R+1Zgs+T8vB7a9sw/yvD0kLuRIROYMWD+e9+uqreOCBB7B582YMGTIEALBv3z4cP34cX331FQBg7969mDhxom0rJbuzWARySxrOGHChTboZgyP8sWZmHNLOFuHNlFPYdaYIX+7Jwfp9ubj/llDMuqM7L14gIoenEK1Y5CUrKwvvvfceTp48CQDo2bMnHnnkEURERNi6PofW3LtAtxeXjFWIW7oVLkoFTvxjDFxduIwY2cbec8VYkXIKP58qBAC4KBWYMDAYj47qhiidt8zVERFZa+7nd6tCFDWPo4WotLNFmPj+bnTx98DOZ++QuxxyQunZJViRcsrqpsbxvQPxyKhuGBLhL2NlRES/ae7nd4uH8wCgtLQUe/bsQX5+/lXrQU2dOrU1h6R2IJv3zCM7iwn3wyd/HYrM3FKs2n4GPxw14Kdj+fjpWD5iwv3wt1HdMLpXIJRKhdylEhHdUItD1HfffYcpU6agvLwcPj4+UCh++8dOoVAwRDmwxkm/YZxUTnY2MMwXqx6KwdmCcvz757P4T/oFpGeX4OFP96F7oBdm3tYViQNDoHLlkDIRtV8t/hdq7ty5+Otf/4ry8nKUlpaipKREehQXF9ujRmojvDKP2lrXzl5Y+sf++GXeHXj09m7w1rjidH45nv3qIEYu34qV206juKJW7jKJiJrU4hB14cIFPPHEE/Dw4Aets2m85Us4QxS1sUAfDeaN6YVdz/0BC8b2gs5HjTxTDV754QSGLU3Bs18dwLFLTd+3k4hILi0OUQkJCdi3b589aiGZcTiP5OatccPM27rh52f/gNcnDkD/UC1q6y1Yt+887n7zZ0x6PxU/HDHAbOH1MEQkvxbPiRo3bhyeeeYZHD16FNHR0XBzc7N6/t5777VZcdR2ymvqUXR52ITr95DcVK5K3DcoFIkDQ7A/pwQf/XoOWw4bsPtsMXafLUaonzumxUXgwcFh0Hq43fiARER20OIlDpTKa5+8UigUMJvNN12Us3CkJQ6OXjRh7Iqf4efhhoyku+Quh+gqF0ur8NnubHy5JwcllXUAAI2bEvf0D8aU2C4YGOZrdaELEVFr2W2Jg98vaUDOgZPKqb0L9nXHs2N64YnRUdiQcQGrd53DcUMZvko/j6/Sz6NPkA/+HNsFiYNC4KVu1eotREQtwn9pCACQU1wBAOjSyVPmSoiuT+PmgklDu2DikDDszynB52k52HjwEo5eMmHhhsNYuukY7h0YgimxXdAvRCt3uUTkxJoVolasWIGZM2dCo9FgxYoV1237xBNP2KQwalu/nYlyl7kSouZRKBSICfdHTLg/ku7pg6/Sz+OLPTk4W1CBL/fk4Ms9ORgQqsXEIV1wz4Ag+Gg4d4qIbKtZc6IiIyOxb98+dOrUCZGRkdc+mEKBs2fP2rRAR+ZIc6KmfrQHO08W4OX7ozFxSBe5yyFqFSEEdp8txudp2fjhiAF15oZ/3jRuSozpq8cDg8MQ17UTV0Qnouuy6ZyorKysJr8n55FTdHk4z5/DeeS4FAoF4rp1Qly3Tigsr8F/0s9jffp5nM4vx4bMi9iQeREhvu64PyYUD8SEcjkPIropvAGxHTnKmSizRaDnws2otwj8+twfEOLLIT1yHkIIZOaWYn36eXyXeRFlNfXSc8O6+uOBmDCM6aeHJyejE9Flzf38bnGIMpvNWL16NVJSUpq8AfHWrVtbV7ETcpQQdb6kEiNe3gY3FwWO/+NuuHCog5xUdZ0ZPxwxYP2+8/j1TCEa//Vzd3PBnX10SBwUjJFRneHmwnv2EXVkdlvi4Mknn8Tq1asxbtw49OvXj+uyOIGcy7d7CfPzYIAip6Zxc8GEgSGYMDAEF0qr8J/08/jP/vPILqrEtwcu4tsDF+Hn4YZx/YOQODAEMeF+/DeOiK6pxSFqzZo1WLduHcaOHWuPekgGObzdC3VAIb7ueGJ0FB7/Q3ccOG/EhowL2HjwIgrLa/HZ7hx8tjsHoX7uuHdAMBIHhaCHzlvukomonWlxiFKpVOjevbs9aiGZcKFN6sgUCgUGhvliYJgvFo7rjV/PFOG/mRfww2EDzpdU4Z3tZ/DO9jPoHeSDe/oHYWx0ECIDeAEGEbUiRM2dOxdvvvkm3n77bZ7mdhLZl0NUOO+ZRx2cq4sSo3p0xqgenVGVaMZPx/Lw38yL2HEyH8cumXDskgmv/HACvYN8MLafHmP7B6FbZy+5yyYimbQ4RP3yyy/Ytm0bNm/ejL59+151A+Kvv/7aZsVR28jlcB7RVdxVLhg/IBjjBwSjpKIWW44YsOnQJew6UyQFqteST6Knzhtjo4Mwrr8e3QM55EfUkbQ4RPn6+uK+++6zRy0kkxyeiSK6Lj9PFSYP7YLJQ7ugpKIWPx41YNMhA349XYgTeWU4kVeG1386iahAL4yNbhjy66Hz4tl6IifXoiUO6uvr8cUXX+Cuu+6CXq+3Z11OwRGWODBW1WHACz8CAI68kMC1cohaoLSyFslH87Dp0CX8crpQWiEdaPhPSXxvHe7so8PgcD+4ctkEIofR3M/vFv2tdnV1xd/+9jfU1NTcdIGNVq5ciYiICGg0GsTGxmLPnj3Xbb9+/Xr06tULGo0G0dHR2LRpk9XzQggkJSUhKCgI7u7uiI+Px6lTp6zaFBcXY8qUKfDx8YGvry9mzJiB8vLyq47z6quvokePHlCr1QgJCcFLL71km063I41DeQFeKgYoohby9VDhgcFh+Hj6UOz7+5147YEBGN0rECpXJbKLKvHhL1mY9P5uDHnpJ8xZl4kthy+h4orFPonIsbX4v0ZDhw5FRkaGTV587dq1mDNnDhYvXoz9+/djwIABSEhIQH5+fpPtd+3ahcmTJ2PGjBnIyMhAYmIiEhMTcfjwYanN8uXLsWLFCqxatQppaWnw9PREQkICqqurpTZTpkzBkSNHkJycjI0bN2Lnzp2YOXOm1Ws9+eST+OCDD/Dqq6/i+PHj+PbbbzF06FCb9Ls94ZV5RLah9XDD/TGh+PAvQ5Cx6E68O+UW/HFQCHw93FBSWYev91/A3z7bj0H/SMZfV+/Fl3tykF9WfeMDE1G71eIVy9etW4f58+fjqaeeQkxMDDw9rS/17d+/f7OPFRsbiyFDhuDtt98GAFgsFoSFheHxxx/Hc889d1X7iRMnoqKiAhs3bpS2DRs2DAMHDsSqVasghEBwcDDmzp2Lp59+GgBgNBqh0+mwevVqTJo0CceOHUOfPn2wd+9eDB48GACwZcsWjB07FufPn0dwcDCOHTuG/v374/Dhw+jZs2dLfj1WHGE4793tZ/DyluNIHBiMNyYNkrscIqdTb7ZgX3YJko/mIflonvQfFwBQKIABob64o2cgbu/ZGdEhWt4cmagdsNuK5ZMmTQIAPPHEE9I2hUIBIQQUCgXMZnOzjlNbW4v09HTMnz9f2qZUKhEfH4/U1NQm90lNTcWcOXOstiUkJGDDhg0AGm6ObDAYEB8fLz2v1WoRGxuL1NRUTJo0CampqfD19ZUCFADEx8dDqVQiLS0N9913H7777jt07doVGzduxJgxYyCEQHx8PJYvXw5/f/9r9qmmpsZqqNNkMjXrdyEnnokisi9XFyWGde2EYV07YeG43jiZV47kowYkH83DgfNGZOaWIjO3FK//dBKdPFUY1bMzbu8ZiNuiAuDroZK7fCK6