transformer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_qNSzzyaCbD"
      },
      "source": [
        "<table style=\"width:100%\">\n",
        "  <tr>\n",
        "    <td><img src=\"https://drive.google.com/uc?id=1ooq-pOOCQ_HSpFAFUwiM9pLjQTf6-f2r\" width=\"300\">\n",
        "    </td>\n",
        "    <td>\n",
        "    <img src=\"https://drive.google.com/uc?id=1iQAIvm1BAoTUcJX__u-MBWNTc7Q-4lcM\" width=\"300\">\n",
        "    </td>\n",
        "  </tr>\n",
        "</table>\n",
        "\n",
        "Hadi Abdi Khojasteh; https://github.com/hkhojasteh/PyData-Yerevan\n",
        "\n",
        "References: TensorFlow (under Apache License V2.0), cs231n, Attention is All you Need\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J0Qjg6vuaHNt"
      },
      "source": [
        "# Transformer model for language understanding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OF6MI3ya7lKi"
      },
      "source": [
        "<img src=\"https://drive.google.com/uc?id=14uqZWPnoWu5nN37UGdU-Fq4-26REUHzY\" width=\"1200\">\n",
        "\n",
        "Vanilla Neural Networks\n",
        "\n",
        "Image Captioning: image -> sequence of words\n",
        "\n",
        "Sentiment Classification: sequence of words -> sentiment\n",
        "\n",
        "Machine Translation: seq of words -> seq of words\n",
        "\n",
        "Video classification on frame level\n",
        "\n",
        "<br>\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1E354oo9VucX9Bz2zWv_QarNwkMiAkXl5\" width=\"600\">\n",
        "\n",
        "<br>\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1SnZuRS6JX0dYFvJKLu4rfu1Ic-UTTYjp\" width=\"400\">\n",
        "<img src=\"https://drive.google.com/uc?id=1rAkUvxP0oB5nEF6WFepXAVmw7fhf2YXb\" width=\"400\">\n",
        "<img src=\"https://drive.google.com/uc?id=1B485QbIdWe3T1CD_AUfsOgk9aB2tLCn5\" width=\"400\">\n",
        "\n",
        "<br>\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1mfNd5_T6_uUgSidgbwaHyZ9P1lGdCvhZ\" width=\"900\">\n",
        "\n",
        "<br>\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1yCoAnC2F9St8Aq4-ObEUREs_ApXXMHfu\" width=\"600\">\n",
        "\n",
        "<br>\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1L-BgReFd7WEvroflwIzXH226heOpcPwI\" width=\"300\">\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-f8TnGpE_ex"
      },
      "source": [
        "This tutorial trains a <a href=\"https://arxiv.org/abs/1706.03762\" class=\"external\">transformer model</a> to translate a [Portuguese to English dataset](https://www.tensorflow.org/datasets/catalog/ted_hrlr_translate#ted_hrlr_translatept_to_en).\n",
        "\n",
        "This is an advanced example that assumes knowledge of [text generation](https://www.tensorflow.org/text/tutorials/text_generation) and [attention](https://www.tensorflow.org/text/tutorials/nmt_with_attention).\n",
        "\n",
        "This tutorial demonstrates how to build a transformer model and most of its components *from scratch* using low-level TensorFlow and Keras functionalities. Some of this could be minimized if you took advantage of built-in APIs like `tf.keras.layers.MultiHeadAttention`.\n",
        "\n",
        "The core idea behind a transformer model is *self-attention*—the ability to attend to different positions of the input sequence to compute a representation of that sequence. Transformer creates stacks of self-attention layers and is explained below in the sections *Scaled dot product attention* and *Multi-head attention*.\n",
        "\n",
        "A transformer model handles variable-sized input using stacks of self-attention layers instead of [RNNs](https://www.tensorflow.org/text/tutorials/text_classification_rnn) or [CNNs](https://www.tensorflow.org/tutorials/images/cnn). This general architecture has a number of advantages:\n",
        "\n",
        "* It makes no assumptions about the temporal/spatial relationships across the data. This is ideal for processing a set of objects (for example, [StarCraft units](https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/#block-8)).\n",
        "* Layer outputs can be calculated in parallel, instead of a series like an RNN.\n",
        "* Distant items can affect each other's output without passing through many RNN-steps, or convolution layers (see [Scene Memory Transformer](https://arxiv.org/pdf/1903.03878.pdf) for example).\n",
        "* It can learn long-range dependencies. This is a challenge in many sequence tasks.\n",
        "\n",
        "The downsides of this architecture are:\n",
        "\n",
        "* For a time-series, the output for a time-step is calculated from the *entire history* instead of only the inputs and current hidden-state. This _may_ be less efficient.   \n",
        "* If the input *does* have a  temporal/spatial relationship, like text, some positional encoding must be added or the model will effectively see a bag of words. \n",
        "\n",
        "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\" width=\"800\" alt=\"Attention heatmap\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6a3Z0gP4IHY"
      },
      "source": [
        "## Mounting Google Drive locally\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJ28-5iT4M7E",
        "outputId": "8320f32e-9d34-4e58-9b85-57d648d7bafa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swymtxpl7W7w"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XFG0NDRu5mYQ",
        "outputId": "9a308970-830e-4d21-e4ed-11d9ef2da7c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tensorflow_datasets in /usr/local/lib/python3.7/dist-packages (4.6.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (4.1.1)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (0.3.5.1)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.2.0)\n",
            "Requirement already satisfied: toml in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (0.10.2)\n",
            "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.9.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.15.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (4.64.0)\n",
            "Requirement already satisfied: etils[epath] in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (0.6.0)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.1.0)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (5.9.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.21.6)\n",
            "Requirement already satisfied: promise in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (2.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (2.23.0)\n",
            "Requirement already satisfied: protobuf>=3.12.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (3.17.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2.10)\n",
            "Requirement already satisfied: zipp in /usr/local/lib/python3.7/dist-packages (from etils[epath]->tensorflow_datasets) (3.8.1)\n",
            "Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-metadata->tensorflow_datasets) (1.56.4)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow-text==2.8.*\n",
            "  Downloading tensorflow_text-2.8.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.9 MB 8.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tensorflow<2.9,>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-text==2.8.*) (2.8.2+zzzcolab20220719082949)\n",
            "Requirement already satisfied: tensorflow-hub>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-text==2.8.*) (0.12.0)\n",
            "Requirement already satisfied: tensorflow-estimator<2.9,>=2.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (2.8.0)\n",
            "Requirement already satisfied: protobuf<3.20,>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (3.17.3)\n",
            "Requirement already satisfied: keras<2.9,>=2.8.0rc0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (2.8.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.1.0)\n",
            "Requirement already satisfied: gast>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (0.5.3)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (0.26.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (0.2.0)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.14.1)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (3.1.0)\n",
            "Requirement already satisfied: tensorboard<2.9,>=2.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (2.8.0)\n",
            "Requirement already satisfied: libclang>=9.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (14.0.6)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.47.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.15.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.6.3)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.21.6)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (3.3.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.1.2)\n",
            "Requirement already satisfied: absl-py>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.2.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (4.1.1)\n",
            "Requirement already satisfied: flatbuffers>=1.12 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (2.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (57.4.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from astunparse>=1.6.0->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (0.37.1)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py>=2.9.0->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.5.2)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.0.1)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.35.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (0.4.6)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (3.4.1)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (0.6.1)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.8.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (2.23.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (4.9)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (4.2.4)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (4.12.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (3.8.1)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (0.4.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (2022.6.15)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow<2.9,>=2.8.0->tensorflow-text==2.8.*) (3.2.0)\n",
            "Installing collected packages: tensorflow-text\n",
            "Successfully installed tensorflow-text-2.8.2\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow_datasets\n",
        "!pip install -U 'tensorflow-text==2.8.*'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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 tf_text to load the ops used by the tokenizer saved model\n",
        "import tensorflow_text  # pylint: disable=unused-import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pXzVhU34zWEU"
      },
      "outputs": [],
      "source": [
        "logging.getLogger('tensorflow').setLevel(logging.ERROR)  # suppress warnings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZbylQPWJMUp",
        "outputId": "ab5ad044-0232-4159-9a7f-c371df0ba720"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting spacy==3.4.0\n",
            "  Downloading spacy-3.4.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 7.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (4.64.0)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (2.0.6)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (1.21.6)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.9 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions<4.2.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (4.1.1)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (3.0.6)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (1.0.7)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.10.0,>=1.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (1.9.1)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (1.0.3)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (2.0.8)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (2.23.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (57.4.0)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (0.10.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (2.11.3)\n",
            "Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (0.6.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (2.4.4)\n",
            "Requirement already satisfied: typer<0.5.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (0.4.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (21.3)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (8.1.0)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from spacy==3.4.0) (3.3.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from catalogue<2.1.0,>=2.0.6->spacy==3.4.0) (3.8.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy==3.4.0) (3.0.9)\n",
            "Requirement already satisfied: smart-open<6.0.0,>=5.2.1 in /usr/local/lib/python3.7/dist-packages (from pathy>=0.3.5->spacy==3.4.0) (5.2.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy==3.4.0) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy==3.4.0) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy==3.4.0) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy==3.4.0) (2.10)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.7/dist-packages (from thinc<8.2.0,>=8.1.0->spacy==3.4.0) (0.7.8)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.5.0,>=0.3.0->spacy==3.4.0) (7.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy==3.4.0) (2.0.1)\n",
            "Installing collected packages: spacy\n",
            "  Attempting uninstall: spacy\n",
            "    Found existing installation: spacy 3.4.1\n",
            "    Uninstalling spacy-3.4.1:\n",
            "      Successfully uninstalled spacy-3.4.1\n",
            "Successfully installed spacy-3.4.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting spacy-language-detection\n",
            "  Downloading spacy_language_detection-0.2.1-py3-none-any.whl (6.5 kB)\n",
            "Collecting langdetect==1.0.9\n",
            "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
            "\u001b[K     |████████████████████████████████| 981 kB 5.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy-language-detection) (3.4.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from langdetect==1.0.9->spacy-language-detection) (1.15.0)\n",
            "Requirement already satisfied: typing-extensions<4.2.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (4.1.1)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (3.3.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (2.11.3)\n",
            "Requirement already satisfied: typer<0.5.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (0.4.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (21.3)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (2.0.6)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (2.23.0)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.9 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (3.0.9)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (57.4.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (1.21.6)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (2.0.8)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (0.10.1)\n",
            "Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (0.6.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (2.4.4)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (4.64.0)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.10.0,>=1.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (1.9.1)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (8.1.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (3.0.6)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (1.0.7)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.0.0->spacy-language-detection) (1.0.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from catalogue<2.1.0,>=2.0.6->spacy>=3.0.0->spacy-language-detection) (3.8.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy>=3.0.0->spacy-language-detection) (3.0.9)\n",
            "Requirement already satisfied: smart-open<6.0.0,>=5.2.1 in /usr/local/lib/python3.7/dist-packages (from pathy>=0.3.5->spacy>=3.0.0->spacy-language-detection) (5.2.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=3.0.0->spacy-language-detection) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=3.0.0->spacy-language-detection) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=3.0.0->spacy-language-detection) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=3.0.0->spacy-language-detection) (2022.6.15)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.7/dist-packages (from thinc<8.2.0,>=8.1.0->spacy>=3.0.0->spacy-language-detection) (0.7.8)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.5.0,>=0.3.0->spacy>=3.0.0->spacy-language-detection) (7.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy>=3.0.0->spacy-language-detection) (2.0.1)\n",
