nmt_with_bert_tf2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "urxCUVCGGeWu",
    "colab_type": "text"
   },
   "source": [
    "This tutorial trains a Transformer model to translate Chinese to English using pre-trained model BERT as encoder in tensorflow 2.0.\n",
    "\n",
    "You will install tensorflow 2.0 first:\n",
    "```bash\n",
    "pip install tensorflow-gpu==2.0.0-beta1\n",
    "pip install tensorflow-datasets\n",
    "pip install bert-for-tf2\n",
    "```\n",
    "\n",
    "And download the BERT chinese pre-trained model:\n",
    "```bash\n",
    "wget https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pne5z4cTGeWv",
    "colab_type": "text"
   },
   "source": [
    "##Prepare the package requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "Ggahts4FGeWv",
    "colab_type": "code",
    "outputId": "b24fb71a-dbfc-48f0-f7ee-c2a733bc9cdd",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561509325233E12,
     "user_tz": -480.0,
     "elapsed": 64019.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow-gpu==2.0.0-beta1\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2b/53/e18c5e7a2263d3581a979645a185804782e59b8e13f42b9c3c3cfb5bb503/tensorflow_gpu-2.0.0b1-cp36-cp36m-manylinux1_x86_64.whl (348.9MB)\n",
      "\u001b[K     |████████████████████████████████| 348.9MB 46kB/s \n",
      "\u001b[?25hRequirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.0.8)\n",
      "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.16.4)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (3.7.1)\n",
      "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.12.0)\n",
      "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.2.2)\n",
      "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.1.7)\n",
      "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.33.4)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.1.0)\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.15.0)\n",
      "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.8.0)\n",
      "Collecting tf-estimator-nightly<1.14.0.dev2019060502,>=1.14.0.dev2019060501 (from tensorflow-gpu==2.0.0-beta1)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/32/dd/99c47dd007dcf10d63fd895611b063732646f23059c618a373e85019eb0e/tf_estimator_nightly-1.14.0.dev2019060501-py2.py3-none-any.whl (496kB)\n",
      "\u001b[K     |████████████████████████████████| 501kB 48.9MB/s \n",
      "\u001b[?25hRequirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.7.1)\n",
      "Collecting tb-nightly<1.14.0a20190604,>=1.14.0a20190603 (from tensorflow-gpu==2.0.0-beta1)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a4/96/571b875cd81dda9d5dfa1422a4f9d749e67c0a8d4f4f0b33a4e5f5f35e27/tb_nightly-1.14.0a20190603-py3-none-any.whl (3.1MB)\n",
      "\u001b[K     |████████████████████████████████| 3.1MB 45.3MB/s \n",
      "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.11.1)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.1.0)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-beta1) (2.8.0)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0-beta1) (41.0.1)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta1) (0.15.4)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta1) (3.1.1)\n",
      "Installing collected packages: tf-estimator-nightly, tb-nightly, tensorflow-gpu\n",
      "Successfully installed tb-nightly-1.14.0a20190603 tensorflow-gpu-2.0.0b1 tf-estimator-nightly-1.14.0.dev2019060501\n",
      "Collecting tensorflow-datasets\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/15/11/ce86b45cb41de8490d2f27f76ad5fe2f75d6eaac1c21f4a12baf1875882e/tensorflow_datasets-1.0.2-py3-none-any.whl (682kB)\n",
      "\u001b[K     |████████████████████████████████| 686kB 41.7MB/s \n",
      "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (2.21.0)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (3.7.1)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (1.12.0)\n",
      "Requirement already satisfied: promise in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (2.2.1)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (1.16.4)\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (0.16.0)\n",
      "Requirement already satisfied: dill in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (0.2.9)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (4.28.1)\n",
      "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (0.13.0)\n",
      "Requirement already satisfied: wrapt in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (1.11.1)\n",
      "Requirement already satisfied: psutil in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (5.4.8)\n",
      "Requirement already satisfied: absl-py in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (0.7.1)\n",
      "Requirement already satisfied: termcolor in /usr/local/lib/python3.6/dist-packages (from tensorflow-datasets) (1.1.0)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->tensorflow-datasets) (2.8)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->tensorflow-datasets) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->tensorflow-datasets) (2019.3.9)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->tensorflow-datasets) (1.24.3)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-datasets) (41.0.1)\n",
      "Requirement already satisfied: googleapis-common-protos in /usr/local/lib/python3.6/dist-packages (from tensorflow-metadata->tensorflow-datasets) (1.6.0)\n",
      "Installing collected packages: tensorflow-datasets\n",
      "Successfully installed tensorflow-datasets-1.0.2\n",
      "Collecting bert-for-tf2\n",
      "  Downloading https://files.pythonhosted.org/packages/0e/94/f55cbdb7efde6dbf8c410142f6555288a7061688ea4df2a619bbd07b0aa3/bert-for-tf2-0.2.0.tar.gz\n",
      "Collecting py-params>=0.6.3 (from bert-for-tf2)\n",
      "  Downloading https://files.pythonhosted.org/packages/a3/d0/811e7380e20724c2700c9afa9cf439aaa2031c3c21db0c26d2df9afc331f/py-params-0.6.3.tar.gz\n",
      "Collecting params-flow>=0.2.2 (from bert-for-tf2)\n",
      "  Downloading https://files.pythonhosted.org/packages/84/5d/1cde5d596f5f9a5bec0e9d8d7eaf4baedf438a49524ff632723a4657e277/params-flow-0.2.2.tar.gz\n",
      "Requirement already satisfied: tensorflow in /usr/local/lib/python3.6/dist-packages (from params-flow>=0.2.2->bert-for-tf2) (1.14.0rc1)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from params-flow>=0.2.2->bert-for-tf2) (1.16.4)\n",
      "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (1.12.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (1.1.0)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (1.1.0)\n",
      "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (0.33.4)\n",
      "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (0.1.7)\n",
      "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (0.7.1)\n",
      "Requirement already satisfied: tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (1.14.0rc1)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (3.7.1)\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (1.15.0)\n",
      "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (1.0.8)\n",
      "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (0.2.2)\n",
      "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (1.11.1)\n",
      "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (0.8.0)\n",
      "Requirement already satisfied: tensorboard<1.14.0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow->params-flow>=0.2.2->bert-for-tf2) (1.13.1)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow->params-flow>=0.2.2->bert-for-tf2) (41.0.1)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow->params-flow>=0.2.2->bert-for-tf2) (2.8.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow->params-flow>=0.2.2->bert-for-tf2) (3.1.1)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow->params-flow>=0.2.2->bert-for-tf2) (0.15.4)\n",
      "Building wheels for collected packages: bert-for-tf2, py-params, params-flow\n",
      "  Building wheel for bert-for-tf2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Stored in directory: /root/.cache/pip/wheels/dd/38/d7/ced0f6a8658d616e2db8243868de392e2a2e9a01e62f16ad1b\n",
      "  Building wheel for py-params (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Stored in directory: /root/.cache/pip/wheels/33/66/2a/5593608fbe4cbcea004c3f2432f3b00f01430d9bb218bb23c6\n",
      "  Building wheel for params-flow (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Stored in directory: /root/.cache/pip/wheels/3b/3b/0c/3c434295eb5f16ffaf638eff3ce0698adbc161107cd063f016\n",
      "Successfully built bert-for-tf2 py-params params-flow\n",
      "Installing collected packages: py-params, params-flow, bert-for-tf2\n",
      "Successfully installed bert-for-tf2-0.2.0 params-flow-0.2.2 py-params-0.6.3\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "!pip install tensorflow-gpu==2.0.0-beta1\n",
    "!pip install tensorflow-datasets\n",
    "!pip install bert-for-tf2\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "import tensorflow_datasets as tfds\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import collections\n",
    "import unicodedata\n",
    "\n",
    "import os\n",
    "from bert import BertModelLayer\n",
    "from bert.loader import StockBertConfig, load_stock_weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "BOEQJQTGu9QO",
    "colab_type": "code",
    "outputId": "b773a65c-3bea-4485-92e1-003e2f35fa4a",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.56150932636E12,
     "user_tz": -480.0,
     "elapsed": 64934.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU Available:  True\n"
     ]
    }
   ],
   "source": [
    "print(\"GPU Available: \", tf.test.is_gpu_available())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "nLxQ9MlD8Nmj",
    "colab_type": "code",
    "outputId": "da5e9c4d-3c59-4988-b9bd-484fff3602fd",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561509336411E12,
     "user_tz": -480.0,
     "elapsed": 74877.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-06-26 00:35:27--  https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip\n",
      "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.161.80, 2404:6800:4004:808::2010\n",
      "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.161.80|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 381892918 (364M) [application/zip]\n",
      "Saving to: ‘chinese_L-12_H-768_A-12.zip’\n",
      "\n",
      "chinese_L-12_H-768_ 100%[===================>] 364.20M   291MB/s    in 1.3s    \n",
      "\n",
      "2019-06-26 00:35:28 (291 MB/s) - ‘chinese_L-12_H-768_A-12.zip’ saved [381892918/381892918]\n",
      "\n",
      "Archive:  chinese_L-12_H-768_A-12.zip\n",
      "   creating: chinese_L-12_H-768_A-12/\n",
      "  inflating: chinese_L-12_H-768_A-12/bert_model.ckpt.meta  \n",
      "  inflating: chinese_L-12_H-768_A-12/bert_model.ckpt.data-00000-of-00001  \n",
      "  inflating: chinese_L-12_H-768_A-12/vocab.txt  \n",
      "  inflating: chinese_L-12_H-768_A-12/bert_model.ckpt.index  \n",
      "  inflating: chinese_L-12_H-768_A-12/bert_config.json  \n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists('chinese_L-12_H-768_A-12'):\n",
    "  !wget https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip\n",
    "  !unzip chinese_L-12_H-768_A-12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tan_kJ7RGeWy",
    "colab_type": "text"
   },
   "source": [
    "## Add BERT tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "WFB-GPy2GeWz",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def convert_to_unicode(text):\n",
    "    \"\"\"Converts `text` to Unicode (if it's not already), assuming utf-8 input.\"\"\"\n",
    "    if isinstance(text, str):\n",
    "        return text\n",
    "    elif isinstance(text, bytes):\n",
    "        return text.decode(\"utf-8\", \"ignore\")\n",
    "    else:\n",
    "        raise ValueError(\"Unsupported string type: %s\" % (type(text)))\n",
    "\n",
    "\n",
    "def load_vocab(vocab_file):\n",
    "    \"\"\"Loads a vocabulary file into a dictionary.\"\"\"\n",
    "    vocab = collections.OrderedDict()\n",
    "    index = 0\n",
    "    with tf.io.gfile.GFile(vocab_file, \"r\") as reader:\n",
    "        while True:\n",