jhaHqKysLJu8cGFhIcxmM3Q6ndV2nU6H48ePN7mPwWBosr3BYJCeb9x2vTaBgYFWz7u6usLf319qc/bsWWRnZ2P9+vX49NNPYTab8dRTT+FPf/rTde8NuHTpUrzwwgs36nq7klNcAQDo0omLBxLZm0KhQE+9N3rqvfHYH6KQZ6rG9hP52Ha8AL+cLkRRRS2+3n8BX++/AKUCuKWLH+7oFYhRPTqjb7APr/YjamdaHKLCw8PtUUe7YrFYUFNTg08//RQ9evQAAHz44YeIiYnBiRMnrjnEN3/+fKszZSaTCWFhYW1Sc2vxTBSRfHQ+Gkwc0gUTh3RBbb0F6dklDaHqRD5O5pVjX3YJ9mWX4JUfTiDQW41RPTpjZI/OuLVbJ3TyUstdPlGH1+rLsY4ePYqcnBzU1tZabb/33nubtX9AQABcXFyQl5dntT0vL++ayyfo9frrtm/8mpeXh6CgIKs2AwcOlNr8fuJ6fX09iouLpf2DgoLg6uoqBSgA6N27NwAgJyfnmiFKrVZDrXacf9jqzBZcLG2Y2MoQRSQvlasScd06Ia5bJ8wf2xsXSquks1S/ni5EflkN1qefx/r08wCAvsE+GNE9ACOiAjAkwh8aNxeZe0DU8bQ4RJ09exb33XcfDh06JM2FAiCdZm7unCiVSoWYmBikpKQgMTERQMMZoJSUFDz22GNN7hMXF4eUlBTMnj1b2pacnIy4uDgAQGRkJPR6PVJSUqTQZDKZkJaWhkcffVQ6RmlpKdLT0xETEwMA2Lp1KywWC2JjYwEAt956K+rr63HmzBl069YNAHDy5EkAznUm7mJpFcwWAbWrEoHejhP+iDqCEF93TIkNx5TYcNTUm7E3q+Es1S+nC3HcUIYjF004ctGE93aehcpViSERfhjRvTNGRgWgT5APJ6gTtYEWX503fvx4uLi44IMPPkBkZCT27NmDoqIizJ07F6+++ipGjhzZ7GOtXbsW06ZNw3vvvYehQ4fijTfewLp163D8+HHodDpMnToVISEhWLp0KYCGJQ5GjRqFZcuWYdy4cVizZg3++c9/Yv/+/ejXrx8A4OWXX8ayZcvwySefIDIyEosWLcLBgwdx9OhRaDQaAMDdd9+NvLw8rFq1CnV1dZg+fToGDx6ML774AkBDmBsyZAi8vLzwxhtvwGKxYNasWfDx8cGPP/7Y7P6196vzfj5VgIc+3IPugV74ac4oucshombKL6vGrtNF+PlUIX45XYA8k/XafX4ebhjePQAjuwdgeLcAhPm7cz4VUQvY7eq81NRUbN26FQEBAVAqlVAqlRgxYgSWLl2KJ554okVrSE2cOBEFBQVISkqCwWDAwIEDsWXLFmlieE5ODpTK35ayGj58OL744gssXLgQCxYsQFRUFDZs2CAFKAB49tlnUVFRgZkzZ6K0tBQjRozAli1bpAAFAJ9//jkee+wxjB49GkqlEvfffz9WrFghPa9UKvHdd9/h8ccfx2233QZPT0/cfffdeO2111r662rXOB+KyDEFemuQOCgEiYNCIITAmYLyhkB1qhC7zxahpLIO3x+8hO8PXgIABGs1DVcIduuEuK6deJ9MIhtp8ZkoPz8/7N+/H5GRkejWrRs++OAD3HHHHThz5gyio6NRWVl544N0EO39TNTSTcfw3s6z+MvwCDx/b1+5yyEiG6gzW5CZW4qfTxXi19OFOJBbinqL9T/zIb7ul5dd8Edct04I9WOoIrqS3c5E9evXDwcOHEBkZCRiY2OxfPlyqFQqvP/+++jatetNFU1ti2eiiJyPm4sSQyL8MSTCH3Pu7IHK2nqkZ5dg99ki7D5bjAO5pbhQWoX/7D+P/+xvmKQe6ucurWU1NMKfw39EzdTiELVw4UJUVDSsLfTiiy/innvuwciRI9GpUyesXbvW5gWS/TSGqPBODFFEzspD5YqRUZ0xMqozAKCi5spQVYSD5404X1KFr9LP46vLV/4FeqsxJMIfgyP8MCTCH7303ryBMlETWjyc15Ti4mL4+fnxfy6/056H84QQ6P/8jyirqUfyU7chSuctd0lEJIOKmnrsuxyq0s4W4dAFI+rM1h8LXmpXDOriKwWrgWG+8FDxhuXkvOw2nNfo9OnTOHPmDG677Tb4+/vDBlmM2lBpZR3KLt9NnpNMiTouT7UrRvXojFE9Gs5UVdeZcSC3FPuyS7D3XDHSz5WgrKYeP58qxM+nCgEArkoF+oZoMSTcD4Mj/HFLuC8CvTXXexkip9TiEFVUVIQHH3wQ27Ztg0KhwKlTp9C1a1fMmDEDfn5+TncFm7NqHMrT+ai5SB8RSTRuLojt2gmxXTsBAMwWgZN5Zdh3rhh7zzUEq0vGahzILcWB3FJ88EvDrcBCfN0xqIsvBnXxw6Auvugb7AO1K/9tIefW4hD11FNPwc3NDTk5OdIq3kDDcgVz5sxhiHIQ2ZxUTkTN4KJUoHeQD3oH+eChuAgAwIXSqsuhqhj7zpXgRF4ZLpRW4UJpFTZeXlZB5aJEn2Cf34JVmC9C/ThhnZxLi0PUjz/+iB9++AGhoaFW26OiopCdnW2zwsi+ci+HKA7lEVFLhfi6I2RgCCYMDAEAlFXX4dB5IzJyS5GRU4KMnFIUVdQiM7cUmbml+PjXcwCAAC/15VDli0FhfugX4gNvjZuMPSG6OS0OURUVFfDwuPqDt7i42KHuG9fRZRc1XGEZ7u8pcyVE5Oi8NQ0rpA/vHgCg4cKV3OIqZOQ2BKqMnBIcuWhCYXkNko/mIflowz1QFQqga4An+of6IjpEiwFhWvQJ0sJdxWFAcgwtDlEjR47Ep59+in/84x8AGu6ZZ7FYsHz5ctxxxx02L5DsQ1ojqpO7zJUQkbNRKBTo0skDXTp5SGerquvMOHLReDlUNQSri8ZqnCmowJmCCnyTcQEAoFQAPXTeiA7Ron+oFtGhvugd5M35VdQutThELV++HKNHj8a+fftQW1uLZ599FkeOHEFxcTF+/fVXe9RIdpBbXAWAc6KIqG1o3FwQE+6PmHB/aVtheQ0OXTDiYK4Rhy6U4sB5IwrKanDcUIbjhjKsv7xulZuLAj313ogO8W0IViFa9NR7w41rV5HMWrVOlNFoxNtvv40DBw6gvLwct9xyC2bNmoWgoCB71Oiw2us6UTX1ZvRatAVCAHv/Ho/O3hyGJaL2Ic9UjYPnjTh0viFUHbpgRHFF7VXtVC5KROm80DfYB32DtegT3DD53UvN9avo5jX389smi20CwPnz5/Hiiy/i/ffft8XhnEJ7DVFnC8rxh9d2wN3NBUdfTODVMkTUbgkhcKG0CofOG3HwgrHh6/lSmKrrm2wf0clDClV9gn3QN8gHgT5cw4paxu6Lbf5eUVERPvzwQ4YoB3DlPfMYoIioPVMoFAj180Conwfujm4Y7RBC4HxJFY5cNOLoRROOXH4YTNU4V1SJc0WV+P7QJekYAV5q9G0MVcE+6KX3QUQnD97Khm4az3t2QL9NKud8KCJyPAqFAmH+Hgjz98CYfr9NIykqr8HRSyYpWB29ZMLZgnIUltdgx8kC7DhZILVVuzYMB/bU+aCX3hu9grzRU++Nzl5q/ueSmo0hqgPKKeJCm0TkfDp5qa1utgwAlbX1OG4o+y1YXTTiZF45qurMOHzBhMMXTFbH8PdUoZe+IVA1fPVBD50X7xVITeKfig6