            "Building wheels for collected packages: langdetect\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993242 sha256=f4b1be5ddea463f06811f458cfd405930b81dd5dc1fbeb70c8e462bb57c5d405\n",
            "  Stored in directory: /root/.cache/pip/wheels/c5/96/8a/f90c59ed25d75e50a8c10a1b1c2d4c402e4dacfa87f3aff36a\n",
            "Successfully built langdetect\n",
            "Installing collected packages: langdetect, spacy-language-detection\n",
            "Successfully installed langdetect-1.0.9 spacy-language-detection-0.2.1\n"
          ]
        }
      ],
      "source": [
        "!pip install spacy==3.4.0\n",
        "!pip install spacy-language-detection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-cCvXbPkccV1"
      },
      "source": [
        "## Download the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4_Qt8W1hJE_"
      },
      "source": [
        "Use [TensorFlow datasets](https://www.tensorflow.org/datasets) to load the [Portuguese-English translation dataset](https://github.com/neulab/word-embeddings-for-nmt) from the [TED Talks Open Translation Project](https://www.ted.com/participate/translate).\n",
        "\n",
        "This dataset contains approximately 50000 training examples, 1100 validation examples, and 2000 test examples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
          "referenced_widgets": [
            "97d18fde951f443abc3a0fa9c1cebd1d",
            "c764b25b0da54e4ab63d875088934768",
            "394b0d069c534edd8cc297b9acd11e6e",
            "c4843efb7b344798ada22d1b1bb5b3e6",
            "4b00b22336ae40d29a831bfe87d37aa7",
            "b4ffdf9961234e0f8222ca6d25c2a788",
            "7cf493c758184d1e9fe458219be86894",
            "dbafab1eb8d54868aef8b05fe4f1bdbf",
            "becf7205afce467b80c21c6eee4e8098",
            "794ba813a39a4c76b2025fc81cbc6a71",
            "dc80d87d7b5943c5bf3992b49ecbd801",
            "18cd6a395788467095618ce933330e4c",
            "72d024cc5a7e4bcca82ef7704be365c0",
            "01c631f7fb8949328e6678b30370fba8",
            "77bf1837391b4b6ca1f1240b8495f6d7",
            "44899ef88d284f8182f94b3cca1a7a64",
            "dab349dc000f4dd1adfc6be57d371c87",
            "4f38696b86b04227af50e8ef9125b05f",
            "6b2a7cd0ce7b46b4a78fadd0ee6e13df",
            "acff79c589dc4563947fa23daf5d8972",
            "0e3456cc744241f29f1a99ad54572712",
            "500e6111ac1e4f439a46fe55a2d4cd35",
            "83ce5057fa824a8bb1dc818ddd01e7e6",
            "523a3080de8246e9998851b94aa289b2",
            "d529e3287cce44fc9ec76f470e165c57",
            "a8c06c5c47644badb3fbdf1712bb773b",
            "4118a878546c4ed98bc1ebd41e949656",
            "a4b7443bc3304573accc72c2f1c926b8",
            "7abc5abb872945a0acbb80325c23fe75",
            "acab89080711461f93395f7e041bd24c",
            "31ad05bf5eb24d25be8fced29a306f6d",
            "a063e605ad0c4a96aeb8556d93537d1e",
            "7741a651675a4105a2dd247e63ff1045",
            "c52c07226eb74a8bb2365ad78d0f8983",
            "6d81341815ec417f82fe219f4522d987",
            "faecef990da948c3ae31857fd1e8c666",
            "9c5d853b616f48929dd851903c8e3a85",
            "daffc8afd11447709011549d92900208",
            "db1ab1178aaa46b190c0eccd7bdebd06",
            "68f287400c924929817364041c41cdd3",
            "54b5b0d36b5c4a9cacc95f0f734bd597",
            "3bcfc4c83fbe4a3e87be1382d516e0d1",
            "70a92596cfcb4a3bbae1ab8a1d1e21c9",
            "14f8889ad5884fae98faa7448d484359",
            "45d8f4f3d5e94ea9814637b1ad958e8b",
            "4867c670481c49e091f02971af1ef124",
            "9753a752274140af9ba91ec9f03e535f",
            "847c2b88aa6c47048a9c742954962ca0",
            "fa900b88b21d401e8e7d97892b2261c9",
            "9173f888fb7d43b390ef33cbc202264a",
            "3b1d080b1b0845e9bed7db128ffa0a31",
            "cca764db6814446fac7daf44017fffc4",
            "63da4d272c8a465c8ade02b8cd53e93f",
            "47676cb41cde42969ce4914c5f46da34",
            "d18d37985d8248f29576897b9d39558e",
            "5287e6bbaf0b4ae0a2dbf94cf9d70df5",
            "f37cfda2d67d4786860aedd6c524859e",
            "6e75bfdf510b43889f76323efad42d5a",
            "f16a3703489d4acbbf671139ef850eca",
            "003103fe34db46bb8937c62830fa198b",
            "0772e3bc35e5443ebc95b6779fb08602",
            "deb0a595f103463892dc639e28300a2a",
            "5d6f2ab779314a71b560c26a2e205105",
            "ee69d6537a204d768f6f5067e9f9d9aa",
            "15ed632e98554200b0d900d61497efe1",
            "6118ba2628b04d30a5788b6c1c03e1f0",
            "d280c534ec2c4b82a56770daf370150f",
            "9e5e532113944781aa4c79c434c393a2",
            "8fb44e5e5f9b461bab8ede06ad4cc0ac",
            "4f74a91bd52b43c286ea57cfb90eaccf",
            "21bb0722c0b1434198a9e7d382795f3e",
            "aea96ae561c84798920f27b645dce72e",
            "93d903d7a38d4acc8619fd9242a89334",
            "0936404803eb4a608c113fd9423f7fc0",
            "5cdf2f4e30454fc19501020f618a8820",
            "acb7347b2c8a4e4d9bbbba8e456b87a6",
            "4855b06812314c03a5f8b91abb374531",
            "ef1350a3defb4750897367648283390a",
            "443a46e4507347e4b4c5b159906f7ca0",
            "3dbb2365a41847079ce0375cab09d471",
            "411bb64c33464c0e89c95e9105fc298d",
            "cb8157d46d2043668bfa7a3397f18565",
            "bd1505edd0ce4c5c88670beb0d61d69e",
            "7e2cad2d9c9340aeb09d685a87c4d405",
            "79781492d22c40d49d84fd918b60dcfc",
            "1a9b650049e94d878c481d9b714e9de5",
            "5e71297a1ae244cfbab29834d135af87",
            "4a342aaa89c840cc84be20975ab383fb",
            "edcf870de96246d4971bc83e0b40b3f5",
            "14a516d260214caa92c9fabdd99b31a2",
            "2cadadc18f164b6c9872d12421b8c654",
            "1d639a743f3d4b71bbb5e7adb24f6387",
            "aae1d7a927f348d0a0fa3aa3c28a8834",
            "137a362b35884826800bd53648dd6817",
            "50901fcec59143928d4aeaaaed5b3bcf",
            "6af90d2e89934007b7d8d15e2857763a",
            "bd9f7c6d56ef461f98f425635877b914",
            "1d09b0fdaf51409d8b70391ebb01a5b3",
            "40d2ce5a27b64e6f9f35b7b1eb132363",
            "f1453920ecb448a7afd59b31ba18576a",
            "e337e7b6764a46f1aae4a7dd63753452",
            "259b9a8478004eb2abd2286b8835dbfb",
            "29b72790c7644824b9d6171a3f8072c0",
            "2e8397937efd418883a906d986c4b3e5",
            "cd75a7666cc446708affada1548d97f9",
            "668bb06adb2c40c6919886e1144a7da5",
            "5eeab2cb7f51418e9e2556ceb490936e",
            "480f54d31d63464d9da3ac46f3428741",
            "128dea962396413b9d3ec3cbfa69f736",
            "0e85e21dff104025a0c69b71e53c90c9"
          ]
        },
        "id": "8q9t4FmN96eN",
        "outputId": "469a73ee-f13b-4d55-97d4-57c06d57fd90"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset 124.94 MiB (download: 124.94 MiB, generated: Unknown size, total: 124.94 MiB) to ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0...\u001b[0m\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "97d18fde951f443abc3a0fa9c1cebd1d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "18cd6a395788467095618ce933330e4c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "83ce5057fa824a8bb1dc818ddd01e7e6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c52c07226eb74a8bb2365ad78d0f8983",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "45d8f4f3d5e94ea9814637b1ad958e8b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train examples...:   0%|          | 0/51785 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5287e6bbaf0b4ae0a2dbf94cf9d70df5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteRVR7GL/ted_hrlr_translate-train.tf…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d280c534ec2c4b82a56770daf370150f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation examples...:   0%|          | 0/1193 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ef1350a3defb4750897367648283390a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteRVR7GL/ted_hrlr_translate-validati…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "edcf870de96246d4971bc83e0b40b3f5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test examples...:   0%|          | 0/1803 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1453920ecb448a7afd59b31ba18576a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteRVR7GL/ted_hrlr_translate-test.tfr…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1mDataset ted_hrlr_translate downloaded and prepared to ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "examples, metadata = tfds.load('ted_hrlr_translate/pt_to_en', with_info=True,\n",
        "                               as_supervised=True)\n",
        "train_examples, val_examples = examples['train'], examples['validation']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1OnwQZ2IpTbl"
      },
      "source": [
        "The `tf.data.Dataset` object returned by TensorFlow datasets yields pairs of text examples:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AtuMLTQGlHZ1",
        "outputId": "4b562430-c47d-40b7-c9cf-9f5f2f6a41d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "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",
            "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",
        "  for pt in pt_examples.numpy():\n",
        "    print(pt.decode('utf-8'))\n",
        "\n",
        "  print()\n",
        "\n",
        "  for en in en_examples.numpy():\n",
        "    print(en.decode('utf-8'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJxTd6aVnZyh"
      },
      "source": [
        "## Text tokenization & detokenization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlHAFNaopq6U"
      },
      "source": [
        "You can't train a model directly on text. The text needs to be converted to some numeric representation first. Typically, you convert the text to sequences of token IDs, which are used as indices into an embedding."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2sPvbCEePzOC"
      },
      "source": [
        "One popular implementation is demonstrated in the [Subword tokenizer tutorial](https://www.tensorflow.org/text/guide/subwords_tokenizer) builds subword tokenizers (`text.BertTokenizer`) optimized for this dataset and exports them in a [saved_model](https://www.tensorflow.org/guide/saved_model).\n",
        "\n",
        "Download and unzip and import the `saved_model`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "QToMl0NanZPr",
        "outputId": "c8895fd6-1639-4b8f-c01b-32d782530199"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/download.tensorflow.org/models/ted_hrlr_translate_pt_en_converter.zip\n",
            "188416/184801 [==============================] - 0s 0us/step\n",
            "196608/184801 [===============================] - 0s 0us/step\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'./ted_hrlr_translate_pt_en_converter.zip'"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": null,
      "metadata": {
        "id": "h5dbGnPXnuI1"
      },
      "outputs": [],
      "source": [
        "tokenizers = tf.saved_model.load(model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQYc0CJTpnvg"
      },
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-PCJijfcZ9_",
        "outputId": "61b2e624-683d-49a9-8ad5-495cd765babf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['detokenize',\n",
              " 'get_reserved_tokens',\n",
              " 'get_vocab_path',\n",
              " 'get_vocab_size',\n",
              " 'lookup',\n",
              " 'tokenize',\n",
              " 'tokenizer',\n",
              " 'vocab']"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "[item for item in dir(tokenizers.en) if not item.startswith('_')]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-pv_nc5lrAiD"
      },
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GV-smAAQJtM",
        "outputId": "2e3e84fb-523b-4523-c3b6-59749e75edbb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "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 en in en_examples.numpy():\n",
        "  print(en.decode('utf-8'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fQJq1xB-tOn",
        "outputId": "1085e75d-4632-47bf-db45-46fad3d90dba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[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",
        "for row in encoded.to_list():\n",
        "  print(row)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBRlikwDR2Lu"
      },
      "source": [
        "The `detokenize` method attempts to convert these token IDs back to human readable text: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tpqx1aug3W31",
        "outputId": "0c212f14-bdfa-44ee-9c01-877b340eaacb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "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",
        "for line in round_trip.numpy():\n",
        "  print(line.decode('utf-8'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5mGvytArL9g"
      },
      "source": [
        "The lower level `lookup` method converts from token-IDs to token text:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RpzRLzvIuN3R",
        "outputId": "28510b90-5ed0-4dac-a1b6-707fcac83ac6"
      },
      "outputs": [
        {
          "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]']                                          ]>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokens = tokenizers.en.lookup(encoded)\n",
        "tokens"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5V8Ix_PNSfhV"
      },
      "source": [
        "Here you can see the \"subword\" aspect of the tokenizers. The word \"searchability\" is decomposed into \"search ##ability\" and the word \"serendipity\" into \"s ##ere ##nd ##ip ##ity\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VhW8Gc8wJ98T"
      },
      "source": [
        "Now take a minute to investigate the distribution of tokens per example in the dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "egIny1M8HX0w",
        "outputId": "82683125-9b52-4c07-833a-eed12138028c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "..................................................."
          ]
        }
      ],
      "source": [
        "lengths = []\n",
        "\n",
        "for pt_examples, en_examples in train_examples.batch(1024):\n",
        "  pt_tokens = tokenizers.en.tokenize(pt_examples)\n",
        "  lengths.append(pt_tokens.row_lengths())\n",
        "\n",
        "  en_tokens = tokenizers.en.tokenize(en_examples)\n",
        "  lengths.append(en_tokens.row_lengths())\n",
        "  print('.', end='', flush=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "IIAV663YH3P3",
        "outputId": "57e1a3a3-30e0-44f1-e8d8-82d190ed72f3"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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'Max tokens per example: {max_length}');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1v_e7nu3SISr"
      },
      "outputs": [],
      "source": [
        "MAX_TOKENS = 128"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://drive.google.com/uc?id=1rdwZmqOHQYuZv99zTcSOxlQRXFQCA9g-\" width=\"900\">"
      ],
      "metadata": {
        "id": "BtiPou9plwa7"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fd1NWMxjfsDd"
      },
      "source": [
        "## Setup input pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E7JHK7N7tNZy"
      },
      "source": [
        "To build an input pipeline suitable for training define some functions to transform the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_F1tg9BMSTva"
      },
      "source": [
        "Define a function to drop the examples longer than `MAX_TOKENS`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "81fPafMpSVJ-"
      },
      "outputs": [],
      "source": [
        "def filter_max_tokens(pt, en):\n",
        "  num_tokens = tf.maximum(tf.shape(pt)[1],tf.shape(en)[1])\n",
        "  return num_tokens < MAX_TOKENS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eHuRZ_rTD7i"
      },
      "source": [
        "Define a function that tokenizes the batches of raw text:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6shgzEck3FiV"
      },
      "outputs": [],
      "source": [
        "def tokenize_pairs(pt, en):\n",
        "    pt = tokenizers.pt.tokenize(pt)\n",
        "    # Convert from ragged to dense, padding with zeros.\n",
        "    pt = pt.to_tensor()\n",
        "\n",
        "    en = tokenizers.en.tokenize(en)\n",
        "    # Convert from ragged to dense, padding with zeros.\n",
        "    en = en.to_tensor()\n",
        "    return pt, en"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cf2HwC_0wG0v"
      },
      "source": [
        "Here's a simple input pipeline that processes, shuffles and batches the data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bcRp7VcQ5m6g"
      },
      "outputs": [],
      "source": [
        "BUFFER_SIZE = 20000\n",
        "BATCH_SIZE = 64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BUN_jLBTwNxk"
