google.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s_qNSzzyaCbD"
   },
   "source": [
    "##### Copyright 2019 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
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     "shell.execute_reply": "2021-02-04T02:21:49.270768Z"
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    "id": "jmjh290raIky"
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   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J0Qjg6vuaHNt"
   },
   "source": [
    "# Transformer model for language understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AOpGoE2T-YXS"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/text/transformer\">\n",
    "    <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />\n",
    "    View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/text/transformer.ipynb\">\n",
    "    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />\n",
    "    Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/text/transformer.ipynb\">\n",
    "    <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />\n",
    "    View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/text/transformer.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "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). This is an advanced example that assumes knowledge of [text generation](text_generation.ipynb) and [attention](nmt_with_attention.ipynb).\n",
    "\n",
    "The core idea behind the 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](text_classification_rnn.ipynb) or [CNNs](../images/intro_to_cnns.ipynb). 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": "swymtxpl7W7w"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:21:49.283016Z",
     "iopub.status.busy": "2021-02-04T02:21:49.282180Z",
     "iopub.status.idle": "2021-02-04T02:21:53.349513Z",
     "shell.execute_reply": "2021-02-04T02:21:53.348883Z"
    },
    "id": "XFG0NDRu5mYQ"
   },
   "outputs": [],
   "source": [
    "# !pip install -q tensorflow_datasets\n",
    "# !pip install -q tensorflow_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:21:53.356196Z",
     "iopub.status.busy": "2021-02-04T02:21:53.354999Z",
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     "shell.execute_reply": "2021-02-04T02:22:01.898318Z"
    },
    "id": "JjJJyJTZYebt"
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "import logging\n",
    "import os\n",
    "import pathlib\n",
    "import re\n",
    "import string\n",
    "import sys\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_text as text\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:01.904707Z",
     "iopub.status.busy": "2021-02-04T02:22:01.903568Z",
     "iopub.status.idle": "2021-02-04T02:22:01.906365Z",
     "shell.execute_reply": "2021-02-04T02:22:01.905813Z"
    },
    "id": "pXzVhU34zWEU"
   },
   "outputs": [],
   "source": [
    "logging.getLogger('tensorflow').setLevel(logging.ERROR)  # suppress warnings"
   ]
  },
  {
   "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": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:01.911940Z",
     "iopub.status.busy": "2021-02-04T02:22:01.911191Z",
     "iopub.status.idle": "2021-02-04T02:22:13.997782Z",
     "shell.execute_reply": "2021-02-04T02:22:13.997164Z"
    },
    "id": "8q9t4FmN96eN"
   },
   "outputs": [],
   "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": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:14.003894Z",
     "iopub.status.busy": "2021-02-04T02:22:14.003236Z",
     "iopub.status.idle": "2021-02-04T02:22:15.566165Z",
     "shell.execute_reply": "2021-02-04T02:22:15.566611Z"
    },
    "id": "AtuMLTQGlHZ1"
   },
   "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 some numeric representation first. Typically you convert the text to sequences of token IDs, which are as indexes into an embedding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2sPvbCEePzOC"
   },
   "source": [
    "One popular implementation is demonstrated in the [Subword tokenizer tutorial](https://www.tensorflow.org/tutorials/tensorflow_text/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": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:15.573461Z",
     "iopub.status.busy": "2021-02-04T02:22:15.572803Z",
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     "shell.execute_reply": "2021-02-04T02:22:15.779289Z"
    },
    "id": "QToMl0NanZPr"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./ted_hrlr_translate_pt_en_converter.zip'"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:15.785154Z",
     "iopub.status.busy": "2021-02-04T02:22:15.784392Z",
     "iopub.status.idle": "2021-02-04T02:22:17.109636Z",
     "shell.execute_reply": "2021-02-04T02:22:17.108974Z"
    },
    "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 Portugese. Both have the same methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:17.115120Z",
     "iopub.status.busy": "2021-02-04T02:22:17.114323Z",
     "iopub.status.idle": "2021-02-04T02:22:17.116847Z",
     "shell.execute_reply": "2021-02-04T02:22:17.117288Z"
    },
    "id": "s-PCJijfcZ9_"
   },
   "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": 9,
     "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": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:17.121754Z",
     "iopub.status.busy": "2021-02-04T02:22:17.121037Z",
     "iopub.status.idle": "2021-02-04T02:22:17.124198Z",
     "shell.execute_reply": "2021-02-04T02:22:17.123650Z"
    },
    "id": "0GV-smAAQJtM"
   },
   "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": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:17.131131Z",
     "iopub.status.busy": "2021-02-04T02:22:17.129279Z",
     "iopub.status.idle": "2021-02-04T02:22:17.441739Z",
     "shell.execute_reply": "2021-02-04T02:22:17.441111Z"
    },
    "id": "2fQJq1xB-tOn"
   },
   "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": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:17.448557Z",
     "iopub.status.busy": "2021-02-04T02:22:17.447832Z",
     "iopub.status.idle": "2021-02-04T02:22:17.572580Z",
     "shell.execute_reply": "2021-02-04T02:22:17.572026Z"
    },
    "id": "Tpqx1aug3W31"
   },
   "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": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:17.577439Z",
     "iopub.status.busy": "2021-02-04T02:22:17.576109Z",
     "iopub.status.idle": "2021-02-04T02:22:17.595660Z",
     "shell.execute_reply": "2021-02-04T02:22:17.595021Z"
    },
    "id": "RpzRLzvIuN3R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.RaggedTensor [[b'[START]', b'and', b'when', b'you', b'improve', b'search', b'##ability', b',', b'you', b'actually', b'take', b'away', b'the', b'one', b'advantage', b'of', b'print', b',', b'which', b'is', b's', b'##ere', b'##nd', b'##ip', b'##ity', b'.', b'[END]'], [b'[START]', b'but', b'what', b'if', b'it', b'were', b'active', b'?', b'[END]'], [b'[START]', b'but', b'they', b'did', b'n', b\"'\", b't', b'test', b'for', b'curiosity', b'.', b'[END]']]>"
      ]
     },
     "execution_count": 13,
     "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 \"serindipity\" into \"s ##ere ##nd ##ip ##ity\""
   ]
  },
  {
   "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 you'll apply some transformations to the dataset.\n",
    "\n",
    "This function will be used to encode the batches of raw text:"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concatenate elements of a list 'x' of multiple integers to a single integer\n",
      "sum(d * 10 ** i for i, d in enumerate(x[::-1]))\n"
     ]
    }
   ],
   "source": [
    "# Load CoNaLa dataset\n",
    "import json\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tokenize import tokenize\n",
    "from io import BytesIO\n",
    "\n",
    "train_file = open(\"conala-corpus-v1.1/conala-corpus/conala-train.json\", \"r\")\n",
    "train_json = json.load(train_file)\n",
    "train_intents = [item[\"rewritten_intent\"] if item[\"rewritten_intent\"] is not None else '' for item in train_json]\n",
    "train_snippets = [item[\"snippet\"] if item[\"snippet\"] is not None else '' for item in train_json]\n",
    "\n",
    "val_file = open(\"conala-corpus-v1.1/conala-corpus/conala-test.json\", \"r\")\n",
    "val_json = json.load(val_file)\n",
    "val_intents = [item[\"rewritten_intent\"] if item[\"rewritten_intent\"] is not None else '' for item in train_json]\n",
    "val_snippets = [item[\"snippet\"] if item[\"snippet\"] is not None else '' for item in train_json]\n",
    "\n",
    "print(train_intents[0])\n",
    "print(train_snippets[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(28,), dtype=string, numpy=\n",
       "array([b'[START]', b'utf-8', b'sum', b'(', b'd', b'*', b'10', b'**', b'i',\n",
       "       b'for', b'i', b',', b'd', b'in', b'enumerate', b'(', b'x', b'[',\n",
       "       b':', b':', b'-', b'1', b']', b')', b')', b'', b'', b'[END]'],\n",
       "      dtype=object)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use custom tokenize function for code snippets\n",
    "start_token, end_token = \"[START]\", \"[END]\"\n",
    "def tokenize_code(snippet):\n",
    "    tokenized = tokenize(BytesIO(snippet.numpy()).readline)\n",
    "    tokens = [start_token] + [token[1] for token in tokenized] + [end_token]\n",
    "    tensor = tf.convert_to_tensor(tokens, dtype=tf.string)\n",
    "    \n",
    "    return tensor\n",
    "\n",
    "example = tf.constant(train_snippets[0])\n",
    "tokenize_code(example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_intent(intents):  \n",
    "    intents = tokenizers.en.tokenize(intents)\n",
    "    intents = intents.to_tensor()\n",
    "    \n",
    "    return intents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:17.607717Z",
     "iopub.status.busy": "2021-02-04T02:22:17.607016Z",
     "iopub.status.idle": "2021-02-04T02:22:17.609308Z",
     "shell.execute_reply": "2021-02-04T02:22:17.608765Z"
    },
    "id": "bcRp7VcQ5m6g"
   },
   "outputs": [],
   "source": [
    "BUFFER_SIZE = 20000\n",
    "BATCH_SIZE = 64\n",
    "CODE_TOKENS = 8000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[   2 2356   71 ...    0    0    0]\n",
      " [   2 2356   71 ...    0    0    0]\n",