o8UxUOM9EEZGT81C54pYufrili5+0zWIRyCmuxHGDCccNZThx+ZFVVIHiilrsOlOEXWeKpPYKBRDu73E5WDWcuYrSeSG8kyevEOzgmh2i/vjHP173+dLS0puthdpIDlcrJ6IOTKlUICLAExEBnlbDgVW1ZpzKb1he4filMpzIM+GEoQyF5bXSXKsfjuRJ7d1cFIgM8ERUoDe6B3ohSueFqEBvRAZ4QuXKcNURNDtEabXaGz4/derUmy6I7EsIYTWxnIiIGrirXNA/1Bf9Q32ttheU1eCEoQzHDQ2h6kReGU7nl6Oy1oyTeeU4mVdu1d5FqUBEJw9EBTacseoe2BCuunb25A3fnUyzQ9THH39szzqojRRV1KKy1gyFAgj142rlREQ30tlbjc7eaoyICpC2WSwCF41VOJVfjtN55TiVXyZ9X1ZTL63EvuXIb8dRKoDwTp6XQ5UXunb2QtfOnugW4AWtB+8h6Ig4J6qDyb48qTzIR8PbKBARtZJS+dvSC3f0DJS2CyFgMFXjVF55Q6jKL8OpvHKczCuDqboeWYUVyCqskO4f2KiTpwrdLoeqrp090TWg4fsu/lyKoT1jiOpgcjkfiojIbhQKBYK07gjSuuO2Hr9dJSiEQEFZDU7ll+NUXsNZq7MFFThbWI48Uw2KKmpRVFGMPeeKrY7n5qJAF38Pq7NWDUHLC/6eqrbuHv0OQ1QH03gmilfmERG1HYVCgUAfDQJ9NLi1e4DVc+U19ci6HKjO5JfjTGEFzhZUIKuwHNV1Fmlo8Pf8PNwawtXlSfIRnTwREeCB8E6evP1NG+FvuYPhpHIiovbFS+2K6FAtokOtL+CyWAQumapxtqAhXJ29HK7OFpTjorEaJZV1SM8uQXp2yVXH7OytRkSnhkAVGeCJ8E4eiOjU8NVbw/lXtsIQ1cFwOI+IyDEolQqE+LojxNfdagFRoGER0azChjNUWQUVyC6qQFZRBbKLKlFcUYuCshoUlNVg77mrA1aAl+pyoPJERCcP6SxWeIAHfBiwWoQhqoPJLm44JRzeyVPmSoiIqLU8VK7oG6xF3+Crlx8yVtUhu6gC54oqkV34W7jKLqpAYXmt9NjXxBmsTp4qdOnkgS7+DY8wfw+E+XmgSycP6H00cFHyljhXYojqQKrrzMgz1QDgcB4RkbPSurs1ud4VAJiq65BTVIlzRRU4V3g5aBVVIKuwEoXljRPca5GRU3rVvm4uDVckNgQrd+ug5e8BrXvHO4vFENWBnC9pGMrzUrvCj2uSEBF1OD4aN/QL0aJfyNVnsMpr6nGusAK5xZXIufzILalCbnElzpdUos4spCUamqJ1d5OCVaj/byGri78Hgn3dnfIWOQxRHUj2FTce5l3KiYjoSl5q12sGLLOlYf2rnKJK5JZUWget4koUltfCWFWHQxeMOHTBeNX+CgWg99E0zPHyc0eonztCfD2u+N7dIVdzZ4jqQHhlHhERtYbLFZPc49DpqucraupxvqTKKlhdGbRq6i24ZKzGJWN1k3OxgIYJ7yG+7gj1awhXDd+7S9+3x6sKGaI6EClEcY0oIiKyIU+1K3rqvdFT733Vc0IIFJbX4kJpFc6XVOJCSdXl76uk78tr6qUJ7wfOX30mCwB8NK5XBaxQP3fc3jNQtrNYDFEdSE4Rz0QREVHbUigU0v0HB4b5XvW8EALGqrqGUFXaEKwavq+UwlZpZR1M1fU4esmEo5dMVvsfev4uhiiyPw7nERFRe6NQKODroYKvh6rJ+VhAw3DhlWeyzl8OWyWVtbIO8zFEdRBCCClE8ZYvRETkSDzVruih80YP3dXDhXJyvusNqUn5ZTWoqbdAqQCCfd3lLoeIiMjhMUR1EI1noZx1rQ4iIqK2xk/TDqJxjSgO5REREdkGQ1QHwUnlREREttUuQtTKlSsREREBjUaD2NhY7Nmz57rt169fj169ekGj0SA6OhqbNm2yel4IgaSkJAQFBcHd3R3x8fE4deqUVZvi4mJMmTIFPj4+8PX1xYwZM1BeXt7k650+fRre3t7w9fW9qX7KKfdyiApjiCIiIrIJ2UPU2rVrMWfOHCxevBj79+/HgAEDkJCQgPz8/Cbb79q1C5MnT8aMGTOQkZGBxMREJCYm4vDhw1Kb5cuXY8WKFVi1ahXS0tLg6emJhIQEVFdXS22mTJmCI0eOIDk5GRs3bsTOnTsxc+bMq16vrq4OkydPxsiRI23f+TaUXdRwr6Nwf0+ZKyEiInIOCiGEkLOA2NhYDBkyBG+//TYAwGKxICwsDI8//jiee+65q9pPnDgRFRUV2Lhxo7Rt2LBhGDhwIFatWgUhBIKDgzF37lw8/fTTAACj0QidTofVq1dj0qRJOHbsGPr06YO9e/di8ODBAIAtW7Zg7NixOH/+PIKDg6Vjz5s3DxcvXsTo0aMxe/ZslJaWNrtvJpMJWq0WRqMRPj4+rfn12MzgJT+hsLwG3z02AtGhTa/DQURERM3//Jb1TFRtbS3S09MRHx8vbVMqlYiPj0dqamqT+6Smplq1B4CEhASpfVZWFgwGg1UbrVaL2NhYqU1qaip8fX2lAAUA8fHxUCqVSEtLk7Zt3boV69evx8qVK5vVn5qaGphMJqtHe1BZW4/C8hoAnBNFRERkK7KGqMLCQpjNZuh0OqvtOp0OBoOhyX0MBsN12zd+vVGbwMBAq+ddXV3h7+8vtSkqKsJf/vIXrF69utlnkZYuXQqtVis9wsLCmrWfvTVOKte6u0Hr0f5u4EhEROSIZJ8T1V49/PDD+POf/4zbbrut2fvMnz8fRqNReuTm5tqxwubjPfOIiIhsT9YQFRAQABcXF+Tl5Vltz8vLg16vb3IfvV5/3faNX2/U5vcT1+vr61FcXCy12bp1K1599VW4urrC1dUVM2bMgNFohKurKz766KMma1Or1fDx8bF6tAdc3oCIiMj2ZA1RKpUKMTExSElJkbZZLBakpKQgLi6uyX3i4uKs2gNAcnKy1D4yMhJ6vd6qjclkQlpamtQmLi4OpaWlSE9Pl9ps3boVFosFsbGxABrmTWVmZkqPF198Ed7e3sjMzMR9991nm19AG5FCFBfaJCIishnZb0A8Z84cTJs2DYMHD8bQoUPxxhtvoKKiAtOnTwcATJ06FSEhIVi6dCkA4Mknn8SoUaPw2muvYdy4cVizZg327duH999/H0DD3aBnz56NJUuWICoqCpGRkVi0aBGCg4ORmJgIAOjduzfGjBmDhx9+GKtWrUJdXR0ee+wxTJo0Sboyr3fv3lZ17tu3D0qlEv369Wuj34zt8EwUERGR7ckeoiZOnIiCggIkJSXBYDBg4MCB2LJlizQxPCcnB0rlbyfMhg8fji+++AILFy7EggULEBUVhQ0bNliFm2effRYVFRWYOXMmSktLMWLECGzZsgUajUZq8/nnn+Oxxx7D6NGjoVQqcf/992PFihVt1/E21BiiwhmiiIiIbEb2daKcWXtYJ8psEei9aAtqzRb8/OwdXLGciIjoBhxinSiyvzxTNWrNFrgqFQjSam68AxERETULQ5STaxzKC/Vzh6sL324iIiJb4aeqk2tcI4rDeERERLbFEOXkeGUeERGRfTBEObnsxivzuEYUERGRTTFEOTmeiSIiIrIPhignl1vMOVFERET2wBDlxMqq61BcUQuAZ6KIiIhsjSHKiTUO5fl7quCtcZO5GiIiIufCEOXEOJRHRERkPwxRTiy7iPfMIyIisheGKCfGK/OIiIjshyHKiUkhimtEERER2RxDlBPjmSgiIiL7YYhyUvVmCy6UVAFgiCIiIrIHhigndclYjXqLgMpFCb2PRu5yiIiInA5DlJNqHMoL9XeHUqmQuRoiIiLnwxDlpDgfioiIyL4YopwU14giIiKyL4YoJ8XVyomIiOyLIcpJcTiPiIjIvhiinFR2UQUAILyTp8yVEBEROSeGKCdkrKyDqboeABDm7y5zNURERM6JIcoJNQ7lBXip4aFylbkaIiIi58QQ5YSyixuH8jgfioiIyF4YopwQJ5UTERHZH0OUE8pliCIiIrI7hign1LjQJkMUERGR/TBEOSFpOI9zooiIiOyGIcrJ1JktuFhaBYC3fCEiIrInhignc6GkChYBqF2V6OytlrscIiIip8UQ5WSuvDJPoVDIXA0REZHzYohyMtmXQxTXiCIiIrIvhign07i8QRjnQxEREdkVQ5STyeHyBkRERG2CIcrJcDiPiIiobTBEOREhBFcrJyIiaiPtIkStXLkSERER0Gg0iI2NxZ49e67bfv369ejVqxc0Gg2io6OxadMmq+eFEEhKSkJQUBDc3d0RHx+PU6dOWbUpLi7GlClT4OPjA19fX8yYMQPl5eXS89u3b8eECRMQFBQET09PDBw4EJ9//rntOm0HJZV1KK+pBwCE+jFEERER2ZPsIWrt2rWYM2cOFi9ejP3792PAgAFISEhAfn5+k+137dqFyZMnY8aMGcjIyEBiYiISExNx+PBhqc3y5cuxYsUKrFq1CmlpafD09ERCQgKqq6ulNlOmTMGRI0eQnJyMjRs3YufOnZg5c6bV6/Tv3x//+c9/cPDgQUyfPh1Tp07Fxo0b7ffLuEnZRRUAAL2PBho3F5mrISIicnJCZkOHDhWzZs2SfjabzSI4OFgsXbq0yfYPPvigGDdunNW22NhY8cgjjwghhLBYLEKv14tXXnlFer60tFSo1Wrx5ZdfCiGEOHr0qAAg9u7dK7XZvHmzUCgU4sKFC9esdezYsWL69OnN7pvRaBQAhNFobPY+N2NDxnkRPm+jeODdXW3yekRERM6ouZ/fsp6Jqq2tRXp6OuLj46VtSqUS8fHxSE1NbXKf1NRUq/YAkJCQILXPysqCwWCwaqPVahEbGyu1SU1Nha+vLwYPHiy1iY+Ph1KpRFpa2jXrNRqN8Pf3v+bzNTU1MJlMVo+2xOUNiIiI2o6sIaqwsBBmsxk6nc5qu06ng8FgaHIfg8Fw3faNX2/UJjAw0Op5V1dX+Pv7X/N1161bh71792L69OnX7M/SpUuh1WqlR1hY2DXb2kN2Ea/MIyIiaiuyz4lyBNu2bcP06dPx73//G3379r1mu/nz58NoNEqP3NzcNqzS+pYvREREZF+yhqiAgAC4uLggLy/PanteXh70en2T++j1+uu2b/x6oza/n7heX1+P4uLiq153x44dGD9+PF5//XVMnTr1uv1Rq9Xw8fGxerQlaXkDnokiIiKyO1lDlEqlQkxMDFJSUqRtFosFKSkpiIuLa3KfuLg4q/YAkJycLLWPjIyEXq+3amMymZCWlia1iYuLQ2lpKdLT06U2W7duhcViQWxsrLRt+/btGDduHF5++WWrK/fao5p6My6ZGq4+5JkoIiIi+3OVu4A5c+Zg2rRpGDx4MIYOHYo33ngDFRUV0tyjqVOnIiQkBEuXLgUAPPnkkxg1ahRee+01jBs3DmvWrMG+ffvw/vvvAwAUCgVmz56NJUuWICoqCpGRkVi0aBGCg4ORmJgIAOjduzfGjBmDhx9+GKtWrUJdXR0ee+wxTJo0CcHBwQAahvDuuecePPnkk7j//vuluVIqleq6k8vlcr6kCkIAHioXdPJUyV0OERGR82ujqwWv66233hJdunQRKpVKDB06VOzevVt6btSoUWLatGlW7detWyd69OghVCqV6Nu3r/j++++tnrdYLGLRokVCp9MJtVotRo8eLU6cOGHVpqioSEyePFl4eXkJHx8fMX36dFFWViY9P23aNAHgqseoUaOa3a+2XOJg67E8ET5vo0h4fYfdX4uIiMiZNffzWyGEEDJmOKdmMpmg1WphNBrtPj/qk13nsPjbI7irjw7vTx184x2IiIioSc39/ObVeU6CV+YRERG1LYYoJ8E1ooiIiNoWQ5ST4GrlREREbYshygkIITicR0RE1MYYopxAQXkNqurMUCiAUD+GKCIiorbAEOUEGofygrXuULnyLSUiImoL/MR1AjnSfCh3mSshIiLqOBiinIB0ZZ6/p8yVEBERdRwMUU4ghzceJiIianMMUU4gl1fmERERtTmGKCfQOJzHEEVERNR2GKIcXFWtGfllNQAYooiIiNoSQ5SDO1/ScBbKW+MKXw83mashIiLqOBiiHNyVQ3kKhULmaoiIiDoOhigHx9u9EBERyYMhysFxeQMiIiJ5MEQ5OJ6JIiIikgdDlINjiCIiIpIHQ5QDs1iEFKJ4yxciIqK2xRDlwPLLalBbb4GLUoEgX43c5RAREXUoDFEOrPEsVLCvBm4ufCuJiIjaEj95HVh2UQUADuURERHJgSHKgTXeeDiMk8qJiIjaHEOUA5MmlXONKCIiojbHEOXAsrm8ARERkWwYohxYLkMUERGRbBiiHFRFTT0Ky2sB8JYvREREcmCIclCN86F8Pdzgo3GTuRoiIqKOhyHKQfF2L0RERPJiiHJQOUUMUURERHJiiHJQPBNFREQkL4YoB8UQRUREJC+GKAclhShemUdERCQLhigHZLYInC/hmSgiIiI5MUQ5IIOpGnVmATcXBYK07nKXQ0RE1CExRDmg7KIKAEConwdclAqZqyEiIuqYGKIcUOPtXsI4lEdERCSbdhGiVq5ciYiICGg0GsTGxmLPnj3Xbb9+/Xr06tULGo0G0dHR2LRpk9XzQggkJSUhKCgI7u7uiI+Px6lTp6zaFBcXY8qUKfDx8YGvry9mzJiB8vJyqzYHDx7EyJEjodFoEBYWhuXLl9umwzfptyvzOJRHREQkF9lD1Nq1azFnzhwsXrwY+/fvx4ABA5CQkID8/Pwm2+/atQuTJ0/GjBkzkJGRgcTERCQmJuLw4cNSm+XLl2PFihVYtWoV0tLS4OnpiYSEBFRXV0ttpkyZgiNHjiA5ORkbN27Ezp07MXPmTOl5k8mEu+66C+Hh4UhPT8crr7yC559/Hu+//779fhnNlH15oc1wf0+ZKyEiIurAhMyGDh0qZs2aJf1sNptFcHCwWLp0aZPtH3zwQTFu3DirbbGxseKRRx4RQghhsViEXq8Xr7zyivR8aWmpUKvV4ssvvxRCCHH06FEBQOzdu1dqs3nzZqFQKMSFCxeEEEK88847ws/PT9TU1Eht5s2bJ3r27NnsvhmNRgFAGI3GZu/THPe+9bMIn7dRbD50yabHJSIiouZ/fst6Jqq2thbp6emIj4+XtimVSsTHxyM1NbXJfVJTU63aA0BCQoLUPisrCwaDwaqNVqtFbGys1CY1NRW+vr4YPHiw1CY+Ph5KpRJpaWlSm9tuuw0qlcrqdU6cOIGSkpIma6upqYHJZLJ62EPjcF4414giIiKSjawhqrCwEGazGTqdzmq7TqeDwWBoch+DwXDd9o1fb9QmMDDQ6nlXV1f4+/tbtWnqGFe+xu8tXboUWq1WeoSFhTXd8ZtQVWuGyrXhbePEciIiIvnIPifKmcyfPx9Go1F65Obm2vw13FUuSFsQj+P/GAMvtavNj09ERETNI2uICggIgIuLC/Ly8qy25+XlQa/XN7mPXq+/bvvGrzdq8/uJ6/X19SguLrZq09QxrnyN31Or1fDx8bF62IvGzcVuxyYiIqIbkzVEqVQqxMTEICUlRdpmsViQkpKCuLi4JveJi4uzag8AycnJUvvIyEjo9XqrNiaTCWlpaVKbuLg4lJaWIj09XWqzdetWWCwWxMbGSm127tyJuro6q9fp2bMn/Pz8brLnRERE5PDaaKL7Na1Zs0ao1WqxevVqcfToUTFz5kzh6+srDAaDEEKIhx56SDz33HNS+19//VW4urqKV199VRw7dkwsXrxYuLm5iUOHDkltli1bJnx9fcV///tfcfDgQTFhwgQRGRkpqqqqpDZjxowRgwYNEmlpaeKXX34RUVFRYvLkydLzpaWlQqfTiYceekgcPnxYrFmzRnh4eIj33nuv2X2z19V5REREZD/N/fyWPUQJIcRbb70lunTpIlQqlRg6dKjYvXu39NyoUaPEtGnTrNqvW7dO9OjRQ6hUKtG3b1/x/fffWz1vsVjEokWLhE6nE2q1WowePVqcOHHCqk1RUZGYPHmy8PLyEj4+PmL69OmirKzMqs2BAwfEiBEjhFqtFiEhIWLZsmUt6hdDFBERkeNp7ue3Qggh5D0X5rxMJhO0Wi2MRqNd50cRERGR7TT385tX5xERERG1AkMUERERUSswRBERERG1AkMUERERUSswRBERERG1AkMUERERUSswRBERERG1AkMUERERUSswRBERERG1gqvcBTizxsXgTSaTzJUQERFRczV+bt/opi4MUXZUVlYGAAgLC5O5EiIiImqpsrIyaLXaaz7Pe+fZkcViwcWLF+Ht7Q2FQmGz45pMJoSFhSE3N9cp78nn7P0DnL+Pzt4/wPn7yP45Pmfvoz37J4RAWVkZgoODoVRee+YTz0TZkVKpRGhoqN2O7+Pj45R/MRo5e/8A5++js/cPcP4+sn+Oz9n7aK/+Xe8MVCNOLCciIiJqBYYoIiIiolZgiHJAarUaixcvhlqtlrsUu3D2/gHO30dn7x/g/H1k/xyfs/exPfSPE8uJiIiIWoFnooiIiIhagSGKiIiIqBUYooiIiIhagSGKiIiIqBUYohzQypUrERERAY1Gg9jYWOzZs0fukq7y/PPPQ6FQWD169eolPV9dXY1Zs2ahU6dO8PLywv3334+8vDyrY+Tk5GDcuHHw8PBAYGAgnnnmGdTX11u12b59O2655Rao1Wp0794dq1evtkt/du7cifHjxyM4OBgKhQIbNmywel4IgaSkJAQFBcHd3R3x8fE4deqUVZvi4mJMmTIFPj4+8PX1xYwZM1BeXm7V5uDBgxg5ciQ0Gg3CwsKwfPnyq2pZv349evXqBY1Gg+joaGzatKlN+viXv/zlqvd0zJgxDtPHpUuXYsiQIfD29kZgYCASExNx4sQJqzZt+efS1n+Pm9O/22+//ar38G9/+5tD9O/dd99F//79pYUV4+LisHnzZul5R37vmttHR37/mrJs2TIoFArMnj1b2uZw76Mgh7JmzRqhUqnERx99JI4cOSIefvhh4evrK/Ly8uQuzcrixYtF3759xaVLl6RHQUGB9Pzf/vY3ERYWJlJSUsS+ffvEsGHDxPDhw6Xn6+vrRb9+/UR8fLzIyMgQmzZtEgEBAWL+/PlSm7NnzwoPDw8xZ84ccfToUfHWW28JFxcXsWXLFpv3Z9OmTeLvf/+7+PrrrwUA8c0331g9v2zZMqHVasWGDRvEgQMHxL333isiIyNFVVWV1GbMmDFiwIABYvfu3eLnn38W3bt3F5MnT5aeNxqNQqfTiSlTpojDhw+LL7/8Uri7u4v33ntPavPrr78KFxcXsXz5cnH06FGxcOFC4ebmJg4dOmT3Pk6bNk2MGTPG6j0tLi62atOe+5iQkCA+/vhjcfjwYZGZmSnGjh0runTpIsrLy6U2bfXn0h5/j5vTv1GjRomHH37Y6j00Go0O0b9vv/1WfP/99+LkyZPixIkTYsGCBcLNzU0cPnxYCOHY711z++jI79/v7dmzR0RERIj+/fuLJ598UtruaO8jQ5SDGTp0qJg1a5b0s9lsFsHBwWLp0qUyVnW1xYsXiwEDBjT5XGlpqXBzcxPr16+Xth07dkwAEKmpqUKIhg90pVIpDAaD1Obdd98VPj4+oqamRgghxLPPPiv69u1rdeyJEyeKhIQEG/fG2u8DhsViEXq9XrzyyivSttLSUqFWq8WXX34phBDi6NGjAoDYu3ev1Gbz5s1CoVCICxcuCCGEeOedd4Sfn5/UPyGEmDdvnujZs6f084MPPijGjRtnVU9sbKx45JFH7NpHIRpC1IQJE665j6P1MT8/XwAQO3bsEEK07Z/Ltvh7/Pv+CdHwIXzlB9bvOVL/hBDCz89PfPDBB0733jXVRyGc5/0rKysTUVFRIjk52apPjvg+cjjPgdTW1iI9PR3x8fHSNqVSifj4eKSmpspYWdNOnTqF4OBgdO3aFVOmTEFOTg4AID09HXV1dVb96NWrF7p06SL1IzU1FdHR0dDpdFKbhIQEmEwmHDlyRGpz5TEa27T17yIrKwsGg8GqFq1Wi9jYWKv++Pr6YvDgwVKb+Ph4KJVKpKWlSW1uu+02qFQqqU1CQgJOnDiBkpISqY2cfd6+fTsCAwPRs2dPPProoygqKpKec7Q+Go1GAIC/vz+Atvtz2VZ/j3/fv0aff/45AgIC0K9fP8yfPx+VlZXSc47SP7PZjDVr1qCiogJxcXFO99411cdGzvD+zZo1C+PGjbuqDkd8H3kDYgdSWFgIs9ls9YcHAHQ6HY4fPy5TVU2LjY3F6tWr0bNnT1y6dAkvvPACRo4cicOHD8NgMEClUsHX19dqH51OB4PBAAAwGAxN9rPxueu1MZlMqKqqgru7u516Z62xnqZqubLWwMBAq+ddXV3h7+9v1SYyMvKqYzQ+5+fnd80+Nx7DnsaMGYM//vGPiIyMxJkzZ7BgwQLcfffdSE1NhYuLi0P10WKxYPbs2bj11lvRr18/6fXb4s9lSUmJ3f8eN9U/APjzn/+M8PBwBAcH4+DBg5g3bx5OnDiBr7/+2iH6d+jQIcTFxaG6uhpeXl745ptv0KdPH2RmZjrNe3etPgKO//4BwJo1a7B//37s3bv3qucc8e8gQxTZxd133y19379/f8TGxiI8PBzr1q1rs3BDtjVp0iTp++joaPTv3x/dunXD9u3bMXr0aBkra7lZs2bh8OHD+OWXX+QuxS6u1b+ZM2dK30dHRyMoKAijR4/GmTNn0K1bt7Yus8V69uyJzMxMGI1GfPXVV5g2bRp27Nghd1k2da0+9unTx+Hfv9zcXDz55JNITk6GRqORuxyb4HCeAwkICICLi8tVVyrk5eVBr9fLVFXz+Pr6okePHjh9+jT0ej1qa2tRWlpq1ebKfuj1+ib72fjc9dr4+Pi0aVBrrOd674ter0d+fr7V8/X19SguLrZJn+V4/7t27YqAgACcPn1aqs0R+vjYY49h48aN2LZtG0JDQ6XtbfXn0t5/j6/Vv6bExsYCgNV