    "            token = convert_to_unicode(reader.readline())\n",
    "            if not token:\n",
    "                break\n",
    "            token = token.strip()\n",
    "            vocab[token] = index\n",
    "            index += 1\n",
    "    return vocab\n",
    "\n",
    "\n",
    "def whitespace_tokenize(text):\n",
    "    \"\"\"Runs basic whitespace cleaning and splitting on a piece of text.\"\"\"\n",
    "    text = text.strip()\n",
    "    if not text:\n",
    "        return []\n",
    "    tokens = text.split()\n",
    "    return tokens\n",
    "\n",
    "def convert_by_vocab(vocab, items):\n",
    "    \"\"\"Converts a sequence of [tokens|ids] using the vocab.\"\"\"\n",
    "    output = []\n",
    "    for item in items:\n",
    "        output.append(vocab[item])\n",
    "    return output\n",
    "\n",
    "class FullTokenizer(object):\n",
    "    \"\"\"Runs end-to-end tokenziation.\"\"\"\n",
    "\n",
    "    def __init__(self, vocab_file, do_lower_case=True):\n",
    "        self.vocab = load_vocab(vocab_file)\n",
    "        self.inv_vocab = {v: k for k, v in self.vocab.items()}\n",
    "        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)\n",
    "        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)\n",
    "\n",
    "    def tokenize(self, text):\n",
    "        split_tokens = []\n",
    "        for token in self.basic_tokenizer.tokenize(text):\n",
    "            for sub_token in self.wordpiece_tokenizer.tokenize(token):\n",
    "                split_tokens.append(sub_token)\n",
    "\n",
    "        return split_tokens\n",
    "    \n",
    "    def convert_tokens_to_ids(self, tokens):\n",
    "        return convert_by_vocab(self.vocab, tokens)\n",
    "\n",
    "    def convert_ids_to_tokens(self, ids):\n",
    "        return convert_by_vocab(self.inv_vocab, ids)\n",
    "\n",
    "\n",
    "class BasicTokenizer(object):\n",
    "    \"\"\"Runs basic tokenization (punctuation splitting, lower casing, etc.).\"\"\"\n",
    "\n",
    "    def __init__(self, do_lower_case=True):\n",
    "        \"\"\"Constructs a BasicTokenizer.\n",
    "    \n",
    "        Args:\n",
    "          do_lower_case: Whether to lower case the input.\n",
    "        \"\"\"\n",
    "        self.do_lower_case = do_lower_case\n",
    "\n",
    "    def tokenize(self, text):\n",
    "        \"\"\"Tokenizes a piece of text.\"\"\"\n",
    "        text = convert_to_unicode(text)\n",
    "        text = self._clean_text(text)\n",
    "\n",
    "        # This was added on November 1st, 2018 for the multilingual and Chinese\n",
    "        # models. This is also applied to the English models now, but it doesn't\n",
    "        # matter since the English models were not trained on any Chinese data\n",
    "        # and generally don't have any Chinese data in them (there are Chinese\n",
    "        # characters in the vocabulary because Wikipedia does have some Chinese\n",
    "        # words in the English Wikipedia.).\n",
    "        text = self._tokenize_chinese_chars(text)\n",
    "\n",
    "        orig_tokens = whitespace_tokenize(text)\n",
    "        split_tokens = []\n",
    "        for token in orig_tokens:\n",
    "            if self.do_lower_case:\n",
    "                token = token.lower()\n",
    "                token = self._run_strip_accents(token)\n",
    "            split_tokens.extend(self._run_split_on_punc(token))\n",
    "\n",
    "        output_tokens = whitespace_tokenize(\" \".join(split_tokens))\n",
    "        return output_tokens\n",
    "\n",
    "    def _run_strip_accents(self, text):\n",
    "        \"\"\"Strips accents from a piece of text.\"\"\"\n",
    "        text = unicodedata.normalize(\"NFD\", text)\n",
    "        output = []\n",
    "        for char in text:\n",
    "            cat = unicodedata.category(char)\n",
    "            if cat == \"Mn\":\n",
    "                continue\n",
    "            output.append(char)\n",
    "        return \"\".join(output)\n",
    "\n",
    "    def _run_split_on_punc(self, text):\n",
    "        \"\"\"Splits punctuation on a piece of text.\"\"\"\n",
    "        chars = list(text)\n",
    "        i = 0\n",
    "        start_new_word = True\n",
    "        output = []\n",
    "        while i < len(chars):\n",
    "            char = chars[i]\n",
    "            if _is_punctuation(char):\n",
    "                output.append([char])\n",
    "                start_new_word = True\n",
    "            else:\n",
    "                if start_new_word:\n",
    "                    output.append([])\n",
    "                start_new_word = False\n",
    "                output[-1].append(char)\n",
    "            i += 1\n",
    "\n",
    "        return [\"\".join(x) for x in output]\n",
    "\n",
    "    def _tokenize_chinese_chars(self, text):\n",
    "        \"\"\"Adds whitespace around any CJK character.\"\"\"\n",
    "        output = []\n",
    "        for char in text:\n",
    "            cp = ord(char)\n",
    "            if self._is_chinese_char(cp):\n",
    "                output.append(\" \")\n",
    "                output.append(char)\n",
    "                output.append(\" \")\n",
    "            else:\n",
    "                output.append(char)\n",
    "        return \"\".join(output)\n",
    "\n",
    "    def _is_chinese_char(self, cp):\n",
    "        \"\"\"Checks whether CP is the codepoint of a CJK character.\"\"\"\n",
    "        # This defines a \"chinese character\" as anything in the CJK Unicode block:\n",
    "        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)\n",
    "        #\n",
    "        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,\n",
    "        # despite its name. The modern Korean Hangul alphabet is a different block,\n",
    "        # as is Japanese Hiragana and Katakana. Those alphabets are used to write\n",
    "        # space-separated words, so they are not treated specially and handled\n",
    "        # like the all of the other languages.\n",
    "        if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #\n",
    "                (cp >= 0x3400 and cp <= 0x4DBF) or  #\n",
    "                (cp >= 0x20000 and cp <= 0x2A6DF) or  #\n",
    "                (cp >= 0x2A700 and cp <= 0x2B73F) or  #\n",
    "                (cp >= 0x2B740 and cp <= 0x2B81F) or  #\n",
    "                (cp >= 0x2B820 and cp <= 0x2CEAF) or\n",
    "                (cp >= 0xF900 and cp <= 0xFAFF) or  #\n",
    "                (cp >= 0x2F800 and cp <= 0x2FA1F)):  #\n",
    "            return True\n",
    "\n",
    "        return False\n",
    "\n",
    "    def _clean_text(self, text):\n",
    "        \"\"\"Performs invalid character removal and whitespace cleanup on text.\"\"\"\n",
    "        output = []\n",
    "        for char in text:\n",
    "            cp = ord(char)\n",
    "            if cp == 0 or cp == 0xfffd or _is_control(char):\n",
    "                continue\n",
    "            if _is_whitespace(char):\n",
    "                output.append(\" \")\n",
    "            else:\n",
    "                output.append(char)\n",
    "        return \"\".join(output)\n",
    "\n",
    "\n",
    "class WordpieceTokenizer(object):\n",
    "    \"\"\"Runs WordPiece tokenziation.\"\"\"\n",
    "\n",
    "    def __init__(self, vocab, unk_token=\"[UNK]\", max_input_chars_per_word=200):\n",
    "        self.vocab = vocab\n",
    "        self.unk_token = unk_token\n",
    "        self.max_input_chars_per_word = max_input_chars_per_word\n",
    "\n",
    "    def tokenize(self, text):\n",
    "        \"\"\"Tokenizes a piece of text into its word pieces.\n",
    "    \n",
    "        This uses a greedy longest-match-first algorithm to perform tokenization\n",
    "        using the given vocabulary.\n",
    "    \n",
    "        For example:\n",
    "          input = \"unaffable\"\n",
    "          output = [\"un\", \"##aff\", \"##able\"]\n",
    "    \n",
    "        Args:\n",
    "          text: A single token or whitespace separated tokens. This should have\n",
    "            already been passed through `BasicTokenizer.\n",
    "    \n",
    "        Returns:\n",
    "          A list of wordpiece tokens.\n",
    "        \"\"\"\n",
    "\n",
    "        text = convert_to_unicode(text)\n",
    "\n",
    "        output_tokens = []\n",
    "        for token in whitespace_tokenize(text):\n",
    "            chars = list(token)\n",
    "            if len(chars) > self.max_input_chars_per_word:\n",
    "                output_tokens.append(self.unk_token)\n",
    "                continue\n",
    "\n",
    "            is_bad = False\n",
    "            start = 0\n",
    "            sub_tokens = []\n",
    "            while start < len(chars):\n",
    "                end = len(chars)\n",
    "                cur_substr = None\n",
    "                while start < end:\n",
    "                    substr = \"\".join(chars[start:end])\n",
    "                    if start > 0:\n",
    "                        substr = \"##\" + substr\n",
    "                    if substr in self.vocab:\n",
    "                        cur_substr = substr\n",
    "                        break\n",
    "                    end -= 1\n",
    "                if cur_substr is None:\n",
    "                    is_bad = True\n",
    "                    break\n",
    "                sub_tokens.append(cur_substr)\n",
    "                start = end\n",
    "\n",
    "            if is_bad:\n",
    "                output_tokens.append(self.unk_token)\n",
    "            else:\n",
    "                output_tokens.extend(sub_tokens)\n",
    "        return output_tokens\n",
    "\n",
    "def _is_whitespace(char):\n",
    "    \"\"\"Checks whether `chars` is a whitespace character.\"\"\"\n",
    "    # \\t, \\n, and \\r are technically contorl characters but we treat them\n",
    "    # as whitespace since they are generally considered as such.\n",
    "    if char == \" \" or char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n",
    "        return True\n",
    "    cat = unicodedata.category(char)\n",
    "    if cat == \"Zs\":\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "\n",
    "def _is_control(char):\n",
    "    \"\"\"Checks whether `chars` is a control character.\"\"\"\n",
    "    # These are technically control characters but we count them as whitespace\n",
    "    # characters.\n",
    "    if char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n",
    "        return False\n",
    "    cat = unicodedata.category(char)\n",
    "    if cat.startswith(\"C\"):\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "\n",
    "def _is_punctuation(char):\n",
    "    \"\"\"Checks whether `chars` is a punctuation character.\"\"\"\n",
    "    cp = ord(char)\n",
    "    # We treat all non-letter/number ASCII as punctuation.\n",
    "    # Characters such as \"^\", \"$\", and \"`\" are not in the Unicode\n",
    "    # Punctuation class but we treat them as punctuation anyways, for\n",
    "    # consistency.\n",
    "    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or\n",
    "            (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):\n",
    "        return True\n",
    "    cat = unicodedata.category(char)\n",
    "    if cat.startswith(\"P\"):\n",
    "        return True\n",
    "    return False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N1Uwxx4lGeW1",
    "colab_type": "text"
   },
   "source": [
    "## Setup input pipleline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8RQZYo0kGeW2",
    "colab_type": "text"
   },
   "source": [
    "Use TFDS to load the wmt2019 zh-en translation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "Trj0HUmwGeW3",
    "colab_type": "code",
    "outputId": "ba9a7209-7d73-405d-fad4-94784270c234",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561509455737E12,
     "user_tz": -480.0,
     "elapsed": 194083.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css": {
       "data": "/* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /* We import all of these together in a single css file because the Webpack
loader sees only one file at a time. This allows postcss to see the variable
definitions when they are used. */