      " [   2  712   71 ...    0    0    0]\n",
      " ...\n",
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      " [   2  141  168 ...    0    0    0]\n",
      " [   2 2682 2057 ...    0    0    0]], shape=(64, 54), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6   48 ...    0    0    0]\n",
      " [   7    6   48 ...    0    0    0]\n",
      " [   7    6   82 ...    0    0    0]\n",
      " ...\n",
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      " [   7    6   11 ...    0    0    0]\n",
      " [   7    6 1316 ...    0    0    0]], shape=(64, 40), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 2577   71 ...    0    0    0]\n",
      " [   2 2522 1719 ...    0    0    0]\n",
      " [   2  114 4879 ...    0    0    0]\n",
      " ...\n",
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      " [   2 3477 2367 ...    0    0    0]\n",
      " [   2 3609 1261 ...    0    0    0]], shape=(64, 39), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6 446 ...   0   0   0]\n",
      " [  7   6  27 ...   0   0   0]\n",
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      " ...\n",
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      " [  7   6  49 ...   0   0   0]], shape=(64, 70), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 2159  787 ...    0    0    0]\n",
      " [   2 2159  787 ...    0    0    0]\n",
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      " ...\n",
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      "tf.Tensor(\n",
      "[[   7    6 1072 ...    0    0    0]\n",
      " [   7    6 1433 ...    0    0    0]\n",
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      " ...\n",
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      " [   7    6   27 ...    0    0    0]], shape=(64, 40), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  329   37 ...    0    0    0]\n",
      " [   2  389 6250 ...    0    0    0]\n",
      " [   2 3609  138 ...    0    0    0]\n",
      " ...\n",
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      " [   2    3    0 ...    0    0    0]\n",
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      "tf.Tensor(\n",
      "[[  7   6  62 ...   0   0   0]\n",
      " [  7   6  18 ...   0   0   0]\n",
      " [  7   6 811 ...   0   0   0]\n",
      " ...\n",
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      " [  7   6  11 ...   8   0   0]\n",
      " [  7   6 126 ...   0   0   0]], shape=(64, 37), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  715  906 ...    0    0    0]\n",
      " [   2  715   71 ...    0    0    0]\n",
      " [   2 6696 5939 ...    0    0    0]\n",
      " ...\n",
      " [   2 2159 5962 ...    0    0    0]\n",
      " [   2 2159 5962 ...    0    0    0]\n",
      " [   2 5848 5939 ...    0    0    0]], shape=(64, 50), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6  33 ...   0   0   0]\n",
      " [  7   6  33 ...   0   0   0]\n",
      " [  7   6  33 ...   0   0   0]\n",
      " ...\n",
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      " [  7   6 263 ...   0   0   8]\n",
      " [  7   6 302 ...   0   0   0]], shape=(64, 42), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  228  101 ...    0    0    0]\n",
      " [   2 3532 3267 ...    0    0    0]\n",
      " [   2  520 5070 ...    0    0    0]\n",
      " ...\n",
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      " [   2 3145  740 ...    0    0    0]\n",
      " [   2 2159  773 ...    0    0    0]], shape=(64, 48), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[ 7  6 22 ...  0  0  0]\n",
      " [ 7  6 26 ...  0  0  0]\n",
      " [ 7  6 92 ...  0  0  0]\n",
      " ...\n",
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      " [ 7  6 22 ...  0  0  0]\n",
      " [ 7  6 20 ...  0  0  0]], shape=(64, 39), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 1512  107 ...    0    0    0]\n",
      " [   2 3011  107 ...    0    0    0]\n",
      " [   2 2682   36 ...    0    0    0]\n",
      " ...\n",
      " [   2 2356   71 ...    0    0    0]\n",
      " [   2  329 1261 ...    0    0    0]\n",
      " [   2  228   44 ...    0    0    0]], shape=(64, 62), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6  126 ...    0    0    0]\n",
      " [   7    6  126 ...    0    0    0]\n",
      " [   7    6   11 ...    0    0    0]\n",
      " ...\n",
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      " [   7    6   27 ...    0    0    0]], shape=(64, 41), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  450  139 ...    0    0    0]\n",
      " [   2 2569 2969 ... 1167    3    0]\n",
      " [   2  518   71 ...    0    0    0]\n",
      " ...\n",
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      " [   2  141   56 ...    0    0    0]\n",
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      "tf.Tensor(\n",
      "[[   7    6 1733 ...    0    0    0]\n",
      " [   7    6 1596 ...    0    0    0]\n",
      " [   7    6  887 ...    0    0    0]\n",
      " ...\n",
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      " [   7    6   11 ...    0    0    0]\n",
      " [   7    6  184 ...    0    0    0]], shape=(64, 43), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 1404  125 ...    0    0    0]\n",
      " [   2  141   37 ...    0    0    0]\n",
      " [   2 3532 3267 ...    0    0    0]\n",
      " ...\n",
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      " [   2 2522   52 ...    0    0    0]\n",
      " [   2 3145   40 ...    0    0    0]], shape=(64, 60), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6  131 ...    0    0    0]\n",
      " [   7    6 1621 ...    0    0    0]\n",
      " [   7    6   25 ...    0    0    0]\n",
      " ...\n",
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      " [   7    6  236 ...    0    0    8]\n",
      " [   7    6   11 ...    0    0    0]], shape=(64, 41), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 1429   37 ...    0    0    0]\n",
      " [   2  389 5660 ...    0    0    0]\n",
      " [   2  389 5660 ...    0    0    0]\n",
      " ...\n",
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      " [   2  141   71 ...    0    0    0]\n",
      " [   2  105 4325 ...    0    0    0]], shape=(64, 51), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6 582 ...   0   0   0]\n",
      " [  7   6  40 ...   0   0   0]\n",
      " [  7   6  40 ...   0   0   0]\n",
      " ...\n",
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      " [  7   6  18 ...   0   0   0]], shape=(64, 45), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  450 3827 ...    0    0    0]\n",
      " [   2  141   71 ...    0    0    0]\n",
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      " ...\n",
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      " [   2 1512  477 ...    0    0    0]\n",
      " [   2 1512  477 ...    0    0    0]], shape=(64, 47), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6  18 ...   0   0   0]\n",
      " [  7   6 341 ...   0   0   0]\n",
      " [  7   6  26 ...   0   0   0]\n",
      " ...\n",
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      " [  7   6  27 ...   0   0   0]\n",
      " [  7   6  27 ...   0   0   0]], shape=(64, 55), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 3455  269 ...    0    0    0]\n",
      " [   2 3609   37 ...    0    0    0]\n",
      " [   2 3609   37 ...    0    0    0]\n",
      " ...\n",
      " [   2  141   37 ...    0    0    0]\n",
      " [   2  389 1261 ...    0    0    0]\n",
      " [   2   48 5165 ...    0    0    0]], shape=(64, 57), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6  48 ...   0   0   0]\n",
      " [  7   6 245 ...   0   0   0]\n",
      " [  7   6  39 ...   0   0   0]\n",
      " ...\n",
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      " [  7   6 248 ...   0   0   0]], shape=(64, 33), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 4204   42 ...    0    0    0]\n",
      " [   2 4204   42 ...    0    0    0]\n",
      " [   2 4204   42 ...    0    0    0]\n",
      " ...\n",
      " [   2 3609 1255 ...    0    0    0]\n",
      " [   2 3609   38 ...    0    0    0]\n",
      " [   2 3609   38 ...    0    0    0]], shape=(64, 53), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6   4 ...   0   0   0]\n",
      " [  7   6 133 ...   0   0   0]\n",
      " [  7   6 406 ...   0   0   0]\n",
      " ...\n",
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      " [  7   6  45 ...   0   0   0]], shape=(64, 38), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  415   37 ...    0    0    0]\n",
      " [   2  415   71 ...    0    0    0]\n",
      " [   2 3615   71 ...    0    0    0]\n",
      " ...\n",
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      "tf.Tensor(\n",
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      " [   7    6   82 ...    0    0    0]\n",
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      " ...\n",
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      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 4036  328 ...    0    0    0]\n",
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      " ...\n",
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      "tf.Tensor(\n",
      "[[  7   6 733 ...   0   0   0]\n",
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      " [  7   6  20 ...   0   0   0]\n",
      " ...\n",
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      " [  7   6  18 ...   0   0   0]], shape=(64, 33), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 3532   37 ...    0    0    0]\n",
      " [   2 1512  107 ...    0    0    0]\n",
      " [   2 1512  107 ...    0    0    0]\n",
      " ...\n",
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      " [   2 2356   71 ...    0    0    0]], shape=(64, 95), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6   26 ...    0    0    0]\n",
      " [   7    6  127 ...    0    0    0]\n",
      " [   7    6  127 ...    0    0    0]\n",
      " ...\n",
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      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 3609 1444 ...    0    0    0]\n",