      },
      "outputs": [],
      "source": [
        "def make_batches(ds):\n",
        "  return (\n",
        "      ds\n",
        "      .cache()\n",
        "      .shuffle(BUFFER_SIZE)\n",
        "      .batch(BATCH_SIZE)\n",
        "      .map(tokenize_pairs, num_parallel_calls=tf.data.AUTOTUNE)\n",
        "      .filter(filter_max_tokens)\n",
        "      .prefetch(tf.data.AUTOTUNE))\n",
        "\n",
        "\n",
        "train_batches = make_batches(train_examples)\n",
        "val_batches = make_batches(val_examples)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://drive.google.com/uc?id=1IBoM4erhM4moq_xd62nkNN-w2j1k1_UE\" width=\"900\">\n",
        "\n",
        "<br>\n",
        "\n",
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/transformer.png\" width=\"600\" alt=\"transformer\">"
      ],
      "metadata": {
        "id": "T7fdOhSWukKm"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBQuibYA4n0n"
      },
      "source": [
        "## Positional encoding\n",
        "\n",
        "Attention layers see their input as a set of vectors, with no sequential order. This model also doesn't contain any recurrent or convolutional layers. Because of this a \"positional encoding\" is added to give the model some information about the relative position of the tokens in the sentence. \n",
        "\n",
        "The positional encoding vector is added to the embedding vector. Embeddings represent a token in a d-dimensional space where tokens with similar meaning will be closer to each other. But the embeddings do not encode the relative position of tokens in a sentence. So after adding the positional encoding, tokens will be closer to each other based on the *similarity of their meaning and their position in the sentence*, in the d-dimensional space.\n",
        "\n",
        "The formula for calculating the positional encoding 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": null,
      "metadata": {
        "id": "WhIOZjMNKujn"
      },
      "outputs": [],
      "source": [
        "def get_angles(pos, i, d_model):\n",
        "  angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))\n",
        "  return pos * angle_rates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1Rz82wEs5biZ"
      },
      "outputs": [],
      "source": [
        "def positional_encoding(position, d_model):\n",
        "  angle_rads = get_angles(np.arange(position)[:, np.newaxis],\n",
        "                          np.arange(d_model)[np.newaxis, :],\n",
        "                          d_model)\n",
        "\n",
        "  # apply sin to even indices in the array; 2i\n",
        "  angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n",
        "\n",
        "  # apply cos to odd indices in the array; 2i+1\n",
        "  angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n",
        "\n",
        "  pos_encoding = angle_rads[np.newaxis, ...]\n",
        "\n",
        "  return tf.cast(pos_encoding, dtype=tf.float32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1kLCla68EloE",
        "outputId": "fd53c8bf-2926-4312-a8a5-b4453cd06663"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1, 2048, 512)\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "n, d = 2048, 512\n",
        "pos_encoding = positional_encoding(n, d)\n",
        "print(pos_encoding.shape)\n",
        "pos_encoding = pos_encoding[0]\n",
        "\n",
        "# Juggle the dimensions for the plot\n",
        "pos_encoding = tf.reshape(pos_encoding, (n, d//2, 2))\n",
        "pos_encoding = tf.transpose(pos_encoding, (2, 1, 0))\n",
        "pos_encoding = tf.reshape(pos_encoding, (d, n))\n",
        "\n",
        "plt.pcolormesh(pos_encoding, cmap='RdBu')\n",
        "plt.ylabel('Depth')\n",
        "plt.xlabel('Position')\n",
        "plt.colorbar()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_b4ou4TYqUN"
      },
      "source": [
        "## Masking"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s42Uydjkv0hF"
      },
      "source": [
        "Mask all the pad tokens in the batch of sequence. It ensures that the model does not treat padding as the input. The mask indicates where pad value `0` is present: it outputs a `1` at those locations, and a `0` otherwise."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U2i8-e1s8ti9"
      },
      "outputs": [],
      "source": [
        "def create_padding_mask(seq):\n",
        "  seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\n",
        "\n",
        "  # add extra dimensions to add the padding\n",
        "  # to the attention logits.\n",
        "  return seq[:, tf.newaxis, tf.newaxis, :]  # (batch_size, 1, 1, seq_len)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7BYeBCNvi7n",
        "outputId": "ee285b2f-330d-4ee8-d42f-56df700552fb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3, 1, 1, 5), dtype=float32, numpy=\n",
              "array([[[[0., 0., 1., 1., 0.]]],\n",
              "\n",
              "\n",
              "       [[[0., 0., 0., 1., 1.]]],\n",
              "\n",
              "\n",
              "       [[[1., 1., 1., 0., 0.]]]], dtype=float32)>"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])\n",
        "create_padding_mask(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z0hzukDBgVom"
      },
      "source": [
        "The look-ahead mask is used to mask the future tokens in a sequence. In other words, the mask indicates which entries should not be used.\n",
        "\n",
        "This means that to predict the third token, only the first and second token will be used. Similarly to predict the fourth token, only the first, second and the third tokens will be used and so on."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dVxS8OPI9uI0"
      },
      "outputs": [],
      "source": [
        "def create_look_ahead_mask(size):\n",
        "  mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)\n",
        "  return mask  # (seq_len, seq_len)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yxKGuXxaBeeE",
        "outputId": "6fd66512-2736-4344-8949-d9a3d34ad654"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3, 3), dtype=float32, numpy=\n",
              "array([[0., 1., 1.],\n",
              "       [0., 0., 1.],\n",
              "       [0., 0., 0.]], dtype=float32)>"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x = tf.random.uniform((1, 3))\n",
        "temp = create_look_ahead_mask(x.shape[1])\n",
        "temp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1d-ftmdL4tU"
      },
      "source": [
        "Note: This mask indicates which entries to *not* attend to. However, the MultiHeadAttention layer in Keras that was added after the creation of this tutorial considers the inverted mask, where 1 is a token to be attended to and 0 should be ignored. If you are using `tf.keras.layers.MultiHeadAttention` instead of the MultiHeadAttention layer in this tutorial defined below, use the following commented out method to generate the mask. You will need to `pip install tensorflow-probability` and `import tensorflow_probability as tfp`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5lLVSWvTL_oA"
      },
      "outputs": [],
      "source": [
        "# def create_look_ahead_mask(size):\n",
        "#     n = int(size * (size+1) / 2)\n",
        "#     mask = tfp.math.fill_triangular(tf.ones((n,), dtype=tf.int32), upper=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xluDl5cXYy4y"
      },
      "source": [
        "## Scaled dot product attention"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vsxEE_-Wa1gF"
      },
      "source": [
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/scaled_attention.png\" width=\"500\" alt=\"scaled_dot_product_attention\">\n",
        "\n",
        "The attention function used by a transformer takes three inputs: Q (query), K (key), V (value). The equation used to calculate the attention weights is:\n",
        "\n",
        "$${Attention(Q, K, V) = softmax_k({QK^T}) V} $$\n",
        "\n",
        "$$\\Large{Attention(Q, K, V) = softmax_k\\left(\\frac{QK^T}{\\sqrt{d_k}}\\right) V} $$\n",
        "\n",
        "\n",
        "The dot-product attention is scaled by a factor of square root of the depth. This is done because for large values of depth, the dot product grows large in magnitude pushing the softmax function where it has small gradients resulting in a very hard softmax.\n",
        "\n",
        "For example, consider that `Q` and `K` have a mean of 0 and variance of 1. Their matrix multiplication will have a mean of 0 and variance of `dk`. So the *square root of `dk`* is used for scaling, so you get a consistent variance regardless of the value of `dk`. If the variance is too low the output may be too flat to optimize effectively. If the variance is too high the softmax may saturate at initialization making it difficult to learn.\n",
        "\n",
        "The mask is multiplied with -1e9 (close to negative infinity). This is done because the mask is summed with the scaled matrix multiplication of Q and K and is applied immediately before a softmax. The goal is to zero out these cells, and large negative inputs to softmax are near zero in the output."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://drive.google.com/uc?id=1v-6pwG5JKLIAg2zUkq47s-DWVSV9ib7Y\" width=\"900\">\n",
        "\n",
        "<br>\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1ZuLazFyAoMUYS0TxmvPeECbcFuwJBd4V\" width=\"900\">\n",
        "\n",
        "<br>\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=11aqQSyE5FHU10QJGPQ3sVCPfWFxpU1x9\" width=\"900\">"
      ],
      "metadata": {
        "id": "AqbdA6eCmea1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LazzUq3bJ5SH"
      },
      "outputs": [],
      "source": [
        "def scaled_dot_product_attention(q, k, v, mask):\n",
        "  \"\"\"Calculate the attention weights.\n",
        "  q, k, v must have matching leading dimensions.\n",
        "  k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v.\n",
        "  The mask has different shapes depending on its type(padding or look ahead)\n",
        "  but it must be broadcastable for addition.\n",
        "\n",
        "  Args:\n",
        "    q: query shape == (..., seq_len_q, depth)\n",
        "    k: key shape == (..., seq_len_k, depth)\n",
        "    v: value shape == (..., seq_len_v, depth_v)\n",
        "    mask: Float tensor with shape broadcastable\n",
        "          to (..., seq_len_q, seq_len_k). Defaults to None.\n",
        "\n",
        "  Returns:\n",
        "    output, attention_weights\n",
        "  \"\"\"\n",
        "\n",
        "  matmul_qk = tf.matmul(q, k, transpose_b=True)  # (..., seq_len_q, seq_len_k)\n",
        "\n",
        "  # scale matmul_qk\n",
        "  dk = tf.cast(tf.shape(k)[-1], tf.float32)\n",
        "  scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n",
        "\n",
        "  # add the mask to the scaled tensor.\n",
        "  if mask is not None:\n",
        "    scaled_attention_logits += (mask * -1e9)\n",
        "\n",
        "  # softmax is normalized on the last axis (seq_len_k) so that the scores\n",
        "  # add up to 1.\n",
        "  attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)  # (..., seq_len_q, seq_len_k)\n",
        "\n",
        "  output = tf.matmul(attention_weights, v)  # (..., seq_len_q, depth_v)\n",
        "\n",
        "  return output, attention_weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FiqETnhCkoXh"
      },
      "source": [
        "As the softmax normalization is done along the dimension for keys, the attention values decide the amount of importance given to the keys for each query.\n",
        "\n",
        "The output represents the multiplication of the attention weights and the V (value) vector. This ensures that the tokens you want to focus on are kept as-is and the irrelevant tokens are flushed out."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n90YjClyInFy"
      },
      "outputs": [],
      "source": [
        "def print_out(q, k, v):\n",
        "  temp_out, temp_attn = scaled_dot_product_attention(\n",
        "      q, k, v, None)\n",
        "  print('Attention weights are:')\n",
        "  print(temp_attn)\n",
        "  print('Output is:')\n",
        "  print(temp_out)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yAzUAf2DPlNt",
        "outputId": "0aa4a209-f435-48fb-c2fa-288ce8cdce05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Attention weights are:\n",
            "tf.Tensor([[0. 1. 0. 0.]], shape=(1, 4), dtype=float32)\n",
            "Output is:\n",
            "tf.Tensor([[10.  0.]], shape=(1, 2), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "np.set_printoptions(suppress=True)\n",
        "\n",
        "temp_k = tf.constant([[10, 0, 0],\n",
        "                      [0, 10, 0],\n",
        "                      [0, 0, 10],\n",
        "                      [0, 0, 10]], dtype=tf.float32)  # (4, 3)\n",
        "\n",
        "temp_v = tf.constant([[1, 0],\n",
        "                      [10, 0],\n",
        "                      [100, 5],\n",
        "                      [1000, 6]], dtype=tf.float32)  # (4, 2)\n",
        "\n",
        "# This `query` aligns with the second `key`,\n",
        "# so the second `value` is returned.\n",
        "temp_q = tf.constant([[0, 10, 0]], dtype=tf.float32)  # (1, 3)\n",
        "print_out(temp_q, temp_k, temp_v)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zg6k-fGhgXra",
        "outputId": "9a0fe446-f018-47cd-82c8-6bbebab833d3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Attention weights are:\n",
            "tf.Tensor([[0.  0.  0.5 0.5]], shape=(1, 4), dtype=float32)\n",
            "Output is:\n",
            "tf.Tensor([[550.    5.5]], shape=(1, 2), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "# This query aligns with a repeated key (third and fourth),\n",
        "# so all associated values get averaged.\n",
        "temp_q = tf.constant([[0, 0, 10]], dtype=tf.float32)  # (1, 3)\n",
        "print_out(temp_q, temp_k, temp_v)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAq3YOzUgXhb",
        "outputId": "bc750b7f-efd3-4e98-8956-b79646d828de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Attention weights are:\n",
            "tf.Tensor([[0.5 0.5 0.  0. ]], shape=(1, 4), dtype=float32)\n",
            "Output is:\n",
            "tf.Tensor([[5.5 0. ]], shape=(1, 2), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "# This query aligns equally with the first and second key,\n",
        "# so their values get averaged.\n",
        "temp_q = tf.constant([[10, 10, 0]], dtype=tf.float32)  # (1, 3)\n",
        "print_out(temp_q, temp_k, temp_v)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOz-4_XIhaTP"
      },
      "source": [
        "Pass all the queries together."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6dlU8Tm-hYrF",
        "outputId": "7f303288-3fb6-400a-abfd-bb1e985f97c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Attention weights are:\n",
            "tf.Tensor(\n",
            "[[0.  0.  0.5 0.5]\n",
            " [0.  1.  0.  0. ]\n",
            " [0.5 0.5 0.  0. ]], shape=(3, 4), dtype=float32)\n",
            "Output is:\n",
            "tf.Tensor(\n",
            "[[550.    5.5]\n",
            " [ 10.    0. ]\n",
            " [  5.5   0. ]], shape=(3, 2), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "temp_q = tf.constant([[0, 0, 10],\n",
        "                      [0, 10, 0],\n",
        "                      [10, 10, 0]], dtype=tf.float32)  # (3, 3)\n",
        "print_out(temp_q, temp_k, temp_v)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kmzGPEy64qmA"
      },
      "source": [
        "## Multi-head attention"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fz5BMC8Kaoqo"
      },
      "source": [
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/multi_head_attention.png\" width=\"500\" alt=\"multi-head attention\">\n",
        "\n",
        "\n",
        "Multi-head attention consists of four parts:\n",
        "*    Linear layers.\n",
        "*    Scaled dot-product attention.\n",
        "*    Final linear layer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPmbr6F1C-v_"
      },
      "source": [
        "Each multi-head attention block gets three inputs; Q (query), K (key), V (value). These are put through linear (Dense) layers before the multi-head attention function.\n",
        "\n",
        "In the diagram above `(K,Q,V)` are passed through sepearte linear (`Dense`) layers for each attention head. For simplicity/efficiency the code below implements this using a single dense layer with `num_heads` times as many outputs. The output is rearranged to a shape of `(batch, num_heads, ...)` before applying the attention function.\n",
        "\n",