 /*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

 /*
This file is copied from the JupyterLab project to define default styling for
when the widget styling is compiled down to eliminate CSS variables. We make one
change - we comment out the font import below.
*/

 /**
 * The material design colors are adapted from google-material-color v1.2.6
 * https://github.com/danlevan/google-material-color
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/dist/palette.var.css
 *
 * The license for the material design color CSS variables is as follows (see
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Dan Le Van
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

 /*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/

 /*
 * Optional monospace font for input/output prompt.
 */

 /* Commented out in ipywidgets since we don't need it. */

 /* @import url('https://fonts.googleapis.com/css?family=Roboto+Mono'); */

 /*
 * Added for compabitility with output area
 */

 :root {

  /* Borders

  The following variables, specify the visual styling of borders in JupyterLab.
   */

  /* UI Fonts

  The UI font CSS variables are used for the typography all of the JupyterLab
  user interface elements that are not directly user generated content.
  */ /* Base font size */ /* Ensures px perfect FontAwesome icons */

  /* Use these font colors against the corresponding main layout colors.
     In a light theme, these go from dark to light.
  */

  /* Use these against the brand/accent/warn/error colors.
     These will typically go from light to darker, in both a dark and light theme
   */

  /* Content Fonts

  Content font variables are used for typography of user generated content.
  */ /* Base font size */


  /* Layout

  The following are the main layout colors use in JupyterLab. In a light
  theme these would go from light to dark.
  */

  /* Brand/accent */

  /* State colors (warn, error, success, info) */

  /* Cell specific styles */
  /* A custom blend of MD grey and blue 600
   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
  /* A custom blend of MD grey and orange 600
   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */

  /* Notebook specific styles */

  /* Console specific styles */

  /* Toolbar specific styles */
}

 /* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /*
 * We assume that the CSS variables in
 * https://github.com/jupyterlab/jupyterlab/blob/master/src/default-theme/variables.css
 * have been defined.
 */

 /* This file has code derived from PhosphorJS CSS files, as noted below. The license for this PhosphorJS code is:

Copyright (c) 2014-2017, PhosphorJS Contributors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

*/

 /*
 * The following section is derived from https://github.com/phosphorjs/phosphor/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css 
 * We've scoped the rules so that they are consistent with exactly our code.
 */

 .jupyter-widgets.widget-tab > .p-TabBar {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
  margin: 0;
  padding: 0;
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  list-style-type: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] > .p-TabBar-content {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] > .p-TabBar-content {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
  overflow: hidden;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
  -webkit-box-flex: 0;
      -ms-flex: 0 0 auto;
          flex: 0 0 auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel {
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  overflow: hidden;
  white-space: nowrap;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden {
  display: none !important;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab {
  position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab {
  left: 0;
  -webkit-transition: left 150ms ease;
  transition: left 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab {
  top: 0;
  -webkit-transition: top 150ms ease;
  transition: top 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging {
  -webkit-transition: none;
  transition: none;
}

 /* End tabbar.css */

 :root { /* margin between inline elements */

    /* From Material Design Lite */
}

 .jupyter-widgets {
    margin: 2px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    color: black;
    overflow: visible;
}

 .jupyter-widgets.jupyter-widgets-disconnected::before {
    line-height: 28px;
    height: 28px;
}