      " [   2 3609 1444 ...    0    0    0]\n",
      " [   2   49 1224 ...    0    0    0]\n",
      " ...\n",
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      " [   2 3532   37 ...    0    0    0]\n",
      " [   2  329   37 ...    0    0    0]], shape=(64, 41), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6  42 ...   0   0   0]\n",
      " [  7   6  62 ...   0   0   0]\n",
      " [  7   6 140 ...   0   0   0]\n",
      " ...\n",
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      " [  7   6  22 ...   0   0   0]], shape=(64, 35), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2   45  504 ...    0    0    0]\n",
      " [   2   45  504 ...    0    0    0]\n",
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      " ...\n",
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      "tf.Tensor(\n",
      "[[  7   6 560 ...   0   0   0]\n",
      " [  7   6 560 ...   0   0   0]\n",
      " [  7   6  20 ...   0   0   0]\n",
      " ...\n",
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      " [  7   6  11 ...   0   0   8]], shape=(64, 40), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 2159 5962 ...    0    0    0]\n",
      " [   2 3532 3267 ...    0    0    0]\n",
      " [   2   77   40 ...    0    0    0]\n",
      " ...\n",
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      " [   2 2159 5962 ...    0    0    0]\n",
      " [   2 2159 5962 ...    0    0    0]], shape=(64, 49), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6   27 ...    0    0    0]\n",
      " [   7    6  677 ...    0    0    0]\n",
      " [   7    6 1796 ...    0    0    0]\n",
      " ...\n",
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      " [   7    6   14 ...    0    0    0]], shape=(64, 32), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  141   37 ...    0    0    0]\n",
      " [   2 3473  101 ...    0    0    0]\n",
      " [   2  141 1673 ...    0    0    0]\n",
      " ...\n",
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      " [   2    3    0 ...    0    0    0]\n",
      " [   2   41 2566 ...    0    0    0]], shape=(64, 56), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6  49 ...   0   0   0]\n",
      " [  7   6 528 ...   0   0   0]\n",
      " [  7   6  39 ...   0   0   0]\n",
      " ...\n",
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      " [  7   6  22 ...   0   0   0]\n",
      " [  7   6 341 ...   0   0   0]], shape=(64, 38), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  726  842 ...    0    0    0]\n",
      " [   2 3145 2174 ...    0    0    0]\n",
      " [   2  228   71 ...    0    0    0]\n",
      " ...\n",
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      " [   2 3145  101 ...    0    0    0]\n",
      " [   2 3609 5660 ...    0    0    0]], shape=(64, 55), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6   43 ...    0    0    0]\n",
      " [   7    6   40 ...    0    0    0]\n",
      " [   7    6  137 ...    0    0    0]\n",
      " ...\n",
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      " [   7    6 1890 ...    0    0    0]], shape=(64, 39), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 3455  269 ...    0    0    0]\n",
      " [   2   77  967 ...    0    0    0]\n",
      " [   2 2159 5962 ...    0    0    0]\n",
      " ...\n",
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      "tf.Tensor(\n",
      "[[   7    6   99 ...    0    0    0]\n",
      " [   7    6 2840 ...    0    0    0]\n",
      " [   7    6   20 ...    0    0    0]\n",
      " ...\n",
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      " [   7    6  149 ...    0    0    0]], shape=(64, 31), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2   85  246 ...    0    0    0]\n",
      " [   2  141   94 ...    0    0    0]\n",
      " [   2 1512  107 ...    0    0    0]\n",
      " ...\n",
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      " [   2 3147  389 ...    0    0    0]\n",
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      "tf.Tensor(\n",
      "[[ 7  6 11 ...  0  0  0]\n",
      " [ 7  6 33 ...  0  0  0]\n",
      " [ 7  6 84 ...  0  0  0]\n",
      " ...\n",
      " [ 7  6 40 ...  0  0  0]\n",
      " [ 7  6 40 ...  0  0  0]\n",
      " [ 7  6 40 ...  0  0  0]], shape=(64, 48), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 1512  107 ...    0    0    0]\n",
      " [   2 2682 3267 ...    0    0    0]\n",
      " [   2  864 4141 ...    0    0    0]\n",
      " ...\n",
      " [   2 3609   37 ...    0    0    0]\n",
      " [   2  105  899 ...    3    0    0]\n",
      " [   2  306 2502 ...    0    0    0]], shape=(64, 55), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6   84 ...    0    0    0]\n",
      " [   7    6 1142 ...    0    0    0]\n",
      " [   7    6   43 ...    0    0    0]\n",
      " ...\n",
      " [   7    6 1170 ...    0    0    0]\n",
      " [   7    6   18 ...    0    0    0]\n",
      " [   7    6  519 ...    0    0    0]], shape=(64, 60), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 3532 1261 ...    0    0    0]\n",
      " [   2 3145  101 ...    0    0    0]\n",
      " [   2   55 4103 ...    0    0    0]\n",
      " ...\n",
      " [   2 6363 5540 ...    0    0    0]\n",
      " [   2 2679 1261 ...    0    0    0]\n",
      " [   2    3    0 ...    0    0    0]], shape=(64, 59), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6  11 ...   0   0   0]\n",
      " [  7   6  86 ...   0   0   0]\n",
      " [  7   6  27 ...   0   0   0]\n",
      " ...\n",
      " [  7   6 184 ...   0   0   0]\n",
      " [  7   6  27 ...   0   0   0]\n",
      " [  7   6  18 ...   0   0   0]], shape=(64, 37), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 6304 2609 ...    0    0    0]\n",
      " [   2 6304 2609 ...    0    0    0]\n",
      " [   2 6304 2609 ...    0    0    0]\n",
      " ...\n",
      " [   2  389 1261 ...    0    0    0]\n",
      " [   2  389   37 ...    0    0    0]\n",
      " [   2  389   37 ...    0    0    0]], shape=(64, 46), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6  27 ...   0   0   0]\n",
      " [  7   6  18 ...   0   0   0]\n",
      " [  7   6  18 ...   0   0   0]\n",
      " ...\n",
      " [  7   6 157 ...   0   0   0]\n",
      " [  7   6 157 ...   0   0   0]\n",
      " [  7   6  25 ...   0   0   0]], shape=(64, 41), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  306   36 ...    0    0    0]\n",
      " [   2  306   36 ...    0    0    0]\n",
      " [   2  141   37 ...    0    0    0]\n",
      " ...\n",
      " [   2  141 2609 ...    0    0    0]\n",
      " [   2  141   71 ...    0    0    0]\n",
      " [   2 3532 6363 ...    0    0    0]], shape=(64, 52), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6  263 ...    0    0    0]\n",
      " [   7    6  263 ...    0    0    0]\n",
      " [   7    6 1765 ...    0    0    0]\n",
      " ...\n",
      " [   7    6   18 ...    0    0    0]\n",
      " [   7    6   11 ...    0    0    0]\n",
      " [   7    6 2276 ...    0    0    0]], shape=(64, 39), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 3609 2021 ...    0    0    0]\n",
      " [   2    3    0 ...    0    0    0]\n",
      " [   2 3145   76 ...    0    0    0]\n",
      " ...\n",
      " [   2  329   37 ...    0    0    0]\n",
      " [   2  228   71 ...    0    0    0]\n",
      " [   2  228 2438 ...    0    0    0]], shape=(64, 58), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6  39 ...   0   0   0]\n",
      " [  7   6  39 ...   0   0   0]\n",
      " [  7   6  20 ...   0   0   0]\n",
      " ...\n",
      " [  7   6  27 ...   0   0   0]\n",
      " [  7   6 222 ...   0   0   0]\n",
      " [  7   6 138 ...   0   0   0]], shape=(64, 40), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 3609   38 ...    0    0    0]\n",
      " [   2 3609 2887 ...    0    0    0]\n",
      " [   2 3609   38 ...    0    0    0]\n",
      " ...\n",
      " [   2  141 1116 ...    0    0    0]\n",
      " [   2 3609 4465 ...    0    0    0]\n",
      " [   2  141  246 ...    0    0    0]], shape=(64, 51), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6  45 ...   0   0   0]\n",
      " [  7   6 255 ...   0   0   0]\n",
      " [  7   6  45 ...   0   0   0]\n",
      " ...\n",
      " [  7   6 241 ...   0   0   0]\n",
      " [  7   6  11 ...   0   0   0]\n",
      " [  7   6  27 ...   0   0   0]], shape=(64, 31), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2   55  227 ...    0    0    0]\n",
      " [   2  141 5288 ...    0    0    0]\n",
      " [   2  141 1565 ...    0    0    0]\n",
      " ...\n",
      " [   2  427   40 ...    0    0    0]\n",
      " [   2 1512  107 ...    0    0    0]\n",
      " [   2  974   37 ...    0    0    0]], shape=(64, 62), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6  151 ...    0    0    0]\n",
      " [   7    6 1637 ...    0    0    0]\n",
      " [   7    6  125 ...    0    0    0]\n",
      " ...\n",
      " [   7    6   40 ...    0    0    0]\n",
      " [   7    6  127 ...    0    0    0]\n",
      " [   7    6   18 ...    0    0    0]], shape=(64, 42), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  328   52 ...    0    0    0]\n",
      " [   2  141   71 ...    0    0    0]\n",
      " [   2 3145 5866 ...    0    0    0]\n",
      " ...\n",
      " [   2    3    0 ...    0    0    0]\n",
      " [   2 3609 1261 ...    0    0    0]\n",
      " [   2 2028  105 ...    0    0    0]], shape=(64, 51), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6   4 ...   0   0   0]\n",
      " [  7   6 622 ...   0   0   0]\n",
      " [  7   6  22 ...   0   0   0]\n",
      " ...\n",
      " [  7   6  11 ...   0   0   0]\n",
      " [  7   6  27 ...   0   0   0]\n",
      " [  7   6  22 ...   0   0   0]], shape=(64, 52), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  228   71 ...    0    0    0]\n",
      " [   2  228   71 ...    0    0    0]\n",
      " [   2  329 1261 ...    0    0    0]\n",
      " ...\n",
      " [   2   41  269 ...    0    0    0]\n",
      " [   2   77  756 ...    0    0    0]\n",
      " [   2  114 4879 ...    0    0    0]], shape=(64, 62), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6  138 ...    0    0    0]\n",