        "The `scaled_dot_product_attention` function defined above is applied in a single call, broadcasted for efficiency. An appropriate mask must be used in the attention step.  The attention output for each head is then concatenated (using `tf.transpose`, and `tf.reshape`) and put through a final `Dense` layer.\n",
        "\n",
        "Instead of one single attention head, Q, K, and V are split into multiple heads because it allows the model to jointly attend to information from different representation subspaces at different positions. After the split each head has a reduced dimensionality, so the total computation cost is the same as a single head attention with full dimensionality."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://drive.google.com/uc?id=1F8iHdPW7bpUCyo0UuBR3jDAe8WADFYil\" width=\"1100\">"
      ],
      "metadata": {
        "id": "bleABz1knAOs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BSV3PPKsYecw"
      },
      "outputs": [],
      "source": [
        "class MultiHeadAttention(tf.keras.layers.Layer):\n",
        "  def __init__(self,*, d_model, num_heads):\n",
        "    super(MultiHeadAttention, self).__init__()\n",
        "    self.num_heads = num_heads\n",
        "    self.d_model = d_model\n",
        "\n",
        "    assert d_model % self.num_heads == 0\n",
        "\n",
        "    self.depth = d_model // self.num_heads\n",
        "\n",
        "    self.wq = tf.keras.layers.Dense(d_model)\n",
        "    self.wk = tf.keras.layers.Dense(d_model)\n",
        "    self.wv = tf.keras.layers.Dense(d_model)\n",
        "\n",
        "    self.dense = tf.keras.layers.Dense(d_model)\n",
        "\n",
        "  def split_heads(self, x, batch_size):\n",
        "    \"\"\"Split the last dimension into (num_heads, depth).\n",
        "    Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)\n",
        "    \"\"\"\n",
        "    x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))\n",
        "    return tf.transpose(x, perm=[0, 2, 1, 3])\n",
        "\n",
        "  def call(self, v, k, q, mask):\n",
        "    batch_size = tf.shape(q)[0]\n",
        "\n",
        "    q = self.wq(q)  # (batch_size, seq_len, d_model)\n",
        "    k = self.wk(k)  # (batch_size, seq_len, d_model)\n",
        "    v = self.wv(v)  # (batch_size, seq_len, d_model)\n",
        "\n",
        "    q = self.split_heads(q, batch_size)  # (batch_size, num_heads, seq_len_q, depth)\n",
        "    k = self.split_heads(k, batch_size)  # (batch_size, num_heads, seq_len_k, depth)\n",
        "    v = self.split_heads(v, batch_size)  # (batch_size, num_heads, seq_len_v, depth)\n",
        "\n",
        "    # scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)\n",
        "    # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)\n",
        "    scaled_attention, attention_weights = scaled_dot_product_attention(\n",
        "        q, k, v, mask)\n",
        "\n",
        "    scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])  # (batch_size, seq_len_q, num_heads, depth)\n",
        "\n",
        "    concat_attention = tf.reshape(scaled_attention,\n",
        "                                  (batch_size, -1, self.d_model))  # (batch_size, seq_len_q, d_model)\n",
        "\n",
        "    output = self.dense(concat_attention)  # (batch_size, seq_len_q, d_model)\n",
        "\n",
        "    return output, attention_weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0D8FJue5lDyZ"
      },
      "source": [
        "Create a `MultiHeadAttention` layer to try out. At each location in the sequence, `y`, the `MultiHeadAttention` runs all 8 attention heads across all other locations in the sequence, returning a new vector of the same length at each location."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hu94p-_-2_BX",
        "outputId": "702fb364-ce06-473f-8b7f-15d7879faf1e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(TensorShape([1, 60, 512]), TensorShape([1, 8, 60, 60]))"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_mha = MultiHeadAttention(d_model=512, num_heads=8)\n",
        "y = tf.random.uniform((1, 60, 512))  # (batch_size, encoder_sequence, d_model)\n",
        "out, attn = temp_mha(y, k=y, q=y, mask=None)\n",
        "out.shape, attn.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RdDqGayx67vv"
      },
      "source": [
        "## Point wise feed forward network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBqzJXGfHK3X"
      },
      "source": [
        "Point wise feed forward network consists of two fully-connected layers with a ReLU activation in between."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ET7xLt0yCT6Z"
      },
      "outputs": [],
      "source": [
        "def point_wise_feed_forward_network(d_model, dff):\n",
        "  return tf.keras.Sequential([\n",
        "      tf.keras.layers.Dense(dff, activation='relu'),  # (batch_size, seq_len, dff)\n",
        "      tf.keras.layers.Dense(d_model)  # (batch_size, seq_len, d_model)\n",
        "  ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mytb1lPyOHLB",
        "outputId": "9f736490-fc74-4146-c6c0-8d0dec165f90"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "TensorShape([64, 50, 512])"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_ffn = point_wise_feed_forward_network(512, 2048)\n",
        "sample_ffn(tf.random.uniform((64, 50, 512))).shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7e7hKcxn6-zd"
      },
      "source": [
        "## Encoder and decoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yScbC0MUH8dS"
      },
      "source": [
        "<img src=\"https://drive.google.com/uc?id=1IBoM4erhM4moq_xd62nkNN-w2j1k1_UE\" width=\"900\">\n",
        "\n",
        "<br>\n",
        "\n",
        "<img src=\"https://www.tensorflow.org/images/tutorials/transformer/transformer.png\" width=\"600\" alt=\"transformer\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfYJG-Kvgwy2"
      },
      "source": [
        "A transformer model follows the same general pattern as a standard [sequence to sequence with attention model](https://www.tensorflow.org/text/tutorials/nmt_with_attention.ipynb). \n",
        "\n",
        "* The input sentence is passed through `N` encoder layers that generates an output for each token in the sequence.\n",
        "* The decoder attends to the encoder's output and its own input (self-attention) to predict the next word. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QFv-FNYUmvpn"
      },
      "source": [
        "### Encoder layer\n",
        "\n",
        "Each encoder layer consists of sublayers:\n",
        "\n",
        "1.   Multi-head attention (with padding mask)\n",
        "2.    Point wise feed forward networks.\n",
        "\n",
        "Each of these sublayers has a residual connection around it followed by a layer normalization. Residual connections help in avoiding the vanishing gradient problem in deep networks.\n",
        "\n",
        "The output of each sublayer is `LayerNorm(x + Sublayer(x))`. The normalization is done on the `d_model` (last) axis. There are N encoder layers in a transformer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ncyS-Ms3i2x_"
      },
      "outputs": [],
      "source": [
        "class EncoderLayer(tf.keras.layers.Layer):\n",
        "  def __init__(self,*, d_model, num_heads, dff, rate=0.1):\n",
        "    super(EncoderLayer, self).__init__()\n",
        "\n",
        "    self.mha = MultiHeadAttention(d_model=d_model, num_heads=num_heads)\n",
        "    self.ffn = point_wise_feed_forward_network(d_model, dff)\n",
        "\n",
        "    self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "    self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "\n",
        "    self.dropout1 = tf.keras.layers.Dropout(rate)\n",
        "    self.dropout2 = tf.keras.layers.Dropout(rate)\n",
        "\n",
        "  def call(self, x, training, mask):\n",
        "\n",
        "    attn_output, _ = self.mha(x, x, x, mask)  # (batch_size, input_seq_len, d_model)\n",
        "    attn_output = self.dropout1(attn_output, training=training)\n",
        "    out1 = self.layernorm1(x + attn_output)  # (batch_size, input_seq_len, d_model)\n",
        "\n",
        "    ffn_output = self.ffn(out1)  # (batch_size, input_seq_len, d_model)\n",
        "    ffn_output = self.dropout2(ffn_output, training=training)\n",
        "    out2 = self.layernorm2(out1 + ffn_output)  # (batch_size, input_seq_len, d_model)\n",
        "\n",
        "    return out2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AzZRXdO0mI48",
        "outputId": "1e063a45-a413-4931-a662-a9b0e5ffd132"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "TensorShape([64, 43, 512])"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_encoder_layer = EncoderLayer(d_model=512, num_heads=8, dff=2048)\n",
        "\n",
        "sample_encoder_layer_output = sample_encoder_layer(\n",
        "    tf.random.uniform((64, 43, 512)), False, None)\n",
        "\n",
        "sample_encoder_layer_output.shape  # (batch_size, input_seq_len, d_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6LO_48Owmx_o"
      },
      "source": [
        "### Decoder layer\n",
        "\n",
        "Each decoder layer consists of sublayers:\n",
        "\n",
        "1.   Masked multi-head attention (with look ahead mask and padding mask)\n",
        "2.   Multi-head attention (with padding mask). V (value) and K (key) receive the *encoder output* as inputs. Q (query) receives the *output from the masked multi-head attention sublayer.*\n",
        "3.   Point wise feed forward networks\n",
        "\n",
        "Each of these sublayers has a residual connection around it followed by a layer normalization. The output of each sublayer is `LayerNorm(x + Sublayer(x))`. The normalization is done on the `d_model` (last) axis.\n",
        "\n",
        "There are a number of decoder layers in the model.\n",
        "\n",
        "As Q receives the output from decoder's first attention block, and K receives the encoder output, the attention weights represent the importance given to the decoder's input based on the encoder's output. In other words, the decoder predicts the next token by looking at the encoder output and self-attending to its own output. See the demonstration above in the scaled dot product attention section."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9SoX0-vd1hue"
      },
      "outputs": [],
      "source": [
        "class DecoderLayer(tf.keras.layers.Layer):\n",
        "  def __init__(self,*, d_model, num_heads, dff, rate=0.1):\n",
        "    super(DecoderLayer, self).__init__()\n",
        "\n",
        "    self.mha1 = MultiHeadAttention(d_model=d_model, num_heads=num_heads)\n",
        "    self.mha2 = MultiHeadAttention(d_model=d_model, num_heads=num_heads)\n",
        "\n",
        "    self.ffn = point_wise_feed_forward_network(d_model, dff)\n",
        "\n",
        "    self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "    self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "    self.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "\n",
        "    self.dropout1 = tf.keras.layers.Dropout(rate)\n",
        "    self.dropout2 = tf.keras.layers.Dropout(rate)\n",
        "    self.dropout3 = tf.keras.layers.Dropout(rate)\n",
        "\n",
        "  def call(self, x, enc_output, training,\n",
        "           look_ahead_mask, padding_mask):\n",
        "    # enc_output.shape == (batch_size, input_seq_len, d_model)\n",
        "\n",
        "    attn1, attn_weights_block1 = self.mha1(x, x, x, look_ahead_mask)  # (batch_size, target_seq_len, d_model)\n",
        "    attn1 = self.dropout1(attn1, training=training)\n",
        "    out1 = self.layernorm1(attn1 + x)\n",
        "\n",
        "    attn2, attn_weights_block2 = self.mha2(\n",
        "        enc_output, enc_output, out1, padding_mask)  # (batch_size, target_seq_len, d_model)\n",
        "    attn2 = self.dropout2(attn2, training=training)\n",
        "    out2 = self.layernorm2(attn2 + out1)  # (batch_size, target_seq_len, d_model)\n",
        "\n",
        "    ffn_output = self.ffn(out2)  # (batch_size, target_seq_len, d_model)\n",
        "    ffn_output = self.dropout3(ffn_output, training=training)\n",
        "    out3 = self.layernorm3(ffn_output + out2)  # (batch_size, target_seq_len, d_model)\n",
        "\n",
        "    return out3, attn_weights_block1, attn_weights_block2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ne2Bqx8k71l0",
        "outputId": "b42bd067-903c-447d-bbef-02502915ad9b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "TensorShape([64, 50, 512])"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_decoder_layer = DecoderLayer(d_model=512, num_heads=8, dff=2048)\n",
        "\n",
        "sample_decoder_layer_output, _, _ = sample_decoder_layer(\n",
        "    tf.random.uniform((64, 50, 512)), sample_encoder_layer_output,\n",
        "    False, None, None)\n",
        "\n",
        "sample_decoder_layer_output.shape  # (batch_size, target_seq_len, d_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SE1H51Ajm0q1"
      },
      "source": [
        "### Encoder\n",
        "\n",
        "The `Encoder` consists of:\n",
        "1.   Input Embedding\n",
        "2.   Positional Encoding\n",
        "3.   N encoder layers\n",
        "\n",
        "The input is put through an embedding which is summed with the positional encoding. The output of this summation is the input to the encoder layers. The output of the encoder is the input to the decoder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jpEox7gJ8FCI"
      },
      "outputs": [],
      "source": [
        "class Encoder(tf.keras.layers.Layer):\n",
        "  def __init__(self,*, num_layers, d_model, num_heads, dff, input_vocab_size,\n",
        "               rate=0.1):\n",
        "    super(Encoder, self).__init__()\n",
        "\n",
        "    self.d_model = d_model\n",
        "    self.num_layers = num_layers\n",
        "\n",
        "    self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)\n",
        "    self.pos_encoding = positional_encoding(MAX_TOKENS, self.d_model)\n",
        "\n",
        "    self.enc_layers = [\n",
        "        EncoderLayer(d_model=d_model, num_heads=num_heads, dff=dff, rate=rate)\n",
        "        for _ in range(num_layers)]\n",
        "\n",
        "    self.dropout = tf.keras.layers.Dropout(rate)\n",
        "\n",
        "  def call(self, x, training, mask):\n",
        "\n",
        "    seq_len = tf.shape(x)[1]\n",
        "\n",
        "    # adding embedding and position encoding.\n",
        "    x = self.embedding(x)  # (batch_size, input_seq_len, d_model)\n",
        "    x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n",
        "    x += self.pos_encoding[:, :seq_len, :]\n",
        "\n",
        "    x = self.dropout(x, training=training)\n",
        "\n",
        "    for i in range(self.num_layers):\n",
        "      x = self.enc_layers[i](x, training, mask)\n",
        "\n",
        "    return x  # (batch_size, input_seq_len, d_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QG9nueFQKXx",
        "outputId": "6382a8fd-1605-4688-8182-bf73b66f5bc1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(64, 62, 512)\n"
          ]
        }
      ],
      "source": [
        "sample_encoder = Encoder(num_layers=2, d_model=512, num_heads=8,\n",
        "                         dff=2048, input_vocab_size=8500)\n",
        "temp_input = tf.random.uniform((64, 62), dtype=tf.int64, minval=0, maxval=200)\n",
        "\n",
        "sample_encoder_output = sample_encoder(temp_input, training=False, mask=None)\n",
        "\n",
        "print(sample_encoder_output.shape)  # (batch_size, input_seq_len, d_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-uO6ls8m2O5"
      },
      "source": [
        "### Decoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZtT7PKzrXkNr"
      },
      "source": [
        " The `Decoder` consists of:\n",
        "1.   Output Embedding\n",
        "2.   Positional Encoding\n",
        "3.   N decoder layers\n",
        "\n",
        "The target is put through an embedding which is summed with the positional encoding. The output of this summation is the input to the decoder layers. The output of the decoder is the input to the final linear layer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d5_d5-PLQXwY"
      },
      "outputs": [],
      "source": [
        "class Decoder(tf.keras.layers.Layer):\n",
        "  def __init__(self,*, num_layers, d_model, num_heads, dff, target_vocab_size,\n",
        "               rate=0.1):\n",
        "    super(Decoder, self).__init__()\n",
        "\n",
        "    self.d_model = d_model\n",
        "    self.num_layers = num_layers\n",
        "\n",
        "    self.embedding = tf.keras.layers.Embedding(target_vocab_size, d_model)\n",
        "    self.pos_encoding = positional_encoding(MAX_TOKENS, d_model)\n",
        "\n",
        "    self.dec_layers = [\n",