72J77p1Kp0L17d8TExGDp0qUYMGAA3nzzTad5767Xx6Y42vuXnp6O/Px83HLLLXB1dYWrqyt27NiBFStWwNXVFTqdzuHeR4YoB6JSqRATE4OUlBRpm8ViQUpKitWYeXtUXl6OM2fOICgoCDExMXBzc7Pqx4kTJ5CTkyP1Iy4uDocOHbL6UE5OToaPj490ajsuLs7qGI1t2vp3ERkZCb1eb1WLyWRCWlqaVX9KS0uRnp4utdm6dSssFov0D2FcXBx27tyJuro6qU1ycjJ69uwJPz8/qU176DMAnD9/HkVFRQgKCpJqa899FELgsccewzfffIOtW7deNazYVn8u7fX3+Eb9a0pmZiYAWL2H7bV/TbFYLKipqXH49645fWyKo71/o0ePxqFDh5CZmSk9Bg8ejClTpkjfO9z72KJp6CS7NWvWCLVaLVavXi2OHj0qZs6cKXx9fa2uVGgP5s6dK7Zv3y6ysrLEr7/+KuLj40VAQIDIz88XQjRcxtqlSxexdetWsW/fPhEXFyfi4uKk/RsvY73rrrtEZmam2LJli+jcuXOTl7E+88wz4tixY2LlypV2W+KgrKxMZGRkiIyMDAFA/Otf/xIZGRkiOztbCNGwxIGvr6/473//Kw4ePCgmTJjQ5BIHgwYNEmlpaeKXX34RUVFRVpf/l5aWCp1OJx566CFx+PBhsWbNGuHh4XHV5f+urq7i1VdfFceOHROLFy+22RIH1+tjWVmZePrpp0VqaqrIysoSP/30k7jllltEVFSUqK6udog+Pvroo0Kr1Yrt27dbXSJeWVkptWmrP5f2+Ht8o/6dPn1avPjii2Lfvn0iKytL/Pe//xVdu3YVt912m0P077nnnhM7duwQWVlZ4uDBg+K5554TCoVC/Pjjj0IIx37vmtNHR3//ruX3Vxw62vvIEOWA3nrrLdGlSxehUqnE0KFDxe7du+Uu6SoTJ04UQUFBQqVSiZCQEDFx4kRx+vRp6fmqqirx//7f/xN+fn7Cw8ND3HfffeLSpUtWxzh37py4++67hbu7uwgICBBz584VdXV1Vm22bdsmBg4cKFQqlejatav4+OOP7dKfbdu2CQBXPaZNmyaEaFjmYNGiRUKn0wm1Wi1Gjx4tTpw4YXWMoqIiMXnyZOHl5SV8fHzE9OnTRVlZmVWbAwcOiBEjRgi1Wi1CQkLEsmXLrqpl3bp1okePHkKlUom+ffuK77//3u59rKysFHfddZfo3LmzcHNzE+Hh4eLhhx++6h+c9tzHpvoGwOrPTFv+ubT13+Mb9S8nJ0fcdtttwt/fX6jVatG9e3fxzDPPWK0z1J7799e//lWEh4cLlUolOnfuLEaPHi0FKCEc+71rTh8d/f27lt+HKEd7HxVCCNGyc1dERERExDlRRERERK3AEEVERETUCgxRRERERK3AEEVERETUCgxRRERERK3AEEVERETUCgxRRERERK3AEEVERETUCgxRREQAIiIi8MYbb8hdBhE5EIYoInIoCoXiuo/nn3++Vcfdu3cvZs6ceVO1ZWVl4c9//jOCg4Oh0WgQGhqKCRMm4Pjx4wCAc+fOQaFQSDeOJSLH5ip3AURELXHp0iXp+7Vr1yIpKQknTpyQtnl5eUnfCyFgNpvh6nrjf+o6d+58U3XV1dXhzjvvRM+ePfH1118jKCgI58+fx+bNm1FaWnpTxyai9olnoojIoej1eumh1WqhUCikn48fPw5vb29s3rwZMTExUKvV+OWXX3DmzBlMmDABOp0OXl5eGDJkCH766Ser4/5+OE+hUOCDDz7AfffdBw8PD0RFReHbb7+9Zl1HjhzBmTNn8M4772DYsGEIDw/HrbfeiiVLlmDYsGEAgMjISADAoEGDoFAocPvtt0v7f/DBB+jduzc0Gg169eqFd955R3qu8QzWmjVrMHz4cGg0GvTr1w87duywwW+UiFqLIYqInM5zzz2HZcuW4dixY+jfvz/Ky8sxduxYpKSkICMjA2PGjMH48eORk5Nz3eO88MILePDBB3Hw4EGMHTsWU6ZMQXFxcZNtO3fuDKVSia+++gpms7nJNnv27AEA/PTTT7h06RK+/vprAMDnn3+OpKQkvPTSSzh27Bj++c9/YtGiRfjkk0+s9n/mmWcwd+5cZGRkIC4uDuPHj0dRUVFLfz1EZCuCiMhBffzxx0Kr1Uo/b9u2TQAQGzZsuOG+ffv2FW+99Zb0c3h4uHj99delnwGIhQsXSj+Xl5cLAGLz5s3XPObbb78tPDw8hLe3t7jjjjvEiy++KM6cOSM9n5WVJQCIjIwMq/26desmvvjiC6tt//jHP0RcXJzVfsuWLZOer6urE6GhoeLll1++YV+JyD54JoqInM7gwYOtfi4vL8fTTz+N3r17w9fXF15eXjh27NgNz0T1799f+t7T0xM+Pj7Iz8+/ZvtZs2bBYDDg888/R1xcHNavX4++ffsiOTn5mvtUVFTgzJkzmDFjBry8vKTHkiVLcObMGau2cXFx0veurq4YPHgwjh07dt0+EJH9cGI5ETkdT09Pq5+ffvppJCcn49VXX0X37t3h7u6OP/3pT6itrb3ucdzc3Kx+VigUsFgs193H29sb48ePx/jx47FkyRIkJCRgyZIluPPOO5tsX15eDgD497//jdjYWKvnXFxcrvtaRCQvnokiIqf366+/4i9/+Qvuu+8+REdHQ6/X49y5c3Z/XYVCgV69eqGiogIAoFKpAMBqzpROp0NwcDDOnj2L7t27Wz0aJ6I32r17t/R9fX090tPT0bt3b7v3g4iaxjNRROT0oqKi8PXXX2P8+PFQKBRYtGjRDc8otVRmZiYWL16Mhx56CH369IFKpcKOHTvw0UcfYd68eQCAwMBAuLu7Y8uWLQgNDYVGo4FWq8ULL7yAJ554AlqtFmPGjEFNTQ327duHkpISzJkzR3qNlStXIioqCr1798brr7+OkpIS/PWvf7VpP4io+RiiiMjp/etf/8Jf//pXDB8+HAEBAZg3bx5MJpNNXyM0NBQRERF44YUXpCUJGn9+6qmnADTMY1qxYgVefPFFJCUlYeTIkdi+fTv+93//Fx4eHnjllVfwzDPPwNPTE9HR0Zg9e7bVayxbtgzLli1DZmYmunfvjm+//RYBAQE27QcRNZ9CCCHkLoKIiK7t3LlziIyMREZGBgYOHCh3OUR0GedEEREREbUCQxQRERFRK3A4j4iIiKgVeCaKiIiIqBUYooiIiIhagSGKiIiIqBUYooiIiIhagSGKiIiIqBUYooiIiIhagSGKiIiIqBUYooiIiIha4f8D1K7jBc0D1zkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(learning_rate(tf.range(40000, dtype=tf.float32)))\n",
        "plt.ylabel('Learning Rate')\n",
        "plt.xlabel('Train Step')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YgkDE7hzo8r5"
      },
      "source": [
        "### Set up the loss 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": 54,