      " [   7    6  138 ...    0    0    0]\n",
      " [   7    6   80 ...    0    0    0]\n",
      " ...\n",
      " [   7    6 1033 ...    0    0    0]\n",
      " [   7    6   11 ...    0    0    0]\n",
      " [   7    6   11 ...    0    0    0]], shape=(64, 36), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  260   37 ...    0    0    0]\n",
      " [   2  260   37 ...    0    0    0]\n",
      " [   2  141 5728 ...    0    0    0]\n",
      " ...\n",
      " [   2 2679   42 ...    0    0    0]\n",
      " [   2 2679   42 ...    0    0    0]\n",
      " [   2 4204   42 ...    0    0    0]], shape=(64, 45), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6 701 ...   0   0   0]\n",
      " [  7   6 701 ...   0   0   0]\n",
      " [  7   6  20 ...   0   0   0]\n",
      " ...\n",
      " [  7   6  27 ...   0   0   0]\n",
      " [  7   6  27 ...   0   0   0]\n",
      " [  7   6   4 ...   0   0   0]], shape=(64, 55), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 1512  107 ...    0    0    0]\n",
      " [   2 1512  107 ...    0    0    0]\n",
      " [   2 1512  107 ...    0    0    0]\n",
      " ...\n",
      " [   2  223 1673 ...    0    0    0]\n",
      " [   2  223 1673 ...    0    0    0]\n",
      " [   2  223 1673 ...    0    0    0]], shape=(64, 44), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[ 7  6  4 ...  0  0  0]\n",
      " [ 7  6  4 ...  0  8  0]\n",
      " [ 7  6  4 ...  0  0  0]\n",
      " ...\n",
      " [ 7  6 33 ...  0  0  0]\n",
      " [ 7  6 33 ...  0  0  0]\n",
      " [ 7  6 33 ...  0  0  0]], shape=(64, 34), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 2356   74  101 1694   77   37   52  240  587  485   40  483  271\n",
      "    36   40   36    3    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0]\n",
      " [   2    3    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0]\n",
      " [   2 3609   52  240  587  485 5660   36  135   35  287   36   73   46\n",
      "  2010 4141    3    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0]\n",
      " [   2  100 5555  111 4141   74   42 2425 4282   77 1221  109   17   73\n",
      "   551   73   37 5465   36 1694   36    3    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0]\n",
      " [   2  389   37 1261   74   40 4848 2966   36 1261   35   74   35   40\n",
      "  1413   36  118 1694   77  111  538   36  538   36    3    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0]\n",
      " [   2  223   71  385   74   71  168 1405   77 3267   36   55   36    3\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0]\n",
      " [   2 1865 1261   74  663   36   32   18   13   19   13   20   13   21\n",
      "    33   36   73 3267   74  663   15    3    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0]\n",
      " [   2 2159 5962  198 1187   36   57  271 1192   14   25   36 2017  109\n",
      "    37 3267   36  599   36    3    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0]\n",
      " [   2 6696 5939 2432   37 3456  950  178   36 3456  950  178   36   77\n",
      "    71  906 2562  109   37   52  240  587  485 4246    3    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0]\n",
      " [   2   99   88 5992  392 6696 5939 2432   53 1549  610    9 6304    1\n",
      "   109   42 3178  151 3438   29    7   55   72   38  303 1061   29    7\n",
      "    55    9   93   52  899 2199  743  343   36   52  899 2199 1205   36\n",
      "    72   36   52  899 2199 1222   36    3]\n",
      " [   2   52 6388 3267   36 4658  271  258   36  156   37 2021 2243  963\n",
      "   328 6716 1318    9    7   61   14    7   49   14    7   40    9    3\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0]], shape=(11, 50), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6   48    4   28    5   72    4    3    3    0    0    8    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0]\n",
      " [   7    6   48    4   28    5   72    4    3    3    0    0    8    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0]\n",
      " [   7    6  151    5  280    4 1044    9 1541   12   97    3    0    0\n",
      "     8    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0]\n",
      " [   7    6   72   12   43    5  116    4   11   21   15   21   13   56\n",
      "     4  180    3   10    9  281   12   43    5  812    3    0    0    8\n",
      "     0    0    0]\n",
      " [   7    6   40    4 1392    9   24   12   31   14   16  329    5   74\n",
      "     4   25    4   14    5   72    4    3    3   11   19   10    3    3\n",
      "     0    0    8]\n",
      " [   7    6  115   26   11   19   10    5  238    4    3   50   26   11\n",
      "    17   16   10    0    0    8    0    0    0    0    0    0    0    0\n",
      "     0    0    0]\n",
      " [   7    6   99    5   44    4   11   17    9   23    9   34    9   60\n",
      "    10    3    0    0    8    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0]\n",
      " [   7    6   86   12   86    5   83    4  130    9  346    3    5   88\n",
      "     4  130    3    0    0    8    0    0    0    0    0    0    0    0\n",
      "     0    0    0]\n",
      " [   7    6   33    5  112    4 1641    3    0    0    8    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0]\n",
      " [   7    6   80    5  227    4 2405    9    4 1280    9 1279    3    3\n",
      "     0    0    8    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0]\n",
      " [   7    6 1600   12   39    5   39    5  113    4 1595    9  294    3\n",
      "     5  122    4    3    0    0    8    0    0    0    0    0    0    0\n",
      "     0    0    0]], shape=(11, 31), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  114 4879 ...    0    0    0]\n",
      " [   2  141   71 ...    0    0    0]\n",
      " [   2   49 1224 ...    0    0    0]\n",
      " ...\n",
      " [   2    3    0 ...    0    0    0]\n",
      " [   2 4395 1673 ...    0    0    0]\n",
      " [   2  228   71 ...    0    0    0]], shape=(64, 57), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6   64 ...    0    0    0]\n",
      " [   7    6   43 ...    0    0    0]\n",
      " [   7    6  727 ...    0    0    0]\n",
      " ...\n",
      " [   7    6   40 ...    0    0    0]\n",
      " [   7    6  166 ...    0    0    0]\n",
      " [   7    6 2819 ...    0    0    0]], shape=(64, 53), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2  389   53 ...    0    0    0]\n",
      " [   2  141   37 ...    0    0    0]\n",
      " [   2  141   71 ...    0    0    0]\n",
      " ...\n",
      " [   2  389 4141 ...    0    0    0]\n",
      " [   2  389 2609 ...    0    0    0]\n",
      " [   2 3532 3267 ...    0    0    0]], shape=(64, 61), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[  7   6 403 ...   0   0   0]\n",
      " [  7   6 222 ...   0   0   0]\n",
      " [  7   6 224 ...   0   0   0]\n",
      " ...\n",
      " [  7   6 190 ...   0   0   0]\n",
      " [  7   6 100 ...   0   0   0]\n",
      " [  7   6  22 ...   0   0   0]], shape=(64, 49), dtype=int64)\n",
      "\n",
      "\n",
      "tf.Tensor(\n",
      "[[   2 1512  477 ...    0    0    0]\n",
      " [   2 3532 3267 ...    0    0    0]\n",
      " [   2  141   37 ...    0    0    0]\n",
      " ...\n",
      " [   2 2682   37 ...    0    0    0]\n",
      " [   2 5660   36 ...    0    0    0]\n",
      " [   2    3    0 ...    0    0    0]], shape=(64, 64), dtype=int64)\n",
      "tf.Tensor(\n",
      "[[   7    6   27 ...    0    0    0]\n",
      " [   7    6 2822 ...    0    0    0]\n",
      " [   7    6   25 ...    0    0    0]\n",
      " ...\n",
      " [   7    6 2832 ...    0    0    0]\n",
      " [   7    6 2833 ...    0    0    0]\n",
      " [   7    6  176 ...    0    0    0]], shape=(64, 61), dtype=int64)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def make_batches(intents, snippets):\n",
    "    intents = tf.data.Dataset.from_tensor_slices(intents)\n",
    "    batched_intents = intents.batch(BATCH_SIZE).map(tokenize_intent)\n",
    "    # preprocessor = hub.load(\"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\")\n",
    "    # tokenize_intent = hub.KerasLayer(preprocessor.tokenize)\n",
    "    \n",
    "    snippets = tf.data.Dataset.from_tensor_slices(snippets)\n",
    "    snippets = snippets.map(lambda x : tf.py_function(func=tokenize_code, inp=[x], Tout=tf.string))\n",
    "    vectorize_layer = keras.layers.experimental.preprocessing.StringLookup(max_tokens=CODE_TOKENS)\n",
    "    vectorize_layer.adapt(snippets)\n",
    "    inverse_vectorize = keras.layers.experimental.preprocessing.StringLookup(vocabulary=vectorize_layer.get_vocabulary(), invert=True)\n",
    "    \n",
    "    snippets = snippets.map(lambda x : vectorize_layer(x))\n",
    "    batched_snippets = snippets.padded_batch(BATCH_SIZE, padded_shapes=(None,))\n",
    "\n",
    "    batched_dataset = tf.data.Dataset.zip((batched_intents, batched_snippets))\n",
    "    batched_dataset = batched_dataset.cache().shuffle(BUFFER_SIZE).prefetch(tf.data.AUTOTUNE)\n",
    "    \n",
    "    return batched_dataset, vectorize_layer, inverse_vectorize\n",
    "\n",
    "\n",
    "train_batches, vectorize, inverse_vectorize = make_batches(train_intents, train_snippets)\n",
    "# val_batches = make_batches(val_intents, val_snippets) Not used anywhere in the notebook?\n",
    "\n",
    "for intent, code in train_batches:\n",
    "    print(intent)\n",
    "    print(code)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nBQuibYA4n0n"
   },
   "source": [
    "## Positional encoding\n",
    "\n",
    "Since this model doesn't contain any recurrence or convolution, positional encoding is added to give the model some information about the relative position of the words 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 words in a sentence. So after adding the positional encoding, words 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",
    "See the notebook on [positional encoding](https://github.com/tensorflow/examples/blob/master/community/en/position_encoding.ipynb) to learn more about it. 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": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:17.843536Z",