        "        DecoderLayer(d_model=d_model, num_heads=num_heads, dff=dff, rate=rate)\n",
        "        for _ in range(num_layers)]\n",
        "    self.dropout = tf.keras.layers.Dropout(rate)\n",
        "\n",
        "  def call(self, x, enc_output, training,\n",
        "           look_ahead_mask, padding_mask):\n",
        "\n",
        "    seq_len = tf.shape(x)[1]\n",
        "    attention_weights = {}\n",
        "\n",
        "    x = self.embedding(x)  # (batch_size, target_seq_len, d_model)\n",
        "    x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n",
        "    x += self.pos_encoding[:, :seq_len, :]\n",
        "\n",
        "    x = self.dropout(x, training=training)\n",
        "\n",
        "    for i in range(self.num_layers):\n",
        "      x, block1, block2 = self.dec_layers[i](x, enc_output, training,\n",
        "                                             look_ahead_mask, padding_mask)\n",
        "\n",
        "      attention_weights[f'decoder_layer{i+1}_block1'] = block1\n",
        "      attention_weights[f'decoder_layer{i+1}_block2'] = block2\n",
        "\n",
        "    # x.shape == (batch_size, target_seq_len, d_model)\n",
        "    return x, attention_weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1jXoAMRZyvu",
        "outputId": "7170c3c7-e8d4-4305-91ce-889469caa708"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(TensorShape([64, 26, 512]), TensorShape([64, 8, 26, 62]))"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_decoder = Decoder(num_layers=2, d_model=512, num_heads=8,\n",
        "                         dff=2048, target_vocab_size=8000)\n",
        "temp_input = tf.random.uniform((64, 26), dtype=tf.int64, minval=0, maxval=200)\n",
        "\n",
        "output, attn = sample_decoder(temp_input,\n",
        "                              enc_output=sample_encoder_output,\n",
        "                              training=False,\n",
        "                              look_ahead_mask=None,\n",
        "                              padding_mask=None)\n",
        "\n",
        "output.shape, attn['decoder_layer2_block2'].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y54xnJnuYgJ7"
      },
      "source": [
        "## Create the transformer model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uERO1y54cOKq"
      },
      "source": [
        "A transformer consists of the encoder, decoder, and a final linear layer. The output of the decoder is the input to the linear layer and its output is returned."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PED3bIpOYkBu"
      },
      "outputs": [],
      "source": [
        "class Transformer(tf.keras.Model):\n",
        "  def __init__(self,*, num_layers, d_model, num_heads, dff, input_vocab_size,\n",
        "               target_vocab_size, 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",
        "                           input_vocab_size=input_vocab_size, rate=rate)\n",
        "\n",
        "    self.decoder = Decoder(num_layers=num_layers, d_model=d_model,\n",
        "                           num_heads=num_heads, dff=dff,\n",
        "                           target_vocab_size=target_vocab_size, rate=rate)\n",
        "\n",
        "    self.final_layer = tf.keras.layers.Dense(target_vocab_size)\n",
        "\n",
        "  def call(self, inputs, training):\n",
        "    # Keras models prefer if you pass all your inputs in the first argument\n",
        "    inp, tar = inputs\n",
        "\n",
        "    padding_mask, look_ahead_mask = self.create_masks(inp, tar)\n",
        "\n",
        "    enc_output = self.encoder(inp, training, padding_mask)  # (batch_size, inp_seq_len, d_model)\n",
        "\n",
        "    # dec_output.shape == (batch_size, tar_seq_len, d_model)\n",
        "    dec_output, attention_weights = self.decoder(\n",
        "        tar, enc_output, training, look_ahead_mask, padding_mask)\n",
        "\n",
        "    final_output = self.final_layer(dec_output)  # (batch_size, tar_seq_len, target_vocab_size)\n",
        "\n",
        "    return final_output, attention_weights\n",
        "\n",
        "  def create_masks(self, inp, tar):\n",
        "    # Encoder padding mask (Used in the 2nd attention block in the decoder too.)\n",
        "    padding_mask = create_padding_mask(inp)\n",
        "\n",
        "    # Used in the 1st attention block in the decoder.\n",
        "    # It is used to pad and mask future tokens in the input received by\n",
        "    # the decoder.\n",
        "    look_ahead_mask = create_look_ahead_mask(tf.shape(tar)[1])\n",
        "    dec_target_padding_mask = create_padding_mask(tar)\n",
        "    look_ahead_mask = tf.maximum(dec_target_padding_mask, look_ahead_mask)\n",
        "\n",
        "    return padding_mask, look_ahead_mask"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJ4fbQcIkHW1",
        "outputId": "5f672734-0418-4782-f92a-d1cf2d7b592b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "TensorShape([64, 36, 8000])"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_transformer = Transformer(\n",
        "    num_layers=2, d_model=512, num_heads=8, dff=2048,\n",
        "    input_vocab_size=8500, target_vocab_size=8000)\n",
        "\n",
        "temp_input = tf.random.uniform((64, 38), dtype=tf.int64, minval=0, maxval=200)\n",
        "temp_target = tf.random.uniform((64, 36), dtype=tf.int64, minval=0, maxval=200)\n",
        "\n",
        "fn_out, _ = sample_transformer([temp_input, temp_target], training=False)\n",
        "\n",
        "fn_out.shape  # (batch_size, tar_seq_len, target_vocab_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsINyf1VEQLC"
      },
      "source": [
        "## Set hyperparameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVjWCxFNcgbt"
      },
      "source": [
        "To keep this example small and relatively fast, the values for `num_layers, d_model, dff` have been reduced. \n",
        "\n",
        "The base model described in the [paper](https://arxiv.org/abs/1706.03762) used: `num_layers=6, d_model=512, dff=2048`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lnJn5SLA2ahP"
      },
      "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": "xYEGhEOtzn5W"
      },
      "source": [
        "## Optimizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOmWW--yP3zx"
      },
      "source": [
        "Use the Adam optimizer with a custom learning rate scheduler according to the formula in the [paper](https://arxiv.org/abs/1706.03762).\n",
        "\n",
        "$$\\Large{lrate = d_{model}^{-0.5} * \\min(step{\\_}num^{-0.5}, step{\\_}num \\cdot warmup{\\_}steps^{-1.5})}$$\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iYQdOO1axwEI"
      },
      "outputs": [],
      "source": [
        "class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
        "  def __init__(self, d_model, warmup_steps=4000):\n",
        "    super(CustomSchedule, self).__init__()\n",
        "\n",
        "    self.d_model = d_model\n",
        "    self.d_model = tf.cast(self.d_model, tf.float32)\n",
        "\n",
        "    self.warmup_steps = warmup_steps\n",
        "\n",
        "  def __call__(self, step):\n",
        "    arg1 = tf.math.rsqrt(step)\n",
        "    arg2 = step * (self.warmup_steps ** -1.5)\n",
        "\n",
        "    return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "f33ZCgvHpPdG",
        "outputId": "4b9b1243-64dc-4eaf-8bca-490ac601bc90"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Train Step')"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "temp_learning_rate_schedule = CustomSchedule(d_model)\n",
        "\n",
        "plt.plot(temp_learning_rate_schedule(tf.range(40000, dtype=tf.float32)))\n",
        "plt.ylabel('Learning Rate')\n",
        "plt.xlabel('Train Step')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YgkDE7hzo8r5"
      },
      "source": [
        "## Loss and metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxGJtoDuYIHL"
      },
      "source": [
        "Since the target sequences are padded, it is important to apply a padding mask when calculating the loss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MlhsJMm0TW_B"
      },
      "outputs": [],
      "source": [
        "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n",
        "    from_logits=True, reduction='none')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "67oqVHiT0Eiu"
      },
      "outputs": [],
      "source": [
        "def loss_function(real, pred):\n",
        "  mask = tf.math.logical_not(tf.math.equal(real, 0))\n",
        "  loss_ = loss_object(real, pred)\n",
        "\n",
        "  mask = tf.cast(mask, dtype=loss_.dtype)\n",
        "  loss_ *= mask\n",
        "\n",
        "  return tf.reduce_sum(loss_)/tf.reduce_sum(mask)\n",
        "\n",
        "\n",
        "def accuracy_function(real, pred):\n",
        "  accuracies = tf.equal(real, tf.argmax(pred, axis=2))\n",
        "\n",
        "  mask = tf.math.logical_not(tf.math.equal(real, 0))\n",
        "  accuracies = tf.math.logical_and(mask, accuracies)\n",
        "\n",
        "  accuracies = tf.cast(accuracies, dtype=tf.float32)\n",
        "  mask = tf.cast(mask, dtype=tf.float32)\n",
        "  return tf.reduce_sum(accuracies)/tf.reduce_sum(mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "phlyxMnm-Tpx"
      },
      "outputs": [],
      "source": [
        "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
        "train_accuracy = tf.keras.metrics.Mean(name='train_accuracy')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aeHumfr7zmMa"
      },
      "source": [
        "## Training and checkpointing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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",
        "    rate=dropout_rate)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fzuf06YZp66w"
      },
      "source": [
        "Create the checkpoint path and the checkpoint manager. This will be used to save checkpoints every `n` epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hNhuYfllndLZ"
      },
      "outputs": [],
      "source": [
        "checkpoint_path = './checkpoints/train'\n",
        "\n",
        "ckpt = tf.train.Checkpoint(transformer=transformer,\n",
        "                           optimizer=optimizer)\n",
        "\n",
        "ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)\n",
        "\n",
        "# if a checkpoint exists, restore the latest checkpoint.\n",
        "if ckpt_manager.latest_checkpoint:\n",
        "  ckpt.restore(ckpt_manager.latest_checkpoint)\n",
        "  print('Latest checkpoint restored!!')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Di_Yaa1gf9r"
      },
      "source": [
        "The target is divided into tar_inp and tar_real. tar_inp is passed as an input to the decoder. `tar_real` is that same input shifted by 1: At each location in `tar_input`, `tar_real` contains the  next token that should be predicted.\n",
        "\n",
        "For example, `sentence = 'SOS A lion in the jungle is sleeping EOS'` becomes:\n",
        "\n",
        "* `tar_inp =  'SOS A lion in the jungle is sleeping'`\n",
        "* `tar_real = 'A lion in the jungle is sleeping EOS'`\n",
        "\n",
        "A transformer is an auto-regressive model: it makes predictions one part at a time, and uses its output so far to decide what to do next.\n",
        "\n",
        "During training this example uses teacher-forcing (like in the [text generation tutorial](https://www.tensorflow.org/text/tutorials/text_generation)). Teacher forcing is passing the true output to the next time step regardless of what the model predicts at the current time step.\n",
        "\n",
        "As the model predicts each token, *self-attention* allows it to look at the previous tokens in the input sequence to better predict the next token.\n",
        "\n",
        "To prevent the model from peeking at the expected output the model uses a look-ahead mask."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LKpoA6q1sJFj"
      },
      "outputs": [],
      "source": [
        "#EPOCHS = 20\n",
        "EPOCHS = 1  #Play with smaller epochs :)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iJwmp9OE29oj"
      },
      "outputs": [],
      "source": [
        "# The @tf.function trace-compiles train_step into a TF graph for faster\n",
        "# execution. The function specializes to the precise shape of the argument\n",
        "# tensors. To avoid re-tracing due to the variable sequence lengths or variable\n",
        "# batch sizes (the last batch is smaller), use input_signature to specify\n",
        "# more generic shapes.\n",
        "\n",
        "train_step_signature = [\n",
        "    tf.TensorSpec(shape=(None, None), dtype=tf.int64),\n",
        "    tf.TensorSpec(shape=(None, None), dtype=tf.int64),\n",
        "]\n",
        "\n",
        "\n",
        "@tf.function(input_signature=train_step_signature)\n",
        "def train_step(inp, tar):\n",
        "  tar_inp = tar[:, :-1]\n",
        "  tar_real = tar[:, 1:]\n",
        "\n",
        "  with tf.GradientTape() as tape:\n",
        "    predictions, _ = transformer([inp, tar_inp],\n",
        "                                 training = True)\n",
        "    loss = loss_function(tar_real, predictions)\n",
        "\n",
        "  gradients = tape.gradient(loss, transformer.trainable_variables)\n",
        "  optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))\n",
        "\n",
        "  train_loss(loss)\n",
        "  train_accuracy(accuracy_function(tar_real, predictions))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qM2PDWGDJ_8V"
      },
      "source": [
        "Portuguese is used as the input language and English is the target language."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbvmaKNiznHZ",
        "outputId": "c32c3714-76e3-4a0f-b50e-ed89083249df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1 Batch 0 Loss 8.9084 Accuracy 0.0000\n",
            "Epoch 1 Batch 50 Loss 8.8436 Accuracy 0.0020\n",
            "Epoch 1 Batch 100 Loss 8.7261 Accuracy 0.0268\n",
            "Epoch 1 Batch 150 Loss 8.6049 Accuracy 0.0373\n",
            "Epoch 1 Batch 200 Loss 8.4592 Accuracy 0.0428\n",
            "Epoch 1 Batch 250 Loss 8.2875 Accuracy 0.0460\n",
            "Epoch 1 Batch 300 Loss 8.0952 Accuracy 0.0497\n",
            "Epoch 1 Batch 350 Loss 7.8967 Accuracy 0.0571\n",
            "Epoch 1 Batch 400 Loss 7.7050 Accuracy 0.0633\n",
            "Epoch 1 Batch 450 Loss 7.5390 Accuracy 0.0697\n",
            "Epoch 1 Batch 500 Loss 7.3914 Accuracy 0.0766\n",
            "Epoch 1 Batch 550 Loss 7.2572 Accuracy 0.0844\n",
            "Epoch 1 Batch 600 Loss 7.1314 Accuracy 0.0922\n",
            "Epoch 1 Batch 650 Loss 7.0123 Accuracy 0.0997\n",
            "Epoch 1 Loss 6.9110 Accuracy 0.1057\n",
            "Time taken for 1 epoch: 96.49 secs\n",
            "\n",
            "Epoch 2 Batch 0 Loss 5.4638 Accuracy 0.1818\n",
            "Epoch 2 Batch 50 Loss 5.3758 Accuracy 0.2018\n",
            "Epoch 2 Batch 100 Loss 5.3359 Accuracy 0.2053\n",
            "Epoch 2 Batch 150 Loss 5.3016 Accuracy 0.2080\n",
            "Epoch 2 Batch 200 Loss 5.2729 Accuracy 0.2103\n",
            "Epoch 2 Batch 250 Loss 5.2458 Accuracy 0.2129\n",
            "Epoch 2 Batch 300 Loss 5.2186 Accuracy 0.2153\n",
            "Epoch 2 Batch 350 Loss 5.1901 Accuracy 0.2180\n",
            "Epoch 2 Batch 400 Loss 5.1605 Accuracy 0.2210\n",
            "Epoch 2 Batch 450 Loss 5.1360 Accuracy 0.2234\n",
            "Epoch 2 Batch 500 Loss 5.1094 Accuracy 0.2261\n",
            "Epoch 2 Batch 550 Loss 5.0867 Accuracy 0.2280\n",
            "Epoch 2 Batch 600 Loss 5.0630 Accuracy 0.2302\n",
            "Epoch 2 Batch 650 Loss 5.0402 Accuracy 0.2322\n",
            "Epoch 2 Batch 700 Loss 5.0208 Accuracy 0.2339\n",
            "Epoch 2 Loss 5.0196 Accuracy 0.2341\n",
            "Time taken for 1 epoch: 75.10 secs\n",
            "\n",
            "Epoch 3 Batch 0 Loss 4.6950 Accuracy 0.2646\n",
            "Epoch 3 Batch 50 Loss 4.7183 Accuracy 0.2611\n",
            "Epoch 3 Batch 100 Loss 4.6974 Accuracy 0.2633\n",
            "Epoch 3 Batch 150 Loss 4.6905 Accuracy 0.2637\n",
            "Epoch 3 Batch 200 Loss 4.6719 Accuracy 0.2648\n",
            "Epoch 3 Batch 250 Loss 4.6539 Accuracy 0.2660\n",
            "Epoch 3 Batch 300 Loss 4.6391 Accuracy 0.2676\n",
            "Epoch 3 Batch 350 Loss 4.6250 Accuracy 0.2690\n",
            "Epoch 3 Batch 400 Loss 4.6123 Accuracy 0.2702\n",
            "Epoch 3 Batch 450 Loss 4.5991 Accuracy 0.2717\n",
            "Epoch 3 Batch 500 Loss 4.5871 Accuracy 0.2729\n",
            "Epoch 3 Batch 550 Loss 4.5727 Accuracy 0.2743\n",
            "Epoch 3 Batch 600 Loss 4.5596 Accuracy 0.2759\n",
            "Epoch 3 Batch 650 Loss 4.5446 Accuracy 0.2776\n",
            "Epoch 3 Batch 700 Loss 4.5308 Accuracy 0.2791\n",
            "Epoch 3 Loss 4.5308 Accuracy 0.2791\n",