 .jp-Output-result > .jupyter-widgets {
    margin-left: 0;
    margin-right: 0;
}

 /* vbox and hbox */

 .widget-inline-hbox {
    /* Horizontal widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
    -webkit-box-align: baseline;
        -ms-flex-align: baseline;
            align-items: baseline;
}

 .widget-inline-vbox {
    /* Vertical Widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
    overflow: auto;
}

 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
}

 .widget-vbox {
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 /* General Button Styling */

 .jupyter-button {
    padding-left: 10px;
    padding-right: 10px;
    padding-top: 0px;
    padding-bottom: 0px;
    display: inline-block;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
    border: 0px solid;
    line-height: 28px;
    -webkit-box-shadow: none;
            box-shadow: none;

    color: rgba(0, 0, 0, .8);
    background-color: #EEEEEE;
    border-color: #E0E0E0;
    border: none;
}

 .jupyter-button i.fa {
    margin-right: 4px;
    pointer-events: none;
}

 .jupyter-button:empty:before {
    content: "\200b"; /* zero-width space */
}

 .jupyter-widgets.jupyter-button:disabled {
    opacity: 0.6;
}

 .jupyter-button i.fa.center {
    margin-right: 0;
}

 .jupyter-button:hover:enabled, .jupyter-button:focus:enabled {
    /* MD Lite 2dp shadow */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0idXRmLTgiPz4KPCEtLSBHZW5lcmF0b3I6IEFkb2JlIElsbHVzdHJhdG9yIDE5LjIuMSwgU1ZHIEV4cG9ydCBQbHVnLUluIC4gU1ZHIFZlcnNpb246IDYuMDAgQnVpbGQgMCkgIC0tPgo8c3ZnIHZlcnNpb249IjEuMSIgaWQ9IkxheWVyXzEiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyIgeG1sbnM6eGxpbms9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkveGxpbmsiIHg9IjBweCIgeT0iMHB4IgoJIHZpZXdCb3g9IjAgMCAxOCAxOCIgc3R5bGU9ImVuYWJsZS1iYWNrZ3JvdW5kOm5ldyAwIDAgMTggMTg7IiB4bWw6c3BhY2U9InByZXNlcnZlIj4KPHN0eWxlIHR5cGU9InRleHQvY3NzIj4KCS5zdDB7ZmlsbDpub25lO30KPC9zdHlsZT4KPHBhdGggZD0iTTUuMiw1LjlMOSw5LjdsMy44LTMuOGwxLjIsMS4ybC00LjksNWwtNC45LTVMNS4yLDUuOXoiLz4KPHBhdGggY2xhc3M9InN0MCIgZD0iTTAtMC42aDE4djE4SDBWLTAuNnoiLz4KPC9zdmc+Cg");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