     "iopub.status.busy": "2021-02-04T02:22:17.842742Z",
     "iopub.status.idle": "2021-02-04T02:22:17.845150Z",
     "shell.execute_reply": "2021-02-04T02:22:17.844601Z"
    },
    "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": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:17.851810Z",
     "iopub.status.busy": "2021-02-04T02:22:17.851073Z",
     "iopub.status.idle": "2021-02-04T02:22:17.853273Z",
     "shell.execute_reply": "2021-02-04T02:22:17.852748Z"
    },
    "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": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:17.859008Z",
     "iopub.status.busy": "2021-02-04T02:22:17.858322Z",
     "iopub.status.idle": "2021-02-04T02:22:18.642792Z",
     "shell.execute_reply": "2021-02-04T02:22:18.643378Z"
    },
    "id": "1kLCla68EloE"
   },
   "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": {
      "needs_background": "light"
     },
     "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": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:18.653836Z",
     "iopub.status.busy": "2021-02-04T02:22:18.653089Z",
     "iopub.status.idle": "2021-02-04T02:22:18.655282Z",
     "shell.execute_reply": "2021-02-04T02:22:18.655694Z"
    },
    "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": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:18.660735Z",
     "iopub.status.busy": "2021-02-04T02:22:18.660038Z",
     "iopub.status.idle": "2021-02-04T02:22:18.665917Z",
     "shell.execute_reply": "2021-02-04T02:22:18.665427Z"
    },
    "id": "A7BYeBCNvi7n"
   },
   "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": 23,
     "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 word, only the first and second word will be used. Similarly to predict the fourth word, only the first, second and the third word will be used and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:18.670548Z",
     "iopub.status.busy": "2021-02-04T02:22:18.669885Z",
     "iopub.status.idle": "2021-02-04T02:22:18.672332Z",
     "shell.execute_reply": "2021-02-04T02:22:18.671687Z"
    },
    "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": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:18.676820Z",
     "iopub.status.busy": "2021-02-04T02:22:18.676147Z",
     "iopub.status.idle": "2021-02-04T02:22:18.681548Z",
     "shell.execute_reply": "2021-02-04T02:22:18.681967Z"
    },
    "id": "yxKGuXxaBeeE"
   },
   "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": 25,
     "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": "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 the transformer takes three inputs: Q (query), K (key), V (value). The equation used to calculate the attention weights is:\n",
    "\n",
    "$$\\Large{Attention(Q, K, V) = softmax_k(\\frac{QK^T}{\\sqrt{d_k}}) V} $$\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 initilization making it dificult 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": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:18.689122Z",
     "iopub.status.busy": "2021-02-04T02:22:18.688450Z",
     "iopub.status.idle": "2021-02-04T02:22:18.690870Z",
     "shell.execute_reply": "2021-02-04T02:22:18.690297Z"
    },
    "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 on K, its values decide the amount of importance given to Q.\n",
    "\n",
    "The output represents the multiplication of the attention weights and the V (value) vector. This ensures that the words you want to focus on are kept as-is and the irrelevant words are flushed out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:18.695523Z",
     "iopub.status.busy": "2021-02-04T02:22:18.694825Z",
     "iopub.status.idle": "2021-02-04T02:22:18.697143Z",
     "shell.execute_reply": "2021-02-04T02:22:18.696653Z"
    },
    "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": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:18.703207Z",
     "iopub.status.busy": "2021-02-04T02:22:18.702379Z",
     "iopub.status.idle": "2021-02-04T02:22:19.124920Z",
     "shell.execute_reply": "2021-02-04T02:22:19.124356Z"
    },
    "id": "yAzUAf2DPlNt"
   },
   "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": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.129766Z",
     "iopub.status.busy": "2021-02-04T02:22:19.128988Z",
     "iopub.status.idle": "2021-02-04T02:22:19.134437Z",
     "shell.execute_reply": "2021-02-04T02:22:19.133928Z"
    },
    "id": "zg6k-fGhgXra"
   },
   "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": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.139424Z",
     "iopub.status.busy": "2021-02-04T02:22:19.138614Z",
     "iopub.status.idle": "2021-02-04T02:22:19.143582Z",
     "shell.execute_reply": "2021-02-04T02:22:19.144021Z"
    },
    "id": "UAq3YOzUgXhb"
   },
   "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": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.149102Z",
     "iopub.status.busy": "2021-02-04T02:22:19.148370Z",
     "iopub.status.idle": "2021-02-04T02:22:19.153775Z",
     "shell.execute_reply": "2021-02-04T02:22:19.153259Z"
    },
    "id": "6dlU8Tm-hYrF"
   },
   "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], [0, 10, 0], [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 and split into heads.\n",
    "*    Scaled dot-product attention.\n",
    "*    Concatenation of heads.\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 and split up into multiple heads. \n",
    "\n",
    "The `scaled_dot_product_attention` defined above is applied to each head (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 at different positions from different representational spaces. 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": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.165473Z",
     "iopub.status.busy": "2021-02-04T02:22:19.164702Z",
     "iopub.status.idle": "2021-02-04T02:22:19.166558Z",
     "shell.execute_reply": "2021-02-04T02:22:19.166997Z"
    },
    "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": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.187962Z",
     "iopub.status.busy": "2021-02-04T02:22:19.171406Z",
     "iopub.status.idle": "2021-02-04T02:22:19.211245Z",
     "shell.execute_reply": "2021-02-04T02:22:19.210618Z"
    },
    "id": "Hu94p-_-2_BX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([1, 60, 512]), TensorShape([1, 8, 60, 60]))"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.216866Z",
     "iopub.status.busy": "2021-02-04T02:22:19.216070Z",
     "iopub.status.idle": "2021-02-04T02:22:19.218685Z",
     "shell.execute_reply": "2021-02-04T02:22:19.218173Z"
    },
    "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": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.227726Z",
     "iopub.status.busy": "2021-02-04T02:22:19.226887Z",
     "iopub.status.idle": "2021-02-04T02:22:19.257532Z",
     "shell.execute_reply": "2021-02-04T02:22:19.256795Z"
    },
    "id": "mytb1lPyOHLB"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([64, 50, 512])"
      ]
     },
     "execution_count": 35,
     "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://www.tensorflow.org/images/tutorials/transformer/transformer.png\" width=\"600\" alt=\"transformer\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MfYJG-Kvgwy2"
   },
   "source": [
    "The transformer model follows the same general pattern as a standard [sequence to sequence with attention model](nmt_with_attention.ipynb). \n",
    "\n",
    "* The input sentence is passed through `N` encoder layers that generates an output for each word/token in the sequence.\n",
    "* The decoder attends on 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 the transformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.266608Z",
     "iopub.status.busy": "2021-02-04T02:22:19.265758Z",
     "iopub.status.idle": "2021-02-04T02:22:19.268529Z",
     "shell.execute_reply": "2021-02-04T02:22:19.268009Z"
    },
    "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, 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": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.283456Z",
     "iopub.status.busy": "2021-02-04T02:22:19.282549Z",
     "iopub.status.idle": "2021-02-04T02:22:19.342241Z",
     "shell.execute_reply": "2021-02-04T02:22:19.341734Z"
    },
    "id": "AzZRXdO0mI48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([64, 43, 512])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_encoder_layer = EncoderLayer(512, 8, 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 N decoder layers in the transformer.\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 word 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": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.352911Z",
     "iopub.status.busy": "2021-02-04T02:22:19.352110Z",
     "iopub.status.idle": "2021-02-04T02:22:19.354443Z",
     "shell.execute_reply": "2021-02-04T02:22:19.353902Z"
    },
    "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, num_heads)\n",
    "    self.mha2 = MultiHeadAttention(d_model, 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",
    "    \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": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.368532Z",
     "iopub.status.busy": "2021-02-04T02:22:19.367849Z",
     "iopub.status.idle": "2021-02-04T02:22:19.423592Z",
     "shell.execute_reply": "2021-02-04T02:22:19.423993Z"
    },
    "id": "Ne2Bqx8k71l0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([64, 50, 512])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_decoder_layer = DecoderLayer(512, 8, 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": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.433437Z",
     "iopub.status.busy": "2021-02-04T02:22:19.432699Z",
     "iopub.status.idle": "2021-02-04T02:22:19.435480Z",
     "shell.execute_reply": "2021-02-04T02:22:19.434814Z"
    },
    "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",
    "               maximum_position_encoding, 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(maximum_position_encoding, \n",
    "                                            self.d_model)\n",
    "    \n",