            "Time taken for 1 epoch: 74.30 secs\n",
            "\n",
            "Epoch 4 Batch 0 Loss 4.2817 Accuracy 0.3002\n",
            "Epoch 4 Batch 50 Loss 4.2355 Accuracy 0.3098\n",
            "Epoch 4 Batch 100 Loss 4.2331 Accuracy 0.3109\n",
            "Epoch 4 Batch 150 Loss 4.2248 Accuracy 0.3123\n",
            "Epoch 4 Batch 200 Loss 4.2063 Accuracy 0.3143\n",
            "Epoch 4 Batch 250 Loss 4.1962 Accuracy 0.3156\n",
            "Epoch 4 Batch 300 Loss 4.1829 Accuracy 0.3173\n",
            "Epoch 4 Batch 350 Loss 4.1642 Accuracy 0.3198\n",
            "Epoch 4 Batch 400 Loss 4.1485 Accuracy 0.3217\n",
            "Epoch 4 Batch 450 Loss 4.1336 Accuracy 0.3236\n",
            "Epoch 4 Batch 500 Loss 4.1186 Accuracy 0.3254\n",
            "Epoch 4 Batch 550 Loss 4.1014 Accuracy 0.3276\n",
            "Epoch 4 Batch 600 Loss 4.0835 Accuracy 0.3300\n",
            "Epoch 4 Batch 650 Loss 4.0717 Accuracy 0.3316\n",
            "Epoch 4 Batch 700 Loss 4.0608 Accuracy 0.3331\n",
            "Epoch 4 Loss 4.0608 Accuracy 0.3331\n",
            "Time taken for 1 epoch: 76.95 secs\n",
            "\n",
            "Epoch 5 Batch 0 Loss 3.8364 Accuracy 0.3631\n",
            "Epoch 5 Batch 50 Loss 3.7421 Accuracy 0.3711\n",
            "Epoch 5 Batch 100 Loss 3.7391 Accuracy 0.3722\n",
            "Epoch 5 Batch 150 Loss 3.7396 Accuracy 0.3722\n",
            "Epoch 5 Batch 200 Loss 3.7255 Accuracy 0.3740\n",
            "Epoch 5 Batch 250 Loss 3.7074 Accuracy 0.3761\n",
            "Epoch 5 Batch 300 Loss 3.6987 Accuracy 0.3772\n",
            "Epoch 5 Batch 350 Loss 3.6869 Accuracy 0.3789\n",
            "Epoch 5 Batch 400 Loss 3.6718 Accuracy 0.3812\n",
            "Epoch 5 Batch 450 Loss 3.6599 Accuracy 0.3828\n",
            "Epoch 5 Batch 500 Loss 3.6479 Accuracy 0.3846\n",
            "Epoch 5 Batch 550 Loss 3.6385 Accuracy 0.3858\n",
            "Epoch 5 Batch 600 Loss 3.6284 Accuracy 0.3871\n",
            "Epoch 5 Batch 650 Loss 3.6189 Accuracy 0.3884\n",
            "Saving checkpoint for epoch 5 at ./checkpoints/train/ckpt-1\n",
            "Epoch 5 Loss 3.6105 Accuracy 0.3894\n",
            "Time taken for 1 epoch: 76.08 secs\n",
            "\n",
            "Epoch 6 Batch 0 Loss 3.2937 Accuracy 0.4080\n",
            "Epoch 6 Batch 50 Loss 3.3592 Accuracy 0.4161\n",
            "Epoch 6 Batch 100 Loss 3.3617 Accuracy 0.4163\n",
            "Epoch 6 Batch 150 Loss 3.3496 Accuracy 0.4185\n",
            "Epoch 6 Batch 200 Loss 3.3346 Accuracy 0.4203\n",
            "Epoch 6 Batch 250 Loss 3.3280 Accuracy 0.4216\n",
            "Epoch 6 Batch 300 Loss 3.3185 Accuracy 0.4228\n",
            "Epoch 6 Batch 350 Loss 3.3115 Accuracy 0.4239\n",
            "Epoch 6 Batch 400 Loss 3.3034 Accuracy 0.4250\n",
            "Epoch 6 Batch 450 Loss 3.3010 Accuracy 0.4253\n",
            "Epoch 6 Batch 500 Loss 3.2928 Accuracy 0.4265\n",
            "Epoch 6 Batch 550 Loss 3.2879 Accuracy 0.4271\n",
            "Epoch 6 Batch 600 Loss 3.2793 Accuracy 0.4282\n",
            "Epoch 6 Batch 650 Loss 3.2683 Accuracy 0.4297\n",
            "Epoch 6 Batch 700 Loss 3.2616 Accuracy 0.4306\n",
            "Epoch 6 Loss 3.2609 Accuracy 0.4306\n",
            "Time taken for 1 epoch: 77.05 secs\n",
            "\n",
            "Epoch 7 Batch 0 Loss 3.1915 Accuracy 0.4205\n",
            "Epoch 7 Batch 50 Loss 3.0146 Accuracy 0.4570\n",
            "Epoch 7 Batch 100 Loss 3.0074 Accuracy 0.4593\n",
            "Epoch 7 Batch 150 Loss 3.0030 Accuracy 0.4605\n",
            "Epoch 7 Batch 200 Loss 2.9915 Accuracy 0.4621\n",
            "Epoch 7 Batch 250 Loss 2.9892 Accuracy 0.4626\n",
            "Epoch 7 Batch 300 Loss 2.9835 Accuracy 0.4633\n",
            "Epoch 7 Batch 350 Loss 2.9703 Accuracy 0.4650\n",
            "Epoch 7 Batch 400 Loss 2.9651 Accuracy 0.4661\n",
            "Epoch 7 Batch 450 Loss 2.9577 Accuracy 0.4672\n",
            "Epoch 7 Batch 500 Loss 2.9526 Accuracy 0.4680\n",
            "Epoch 7 Batch 550 Loss 2.9441 Accuracy 0.4693\n",
            "Epoch 7 Batch 600 Loss 2.9376 Accuracy 0.4702\n",
            "Epoch 7 Batch 650 Loss 2.9308 Accuracy 0.4711\n",
            "Epoch 7 Loss 2.9254 Accuracy 0.4721\n",
            "Time taken for 1 epoch: 74.21 secs\n",
            "\n",
            "Epoch 8 Batch 0 Loss 2.6576 Accuracy 0.5046\n",
            "Epoch 8 Batch 50 Loss 2.6736 Accuracy 0.5009\n",
            "Epoch 8 Batch 100 Loss 2.6600 Accuracy 0.5040\n",
            "Epoch 8 Batch 150 Loss 2.6616 Accuracy 0.5041\n",
            "Epoch 8 Batch 200 Loss 2.6645 Accuracy 0.5041\n",
            "Epoch 8 Batch 250 Loss 2.6643 Accuracy 0.5042\n",
            "Epoch 8 Batch 300 Loss 2.6625 Accuracy 0.5052\n",
            "Epoch 8 Batch 350 Loss 2.6617 Accuracy 0.5052\n",
            "Epoch 8 Batch 400 Loss 2.6590 Accuracy 0.5060\n",
            "Epoch 8 Batch 450 Loss 2.6558 Accuracy 0.5064\n",
            "Epoch 8 Batch 500 Loss 2.6498 Accuracy 0.5076\n",
            "Epoch 8 Batch 550 Loss 2.6446 Accuracy 0.5087\n",
            "Epoch 8 Batch 600 Loss 2.6423 Accuracy 0.5090\n",
            "Epoch 8 Batch 650 Loss 2.6384 Accuracy 0.5099\n",
            "Epoch 8 Loss 2.6340 Accuracy 0.5105\n",
            "Time taken for 1 epoch: 75.19 secs\n",
            "\n",
            "Epoch 9 Batch 0 Loss 2.3523 Accuracy 0.5448\n",
            "Epoch 9 Batch 50 Loss 2.4484 Accuracy 0.5320\n",
            "Epoch 9 Batch 100 Loss 2.4525 Accuracy 0.5306\n",
            "Epoch 9 Batch 150 Loss 2.4248 Accuracy 0.5353\n",
            "Epoch 9 Batch 200 Loss 2.4174 Accuracy 0.5366\n",
            "Epoch 9 Batch 250 Loss 2.4198 Accuracy 0.5371\n",
            "Epoch 9 Batch 300 Loss 2.4216 Accuracy 0.5369\n",
            "Epoch 9 Batch 350 Loss 2.4211 Accuracy 0.5371\n",
            "Epoch 9 Batch 400 Loss 2.4228 Accuracy 0.5371\n",
            "Epoch 9 Batch 450 Loss 2.4181 Accuracy 0.5379\n",
            "Epoch 9 Batch 500 Loss 2.4187 Accuracy 0.5381\n",
            "Epoch 9 Batch 550 Loss 2.4163 Accuracy 0.5387\n",
            "Epoch 9 Batch 600 Loss 2.4146 Accuracy 0.5390\n",
            "Epoch 9 Batch 650 Loss 2.4136 Accuracy 0.5394\n",
            "Epoch 9 Loss 2.4131 Accuracy 0.5397\n",
            "Time taken for 1 epoch: 74.26 secs\n",
            "\n",
            "Epoch 10 Batch 0 Loss 2.0556 Accuracy 0.5943\n",
            "Epoch 10 Batch 50 Loss 2.2365 Accuracy 0.5621\n",
            "Epoch 10 Batch 100 Loss 2.2566 Accuracy 0.5589\n",
            "Epoch 10 Batch 150 Loss 2.2440 Accuracy 0.5620\n",
            "Epoch 10 Batch 200 Loss 2.2454 Accuracy 0.5622\n",
            "Epoch 10 Batch 250 Loss 2.2476 Accuracy 0.5624\n",
            "Epoch 10 Batch 300 Loss 2.2500 Accuracy 0.5621\n",
            "Epoch 10 Batch 350 Loss 2.2518 Accuracy 0.5620\n",
            "Epoch 10 Batch 400 Loss 2.2533 Accuracy 0.5617\n",
            "Epoch 10 Batch 450 Loss 2.2509 Accuracy 0.5625\n",
            "Epoch 10 Batch 500 Loss 2.2501 Accuracy 0.5625\n",
            "Epoch 10 Batch 550 Loss 2.2509 Accuracy 0.5625\n",
            "Epoch 10 Batch 600 Loss 2.2502 Accuracy 0.5627\n",
            "Epoch 10 Batch 650 Loss 2.2495 Accuracy 0.5628\n",
            "Epoch 10 Batch 700 Loss 2.2514 Accuracy 0.5627\n",
            "Saving checkpoint for epoch 10 at ./checkpoints/train/ckpt-2\n",
            "Epoch 10 Loss 2.2514 Accuracy 0.5627\n",
            "Time taken for 1 epoch: 75.03 secs\n",
            "\n",
            "Epoch 11 Batch 0 Loss 2.1073 Accuracy 0.5732\n",
            "Epoch 11 Batch 50 Loss 2.1178 Accuracy 0.5812\n",
            "Epoch 11 Batch 100 Loss 2.1063 Accuracy 0.5828\n",
            "Epoch 11 Batch 150 Loss 2.1180 Accuracy 0.5811\n",
            "Epoch 11 Batch 200 Loss 2.1166 Accuracy 0.5811\n",
            "Epoch 11 Batch 250 Loss 2.1142 Accuracy 0.5814\n",
            "Epoch 11 Batch 300 Loss 2.1194 Accuracy 0.5807\n",
            "Epoch 11 Batch 350 Loss 2.1170 Accuracy 0.5809\n",
            "Epoch 11 Batch 400 Loss 2.1174 Accuracy 0.5807\n",
            "Epoch 11 Batch 450 Loss 2.1199 Accuracy 0.5804\n",
            "Epoch 11 Batch 500 Loss 2.1224 Accuracy 0.5800\n",
            "Epoch 11 Batch 550 Loss 2.1245 Accuracy 0.5798\n",
            "Epoch 11 Batch 600 Loss 2.1249 Accuracy 0.5799\n",
            "Epoch 11 Batch 650 Loss 2.1264 Accuracy 0.5800\n",
            "Epoch 11 Loss 2.1268 Accuracy 0.5799\n",
            "Time taken for 1 epoch: 74.24 secs\n",
            "\n",
            "Epoch 12 Batch 0 Loss 2.0206 Accuracy 0.5845\n",
            "Epoch 12 Batch 50 Loss 1.9801 Accuracy 0.5970\n",
            "Epoch 12 Batch 100 Loss 1.9861 Accuracy 0.5969\n",
            "Epoch 12 Batch 150 Loss 1.9828 Accuracy 0.5977\n",
            "Epoch 12 Batch 200 Loss 1.9838 Accuracy 0.5980\n",
            "Epoch 12 Batch 250 Loss 1.9895 Accuracy 0.5976\n",
            "Epoch 12 Batch 300 Loss 1.9935 Accuracy 0.5971\n",
            "Epoch 12 Batch 350 Loss 1.9987 Accuracy 0.5966\n",
            "Epoch 12 Batch 400 Loss 2.0027 Accuracy 0.5963\n",
            "Epoch 12 Batch 450 Loss 2.0029 Accuracy 0.5963\n",
            "Epoch 12 Batch 500 Loss 2.0099 Accuracy 0.5953\n",
            "Epoch 12 Batch 550 Loss 2.0115 Accuracy 0.5950\n",
            "Epoch 12 Batch 600 Loss 2.0127 Accuracy 0.5949\n",
            "Epoch 12 Batch 650 Loss 2.0140 Accuracy 0.5949\n",
            "Epoch 12 Batch 700 Loss 2.0150 Accuracy 0.5951\n",
            "Epoch 12 Loss 2.0150 Accuracy 0.5951\n",
            "Time taken for 1 epoch: 74.41 secs\n",
            "\n",
            "Epoch 13 Batch 0 Loss 1.9298 Accuracy 0.6137\n",
            "Epoch 13 Batch 50 Loss 1.9002 Accuracy 0.6124\n",
            "Epoch 13 Batch 100 Loss 1.8997 Accuracy 0.6128\n",
            "Epoch 13 Batch 150 Loss 1.8978 Accuracy 0.6128\n",
            "Epoch 13 Batch 200 Loss 1.8982 Accuracy 0.6123\n",
            "Epoch 13 Batch 250 Loss 1.9054 Accuracy 0.6110\n",
            "Epoch 13 Batch 300 Loss 1.9083 Accuracy 0.6103\n",
            "Epoch 13 Batch 350 Loss 1.9141 Accuracy 0.6092\n",
            "Epoch 13 Batch 400 Loss 1.9177 Accuracy 0.6086\n",
            "Epoch 13 Batch 450 Loss 1.9155 Accuracy 0.6091\n",
            "Epoch 13 Batch 500 Loss 1.9182 Accuracy 0.6089\n",
            "Epoch 13 Batch 550 Loss 1.9201 Accuracy 0.6085\n",
            "Epoch 13 Batch 600 Loss 1.9236 Accuracy 0.6082\n",
            "Epoch 13 Batch 650 Loss 1.9264 Accuracy 0.6078\n",
            "Epoch 13 Loss 1.9282 Accuracy 0.6076\n",
            "Time taken for 1 epoch: 76.68 secs\n",
            "\n",
            "Epoch 14 Batch 0 Loss 1.6777 Accuracy 0.6354\n",
            "Epoch 14 Batch 50 Loss 1.8179 Accuracy 0.6227\n",
            "Epoch 14 Batch 100 Loss 1.8283 Accuracy 0.6206\n",
            "Epoch 14 Batch 150 Loss 1.8235 Accuracy 0.6219\n",
            "Epoch 14 Batch 200 Loss 1.8244 Accuracy 0.6217\n",
            "Epoch 14 Batch 250 Loss 1.8238 Accuracy 0.6218\n",
            "Epoch 14 Batch 300 Loss 1.8254 Accuracy 0.6218\n",
            "Epoch 14 Batch 350 Loss 1.8296 Accuracy 0.6210\n",
            "Epoch 14 Batch 400 Loss 1.8314 Accuracy 0.6206\n",
            "Epoch 14 Batch 450 Loss 1.8355 Accuracy 0.6199\n",
            "Epoch 14 Batch 500 Loss 1.8360 Accuracy 0.6199\n",
            "Epoch 14 Batch 550 Loss 1.8383 Accuracy 0.6197\n",
            "Epoch 14 Batch 600 Loss 1.8410 Accuracy 0.6194\n",
            "Epoch 14 Batch 650 Loss 1.8434 Accuracy 0.6192\n",
            "Epoch 14 Loss 1.8460 Accuracy 0.6189\n",
            "Time taken for 1 epoch: 75.07 secs\n",
            "\n",
            "Epoch 15 Batch 0 Loss 1.7661 Accuracy 0.6116\n",
            "Epoch 15 Batch 50 Loss 1.7240 Accuracy 0.6377\n",
            "Epoch 15 Batch 100 Loss 1.7215 Accuracy 0.6382\n",
            "Epoch 15 Batch 150 Loss 1.7357 Accuracy 0.6358\n",
            "Epoch 15 Batch 200 Loss 1.7401 Accuracy 0.6351\n",
            "Epoch 15 Batch 250 Loss 1.7444 Accuracy 0.6343\n",
            "Epoch 15 Batch 300 Loss 1.7488 Accuracy 0.6339\n",
            "Epoch 15 Batch 350 Loss 1.7499 Accuracy 0.6338\n",
            "Epoch 15 Batch 400 Loss 1.7557 Accuracy 0.6330\n",
            "Epoch 15 Batch 450 Loss 1.7583 Accuracy 0.6327\n",
            "Epoch 15 Batch 500 Loss 1.7634 Accuracy 0.6319\n",
            "Epoch 15 Batch 550 Loss 1.7674 Accuracy 0.6314\n",
            "Epoch 15 Batch 600 Loss 1.7711 Accuracy 0.6310\n",
            "Epoch 15 Batch 650 Loss 1.7727 Accuracy 0.6307\n",
            "Saving checkpoint for epoch 15 at ./checkpoints/train/ckpt-3\n",
            "Epoch 15 Loss 1.7760 Accuracy 0.6303\n",
            "Time taken for 1 epoch: 74.29 secs\n",
            "\n",
            "Epoch 16 Batch 0 Loss 1.6235 Accuracy 0.6560\n",
            "Epoch 16 Batch 50 Loss 1.6661 Accuracy 0.6461\n",
            "Epoch 16 Batch 100 Loss 1.6703 Accuracy 0.6461\n",
            "Epoch 16 Batch 150 Loss 1.6799 Accuracy 0.6441\n",
            "Epoch 16 Batch 200 Loss 1.6918 Accuracy 0.6425\n",
            "Epoch 16 Batch 250 Loss 1.6955 Accuracy 0.6416\n",
            "Epoch 16 Batch 300 Loss 1.6969 Accuracy 0.6414\n",
            "Epoch 16 Batch 350 Loss 1.6965 Accuracy 0.6416\n",
            "Epoch 16 Batch 400 Loss 1.6977 Accuracy 0.6414\n",
            "Epoch 16 Batch 450 Loss 1.7003 Accuracy 0.6411\n",
            "Epoch 16 Batch 500 Loss 1.7062 Accuracy 0.6402\n",
            "Epoch 16 Batch 550 Loss 1.7073 Accuracy 0.6400\n",
            "Epoch 16 Batch 600 Loss 1.7117 Accuracy 0.6392\n",
            "Epoch 16 Batch 650 Loss 1.7143 Accuracy 0.6388\n",
            "Epoch 16 Batch 700 Loss 1.7169 Accuracy 0.6384\n",
            "Epoch 16 Loss 1.7176 Accuracy 0.6383\n",
            "Time taken for 1 epoch: 75.20 secs\n",
            "\n",
            "Epoch 17 Batch 0 Loss 1.7149 Accuracy 0.6542\n",
            "Epoch 17 Batch 50 Loss 1.6347 Accuracy 0.6516\n",
            "Epoch 17 Batch 100 Loss 1.6206 Accuracy 0.6534\n",
            "Epoch 17 Batch 150 Loss 1.6276 Accuracy 0.6525\n",
            "Epoch 17 Batch 200 Loss 1.6298 Accuracy 0.6519\n",
            "Epoch 17 Batch 250 Loss 1.6349 Accuracy 0.6505\n",
            "Epoch 17 Batch 300 Loss 1.6392 Accuracy 0.6499\n",
            "Epoch 17 Batch 350 Loss 1.6449 Accuracy 0.6490\n",
            "Epoch 17 Batch 400 Loss 1.6450 Accuracy 0.6491\n",
            "Epoch 17 Batch 450 Loss 1.6506 Accuracy 0.6483\n",
            "Epoch 17 Batch 500 Loss 1.6536 Accuracy 0.6479\n",
            "Epoch 17 Batch 550 Loss 1.6584 Accuracy 0.6472\n",
            "Epoch 17 Batch 600 Loss 1.6604 Accuracy 0.6470\n",
            "Epoch 17 Batch 650 Loss 1.6635 Accuracy 0.6468\n",
            "Epoch 17 Batch 700 Loss 1.6664 Accuracy 0.6462\n",
            "Epoch 17 Loss 1.6667 Accuracy 0.6462\n",
            "Time taken for 1 epoch: 74.71 secs\n",
            "\n",
            "Epoch 18 Batch 0 Loss 1.5980 Accuracy 0.6531\n",
            "Epoch 18 Batch 50 Loss 1.5672 Accuracy 0.6600\n",
            "Epoch 18 Batch 100 Loss 1.5682 Accuracy 0.6607\n",
            "Epoch 18 Batch 150 Loss 1.5693 Accuracy 0.6604\n",
            "Epoch 18 Batch 200 Loss 1.5776 Accuracy 0.6595\n",
            "Epoch 18 Batch 250 Loss 1.5837 Accuracy 0.6586\n",
            "Epoch 18 Batch 300 Loss 1.5893 Accuracy 0.6578\n",
            "Epoch 18 Batch 350 Loss 1.5927 Accuracy 0.6574\n",
            "Epoch 18 Batch 400 Loss 1.5971 Accuracy 0.6563\n",
            "Epoch 18 Batch 450 Loss 1.5979 Accuracy 0.6562\n",
            "Epoch 18 Batch 500 Loss 1.6001 Accuracy 0.6560\n",
            "Epoch 18 Batch 550 Loss 1.6047 Accuracy 0.6554\n",
            "Epoch 18 Batch 600 Loss 1.6057 Accuracy 0.6552\n",
            "Epoch 18 Batch 650 Loss 1.6109 Accuracy 0.6543\n",
            "Epoch 18 Loss 1.6143 Accuracy 0.6539\n",
            "Time taken for 1 epoch: 74.35 secs\n",
            "\n",
            "Epoch 19 Batch 0 Loss 1.4473 Accuracy 0.6775\n",
            "Epoch 19 Batch 50 Loss 1.5130 Accuracy 0.6683\n",
            "Epoch 19 Batch 100 Loss 1.5275 Accuracy 0.6669\n",
            "Epoch 19 Batch 150 Loss 1.5390 Accuracy 0.6655\n",
            "Epoch 19 Batch 200 Loss 1.5390 Accuracy 0.6656\n",
            "Epoch 19 Batch 250 Loss 1.5449 Accuracy 0.6648\n",
            "Epoch 19 Batch 300 Loss 1.5494 Accuracy 0.6642\n",
            "Epoch 19 Batch 350 Loss 1.5523 Accuracy 0.6637\n",
            "Epoch 19 Batch 400 Loss 1.5562 Accuracy 0.6630\n",
            "Epoch 19 Batch 450 Loss 1.5582 Accuracy 0.6628\n",
            "Epoch 19 Batch 500 Loss 1.5602 Accuracy 0.6625\n",
            "Epoch 19 Batch 550 Loss 1.5624 Accuracy 0.6623\n",
            "Epoch 19 Batch 600 Loss 1.5680 Accuracy 0.6613\n",
            "Epoch 19 Batch 650 Loss 1.5700 Accuracy 0.6610\n",
            "Epoch 19 Loss 1.5740 Accuracy 0.6604\n",
            "Time taken for 1 epoch: 75.00 secs\n",
            "\n",
            "Epoch 20 Batch 0 Loss 1.4186 Accuracy 0.6958\n",
            "Epoch 20 Batch 50 Loss 1.4626 Accuracy 0.6766\n",
            "Epoch 20 Batch 100 Loss 1.4833 Accuracy 0.6736\n",