/*# sourceMappingURL=data:application/json;base64,{"version":3,"sources":["../node_modules/@jupyter-widgets/controls/css/widgets.css","../node_modules/@jupyter-widgets/controls/css/labvariables.css","../node_modules/@jupyter-widgets/controls/css/materialcolors.css","../node_modules/@jupyter-widgets/controls/css/widgets-base.css","../node_modules/@jupyter-widgets/controls/css/phosphor.css"],"names":[],"mappings":"AAAA;;GAEG;;CAEF;;kCAEiC;;CCNlC;;;+EAG+E;;CAE/E;;;;EAIE;;CCTF;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GA6BG;;CDhBH;;;;;;;;;;;;;;;;;;;EAmBE;;CAGF;;GAEG;;CACF,yDAAyD;;CAC1D,yEAAyE;;CAEzE;;GAEG;;CAOH;;EAEE;;;KAGG;;EAQH;;;;IAIE,CAIwB,oBAAoB,CAGhB,0CAA0C;;EAGxE;;IAEE;;EAOF;;KAEG;;EAOH;;;IAGE,CAWwB,oBAAoB;;;EAU9C;;;;IAIE;;EAOF,kBAAkB;;EAYlB,+CAA+C;;EAsB/C,0BAA0B;EAa1B;4EAC0E;EAE1E;wEACsE;;EAGtE,8BAA8B;;EAK9B,6BAA6B;;EAI7B,6BAA6B;CAQ9B;;CEzMD;;GAEG;;CAEH;;;;GAIG;;CCRH;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;EA8BE;;CAEF;;;GAGG;;CAEH;EACE,qBAAc;EAAd,qBAAc;EAAd,cAAc;EACd,0BAA0B;EAC1B,uBAAuB;EACvB,sBAAsB;EACtB,kBAAkB;CACnB;;CAGD;EACE,+BAAoB;EAApB,8BAAoB;MAApB,wBAAoB;UAApB,oBAAoB;CACrB;;CAGD;EACE,6BAAuB;EAAvB,8BAAuB;MAAvB,2BAAuB;UAAvB,uBAAuB;CACxB;;CAGD;EACE,UAAU;EACV,WAAW;EACX,qBAAc;EAAd,qBAAc;EAAd,cAAc;EACd,oBAAe;MAAf,mBAAe;UAAf,eAAe;EACf,sBAAsB;CACvB;;CAGD;EACE,+BAAoB;EAApB,8BAAoB;MAApB,wBAAoB;UAApB,oBAAoB;CACrB;;CAGD;EACE,6BAAuB;EAAvB,8BAAuB;MAAvB,2BAAuB;UAAvB,uBAAuB;CACxB;;CAGD;EACE,qBAAc;EAAd,qBAAc;EAAd,cAAc;EACd,+BAAoB;EAApB,8BAAoB;MAApB,wBAAoB;UAApB,oBAAoB;EACpB,+BAAuB;UAAvB,uBAAuB;EACvB,iBAAiB;CAClB;;CAGD;;EAEE,oBAAe;MAAf,mBAAe;UAAf,eAAe;CAChB;;CAGD;EACE,oBAAe;MAAf,mBAAe;UAAf,eAAe;EACf,iBAAiB;EACjB,oBAAoB;CACrB;;CAGD;EACE,yBAAyB;CAC1B;;CAGD;EACE,mBAAmB;CACpB;;CAGD;EACE,QAAQ;EACR,oCAA4B;EAA5B,4BAA4B;CAC7B;;CAGD;EACE,OAAO;EACP,mCAA2B;EAA3B,2BAA2B;CAC5B;;CAGD;EACE,yBAAiB;EAAjB,iBAAiB;CAClB;;CAED,oBAAoB;;CD9GpB,QAUqC,oCAAoC;;IA2BrE,+BAA+B;CAIlC;;CAED;IACI,YAAiC;IACjC,+BAAuB;YAAvB,uBAAuB;IACvB,aAA+B;IAC/B,kBAAkB;CACrB;;CAED;IACI,kBAA6C;IAC7C,aAAwC;CAC3C;;CAED;IACI,eAAe;IACf,gBAAgB;CACnB;;CAED,mBAAmB;;CAEnB;IACI,wBAAwB;IACxB,+BAAuB;YAAvB,uBAAuB;IACvB,qBAAc;IAAd,qBAAc;IAAd,cAAc;IACd,+BAAoB;IAApB,8BAAoB;QAApB,wBAAoB;YAApB,oBAAoB;IACpB,4BAAsB;QAAtB,yBAAsB;YAAtB,sBAAsB;CACzB;;CAED;IACI,sBAAsB;IACtB,+BAAuB;YAAvB,uBAAuB;IACvB,qBAAc;IAAd,qBAAc;IAAd,cAAc;IACd,6BAAuB;IAAvB,8BAAuB;QAAvB,2BAAuB;YAAvB,uBAAuB;IACvB,0BAAoB;QAApB,uBAAoB;YAApB,oBAAoB;CACvB;;CAED;IACI,+BAAuB;YAAvB,uBAAuB;IACvB,qBAAc;IAAd,qBAAc;IAAd,cAAc;IACd,UAAU;IACV,eAAe;CAClB;;CAED;IACI,+BAAuB;YAAvB,uBAAuB;IACvB,cAAc;IACd,UAAU;IACV,eAAe;CAClB;;CAED;IACI,+BAAoB;IAApB,8BAAoB;QAApB,wBAAoB;YAApB,oBAAoB;CACvB;;CAED;IACI,6BAAuB;IAAvB,8BAAuB;QAAvB,2BAAuB;YAAvB,uBAAuB;CAC1B;;CAED,4BAA4B;;CAE5B;IACI,mBAAmB;IACnB,oBAAoB;IACpB,iBAAiB;IACjB,oBAAoB;IACpB,sBAAsB;IACtB,oBAAoB;IACpB,iBAAiB;IACjB,wBAAwB;IACxB,mBAAmB;IACnB,gBAAuC;IACvC,gBAAgB;;IAEhB,aAAwC;IACxC,kBAAkB;IAClB,kBAA6C;IAC7C,yBAAiB;YAAjB,iBAAiB;;IAEjB,yBAAgC;IAChC,0BAA0C;IAC1C,sBAAsC;IACtC,aAAa;CAChB;;CAED;IACI,kBAA8C;IAC9C,qBAAqB;CACxB;;CAED;IACI,iBAAiB,CAAC,sBAAsB;CAC3C;;CAED;IACI,aAA4C;CAC/C;;CAED;IACI,gBAAgB;CACnB;;CAED;IACI,wBAAwB;IACxB;;+CAE+E;YAF/E;;+CAE+E;CAClF;;CAED;IACI,wBAAwB;IACxB;;iDAE6E;YAF7E;;iDAE6E;IAC7E,yBAAgC;IAChC,0BAA0C;CAC7C;;CAED;IACI,2BAA8D;CACjE;;CAED,8BAA8B;;CAE9B;IACI,gCAAwC;IACxC,0BAAyC;CAC5C;;CAED;IACI,8BAAwC;IACxC,0BAAyC;CAC5C;;CAED;IACI,8BAAwC;IACxC,0BAAyC;CAC5C;;CAED,8BAA8B;;CAE9B;IACI,gCAAwC;IACxC,0BAA2C;CAC9C;;CAED;IACI,8BAAwC;IACxC,0BAA2C;EAC7C;;CAEF;IACI,8BAAwC;IACxC,0BAA2C;EAC7C;;CAED,2BAA2B;;CAE5B;IACI,gCAAwC;IACxC,0BAAwC;CAC3C;;CAED;IACI,8BAAwC;IACxC,0BAAwC;CAC3C;;CAED;IACI,8BAAwC;IACxC,0BAAwC;CAC3C;;CAED,8BAA8B;;CAE9B;IACI,gCAAwC;IACxC,0BAAwC;CAC3C;;CAED;IACI,8BAAwC;IACxC,0BAAwC;CAC3C;;CAED;IACI,8BAAwC;IACxC,0BAAwC;CAC3C;;CAED,6BAA6B;;CAE7B;IACI,gCAAwC;IACxC,0BAAyC;CAC5C;;CAED;IACI,8BAAwC;IACxC,0BAAyC;CAC5C;;CAED;IACI,8BAAwC;IACxC,0BAAyC;CAC5C;;CAED,kBAAkB;;CAElB;IACI,aAA4C;CAC/C;;CAED,0BAA0B;;CAE1B,kCAAkC;;CAClC;IACI,iBAAuB;IAAvB,uBAAuB;CAC1B;;CAED;IACI,iBAAiB;IACjB,aAAqC;IACrC,gBAAuC;IACvC,iBAAiB;IACjB,wBAAwB;IACxB,oBAAoB;IACpB,kBAA6C;CAChD;;CAED;IACI,WAAW;IACX,aAAqC;IACrC,gBAAuC;IACvC,iBAAiB;IACjB,wBAAwB;IACxB,oBAAoB;IACpB,kBAA6C;CAChD;;CAED;IACI,6BAA6B;IAC7B,aAAqC;IACrC,kBAAkB;IAClB,kBAA0D;IAC1D,YAA4C;IAC5C,qBAAe;QAAf,eAAe;CAClB;;CAED;IACI,2BAA2B;IAC3B,aAAqC;IACrC,mBAAmB;IACnB,kBAA6C;CAChD;;CAED,4BAA4B;;CAE5B;IACI,aAAuC;IACvC,gBAAuC;IACvC,aAAwC;IACxC,kBAA6C;IAC7C,iBAAiB;IACjB,oBAAoB;IACpB,mBAAmB;CACtB;;CAED;IACI,yBAAyB;;IAEzB;;;;OAIG;IACH;;uDAEoD;;IAMpD;;+CAE4C;CAC/C;;CAED;IACI,wBAAwB;IACxB,mBAAmB;IACnB,iBAAgD;IAChD,gBAA+C;IAC/C,iBAA6C;CAChD;;CAED;IACI,sBAAsB;IACtB,gBAA4C;IAC5C,2BAA2B;IAC3B,eAAe;CAClB;;CAED,6BAA6B;;CAE7B;IACI,aAAsC;IACtC,aAAwC;IACxC,kBAA6C;CAChD;;CAED;IACI,wBAAgE;IAChE,kBAA6C;IAC7C,iBAAiB;IACjB,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,qBAAe;QAAf,eAAe;IACf,4BAAmB;QAAnB,mBAAmB;CACtB;;CAED,0BAA0B;;CAE1B;IACI,aAAwC;IACxC,kBAA6C;IAC7C,aAA4C;IAC5C,gBAAuC;CAC1C;;CAED;IACI,kBAA6C;IAC7C,kBAA8C;IAC9C,iBAA6C;;IAE7C,0JAA0J;IAC1J,sBAAsB;IACtB,8CAA8C;IAC9C,mBAAmB;IACnB,qBAAqB;IACrB,oCAAoC;IACpC,mCAAmC;CACtC;;CAED;IACI,iBAAiB;IACjB,aAAa;CAChB;;CAED;IACI,iBAAiB;IACjB,WAAW;CACd;;CAED;IACI,cAAc;CACjB;;CAED,qCAAqC;;CAErC;IACI,aAAsC;CACzC;;CAED;IACI,aAAwC;IACxC,kBAA6C;CAChD;;CAED;IACI,aAA4C;CAC/C;;CAED;IACI,+BAAuB;YAAvB,uBAAuB;IACvB,0BAAwF;IACxF,wBAA2D;IAC3D,yBAAqC;IACrC,gBAAuC;IACvC,iBAAsF;IACtF,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,aAAa,CAAC,iEAAiE;IAC/E,qBAAe;QAAf,eAAe;IACf,yBAAyB;CAC5B;;CAED;IACI,gBAAgB;IAChB,eAAe;CAClB;;CAED;IACI,sBAAyD;CAC5D;;CAED,mBAAmB;;CAEnB;IACI,kBAAkB;IAClB,0BAA4E;IAC5E,oBAAoC;IACpC,+BAAuB;YAAvB,uBAAuB;IACvB,mBAAmB;IACnB,mBAAmB;CACtB;;CAED;IACI,mBAAmB;IACnB,yBAAyB,CAAC,oDAAoD;IAC9E,mBAAmB;IACnB,wBAAmE;IACnE,0BAAiG;IACjG,+BAAuB;YAAvB,uBAAuB;IACvB,WAAW;IACX,uBAAuB,CAAC,wBAAwB;CACnD;;CAED,wBAAwB;;CACxB;IACI,0BAA+D;IAC/D,0BAAiG;CACpG;;CAED;IACI,0BAA+D;IAC/D,sBAA2D