    "    \n",
    "    self.enc_layers = [EncoderLayer(d_model, num_heads, dff, 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": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.448225Z",
     "iopub.status.busy": "2021-02-04T02:22:19.447426Z",
     "iopub.status.idle": "2021-02-04T02:22:19.763723Z",
     "shell.execute_reply": "2021-02-04T02:22:19.764192Z"
    },
    "id": "8QG9nueFQKXx"
   },
   "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",
    "                         maximum_position_encoding=10000)\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": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.775749Z",
     "iopub.status.busy": "2021-02-04T02:22:19.774605Z",
     "iopub.status.idle": "2021-02-04T02:22:19.777023Z",
     "shell.execute_reply": "2021-02-04T02:22:19.776457Z"
    },
    "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",
    "               maximum_position_encoding, 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(maximum_position_encoding, d_model)\n",
    "    \n",
    "    self.dec_layers = [DecoderLayer(d_model, num_heads, dff, 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['decoder_layer{}_block1'.format(i+1)] = block1\n",
    "      attention_weights['decoder_layer{}_block2'.format(i+1)] = block2\n",
    "    \n",
    "    # x.shape == (batch_size, target_seq_len, d_model)\n",
    "    return x, attention_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:19.784017Z",
     "iopub.status.busy": "2021-02-04T02:22:19.783324Z",
     "iopub.status.idle": "2021-02-04T02:22:20.026095Z",
     "shell.execute_reply": "2021-02-04T02:22:20.026543Z"
    },
    "id": "a1jXoAMRZyvu"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([64, 26, 512]), TensorShape([64, 8, 26, 62]))"
      ]
     },
     "execution_count": 43,
     "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",
    "                         maximum_position_encoding=5000)\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uERO1y54cOKq"
   },
   "source": [
    "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": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.034857Z",
     "iopub.status.busy": "2021-02-04T02:22:20.034176Z",
     "iopub.status.idle": "2021-02-04T02:22:20.035988Z",
     "shell.execute_reply": "2021-02-04T02:22:20.036396Z"
    },
    "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, pe_input, pe_target, rate=0.1):\n",
    "    super(Transformer, self).__init__()\n",
    "\n",
    "    self.tokenizer = Encoder(num_layers, d_model, num_heads, dff, \n",
    "                           input_vocab_size, pe_input, rate)\n",
    "\n",
    "    self.decoder = Decoder(num_layers, d_model, num_heads, dff, \n",
    "                           target_vocab_size, pe_target, rate)\n",
    "\n",
    "    self.final_layer = tf.keras.layers.Dense(target_vocab_size)\n",
    "    \n",
    "  def call(self, inp, tar, training, enc_padding_mask, \n",
    "           look_ahead_mask, dec_padding_mask):\n",
    "\n",
    "    enc_output = self.tokenizer(inp, training, enc_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, dec_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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.044779Z",
     "iopub.status.busy": "2021-02-04T02:22:20.044011Z",
     "iopub.status.idle": "2021-02-04T02:22:20.611954Z",
     "shell.execute_reply": "2021-02-04T02:22:20.612383Z"
    },
    "id": "tJ4fbQcIkHW1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([64, 36, 8000])"
      ]
     },
     "execution_count": 45,
     "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",
    "    pe_input=10000, pe_target=6000)\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",
    "                               enc_padding_mask=None, \n",
    "                               look_ahead_mask=None,\n",
    "                               dec_padding_mask=None)\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, and dff* have been reduced. \n",
    "\n",
    "The values used in the base model of transformer were; *num_layers=6*, *d_model = 512*, *dff = 2048*. See the [paper](https://arxiv.org/abs/1706.03762) for all the other versions of the transformer.\n",
    "\n",
    "Note: By changing the values below, you can get the model that achieved state of the art on many tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.617097Z",
     "iopub.status.busy": "2021-02-04T02:22:20.616437Z",
     "iopub.status.idle": "2021-02-04T02:22:20.618816Z",
     "shell.execute_reply": "2021-02-04T02:22:20.618227Z"
    },
    "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",
    "$$\\Large{lrate = d_{model}^{-0.5} * min(step{\\_}num^{-0.5}, step{\\_}num * warmup{\\_}steps^{-1.5})}$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.625189Z",
     "iopub.status.busy": "2021-02-04T02:22:20.624533Z",
     "iopub.status.idle": "2021-02-04T02:22:20.626877Z",
     "shell.execute_reply": "2021-02-04T02:22:20.626402Z"
    },
    "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": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.631126Z",
     "iopub.status.busy": "2021-02-04T02:22:20.630404Z",
     "iopub.status.idle": "2021-02-04T02:22:20.632992Z",
     "shell.execute_reply": "2021-02-04T02:22:20.633417Z"
    },
    "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": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.638792Z",
     "iopub.status.busy": "2021-02-04T02:22:20.638090Z",
     "iopub.status.idle": "2021-02-04T02:22:20.809074Z",
     "shell.execute_reply": "2021-02-04T02:22:20.808459Z"
    },
    "id": "f33ZCgvHpPdG"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Train Step')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.814239Z",
     "iopub.status.busy": "2021-02-04T02:22:20.813568Z",
     "iopub.status.idle": "2021-02-04T02:22:20.815862Z",
     "shell.execute_reply": "2021-02-04T02:22:20.815258Z"
    },
    "id": "MlhsJMm0TW_B"
   },
   "outputs": [],
   "source": [
    "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n",
    "    from_logits=True, reduction='none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.822691Z",
     "iopub.status.busy": "2021-02-04T02:22:20.822040Z",
     "iopub.status.idle": "2021-02-04T02:22:20.823913Z",
     "shell.execute_reply": "2021-02-04T02:22:20.824316Z"
    },
    "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": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.835541Z",
     "iopub.status.busy": "2021-02-04T02:22:20.834847Z",
     "iopub.status.idle": "2021-02-04T02:22:20.844012Z",
     "shell.execute_reply": "2021-02-04T02:22:20.843498Z"
    },
    "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": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.848653Z",
     "iopub.status.busy": "2021-02-04T02:22:20.848016Z",
     "iopub.status.idle": "2021-02-04T02:22:20.973356Z",
     "shell.execute_reply": "2021-02-04T02:22:20.973821Z"
    },
    "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(),\n",
    "    target_vocab_size=tokenizers.en.get_vocab_size(), \n",
    "    pe_input=1000, \n",
    "    pe_target=1000,\n",
    "    rate=dropout_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.979269Z",
     "iopub.status.busy": "2021-02-04T02:22:20.978563Z",
     "iopub.status.idle": "2021-02-04T02:22:20.980510Z",
     "shell.execute_reply": "2021-02-04T02:22:20.980907Z"
    },
    "id": "ZOJUSB1T8GjM"
   },
   "outputs": [],
   "source": [
    "def create_masks(inp, tar):\n",
    "  # Encoder padding mask\n",
    "  enc_padding_mask = create_padding_mask(inp)\n",
    "  \n",
    "  # Used in the 2nd attention block in the decoder.\n",
    "  # This padding mask is used to mask the encoder outputs.\n",
    "  dec_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",
    "  combined_mask = tf.maximum(dec_target_padding_mask, look_ahead_mask)\n",
    "  \n",
    "  return enc_padding_mask, combined_mask, dec_padding_mask"
   ]
  },
  {
   "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": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.986227Z",
     "iopub.status.busy": "2021-02-04T02:22:20.985579Z",
     "iopub.status.idle": "2021-02-04T02:22:20.987871Z",
     "shell.execute_reply": "2021-02-04T02:22:20.987371Z"
    },
    "id": "hNhuYfllndLZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latest checkpoint restored!!\n"
     ]
    }
   ],
   "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\"\n",
    "\n",
    "`tar_inp` =  \"SOS A lion in the jungle is sleeping\"\n",
    "\n",
    "`tar_real` = \"A lion in the jungle is sleeping EOS\"\n",
    "\n",
    "The 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](./text_generation.ipynb)). 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 transformer predicts each word, *self-attention* allows it to look at the previous words in the input sequence to better predict the next word.\n",
    "\n",
    "To prevent the model from peeking at the expected output the model uses a look-ahead mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:20.991763Z",
     "iopub.status.busy": "2021-02-04T02:22:20.991085Z",
     "iopub.status.idle": "2021-02-04T02:22:20.993341Z",
     "shell.execute_reply": "2021-02-04T02:22:20.992802Z"
    },
    "id": "LKpoA6q1sJFj"
   },
   "outputs": [],
   "source": [
    "EPOCHS = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:21.000627Z",
     "iopub.status.busy": "2021-02-04T02:22:20.999956Z",
     "iopub.status.idle": "2021-02-04T02:22:21.002295Z",
     "shell.execute_reply": "2021-02-04T02:22:21.001804Z"
    },
    "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",
    "@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",
    "  enc_padding_mask, combined_mask, dec_padding_mask = create_masks(inp, tar_inp)\n",
    "  \n",
    "  with tf.GradientTape() as tape:\n",
    "    predictions, _ = transformer(inp, tar_inp, \n",
    "                                 True, \n",
    "                                 enc_padding_mask, \n",
    "                                 combined_mask, \n",
    "                                 dec_padding_mask)\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": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:22:21.008607Z",