            "Epoch 20 Batch 150 Loss 1.4918 Accuracy 0.6730\n",
            "Epoch 20 Batch 200 Loss 1.4976 Accuracy 0.6715\n",
            "Epoch 20 Batch 250 Loss 1.5056 Accuracy 0.6704\n",
            "Epoch 20 Batch 300 Loss 1.5094 Accuracy 0.6697\n",
            "Epoch 20 Batch 350 Loss 1.5087 Accuracy 0.6703\n",
            "Epoch 20 Batch 400 Loss 1.5125 Accuracy 0.6697\n",
            "Epoch 20 Batch 450 Loss 1.5172 Accuracy 0.6689\n",
            "Epoch 20 Batch 500 Loss 1.5201 Accuracy 0.6685\n",
            "Epoch 20 Batch 550 Loss 1.5210 Accuracy 0.6683\n",
            "Epoch 20 Batch 600 Loss 1.5247 Accuracy 0.6677\n",
            "Epoch 20 Batch 650 Loss 1.5288 Accuracy 0.6671\n",
            "Saving checkpoint for epoch 20 at ./checkpoints/train/ckpt-4\n",
            "Epoch 20 Loss 1.5320 Accuracy 0.6668\n",
            "Time taken for 1 epoch: 74.81 secs\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for epoch in range(EPOCHS):\n",
        "  start = time.time()\n",
        "\n",
        "  train_loss.reset_states()\n",
        "  train_accuracy.reset_states()\n",
        "\n",
        "  # inp -> portuguese, tar -> english\n",
        "  for (batch, (inp, tar)) in enumerate(train_batches):\n",
        "    train_step(inp, tar)\n",
        "\n",
        "    if batch % 50 == 0:\n",
        "      print(f'Epoch {epoch + 1} Batch {batch} Loss {train_loss.result():.4f} Accuracy {train_accuracy.result():.4f}')\n",
        "\n",
        "  if (epoch + 1) % 5 == 0:\n",
        "    ckpt_save_path = ckpt_manager.save()\n",
        "    print(f'Saving checkpoint for epoch {epoch+1} at {ckpt_save_path}')\n",
        "\n",
        "  print(f'Epoch {epoch + 1} Loss {train_loss.result():.4f} Accuracy {train_accuracy.result():.4f}')\n",
        "\n",
        "  print(f'Time taken for 1 epoch: {time.time() - start:.2f} secs\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QfcsSWswSdGV"
      },
      "source": [
        "### Run inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y6APsFrgImLW"
      },
      "source": [
        "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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-FQmQwtv9-kk"
      },
      "source": [
        "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`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "5buvMlnvyrFm"
      },
      "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",
        "    # input sentence is portuguese, hence adding the start and end token\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:, :]  # (batch_size, 1, vocab_size)\n",
        "\n",
        "      predicted_id = tf.argmax(predictions, axis=-1)\n",
        "\n",
        "      # concatentate the predicted_id to the output which is given to the decoder\n",
        "      # 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",
        "    # output.shape (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. So recalculate them outside\n",
        "    # the loop.\n",
        "    _, attention_weights = self.transformer([encoder_input, output[:,:-1]], training=False)\n",
        "\n",
        "    return text, tokens, attention_weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FXGLLJulaq_-"
      },
      "source": [
        "Note: This function uses an unrolled loop, not a dynamic loop. It generates `MAX_TOKENS` on every call. Refer to [NMT with attention](nmt_with_attention.ipynb) for an example implementation with a dynamic loop, which can be much more efficient."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofUWszmY3szZ"
      },
      "source": [
        "Create an instance of this `Translator` class, and try it out a few times:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "4OR2D4EXeIRY"
      },
      "outputs": [],
      "source": [
        "translator = Translator(tokenizers, transformer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "lU2_yG_vBGza"
      },
      "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": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "YsxrAlvFG8SZ",
        "outputId": "c39f0a96-7390-485f-f874-3e59e7ad042e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input:         : este é um problema que temos que resolver.\n",
            "Prediction     : this is a problem that we have to solve .\n",
            "Ground truth   : this is a problem we have to solve .\n"
          ]
        }
      ],
      "source": [
        "sentence = 'este é um problema que temos que resolver.'\n",
        "ground_truth = 'this is a problem we have to solve .'\n",
        "\n",
        "translated_text, translated_tokens, attention_weights = translator(\n",
        "    tf.constant(sentence))\n",
        "print_translation(sentence, translated_text, ground_truth)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "7EH5y_aqI4t1",
        "outputId": "dd87985e-3728-46f5-9016-0595331f3c63"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input:         : os meus vizinhos ouviram sobre esta ideia.\n",
            "Prediction     : my neighbors heard about this idea .\n",
            "Ground truth   : and my neighboring homes heard about this idea .\n"
          ]
        }
      ],
      "source": [
        "sentence = 'os meus vizinhos ouviram sobre esta ideia.'\n",
        "ground_truth = 'and my neighboring homes heard about this idea .'\n",
        "\n",
        "translated_text, translated_tokens, attention_weights = translator(\n",
        "    tf.constant(sentence))\n",
        "print_translation(sentence, translated_text, ground_truth)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-hVCTSUMlkb",
        "outputId": "3f0f06cf-396d-4d16-88a7-c51c6ee82257"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input:         : vou então muito rapidamente partilhar convosco algumas histórias de algumas coisas mágicas que aconteceram.\n",
            "Prediction     : so i ' m very quickly sharing with some of the stories of some magic things that will happen .\n",
            "Ground truth   : so i'll just share with you some stories very quickly of some magical things that have happened.\n"
          ]
        }
      ],
      "source": [
        "sentence = 'vou então muito rapidamente partilhar convosco algumas histórias de algumas coisas mágicas que aconteceram.'\n",
        "ground_truth = \"so i'll just share with you some stories very quickly of some magical things that have happened.\"\n",
        "\n",
        "translated_text, translated_tokens, attention_weights = translator(\n",
        "    tf.constant(sentence))\n",
        "print_translation(sentence, translated_text, ground_truth)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3EQiFUC--Ds"
      },
      "source": [
        "## Attention plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hHV2pdXHGz-0"
      },
      "source": [
        "The `Translator` class returns a dictionary of attention maps you can use to visualize the internal working of the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-kFyiOLH0xg",
        "outputId": "246f2a9a-8a05-4fa9-b90c-9c0b8c67d343"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input:         : este é o primeiro livro que eu fiz.\n",
            "Prediction     : this is the first book that i did .\n",
            "Ground truth   : this is the first book i've ever done.\n"
          ]
        }
      ],
      "source": [
        "sentence = 'este é o primeiro livro que eu fiz.'\n",
        "ground_truth = \"this is the first book i've ever done.\"\n",
        "\n",
        "translated_text, translated_tokens, attention_weights = translator(\n",
        "    tf.constant(sentence))\n",
        "print_translation(sentence, translated_text, ground_truth)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "CcI4DxAK5EHY"
      },
      "outputs": [],
      "source": [
        "def plot_attention_head(in_tokens, translated_tokens, attention):\n",
        "  # The plot is of the attention when a token was generated.\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": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "_KY4c2cryuxY",
        "outputId": "d5cbe72a-cc34-4fe4-b640-98737e29866b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "TensorShape([10, 11])"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "head = 0\n",
        "# shape: (batch=1, num_heads, seq_len_q, seq_len_k)\n",
        "attention_heads = tf.squeeze(\n",
        "  attention_weights['decoder_layer4_block2'], 0)\n",
        "attention = attention_heads[head]\n",
        "attention.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdxmakWE6Om3",
        "outputId": "55622b9f-d921-454a-af60-da9a49966e5b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(11,), dtype=string, numpy=\n",
              "array([b'[START]', b'este', b'e', b'o', b'primeiro', b'livro', b'que',\n",
              "       b'eu', b'fiz', b'.', b'[END]'], dtype=object)>"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVdPSfecmrpj",
        "outputId": "9952c958-e25c-4753-9bb0-0d3faa430070"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(11,), dtype=string, numpy=\n",
              "array([b'[START]', b'this', b'is', b'the', b'first', b'book', b'that',\n",
              "       b'i', b'did', b'.', b'[END]'], dtype=object)>"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "translated_tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "XtzyKCFamm4N",
        "outputId": "382a15ff-4dd3-47d7-9ed0-38a08365cf35"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_attention_head(in_tokens, translated_tokens, attention)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "MBliB-PCzNK3"
      },
      "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",
        "  in_tokens\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": {
        "colab": {
          "background_save": true
        },
        "id": "pyRQi7944wru",
        "outputId": "f3c14c53-c9cc-4548-bc05-f6075abd686d"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_attention_weights(sentence, translated_tokens,\n",
        "                       attention_weights['decoder_layer4_block2'][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZJirKUtikTt"
      },
      "source": [
        "The model does okay on unfamiliar words. Neither \"triceratops\" or \"encyclopedia\" are in the input dataset and the model almost learns to transliterate them, even without a shared vocabulary:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "9cxysY7uh3jg",
        "outputId": "8e1dac89-1942-48e7-e784-f36f20c0ac1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input:         : Eu li sobre triceratops na enciclopédia.\n",
            "Prediction     : i read about taysarife in the encyclo hatement .\n",
            "Ground truth   : I read about triceratops in the encyclopedia.\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sentence = 'Eu li sobre triceratops na enciclopédia.'\n",
        "ground_truth = 'I read about triceratops in the encyclopedia.'\n",
        "\n",
        "translated_text, translated_tokens, attention_weights = translator(\n",
        "    tf.constant(sentence))\n",
        "print_translation(sentence, translated_text, ground_truth)\n",
        "\n",
        "plot_attention_weights(sentence, translated_tokens,\n",
        "                       attention_weights['decoder_layer4_block2'][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mOyiOetL2l60"
      },
      "source": [
        "## Export"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTK3g2UL2oMc"
      },
      "source": [
        "That inference model is working, so next you'll export it as a `tf.saved_model`.\n",
        "\n",
        "To do that, wrap it in yet another `tf.Module` sub-class, this time with a `tf.function` on the `__call__` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "GRmzkibLusQi"
      },
      "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": "O9f_pmEA4kql"
      },
      "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": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "EfomoJDP2n5n"
      },
      "outputs": [],
      "source": [
        "translator = ExportTranslator(translator)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUfoCWPS9LuB"
      },
      "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": {
        "colab": {
          "background_save": true
        },
        "id": "hAlqyycz3IYL",
        "outputId": "3ea87683-9345-44c3-db76-39c5fe57d1e2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "b'this is the first book that i did .'"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "translator('este é o primeiro livro que eu fiz.').numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ar3LO-Vuvlnv",
        "outputId": "cee8ec4b-c947-4470-8eeb-6bd2b47a4589"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:Found untraced functions such as embedding_4_layer_call_fn, embedding_4_layer_call_and_return_conditional_losses, dropout_37_layer_call_fn, dropout_37_layer_call_and_return_conditional_losses, embedding_5_layer_call_fn while saving (showing 5 of 224). These functions will not be directly callable after loading.\n"
          ]
        }
      ],
      "source": [
        "tf.saved_model.save(translator, export_dir='translator')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "8WUflwyT1SEF"
      },
      "outputs": [],
      "source": [
        "reloaded = tf.saved_model.load('translator')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-sBTBWwR1XMr"
      },
      "outputs": [],
      "source": [
        "reloaded('este é o primeiro livro que eu fiz.').numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqQ1fIsLwkGE"
      },
      "source": [
        "## Summary\n",
        "\n",
        "In this tutorial you learned about:\n",
        "\n",
        "* recurrent neural networks\n",
        "* positional encoding\n",
        "* multi-head attention\n",
        "* the importance of masking \n",
        "* and how to put it all together to build a transformer.\n",
        "\n",
        "This implementation tried to stay close to the implementation of the \n",
        "[original paper](https://arxiv.org/abs/1706.03762). If you want to practice there are many things you could try with it. For example: \n",
        "\n",
        "* Using 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",
        "* Implement beam search to get better predictions.\n",
        "* Use other implementations such as [HuggingFace Transformers](https://github.com/huggingface/transformers), [ModernMT](https://github.com/modernmt/modernmt) (Neural Adaptive Machine Translation that adapts to context and learns from corrections.) or [MarianNMT](https://github.com/marian-nmt/marian) (Fast Neural Machine Translation in C++)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "transformer.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "003103fe34db46bb8937c62830fa198b": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "01c631f7fb8949328e6678b30370fba8": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6b2a7cd0ce7b46b4a78fadd0ee6e13df",
            "max": 1,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_acff79c589dc4563947fa23daf5d8972",
            "value": 1
          }
        },
        "0772e3bc35e5443ebc95b6779fb08602": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0936404803eb4a608c113fd9423f7fc0": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0e3456cc744241f29f1a99ad54572712": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0e85e21dff104025a0c69b71e53c90c9": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "128dea962396413b9d3ec3cbfa69f736": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "137a362b35884826800bd53648dd6817": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "14a516d260214caa92c9fabdd99b31a2": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_137a362b35884826800bd53648dd6817",
            "placeholder": "​",
            "style": "IPY_MODEL_50901fcec59143928d4aeaaaed5b3bcf",
            "value": "Generating test examples...:  83%"
          }
        },
        "14f8889ad5884fae98faa7448d484359": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "15ed632e98554200b0d900d61497efe1": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "18cd6a395788467095618ce933330e4c": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_72d024cc5a7e4bcca82ef7704be365c0",
              "IPY_MODEL_01c631f7fb8949328e6678b30370fba8",
              "IPY_MODEL_77bf1837391b4b6ca1f1240b8495f6d7"
            ],
            "layout": "IPY_MODEL_44899ef88d284f8182f94b3cca1a7a64"
          }
        },
        "1a9b650049e94d878c481d9b714e9de5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "1d09b0fdaf51409d8b70391ebb01a5b3": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1d639a743f3d4b71bbb5e7adb24f6387": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1d09b0fdaf51409d8b70391ebb01a5b3",
            "placeholder": "​",
            "style": "IPY_MODEL_40d2ce5a27b64e6f9f35b7b1eb132363",