;IAC3D,WAAW;IACX,8BAAsB;YAAtB,sBAAsB;CACzB;;CAED;IACI,iEAAiE;IACjE,mBAAmB;IACnB,oBAAyD;IACzD,WAAW;CACd;;CAED,8BAA8B;;CAE9B;IACI,YAA4C;IAC5C,aAA6C;IAC7C,iBAAgJ;IAChJ,kBAAqG;IACrG,mBAAmB;IACnB,OAAO;CACV;;CAED;IACI,YAA4C;IAC5C,aAA6C;IAC7C,oBAAuG;IACvG,kBAAiJ;IACjJ,mBAAmB;IACnB,QAAQ;CACX;;CAED;IACI,YAA6D;IAC7D,iBAAyJ;CAC5J;;CAED;IACI,WAA4D;IAC5D,kBAA0J;CAC7J;;CAED,uBAAuB;;CAEvB;IACI,aAAsC;IACtC,aAAwC;IACxC,kBAA6C;;IAE7C;;oDAEgD;IAChD,0BAAoB;QAApB,uBAAoB;YAApB,oBAAoB;CACvB;;CAED;IACI,kBAAkB;CACrB;;CAED;IACI,aAAwC;IACxC,iBAAwG;IACxG,kBAAyG;IACzG,oBAA+C;QAA/C,oBAA+C;YAA/C,gBAA+C;CAClD;;CAED;IACI,gCAAgC;IAChC,YAAiD;IACjD,iBAAmG;IACnG,YAAY;CACf;;CAED,qBAAqB;;CAErB;IACI,aAAwC;IACxC,kBAA6C;CAChD;;CAED;IACI,qBAAqB;IACrB,cAA0C;IAC1C,YAA2C;CAC9C;;CAED;IACI,oBAA+C;QAA/C,oBAA+C;YAA/C,gBAA+C;IAC/C,kBAAkB;IAClB,mBAAmB;IACnB,mBAA0G;IAC1G,gBAAuG;IACvG,qBAAc;IAAd,qBAAc;IAAd,cAAc;IACd,6BAAuB;IAAvB,8BAAuB;QAAvB,2BAAuB;YAAvB,uBAAuB;CAC1B;;CAED;IACI,gCAAgC;IAChC,WAAgD;IAChD,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,kBAAkB;IAClB,mBAAmB;CACtB;;CAED,6BAA6B;;CAE7B;IACI,yBAAyB;IAIzB,iBAAiB;CACpB;;CAED;IACI,aAAwC;CAC3C;;CAED;IACI,0BAAyC;CAC5C;;CAED;IACI,0BAA2C;CAC9C;;CAED;IACI,0BAAwC;CAC3C;;CAED;IACI,0BAAwC;CAC3C;;CAED;IACI,0BAAyC;CAC5C;;CAED;IACI,0BAA0C;IAC1C,aAAa;IACb,yBAAiB;YAAjB,iBAAiB;CACpB;;CAED,yBAAyB;;CAEzB;IACI,kBAAkB;IAClB,aAAwC;IACxC,kBAA6C;IAC7C,aAAsC;IACtC,0BAAoB;QAApB,uBAAoB;YAApB,oBAAoB;;CAEvB;;CAED;IACI,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,gBAA4C;IAC5C,mBAA+C;IAC/C,6BAAoB;QAApB,oBAAoB;IACpB,8BAA8B;IAC9B,aAAgB;IAAhB,gBAAgB;CACnB;;CAED,uBAAuB;;CAEvB;IACI,cAA0C;IAC1C,YAA2C;CAC9C;;CAED;IACI,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,YAA4C;IAC5C,kBAAkB;IAClB,mBAAmB;IACnB,iBAAiB;CACpB;;CAED,2BAA2B;;CAE3B;IACI,aAAwC;IACxC,aAAsC;IACtC,kBAA6C;CAChD;;CAED;IACI,oBAAoB;IACpB,0BAAwF;IACxF,iBAAiB;IACjB,gBAAgB;IAChB,oBAA+C;QAA/C,oBAA+C;YAA/C,gBAA+C;IAC/C,aAAa,CAAC,iEAAiE;IAC/E,+BAAuB;YAAvB,uBAAuB;IACvB,yBAAyB;IACzB,yBAAiB;YAAjB,iBAAiB;IACjB,wBAA2D;IAC3D,yBAAqC;IACrC,gBAAuC;IACvC,oBAAoB;IACpB,kBAAyD;CAC5D,iBAAiB;CACjB,yBAAyB;CACzB,sBAAsB;IACnB,6BAA6B;CAChC,sBAAsB;CACtB,kCAAkC;IAC/B,kuBAAmD;CACtD;;CACD;IACI,sBAAyD;CAC5D;;CAED;IACI,aAA4C;CAC/C;;CAED;6CAC6C;;CAC7C;IACI,mBAAmB;IACnB,wBAAwB;CAC3B;;CAED,+BAA+B;;CAE/B;IACI,aAAsC;IACtC,kBAA6C;;IAE7C;;kEAE8D;IAC9D,yBAAwB;QAAxB,sBAAwB;YAAxB,wBAAwB;CAC3B;;CAED;IACI,0BAAwF;IACxF,wBAA2D;IAC3D,yBAAqC;IACrC,gBAAuC;IACvC,oBAA+C;QAA/C,oBAA+C;YAA/C,gBAA+C;IAC/C,yBAAyB;IACzB,eAAe;IACf,gBAAgB;;IAEhB;;kEAE8D;IAC9D,iBAAiB;CACpB;;CAED;IACI,sBAAyD;CAC5D;;CAED;IACI,kBAA8C;IAC9C,kBAA6C;IAC7C,kEAAkE;IAClE,0DAAiF;IACjF,6DAAoF;CACvF;;CAID,4BAA4B;;CAE5B;IACI,kBAA6C;CAChD;;CAED;IACI,iBAAsC;IACtC,kBAAuC;CAC1C;;CAED;IACI,aAA4C;CAC/C;;CAED,2BAA2B;;CAE3B;IACI,aAAsC;IACtC,kBAA6C;CAChD;;CAED;IACI,qBAAc;IAAd,qBAAc;IAAd,cAAc;IACd,6BAAuB;IAAvB,8BAAuB;QAAvB,2BAAuB;YAAvB,uBAAuB;IACvB,2BAAqB;QAArB,wBAAqB;YAArB,qBAAqB;IACrB,+BAAuB;YAAvB,uBAAuB;IACvB,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,mBAA8D;CACjE;;CAED;IACI,aAA4C;IAC5C,kBAAiD;IACjD,gBAAuC;CAC1C;;CAED;IACI,aAA4C;IAC5C,kBAAiD;IACjD,oBAA4D;IAC5D,YAAY;CACf;;CAED,0BAA0B;;CAE1B;IACI,aAAsC;IACtC,aAAwC;IACxC,kBAA6C;CAChD;;CAED;IACI,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,qBAAe;QAAf,eAAe;IACf,gBAA+C;CAClD;;CAED;IACI,YAAuC;IACvC,aAAwC;IACxC,eAAe,CAAC,6DAA6D;IAC7E,kBAAqD;IACrD,yBAAqC;IACrC,0BAAwF;IACxF,kBAAkB;IAClB,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,qBAAe;QAAf,eAAe;IACf,+BAAuB;YAAvB,uBAAuB;IACvB,6BAAoB;QAApB,oBAAoB;IACpB,yBAAyB;CAC5B;;CAED;IACI,+BAA6F;CAChG;;CAED;IACI,sBAAyD;CAC5D;;CAED;IACI,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,yBAAyB;IACzB,aAAwC;IACxC,kBAA6C;IAC7C,kBAAqD;IACrD,yBAAqC;IACrC,0BAAwF;IACxF,gBAAuC;IACvC,iBAAsF;IACtF,aAAa,CAAC,iEAAiE;IAC/E,qBAAe;QAAf,eAAe;IACf,+BAAuB;YAAvB,uBAAuB;CAC1B;;CAED;IACI,aAA4C;CAC/C;;CAED,yBAAyB;;CAEzB;IACI,aAAsC;IACtC,aAAwC;IACxC,kBAA6C;CAChD;;CAED;IACI,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,qBAAe;QAAf,eAAe;IACf,aAAa,CAAC,iEAAiE;IAC/E,yBAAyB;IACzB,aAAwC;IACxC,0BAAwF;IACxF,wBAA2D;IAC3D,yBAAqC;IACrC,gBAAuC;IACvC,iBAAsF;IACtF,+BAAuB;YAAvB,uBAAuB;CAC1B;;CAED;IACI,sBAAyD;CAC5D;;CAED;IACI,sBAAoC;CACvC;;CAED;IACI,aAA4C;CAC/C;;CAED,iBAAiB;;CAEjB;IACI,aAA4C;IAC5C,qBAAc;IAAd,qBAAc;IAAd,cAAc;IACd,2BAAqB;QAArB,wBAAqB;YAArB,qBAAqB;CACxB;;CAED;IACI,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,aAAa;CAChB;;CAED;IACI,aAA4C;CAC/C;;CAED,gBAAgB;;CAEhB;IACI,qBAAc;IAAd,qBAAc;IAAd,cAAc;IACd,6BAAuB;IAAvB,8BAAuB;QAAvB,2BAAuB;YAAvB,uBAAuB;CAC1B;;CAED;IACI,yFAAyF;IACzF,oBAAoB;IACpB,oBAAoB;CACvB;;CAED;IACI,iDAAiD;IACjD,uBAAsB;QAAtB,oBAAsB;YAAtB,sBAAsB;IACtB,aAAa;IACb,cAAc;CACjB;;CAED;IACI,YAAY;IACZ,+BAAuB;YAAvB,uBAAuB;IACvB,UAAU;IACV,kBAAoC;IACpC,yBAAgC;IAChC,0BAA6D;IAC7D,cAA6C;IAC7C,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,eAAe;CAClB;;CAED;IACI,wCAA+D;IAC/D,iBAAmF;CACtF;;CAED;IACI,oBAAiD;QAAjD,oBAAiD;YAAjD,gBAAiD;IACjD,gBAAgB;IAChB,iBAAmF;IACnF,kBAAqD;IACrD,kBAA+C;IAC/C,kBAAkB;IAClB,oBAAoC;IACpC,yBAAgC;IAChC,0BAA6D;IAC7D,oBAAoB;IACpB,mBAAmB;CACtB;;CAED;IACI,0BAAgC;IAChC,gEAAgE;IAChE,kBAAoC;IACpC,iBAAuF;IACvF,mCAA8C;YAA9C,2BAA8C;IAC9C,kBAAkB;CACrB;;CAED;IACI,mBAAmB;IACnB,UAAuC;IACvC,WAAwC;IACxC,YAAY;IACZ,YAAoD;IACpD,wBAA+C;IAC/C,oBAAmC;CACtC;;CAED;IACI,eAAe;CAClB;;CAED;IACI,kBAAoC;IACpC,yBAAgC;CACnC;;CAED;IACI,iBAAiB;CACpB;;CAED;IACI,yBAAyB;IACzB,iBAAiB,CAAC,WAAW;CAChC;;CAED;;;IAGI,kBAAqD;CACxD;;CAED,sBAAsB;;CAEtB;IACI,qBAAc;IAAd,qBAAc;IAAd,cAAc;IACd,6BAAuB;IAAvB,8BAAuB;QAAvB,2BAAuB;YAAvB,uBAAuB;IACvB,2BAAqB;QAArB,wBAAqB;YAArB,qBAAqB;CACxB;;CAED;IACI,aAAyC;IACzC,gBAAgB;IAChB,yBAAgC;IAChC,0BAA0C;IAC1C,0BAAqE;IACrE,mBAA+F;IAC/F,kBAAkB;CACrB;;CAED;IACI,wBAA0C;IAC1C,yBAAgC;CACnC;;CAED;IACI,wBAA0C;IAC1C,0BAAgC;IAChC,gBAAgB;IAChB,oBAAoB;CACvB;;CAED;IACI,sBAAsB,EAAE,qCAAqC;IAC7D,sBAAsB;IACtB,8CAA8C;IAC9C,mBAAmB;IACnB,qBAAqB;IACrB,oCAAoC;IACpC,mCAAmC;CACtC;;CAED;IACI,sBAAsB,CAAC,oCAAoC;CAC9D;;CAED;IACI,cAA6C;IAC7C,wBAA0C;IAC1C,yBAAgC;IAChC,+BAA0E;IAC1E,gCAA2E;IAC3E,iCAA4E;IAC5E,eAAe;CAClB;;CAED;IACI,qBAAc;IAAd,qBAAc;IAAd,cAAc;IACd,6BAAuB;IAAvB,8BAAuB;QAAvB,2BAAuB;YAAvB,uBAAuB;IACvB,2BAAqB;QAArB,wBAAqB;YAArB,qBAAqB;CACxB;;CAED;IACI,iBAAiB;CACpB;;CAED;IACI,gBAAgB;CACnB;;CAID,iBAAiB;;CAEjB;IACI,gBAAuC;CAC1C;;CAED;IACI,0CAA0C;IAC1C,6BAAoB;QAApB,oBAAoB;IACpB,oBAAa;QAAb,qBAAa;YAAb,aAAa;IACb,qBAAe;QAAf,eAAe;IACf,kEAAkE;IAClE,kBAA6C;IAC7C,yEAAyE;IACzE,mBAAmB;CACtB","file":"controls.css","sourcesContent":["/* Copyright (c) Jupyter Development Team.