     "iopub.status.busy": "2021-02-04T02:22:21.007905Z",
     "iopub.status.idle": "2021-02-04T02:39:34.296150Z",
     "shell.execute_reply": "2021-02-04T02:39:34.295520Z"
    },
    "id": "bbvmaKNiznHZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 Batch 0 Loss 0.3398 Accuracy 0.9085\n",
      "Epoch 1 Batch 10 Loss 0.3136 Accuracy 0.9101\n",
      "Epoch 1 Batch 20 Loss 0.2986 Accuracy 0.9141\n",
      "Epoch 1 Batch 30 Loss 0.3088 Accuracy 0.9108\n",
      "Epoch 1 Loss 0.3156 Accuracy 0.9087\n",
      "Time taken for 1 epoch: 20.86 secs\n",
      "\n",
      "Epoch 2 Batch 0 Loss 0.2794 Accuracy 0.9188\n",
      "Epoch 2 Batch 10 Loss 0.3067 Accuracy 0.9103\n",
      "Epoch 2 Batch 20 Loss 0.3107 Accuracy 0.9097\n",
      "Epoch 2 Batch 30 Loss 0.3169 Accuracy 0.9076\n",
      "Epoch 2 Loss 0.3288 Accuracy 0.9034\n",
      "Time taken for 1 epoch: 9.01 secs\n",
      "\n",
      "Epoch 3 Batch 0 Loss 0.2837 Accuracy 0.9183\n",
      "Epoch 3 Batch 10 Loss 0.2979 Accuracy 0.9111\n",
      "Epoch 3 Batch 20 Loss 0.3052 Accuracy 0.9101\n",
      "Epoch 3 Batch 30 Loss 0.3133 Accuracy 0.9068\n",
      "Epoch 3 Loss 0.3197 Accuracy 0.9044\n",
      "Time taken for 1 epoch: 9.58 secs\n",
      "\n",
      "Epoch 4 Batch 0 Loss 0.2653 Accuracy 0.9281\n",
      "Epoch 4 Batch 10 Loss 0.2785 Accuracy 0.9175\n",
      "Epoch 4 Batch 20 Loss 0.2840 Accuracy 0.9159\n",
      "Epoch 4 Batch 30 Loss 0.2895 Accuracy 0.9142\n",
      "Epoch 4 Loss 0.3005 Accuracy 0.9106\n",
      "Time taken for 1 epoch: 9.13 secs\n",
      "\n",
      "Epoch 5 Batch 0 Loss 0.2665 Accuracy 0.9214\n",
      "Epoch 5 Batch 10 Loss 0.2664 Accuracy 0.9196\n",
      "Epoch 5 Batch 20 Loss 0.2836 Accuracy 0.9152\n",
      "Epoch 5 Batch 30 Loss 0.2846 Accuracy 0.9158\n",
      "Saving checkpoint for epoch 5 at ./checkpoints/train/ckpt-17\n",
      "Epoch 5 Loss 0.2860 Accuracy 0.9148\n",
      "Time taken for 1 epoch: 9.33 secs\n",
      "\n",
      "Epoch 6 Batch 0 Loss 0.2786 Accuracy 0.9163\n",
      "Epoch 6 Batch 10 Loss 0.2604 Accuracy 0.9228\n",
      "Epoch 6 Batch 20 Loss 0.2838 Accuracy 0.9149\n",
      "Epoch 6 Batch 30 Loss 0.2789 Accuracy 0.9160\n",
      "Epoch 6 Loss 0.2875 Accuracy 0.9135\n",
      "Time taken for 1 epoch: 8.86 secs\n",
      "\n",
      "Epoch 7 Batch 0 Loss 0.3013 Accuracy 0.9156\n",
      "Epoch 7 Batch 10 Loss 0.2557 Accuracy 0.9258\n",
      "Epoch 7 Batch 20 Loss 0.2692 Accuracy 0.9206\n",
      "Epoch 7 Batch 30 Loss 0.2755 Accuracy 0.9179\n",
      "Epoch 7 Loss 0.2722 Accuracy 0.9190\n",
      "Time taken for 1 epoch: 9.22 secs\n",
      "\n",
      "Epoch 8 Batch 0 Loss 0.2361 Accuracy 0.9362\n",
      "Epoch 8 Batch 10 Loss 0.2348 Accuracy 0.9335\n",
      "Epoch 8 Batch 20 Loss 0.2481 Accuracy 0.9279\n",
      "Epoch 8 Batch 30 Loss 0.2588 Accuracy 0.9246\n",
      "Epoch 8 Loss 0.2690 Accuracy 0.9208\n",
      "Time taken for 1 epoch: 9.01 secs\n",
      "\n",
      "Epoch 9 Batch 0 Loss 0.2151 Accuracy 0.9327\n",
      "Epoch 9 Batch 10 Loss 0.2599 Accuracy 0.9218\n",
      "Epoch 9 Batch 20 Loss 0.2623 Accuracy 0.9214\n",
      "Epoch 9 Batch 30 Loss 0.2581 Accuracy 0.9228\n",
      "Epoch 9 Loss 0.2635 Accuracy 0.9211\n",
      "Time taken for 1 epoch: 8.98 secs\n",
      "\n",
      "Epoch 10 Batch 0 Loss 0.2398 Accuracy 0.9259\n",
      "Epoch 10 Batch 10 Loss 0.2306 Accuracy 0.9325\n",
      "Epoch 10 Batch 20 Loss 0.2547 Accuracy 0.9255\n",
      "Epoch 10 Batch 30 Loss 0.2608 Accuracy 0.9236\n",
      "Saving checkpoint for epoch 10 at ./checkpoints/train/ckpt-18\n",
      "Epoch 10 Loss 0.2637 Accuracy 0.9220\n",
      "Time taken for 1 epoch: 9.36 secs\n",
      "\n",
      "Epoch 11 Batch 0 Loss 0.2376 Accuracy 0.9374\n",
      "Epoch 11 Batch 10 Loss 0.2525 Accuracy 0.9268\n",
      "Epoch 11 Batch 20 Loss 0.2584 Accuracy 0.9234\n",
      "Epoch 11 Batch 30 Loss 0.2550 Accuracy 0.9241\n",
      "Epoch 11 Loss 0.2549 Accuracy 0.9231\n",
      "Time taken for 1 epoch: 8.67 secs\n",
      "\n",
      "Epoch 12 Batch 0 Loss 0.2140 Accuracy 0.9387\n",
      "Epoch 12 Batch 10 Loss 0.2320 Accuracy 0.9322\n",
      "Epoch 12 Batch 20 Loss 0.2289 Accuracy 0.9319\n",
      "Epoch 12 Batch 30 Loss 0.2353 Accuracy 0.9299\n",
      "Epoch 12 Loss 0.2407 Accuracy 0.9278\n",
      "Time taken for 1 epoch: 9.11 secs\n",
      "\n",
      "Epoch 13 Batch 0 Loss 0.2254 Accuracy 0.9365\n",
      "Epoch 13 Batch 10 Loss 0.2331 Accuracy 0.9318\n",
      "Epoch 13 Batch 20 Loss 0.2338 Accuracy 0.9307\n",
      "Epoch 13 Batch 30 Loss 0.2403 Accuracy 0.9283\n",
      "Epoch 13 Loss 0.2457 Accuracy 0.9265\n",
      "Time taken for 1 epoch: 9.14 secs\n",
      "\n",
      "Epoch 14 Batch 0 Loss 0.1625 Accuracy 0.9511\n",
      "Epoch 14 Batch 10 Loss 0.2315 Accuracy 0.9342\n",
      "Epoch 14 Batch 20 Loss 0.2442 Accuracy 0.9299\n",
      "Epoch 14 Batch 30 Loss 0.2412 Accuracy 0.9298\n",
      "Epoch 14 Loss 0.2439 Accuracy 0.9283\n",
      "Time taken for 1 epoch: 9.19 secs\n",
      "\n",
      "Epoch 15 Batch 0 Loss 0.2179 Accuracy 0.9291\n",
      "Epoch 15 Batch 10 Loss 0.2155 Accuracy 0.9349\n",
      "Epoch 15 Batch 20 Loss 0.2290 Accuracy 0.9313\n",
      "Epoch 15 Batch 30 Loss 0.2392 Accuracy 0.9272\n",
      "Saving checkpoint for epoch 15 at ./checkpoints/train/ckpt-19\n",
      "Epoch 15 Loss 0.2430 Accuracy 0.9267\n",
      "Time taken for 1 epoch: 9.72 secs\n",
      "\n",
      "Epoch 16 Batch 0 Loss 0.2156 Accuracy 0.9408\n",
      "Epoch 16 Batch 10 Loss 0.2377 Accuracy 0.9264\n",
      "Epoch 16 Batch 20 Loss 0.2285 Accuracy 0.9306\n",
      "Epoch 16 Batch 30 Loss 0.2354 Accuracy 0.9289\n",
      "Epoch 16 Loss 0.2383 Accuracy 0.9273\n",
      "Time taken for 1 epoch: 9.20 secs\n",
      "\n",
      "Epoch 17 Batch 0 Loss 0.1797 Accuracy 0.9425\n",
      "Epoch 17 Batch 10 Loss 0.1946 Accuracy 0.9412\n",
      "Epoch 17 Batch 20 Loss 0.2141 Accuracy 0.9360\n",
      "Epoch 17 Batch 30 Loss 0.2169 Accuracy 0.9347\n",
      "Epoch 17 Loss 0.2205 Accuracy 0.9335\n",
      "Time taken for 1 epoch: 8.93 secs\n",
      "\n",
      "Epoch 18 Batch 0 Loss 0.1599 Accuracy 0.9589\n",
      "Epoch 18 Batch 10 Loss 0.2058 Accuracy 0.9387\n",
      "Epoch 18 Batch 20 Loss 0.2061 Accuracy 0.9378\n",
      "Epoch 18 Batch 30 Loss 0.2103 Accuracy 0.9358\n",
      "Epoch 18 Loss 0.2174 Accuracy 0.9334\n",
      "Time taken for 1 epoch: 8.90 secs\n",
      "\n",
      "Epoch 19 Batch 0 Loss 0.0618 Accuracy 0.9886\n",
      "Epoch 19 Batch 10 Loss 0.2117 Accuracy 0.9330\n",
      "Epoch 19 Batch 20 Loss 0.2209 Accuracy 0.9302\n",
      "Epoch 19 Batch 30 Loss 0.2209 Accuracy 0.9314\n",
      "Epoch 19 Loss 0.2231 Accuracy 0.9308\n",
      "Time taken for 1 epoch: 9.23 secs\n",
      "\n",
      "Epoch 20 Batch 0 Loss 0.1634 Accuracy 0.9396\n",
      "Epoch 20 Batch 10 Loss 0.2079 Accuracy 0.9346\n",
      "Epoch 20 Batch 20 Loss 0.2027 Accuracy 0.9364\n",
      "Epoch 20 Batch 30 Loss 0.2109 Accuracy 0.9351\n",
      "Saving checkpoint for epoch 20 at ./checkpoints/train/ckpt-20\n",
      "Epoch 20 Loss 0.2137 Accuracy 0.9338\n",
      "Time taken for 1 epoch: 9.82 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 % 10 == 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": [
    "## Evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y6APsFrgImLW"
   },
   "source": [
    "The following steps are used for evaluation:\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",
    "* The model makes predictions of the next word for each word in the output. Most of these are redundant. Use the predictrions from the last word.\n",
    "* Concatentate the predicted word to the decoder input and pass it to the decoder.\n",
    "* In this approach, the decoder predicts the next word based on the previous words it predicted.\n",
    "\n",
    "Note: The model used here has less capacity to keep the example relatively faster so the predictions maybe less right. To reproduce the results in the paper, use the entire dataset and base transformer model or transformer XL, by changing the hyperparameters above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:34.305106Z",
     "iopub.status.busy": "2021-02-04T02:39:34.304418Z",
     "iopub.status.idle": "2021-02-04T02:39:34.306541Z",
     "shell.execute_reply": "2021-02-04T02:39:34.306078Z"
    },
    "id": "5buvMlnvyrFm"
   },
   "outputs": [],
   "source": [
    "def evaluate(sentence, max_length=40):\n",
    "  # inp sentence is portuguese, hence adding the start and end token\n",
    "  sentence = tf.convert_to_tensor([sentence])\n",
    "  sentence = tokenizers.en.tokenize(sentence).to_tensor()\n",
    "\n",
    "  encoder_input = sentence\n",
    "  \n",
    "  # as the target is english, the first word to the transformer should be the\n",
    "  # english start token.\n",
    "  # start, end = tokenizers.en.tokenize([''])[0]\n",
    "  start = vectorize(tf.convert_to_tensor(start_token))\n",
    "  end = vectorize(tf.convert_to_tensor(end_token)) - 1\n",
    "  output = tf.convert_to_tensor([start])\n",
    "  output = tf.expand_dims(output, 0)\n",
    "    \n",
    "  for i in range(max_length):\n",
    "    enc_padding_mask, combined_mask, dec_padding_mask = create_masks(\n",
    "        encoder_input, output)\n",
    "  \n",
    "    # predictions.shape == (batch_size, seq_len, vocab_size)\n",
    "    predictions, attention_weights = transformer(encoder_input, \n",
    "                                                 output,\n",
    "                                                 False,\n",
    "                                                 enc_padding_mask,\n",
    "                                                 combined_mask,\n",
    "                                                 dec_padding_mask)\n",
    "    \n",
    "    # select the last word 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 = tf.concat([output, predicted_id], axis=-1)\n",
    "    \n",
    "    # return the result if the predicted_id is equal to the end token\n",
    "    if predicted_id == end:\n",
    "      break\n",
    "\n",
    "  # output.shape (1, tokens)\n",
    "  # text = tokenizers.en.detokenize(output)[0] # shape: ()\n",