            "value": " 1491/1803 [00:00&lt;00:00, 3790.07 examples/s]"
          }
        },
        "21bb0722c0b1434198a9e7d382795f3e": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "259b9a8478004eb2abd2286b8835dbfb": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5eeab2cb7f51418e9e2556ceb490936e",
            "max": 1803,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_480f54d31d63464d9da3ac46f3428741",
            "value": 1803
          }
        },
        "29b72790c7644824b9d6171a3f8072c0": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_128dea962396413b9d3ec3cbfa69f736",
            "placeholder": "​",
            "style": "IPY_MODEL_0e85e21dff104025a0c69b71e53c90c9",
            "value": " 0/1803 [00:00&lt;?, ? examples/s]"
          }
        },
        "2cadadc18f164b6c9872d12421b8c654": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6af90d2e89934007b7d8d15e2857763a",
            "max": 1803,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_bd9f7c6d56ef461f98f425635877b914",
            "value": 1803
          }
        },
        "2e8397937efd418883a906d986c4b3e5": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "31ad05bf5eb24d25be8fced29a306f6d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "394b0d069c534edd8cc297b9acd11e6e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_dbafab1eb8d54868aef8b05fe4f1bdbf",
            "max": 1,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_becf7205afce467b80c21c6eee4e8098",
            "value": 1
          }
        },
        "3b1d080b1b0845e9bed7db128ffa0a31": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "3bcfc4c83fbe4a3e87be1382d516e0d1": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "3dbb2365a41847079ce0375cab09d471": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_79781492d22c40d49d84fd918b60dcfc",
            "max": 1193,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_1a9b650049e94d878c481d9b714e9de5",
            "value": 1193
          }
        },
        "40d2ce5a27b64e6f9f35b7b1eb132363": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4118a878546c4ed98bc1ebd41e949656": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "411bb64c33464c0e89c95e9105fc298d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5e71297a1ae244cfbab29834d135af87",
            "placeholder": "​",
            "style": "IPY_MODEL_4a342aaa89c840cc84be20975ab383fb",
            "value": " 0/1193 [00:00&lt;?, ? examples/s]"
          }
        },
        "443a46e4507347e4b4c5b159906f7ca0": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bd1505edd0ce4c5c88670beb0d61d69e",
            "placeholder": "​",
            "style": "IPY_MODEL_7e2cad2d9c9340aeb09d685a87c4d405",
            "value": "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteRVR7GL/ted_hrlr_translate-validation.tfrecord*...:   0%"
          }
        },
        "44899ef88d284f8182f94b3cca1a7a64": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "45d8f4f3d5e94ea9814637b1ad958e8b": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_4867c670481c49e091f02971af1ef124",
              "IPY_MODEL_9753a752274140af9ba91ec9f03e535f",
              "IPY_MODEL_847c2b88aa6c47048a9c742954962ca0"
            ],
            "layout": "IPY_MODEL_fa900b88b21d401e8e7d97892b2261c9"
          }
        },
        "47676cb41cde42969ce4914c5f46da34": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "480f54d31d63464d9da3ac46f3428741": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "4855b06812314c03a5f8b91abb374531": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4867c670481c49e091f02971af1ef124": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9173f888fb7d43b390ef33cbc202264a",
            "placeholder": "​",
            "style": "IPY_MODEL_3b1d080b1b0845e9bed7db128ffa0a31",
            "value": "Generating train examples...: 100%"
          }
        },
        "4a342aaa89c840cc84be20975ab383fb": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4b00b22336ae40d29a831bfe87d37aa7": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4f38696b86b04227af50e8ef9125b05f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4f74a91bd52b43c286ea57cfb90eaccf": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_acb7347b2c8a4e4d9bbbba8e456b87a6",
            "placeholder": "​",
            "style": "IPY_MODEL_4855b06812314c03a5f8b91abb374531",
            "value": " 671/1193 [00:00&lt;00:00, 3874.67 examples/s]"
          }
        },
        "500e6111ac1e4f439a46fe55a2d4cd35": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "50901fcec59143928d4aeaaaed5b3bcf": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "523a3080de8246e9998851b94aa289b2": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a4b7443bc3304573accc72c2f1c926b8",
            "placeholder": "​",
            "style": "IPY_MODEL_7abc5abb872945a0acbb80325c23fe75",
            "value": "Extraction completed...: 100%"
          }
        },
        "5287e6bbaf0b4ae0a2dbf94cf9d70df5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_f37cfda2d67d4786860aedd6c524859e",
              "IPY_MODEL_6e75bfdf510b43889f76323efad42d5a",
              "IPY_MODEL_f16a3703489d4acbbf671139ef850eca"
            ],
            "layout": "IPY_MODEL_003103fe34db46bb8937c62830fa198b"
          }
        },
        "54b5b0d36b5c4a9cacc95f0f734bd597": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5cdf2f4e30454fc19501020f618a8820": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "5d6f2ab779314a71b560c26a2e205105": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5e71297a1ae244cfbab29834d135af87": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5eeab2cb7f51418e9e2556ceb490936e": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6118ba2628b04d30a5788b6c1c03e1f0": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "63da4d272c8a465c8ade02b8cd53e93f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "668bb06adb2c40c6919886e1144a7da5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "68f287400c924929817364041c41cdd3": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6af90d2e89934007b7d8d15e2857763a": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6b2a7cd0ce7b46b4a78fadd0ee6e13df": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": "20px"
          }
        },
        "6d81341815ec417f82fe219f4522d987": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_db1ab1178aaa46b190c0eccd7bdebd06",
            "placeholder": "​",
            "style": "IPY_MODEL_68f287400c924929817364041c41cdd3",
            "value": "Generating splits...: 100%"
          }
        },
        "6e75bfdf510b43889f76323efad42d5a": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5d6f2ab779314a71b560c26a2e205105",
            "max": 51785,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_ee69d6537a204d768f6f5067e9f9d9aa",
            "value": 51785
          }
        },
        "70a92596cfcb4a3bbae1ab8a1d1e21c9": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "72d024cc5a7e4bcca82ef7704be365c0": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_dab349dc000f4dd1adfc6be57d371c87",
            "placeholder": "​",
            "style": "IPY_MODEL_4f38696b86b04227af50e8ef9125b05f",
            "value": "Dl Size...: 100%"
          }
        },
        "7741a651675a4105a2dd247e63ff1045": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "77bf1837391b4b6ca1f1240b8495f6d7": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0e3456cc744241f29f1a99ad54572712",
            "placeholder": "​",
            "style": "IPY_MODEL_500e6111ac1e4f439a46fe55a2d4cd35",
            "value": " 124/124 [00:11&lt;00:00, 36.38 MiB/s]"
          }
        },
        "794ba813a39a4c76b2025fc81cbc6a71": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "79781492d22c40d49d84fd918b60dcfc": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7abc5abb872945a0acbb80325c23fe75": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "7cf493c758184d1e9fe458219be86894": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "7e2cad2d9c9340aeb09d685a87c4d405": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "83ce5057fa824a8bb1dc818ddd01e7e6": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_523a3080de8246e9998851b94aa289b2",
              "IPY_MODEL_d529e3287cce44fc9ec76f470e165c57",
              "IPY_MODEL_a8c06c5c47644badb3fbdf1712bb773b"
            ],
            "layout": "IPY_MODEL_4118a878546c4ed98bc1ebd41e949656"
          }
        },
        "847c2b88aa6c47048a9c742954962ca0": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_47676cb41cde42969ce4914c5f46da34",
            "placeholder": "​",
            "style": "IPY_MODEL_d18d37985d8248f29576897b9d39558e",
            "value": " 51766/51785 [00:11&lt;00:00, 3451.30 examples/s]"
          }
        },
        "8fb44e5e5f9b461bab8ede06ad4cc0ac": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0936404803eb4a608c113fd9423f7fc0",
            "max": 1193,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_5cdf2f4e30454fc19501020f618a8820",
            "value": 1193
          }
        },
        "9173f888fb7d43b390ef33cbc202264a": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "93d903d7a38d4acc8619fd9242a89334": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "9753a752274140af9ba91ec9f03e535f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cca764db6814446fac7daf44017fffc4",
            "max": 51785,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_63da4d272c8a465c8ade02b8cd53e93f",
            "value": 51785
          }
        },
        "97d18fde951f443abc3a0fa9c1cebd1d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_c764b25b0da54e4ab63d875088934768",
              "IPY_MODEL_394b0d069c534edd8cc297b9acd11e6e",
              "IPY_MODEL_c4843efb7b344798ada22d1b1bb5b3e6"
            ],
            "layout": "IPY_MODEL_4b00b22336ae40d29a831bfe87d37aa7"
          }
        },
        "9c5d853b616f48929dd851903c8e3a85": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_70a92596cfcb4a3bbae1ab8a1d1e21c9",
            "placeholder": "​",
            "style": "IPY_MODEL_14f8889ad5884fae98faa7448d484359",
            "value": " 3/3 [00:12&lt;00:00,  3.07s/ splits]"
          }
        },
        "9e5e532113944781aa4c79c434c393a2": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_aea96ae561c84798920f27b645dce72e",
            "placeholder": "​",
            "style": "IPY_MODEL_93d903d7a38d4acc8619fd9242a89334",
            "value": "Generating validation examples...:  56%"
          }
        },
        "a063e605ad0c4a96aeb8556d93537d1e": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a4b7443bc3304573accc72c2f1c926b8": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a8c06c5c47644badb3fbdf1712bb773b": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a063e605ad0c4a96aeb8556d93537d1e",
            "placeholder": "​",
            "style": "IPY_MODEL_7741a651675a4105a2dd247e63ff1045",
            "value": " 1/1 [00:11&lt;00:00, 11.60s/ file]"
          }
        },
        "aae1d7a927f348d0a0fa3aa3c28a8834": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "acab89080711461f93395f7e041bd24c": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": "20px"
          }
        },
        "acb7347b2c8a4e4d9bbbba8e456b87a6": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "acff79c589dc4563947fa23daf5d8972": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "aea96ae561c84798920f27b645dce72e": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b4ffdf9961234e0f8222ca6d25c2a788": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bd1505edd0ce4c5c88670beb0d61d69e": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bd9f7c6d56ef461f98f425635877b914": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "becf7205afce467b80c21c6eee4e8098": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "c4843efb7b344798ada22d1b1bb5b3e6": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_794ba813a39a4c76b2025fc81cbc6a71",
            "placeholder": "​",
            "style": "IPY_MODEL_dc80d87d7b5943c5bf3992b49ecbd801",
            "value": " 1/1 [00:11&lt;00:00,  4.10s/ url]"
          }
        },
        "c52c07226eb74a8bb2365ad78d0f8983": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_6d81341815ec417f82fe219f4522d987",
              "IPY_MODEL_faecef990da948c3ae31857fd1e8c666",
              "IPY_MODEL_9c5d853b616f48929dd851903c8e3a85"
            ],
            "layout": "IPY_MODEL_daffc8afd11447709011549d92900208"
          }
        },
        "c764b25b0da54e4ab63d875088934768": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_b4ffdf9961234e0f8222ca6d25c2a788",
            "placeholder": "​",
            "style": "IPY_MODEL_7cf493c758184d1e9fe458219be86894",
            "value": "Dl Completed...: 100%"
          }
        },
        "cb8157d46d2043668bfa7a3397f18565": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cca764db6814446fac7daf44017fffc4": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cd75a7666cc446708affada1548d97f9": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d18d37985d8248f29576897b9d39558e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "d280c534ec2c4b82a56770daf370150f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_9e5e532113944781aa4c79c434c393a2",
              "IPY_MODEL_8fb44e5e5f9b461bab8ede06ad4cc0ac",
              "IPY_MODEL_4f74a91bd52b43c286ea57cfb90eaccf"
            ],
            "layout": "IPY_MODEL_21bb0722c0b1434198a9e7d382795f3e"
          }
        },
        "d529e3287cce44fc9ec76f470e165c57": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_acab89080711461f93395f7e041bd24c",
            "max": 1,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_31ad05bf5eb24d25be8fced29a306f6d",
            "value": 1
          }
        },
        "dab349dc000f4dd1adfc6be57d371c87": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "daffc8afd11447709011549d92900208": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "db1ab1178aaa46b190c0eccd7bdebd06": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "dbafab1eb8d54868aef8b05fe4f1bdbf": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": "20px"
          }
        },
        "dc80d87d7b5943c5bf3992b49ecbd801": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "deb0a595f103463892dc639e28300a2a": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e337e7b6764a46f1aae4a7dd63753452": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cd75a7666cc446708affada1548d97f9",
            "placeholder": "​",
            "style": "IPY_MODEL_668bb06adb2c40c6919886e1144a7da5",
            "value": "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteRVR7GL/ted_hrlr_translate-test.tfrecord*...:   0%"
          }
        },
        "edcf870de96246d4971bc83e0b40b3f5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_14a516d260214caa92c9fabdd99b31a2",
              "IPY_MODEL_2cadadc18f164b6c9872d12421b8c654",
              "IPY_MODEL_1d639a743f3d4b71bbb5e7adb24f6387"
            ],
            "layout": "IPY_MODEL_aae1d7a927f348d0a0fa3aa3c28a8834"
          }
        },
        "ee69d6537a204d768f6f5067e9f9d9aa": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "ef1350a3defb4750897367648283390a": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_443a46e4507347e4b4c5b159906f7ca0",
              "IPY_MODEL_3dbb2365a41847079ce0375cab09d471",
              "IPY_MODEL_411bb64c33464c0e89c95e9105fc298d"
            ],
            "layout": "IPY_MODEL_cb8157d46d2043668bfa7a3397f18565"
          }
        },
        "f1453920ecb448a7afd59b31ba18576a": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_e337e7b6764a46f1aae4a7dd63753452",
              "IPY_MODEL_259b9a8478004eb2abd2286b8835dbfb",
              "IPY_MODEL_29b72790c7644824b9d6171a3f8072c0"
            ],
            "layout": "IPY_MODEL_2e8397937efd418883a906d986c4b3e5"
          }
        },
        "f16a3703489d4acbbf671139ef850eca": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_15ed632e98554200b0d900d61497efe1",
            "placeholder": "​",
            "style": "IPY_MODEL_6118ba2628b04d30a5788b6c1c03e1f0",
            "value": " 40839/51785 [00:00&lt;00:00, 143155.66 examples/s]"
          }
        },
        "f37cfda2d67d4786860aedd6c524859e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0772e3bc35e5443ebc95b6779fb08602",
            "placeholder": "​",
            "style": "IPY_MODEL_deb0a595f103463892dc639e28300a2a",
            "value": "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteRVR7GL/ted_hrlr_translate-train.tfrecord*...:  79%"
          }
        },
        "fa900b88b21d401e8e7d97892b2261c9": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "faecef990da948c3ae31857fd1e8c666": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_54b5b0d36b5c4a9cacc95f0f734bd597",
            "max": 3,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_3bcfc4c83fbe4a3e87be1382d516e0d1",
            "value": 3
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}