\n * Distributed under the terms of the Modified BSD License.\n */\n\n /* We import all of these together in a single css file because the Webpack\nloader sees only one file at a time. This allows postcss to see the variable\ndefinitions when they are used. */\n\n@import \"./labvariables.css\";\n@import \"./widgets-base.css\";\n","/*-----------------------------------------------------------------------------\n| Copyright (c) Jupyter Development Team.\n| Distributed under the terms of the Modified BSD License.\n|----------------------------------------------------------------------------*/\n\n/*\nThis file is copied from the JupyterLab project to define default styling for\nwhen the widget styling is compiled down to eliminate CSS variables. We make one\nchange - we comment out the font import below.\n*/\n\n@import \"./materialcolors.css\";\n\n/*\nThe following CSS variables define the main, public API for styling JupyterLab.\nThese variables should be used by all plugins wherever possible. In other\nwords, plugins should not define custom colors, sizes, etc unless absolutely\nnecessary. This enables users to change the visual theme of JupyterLab\nby changing these variables.\n\nMany variables appear in an ordered sequence (0,1,2,3). These sequences\nare designed to work well together, so for example, `--jp-border-color1` should\nbe used with `--jp-layout-color1`. The numbers have the following meanings:\n\n* 0: super-primary, reserved for special emphasis\n* 1: primary, most important under normal situations\n* 2: secondary, next most important under normal situations\n* 3: tertiary, next most important under normal situations\n\nThroughout JupyterLab, we are mostly following principles from Google's\nMaterial Design when selecting colors. We are not, however, following\nall of MD as it is not optimized for dense, information rich UIs.\n*/\n\n\n/*\n * Optional monospace font for input/output prompt.\n */\n /* Commented out in ipywidgets since we don't need it. */\n/* @import url('https://fonts.googleapis.com/css?family=Roboto+Mono'); */\n\n/*\n * Added for compabitility with output area\n */\n:root {\n  --jp-icon-search: none;\n  --jp-ui-select-caret: none;\n}\n\n\n:root {\n\n  /* Borders\n\n  The following variables, specify the visual styling of borders in JupyterLab.\n   */\n\n  --jp-border-width: 1px;\n  --jp-border-color0: var(--md-grey-700);\n  --jp-border-color1: var(--md-grey-500);\n  --jp-border-color2: var(--md-grey-300);\n  --jp-border-color3: var(--md-grey-100);\n\n  /* UI Fonts\n\n  The UI font CSS variables are used for the typography all of the JupyterLab\n  user interface elements that are not directly user generated content.\n  */\n\n  --jp-ui-font-scale-factor: 1.2;\n  --jp-ui-font-size0: calc(var(--jp-ui-font-size1)/var(--jp-ui-font-scale-factor));\n  --jp-ui-font-size1: 13px; /* Base font size */\n  --jp-ui-font-size2: calc(var(--jp-ui-font-size1)*var(--jp-ui-font-scale-factor));\n  --jp-ui-font-size3: calc(var(--jp-ui-font-size2)*var(--jp-ui-font-scale-factor));\n  --jp-ui-icon-font-size: 14px; /* Ensures px perfect FontAwesome icons */\n  --jp-ui-font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n\n  /* Use these font colors against the corresponding main layout colors.\n     In a light theme, these go from dark to light.\n  */\n\n  --jp-ui-font-color0: rgba(0,0,0,1.0);\n  --jp-ui-font-color1: rgba(0,0,0,0.8);\n  --jp-ui-font-color2: rgba(0,0,0,0.5);\n  --jp-ui-font-color3: rgba(0,0,0,0.3);\n\n  /* Use these against the brand/accent/warn/error colors.\n     These will typically go from light to darker, in both a dark and light theme\n   */\n\n  --jp-inverse-ui-font-color0: rgba(255,255,255,1);\n  --jp-inverse-ui-font-color1: rgba(255,255,255,1.0);\n  --jp-inverse-ui-font-color2: rgba(255,255,255,0.7);\n  --jp-inverse-ui-font-color3: rgba(255,255,255,0.5);\n\n  /* Content Fonts\n\n  Content font variables are used for typography of user generated content.\n  */\n\n  --jp-content-font-size: 13px;\n  --jp-content-line-height: 1.5;\n  --jp-content-font-color0: black;\n  --jp-content-font-color1: black;\n  --jp-content-font-color2: var(--md-grey-700);\n  --jp-content-font-color3: var(--md-grey-500);\n\n  --jp-ui-font-scale-factor: 1.2;\n  --jp-ui-font-size0: calc(var(--jp-ui-font-size1)/var(--jp-ui-font-scale-factor));\n  --jp-ui-font-size1: 13px; /* Base font size */\n  --jp-ui-font-size2: calc(var(--jp-ui-font-size1)*var(--jp-ui-font-scale-factor));\n  --jp-ui-font-size3: calc(var(--jp-ui-font-size2)*var(--jp-ui-font-scale-factor));\n\n  --jp-code-font-size: 13px;\n  --jp-code-line-height: 1.307;\n  --jp-code-padding: 5px;\n  --jp-code-font-family: monospace;\n\n\n  /* Layout\n\n  The following are the main layout colors use in JupyterLab. In a light\n  theme these would go from light to dark.