    "  tokens = inverse_vectorize(output - 1)[0]\n",
    "  text = tf.strings.join(tokens, separator=' ').numpy()\n",
    "\n",
    "  # tokens = tokenizers.en.lookup(output)[0]\n",
    "  \n",
    "  return text, tokens, attention_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=string, numpy=b'[END]'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inverse_vectorize(vectorize(end_token) - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:34.310804Z",
     "iopub.status.busy": "2021-02-04T02:39:34.310172Z",
     "iopub.status.idle": "2021-02-04T02:39:34.312477Z",
     "shell.execute_reply": "2021-02-04T02:39:34.312070Z"
    },
    "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}')\n",
    "  print(f'{\"Ground truth\":15s}: {ground_truth}')\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:34.317525Z",
     "iopub.status.busy": "2021-02-04T02:39:34.316406Z",
     "iopub.status.idle": "2021-02-04T02:39:35.976121Z",
     "shell.execute_reply": "2021-02-04T02:39:35.975584Z"
    },
    "id": "YsxrAlvFG8SZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input:         : send a signal `signal.SIGUSR1` to the current process\n",
      "Prediction     : b\"[START] utf-8 p = subprocess . Popen ( [ 'grep' , 'f' ] , stdout = subprocess . PIPE , stderr = subprocess . STDOUT ) \\n p . from_string ( p . from_string ( '.' ) ) . wait (\"\n",
      "Ground truth   : os.kill(os.getpid(), signal.SIGUSR1)\n"
     ]
    }
   ],
   "source": [
    "sentence = \"send a signal `signal.SIGUSR1` to the current process\"\n",
    "ground_truth = \"os.kill(os.getpid(), signal.SIGUSR1)\"\n",
    "\n",
    "translated_text, translated_tokens, attention_weights = evaluate(sentence)\n",
    "print_translation(sentence, translated_text, ground_truth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:35.980908Z",
     "iopub.status.busy": "2021-02-04T02:39:35.979974Z",
     "iopub.status.idle": "2021-02-04T02:39:36.841973Z",
     "shell.execute_reply": "2021-02-04T02:39:36.841460Z"
    },
    "id": "7EH5y_aqI4t1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input:         : decode a hex string '4a4b4c' to UTF-8.\n",
      "Prediction     : b\"[START] utf-8 print ( '\\\\xc5\\\\xc4\\\\xd6' . encode ( 'ascii' ( 'ascii' ) ) . encode ( 'ascii' ( 'utf-8' ) ) ) . decode ( 'utf-8' ) ) . decode ( 'utf-8' ) ) . encode ( 'hex' ) . encode\"\n",
      "Ground truth   : bytes.fromhex('4a4b4c').decode('utf-8')\n"
     ]
    }
   ],
   "source": [
    "sentence = \"decode a hex string '4a4b4c' to UTF-8.\"\n",
    "ground_truth = \"bytes.fromhex('4a4b4c').decode('utf-8')\"\n",
    "\n",
    "translated_text, translated_tokens, attention_weights = evaluate(sentence)\n",
    "print_translation(sentence, translated_text, ground_truth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:36.847205Z",
     "iopub.status.busy": "2021-02-04T02:39:36.846309Z",
     "iopub.status.idle": "2021-02-04T02:39:38.882353Z",
     "shell.execute_reply": "2021-02-04T02:39:38.881840Z"
    },
    "id": "J-hVCTSUMlkb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input:         : Format a datetime string `when` to extract date only\n",
      "Prediction     : b\"[START] utf-8 [ ( datetime . datetime . now ( ) for datetime in datetime . now ( ) ] ) . strftime ( '%a' ) ] ) ] ) ] for t [END] in s . isoformat ( ) ]\"\n",
      "Ground truth   : then = datetime.datetime.strptime(when, '%Y-%m-%d').date()\n"
     ]
    }
   ],
   "source": [
    "sentence = \"Format a datetime string `when` to extract date only\"\n",
    "ground_truth = \"then = datetime.datetime.strptime(when, '%Y-%m-%d').date()\"\n",
    "\n",
    "translated_text, translated_tokens, attention_weights = evaluate(sentence)\n",
    "print_translation(sentence, translated_text, ground_truth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_1MxkSZvz0jX"
   },
   "source": [
    "You can pass different layers and attention blocks of the decoder to the `plot` parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S3EQiFUC--Ds"
   },
   "source": [
    "## Attention plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hHV2pdXHGz-0"
   },
   "source": [
    "The `evaluate` function also returns a dictionary of attention maps you can use to visualize the internal working of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:38.887671Z",
     "iopub.status.busy": "2021-02-04T02:39:38.886693Z",
     "iopub.status.idle": "2021-02-04T02:39:39.832813Z",
     "shell.execute_reply": "2021-02-04T02:39:39.832327Z"
    },
    "id": "t-kFyiOLH0xg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input:         : Calculate sum over all rows of 2D numpy array\n",
      "Prediction     : b'[START] utf-8 np . where ( np . arange ( len ( x ) ) ) . all ( ) ) , np . sum ( ) ) [ : 2 ] ) ) ) ) . all ( axis ='\n",
      "Ground truth   : a.sum(axis=1)\n"
     ]
    }
   ],
   "source": [
    "sentence = \"Calculate sum over all rows of 2D numpy array\"\n",
    "ground_truth = \"a.sum(axis=1)\"\n",
    "\n",
    "translated_text, translated_tokens, attention_weights = evaluate(sentence)\n",
    "print_translation(sentence, translated_text, ground_truth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:39.838254Z",
     "iopub.status.busy": "2021-02-04T02:39:39.837661Z",
     "iopub.status.idle": "2021-02-04T02:39:39.839958Z",
     "shell.execute_reply": "2021-02-04T02:39:39.839502Z"
    },
    "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",
    "  \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": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:39.843896Z",
     "iopub.status.busy": "2021-02-04T02:39:39.843277Z",
     "iopub.status.idle": "2021-02-04T02:39:39.846540Z",
     "shell.execute_reply": "2021-02-04T02:39:39.846948Z"
    },
    "id": "_KY4c2cryuxY"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([40, 15])"
      ]
     },
     "execution_count": 67,
     "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": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:39.851785Z",
     "iopub.status.busy": "2021-02-04T02:39:39.850800Z",
     "iopub.status.idle": "2021-02-04T02:39:39.870136Z",
     "shell.execute_reply": "2021-02-04T02:39:39.869618Z"
    },
    "id": "XdxmakWE6Om3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(24,), dtype=string, numpy=\n",
       "array([b'[START]', b'ca', b'##l', b'##cula', b'##te', b's', b'##um', b'o',\n",
       "       b'##ver', b'al', b'##l', b'r', b'##o', b'##ws', b'of', b'2',\n",
       "       b'##d', b'num', b'##p', b'##y', b'ar', b'##ra', b'##y', b'[END]'],\n",
       "      dtype=object)>"
      ]
     },
     "execution_count": 68,
     "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": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:39.874725Z",
     "iopub.status.busy": "2021-02-04T02:39:39.873972Z",
     "iopub.status.idle": "2021-02-04T02:39:39.876879Z",
     "shell.execute_reply": "2021-02-04T02:39:39.877309Z"
    },
    "id": "hVdPSfecmrpj"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(41,), dtype=string, numpy=\n",
       "array([b'[START]', b'utf-8', b'np', b'.', b'where', b'(', b'np', b'.',\n",
       "       b'arange', b'(', b'len', b'(', b'x', b')', b')', b')', b'.',\n",
       "       b'all', b'(', b')', b')', b',', b'np', b'.', b'sum', b'(', b')',\n",
       "       b')', b'[', b':', b'2', b']', b')', b')', b')', b')', b'.', b'all',\n",
       "       b'(', b'axis', b'='], dtype=object)>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translated_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:39.881622Z",
     "iopub.status.busy": "2021-02-04T02:39:39.880666Z",
     "iopub.status.idle": "2021-02-04T02:39:40.115448Z",
     "shell.execute_reply": "2021-02-04T02:39:40.114807Z"
    },
    "id": "XtzyKCFamm4N"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attention_head(in_tokens, translated_tokens, attention)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:40.121798Z",
     "iopub.status.busy": "2021-02-04T02:39:40.121127Z",
     "iopub.status.idle": "2021-02-04T02:39:40.123676Z",
     "shell.execute_reply": "2021-02-04T02:39:40.123200Z"
    },
    "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('Head {}'.format(h+1))\n",
    "  \n",
    "  plt.tight_layout()\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:40.128618Z",
     "iopub.status.busy": "2021-02-04T02:39:40.127738Z",
     "iopub.status.idle": "2021-02-04T02:39:41.444542Z",
     "shell.execute_reply": "2021-02-04T02:39:41.444963Z"
    },
    "id": "pyRQi7944wru"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 transliterare them, even withoput a shared vocabulary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-04T02:39:41.450413Z",
     "iopub.status.busy": "2021-02-04T02:39:41.449440Z",
     "iopub.status.idle": "2021-02-04T02:39:45.089463Z",
     "shell.execute_reply": "2021-02-04T02:39:45.089973Z"
    },
    "id": "9cxysY7uh3jg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input:         : concatenate items of list `l` with a space ' '\n",
      "Prediction     : b\"[START] utf-8 [ ( int ( x ) for x in data ( ) ] ) . split ( ',' ) ] ( int ( '.' ) ] ) ] ) ] ) ] ) ] ( data . split (\"\n",
      "Ground truth   : print(' '.join(map(str, l)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sentence = \"concatenate items of list `l` with a space ' '\"\n",
    "ground_truth = \"print(' '.join(map(str, l)))\"\n",
    "\n",
    "translated_text, translated_tokens, attention_weights = evaluate(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": "RqQ1fIsLwkGE"
   },
   "source": [
    "## Summary\n",
    "\n",
    "In this tutorial, you learned about positional encoding, multi-head attention, the importance of masking and how to create a transformer.\n",
    "\n",
    "Try using a different dataset to train the transformer. You can also create the base transformer or transformer XL by changing the hyperparameters above. You can also use the layers defined here to create [BERT](https://arxiv.org/abs/1810.04805) and train state of the art models. Furthermore, you can implement beam search to get better predictions."
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "transformer.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}