      "metadata": {
        "id": "67oqVHiT0Eiu"
      },
      "outputs": [],
      "source": [
        "def masked_loss(label, pred):\n",
        "  mask = label != 0\n",
        "  loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n",
        "    from_logits=True, reduction='none')\n",
        "  loss = loss_object(label, pred)\n",
        "\n",
        "  mask = tf.cast(mask, dtype=loss.dtype)\n",
        "  loss *= mask\n",
        "\n",
        "  loss = tf.reduce_sum(loss)/tf.reduce_sum(mask)\n",
        "  return loss\n",
        "\n",
        "\n",
        "def masked_accuracy(label, pred):\n",
        "  pred = tf.argmax(pred, axis=2)\n",
        "  label = tf.cast(label, pred.dtype)\n",
        "  match = label == pred\n",
        "\n",
        "  mask = label != 0\n",
        "\n",
        "  match = match & mask\n",
        "\n",
        "  match = tf.cast(match, dtype=tf.float32)\n",
        "  mask = tf.cast(mask, dtype=tf.float32)\n",
        "  return tf.reduce_sum(match)/tf.reduce_sum(mask)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xYEasEOsdn5W"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mk8vwuN24hafK"
      },
      "source": [
        "With all the components ready, configure the training procedure using `model.compile`, and then run it with `model.fit`:\n",
        "\n",
        "Note: This takes about an hour to train in Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "Una1v0hDlIsT"
      },
      "outputs": [],
      "source": [
        "transformer.compile(\n",
        "    loss=masked_loss,\n",
        "    optimizer=optimizer,\n",
        "    metrics=[masked_accuracy])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jg35qKvVlctp"
      },
      "outputs": [],
      "source": [
        "transformer.fit(train_batches,\n",
        "                epochs=20,\n",
        "                validation_data=val_batches)"
      ]
    },
    {
      "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": null,
      "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",
        "    # So, recalculate them outside the loop.\n",
        "    self.transformer([encoder_input, output[:,:-1]], training=False)\n",
        "    attention_weights = self.transformer.decoder.last_attn_scores\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": null,
      "metadata": {
        "id": "-NjbvpHUTEia"
      },
      "outputs": [],
      "source": [
        "translator = Translator(tokenizers, transformer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "metadata": {
        "id": "o9CEm4cuTGtw"
      },
      "outputs": [],
      "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": null,
      "metadata": {
        "id": "elmz_Ly7THuJ"
      },
      "outputs": [],
      "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": null,
      "metadata": {
        "id": "bmmtPo3vTOwj"
      },
      "outputs": [],
      "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": null,
      "metadata": {
        "id": "V3m2wcNLTU8K"
      },
      "outputs": [],
      "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": null,
      "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": null,
      "metadata": {
        "id": "yI4YWU2uXDeW"
      },
      "outputs": [],
      "source": [
        "head = 0\n",
        "# Shape: `(batch=1, num_heads, seq_len_q, seq_len_k)`.\n",
        "attention_heads = tf.squeeze(attention_weights, 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": null,
      "metadata": {
        "id": "SMEpyioWTmSN"
      },
      "outputs": [],
      "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": null,
      "metadata": {
        "id": "GzvIo5uYTnHG"
      },
      "outputs": [],
      "source": [
        "translated_tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lrNh47D1ToBD"
      },
      "outputs": [],
      "source": [
        "plot_attention_head(in_tokens, translated_tokens, attention)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "metadata": {
        "id": "lBMujUb1Tr4C"
      },
      "outputs": [],
      "source": [
        "plot_attention_weights(sentence,\n",
        "                       translated_tokens,\n",
        "                       attention_weights[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9N5S5IptTtHI"
      },
      "source": [
        "The model can handle unfamiliar words. Neither `'triceratops'` nor `'encyclopédia'` are in the input dataset, and the model attempts to transliterate them even without a shared vocabulary. For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w0-5gjfWT0CS"
      },
      "outputs": [],
      "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, attention_weights[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": null,
      "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": null,
      "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": null,
      "metadata": {
        "id": "GITRCiAYUE5w"
      },
      "outputs": [],
      "source": [
        "translator('este é o primeiro livro que eu fiz.').numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_v--e1XmUFw3"
      },
      "outputs": [],
      "source": [
        "tf.saved_model.save(translator, export_dir='translator')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5KJSQEzlUGo-"
      },
      "outputs": [],
      "source": [
        "reloaded = tf.saved_model.load('translator')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lIVpKWBNUHhr"
      },
      "outputs": [],
      "source": [
        "reloaded('este é o primeiro livro que eu fiz.').numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ri2i6cTxUI00"
      },
      "source": [
        "## 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 has a temporal/spatial relationship, like text or images, 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)\n",
        "* Use 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) (Tay et al., 2022)\n",
        "* [\"Formal algorithms for Transformers\"](https://arxiv.org/abs/2207.09238) (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) (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).\n",
        "* [LaMDA](https://ai.googleblog.com/2022/01/lamda-towards-safe-grounded-and-high.html)\n",
        "* [MUM](https://blog.google/products/search/introducing-mum/)\n",
        "* [Reformer](https://ai.googleblog.com/2020/01/reformer-efficient-transformer.html)\n",
        "* [BERT](https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html)\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)\n",
        "- [Multi-task multitrack music transcription (MT3)](https://magenta.tensorflow.org/transcription-with-transformers) with a Transformer\n",
        "- [Code generation with AlphaCode](https://www.deepmind.com/blog/competitive-programming-with-alphacode)\n",
        "- [Reinforcement learning with multi-game decision Transformers](https://ai.googleblog.com/2022/07/training-generalist-agents-with-multi.html)\n",
        "- [Protein structure prediction with AlphaFold](https://www.nature.com/articles/s41586-021-03819-2)\n",
        "- [OptFormer: Towards universal hyperparameter optimization with Transformers](http://ai.googleblog.com/2022/08/optformer-towards-universal.html)\n"
      ]
    },
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