\n  */\n\n  --jp-layout-color0: white;\n  --jp-layout-color1: white;\n  --jp-layout-color2: var(--md-grey-200);\n  --jp-layout-color3: var(--md-grey-400);\n\n  /* Brand/accent */\n\n  --jp-brand-color0: var(--md-blue-700);\n  --jp-brand-color1: var(--md-blue-500);\n  --jp-brand-color2: var(--md-blue-300);\n  --jp-brand-color3: var(--md-blue-100);\n\n  --jp-accent-color0: var(--md-green-700);\n  --jp-accent-color1: var(--md-green-500);\n  --jp-accent-color2: var(--md-green-300);\n  --jp-accent-color3: var(--md-green-100);\n\n  /* State colors (warn, error, success, info) */\n\n  --jp-warn-color0: var(--md-orange-700);\n  --jp-warn-color1: var(--md-orange-500);\n  --jp-warn-color2: var(--md-orange-300);\n  --jp-warn-color3: var(--md-orange-100);\n\n  --jp-error-color0: var(--md-red-700);\n  --jp-error-color1: var(--md-red-500);\n  --jp-error-color2: var(--md-red-300);\n  --jp-error-color3: var(--md-red-100);\n\n  --jp-success-color0: var(--md-green-700);\n  --jp-success-color1: var(--md-green-500);\n  --jp-success-color2: var(--md-green-300);\n  --jp-success-color3: var(--md-green-100);\n\n  --jp-info-color0: var(--md-cyan-700);\n  --jp-info-color1: var(--md-cyan-500);\n  --jp-info-color2: var(--md-cyan-300);\n  --jp-info-color3: var(--md-cyan-100);\n\n  /* Cell specific styles */\n\n  --jp-cell-padding: 5px;\n  --jp-cell-editor-background: #f7f7f7;\n  --jp-cell-editor-border-color: #cfcfcf;\n  --jp-cell-editor-background-edit: var(--jp-ui-layout-color1);\n  --jp-cell-editor-border-color-edit: var(--jp-brand-color1);\n  --jp-cell-prompt-width: 100px;\n  --jp-cell-prompt-font-family: 'Roboto Mono', monospace;\n  --jp-cell-prompt-letter-spacing: 0px;\n  --jp-cell-prompt-opacity: 1.0;\n  --jp-cell-prompt-opacity-not-active: 0.4;\n  --jp-cell-prompt-font-color-not-active: var(--md-grey-700);\n  /* A custom blend of MD grey and blue 600\n   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */\n  --jp-cell-inprompt-font-color: #307FC1;\n  /* A custom blend of MD grey and orange 600\n   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */\n  --jp-cell-outprompt-font-color: #BF5B3D;\n\n  /* Notebook specific styles */\n\n  --jp-notebook-padding: 10px;\n  --jp-notebook-scroll-padding: 100px;\n\n  /* Console specific styles */\n\n  --jp-console-background: var(--md-grey-100);\n\n  /* Toolbar specific styles */\n\n  --jp-toolbar-border-color: var(--md-grey-400);\n  --jp-toolbar-micro-height: 8px;\n  --jp-toolbar-background: var(--jp-layout-color0);\n  --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0,0,0,0.24);\n  --jp-toolbar-header-margin: 4px 4px 0px 4px;\n  --jp-toolbar-active-background: var(--md-grey-300);\n}\n","/**\n * The material design colors are adapted from google-material-color v1.2.6\n * https://github.com/danlevan/google-material-color\n * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/dist/palette.var.css\n *\n * The license for the material design color CSS variables is as follows (see\n * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)\n *\n * The MIT License (MIT)\n *\n * Copyright (c) 2014 Dan Le Van\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to deal\n * in the Software without restriction, including without limitation the rights\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n * SOFTWARE.\n */\n:root {\n  --md-red-50: #FFEBEE;\n  --md-red-100: #FFCDD2;\n  --md-red-200: #EF9A9A;\n  --md-red-300: #E57373;\n  --md-red-400: #EF5350;\n  --md-red-500: #F44336;\n  --md-red-600: #E53935;\n  --md-red-700: #D32F2F;\n  --md-red-800: #C62828;\n  --md-red-900: #B71C1C;\n  --md-red-A100: #FF8A80;\n  --md-red-A200: #FF5252;\n  --md-red-A400: #FF1744;\n  --md-red-A700: #D50000;\n\n  --md-pink-50: #FCE4EC;\n  --md-pink-100: #F8BBD0;\n  --md-pink-200: #F48FB1;\n  --md-pink-300: #F06292;\n  --md-pink-400: #EC407A;\n  --md-pink-500: #E91E63;\n  --md-pink-600: #D81B60;\n  --md-pink-700: #C2185B;\n  --md-pink-800: #AD1457;\n  --md-pink-900: #880E4F;\n  --md-pink-A100: #FF80AB;\n  --md-pink-A200: #FF4081;\n  --md-pink-A400: #F50057;\n  --md-pink-A700: #C51162;\n\n  --md-purple-50: #F3E5F5;\n  --md-purple-100: #E1BEE7;\n  --md-purple-200: #CE93D8;\n  --md-purple-300: #BA68C8;\n  --md-purple-400: #AB47BC;\n  --md-purple-500: #9C27B0;\n  --md-purple-600: #8E24AA;\n  --md-purple-700: #7B1FA2;\n  --md-purple-800: #6A1B9A;\n  --md-purple-900: #4A148C;\n  --md-purple-A100: #EA80FC;\n  --md-purple-A200: #E040FB;\n  --md-purple-A400: #D500F9;\n  --md-purple-A700: #AA00FF;\n\n  --md-deep-purple-50: #EDE7F6;\n  --md-deep-purple-100: #D1C4E9;\n  --md-deep-purple-200: #B39DDB;\n  --md-deep-purple-300: #9575CD;\n  --md-deep-purple-400: #7E57C2;\n  --md-deep-purple-500: #673AB7;\n  --md-deep-purple-600: #5E35B1;\n  --md-deep-purple-700: #512DA8;\n  --md-deep-purple-800: #4527A0;\n  --md-deep-purple-900: #311B92;\n  --md-deep-purple-A100: #B388FF;\n  --md-deep-purple-A200: #7C4DFF;\n  --md-deep-purple-A400: #651FFF;\n  --md-deep-purple-A700: #6200EA;\n\n  --md-indigo-50: #E8EAF6;\n  --md-indigo-100: #C5CAE9;\n  --md-indigo-200: #9FA8DA;\n  --md-indigo-300: #7986CB;\n  --md-indigo-400: #5C6BC0;\n  --md-indigo-500: #3F51B5;\n  --md-indigo-600: #3949AB;\n  --md-indigo-700: #303F9F;\n  --md-indigo-800: #283593;\n  --md-indigo-900: #1A237E;\n  --md-indigo-A100: #8C9EFF;\n  --md-indigo-A200: #536DFE;\n  --md-indigo-A400: #3D5AFE;\n  --md-indigo-A700: #304FFE;\n\n  --md-blue-50: #E3F2FD;\n  --md-blue-100: #BBDEFB;\n  --md-blue-200: #90CAF9;\n  --md-blue-300: #64B5F6;\n  --md-blue-400: #42A5F5;\n  --md-blue-500: #2196F3;\n  --md-blue-600: #1E88E5;\n  --md-blue-700: #1976D2;\n  --md-blue-800: #1565C0;\n  --md-blue-900: #0D47A1;\n  --md-blue-A100: #82B1FF;\n  --md-blue-A200: #448AFF;\n  --md-blue-A400: #2979FF;\n  --md-blue-A700: #2962FF;\n\n  --md-light-blue-50: #E1F5FE;\n  --md-light-blue-100: #B3E5FC;\n  --md-light-blue-200: #81D4FA;\n  --md-light-blue-300: #4FC3F7;\n  --md-light-blue-400: #29B6F6;\n  --md-light-blue-500: #03A9F4;\n  --md-light-blue-600: #039BE5;\n  --md-light-blue-700: #0288D1;\n  --md-light-blue-800: #0277BD;\n  --md-light-blue-900: #01579B;\n  --md-light-blue-A100: #80D8FF;\n  --md-light-blue-A200: #40C4FF;\n  --md-light-blue-A400: #00B0FF;\n  --md-light-blue-A700: #0091EA;\n\n  --md-cyan-50: #E0F7FA;\n  --md-cyan-100: #B2EBF2;\n  --md-cyan-200: #80DEEA;\n  --md-cyan-300: #4DD0E1;\n  --md-cyan-400: #26C6DA;\n  --md-cyan-500: #00BCD4;\n  --md-cyan-600: #00ACC1;\n  --md-cyan-700: #0097A7;\n  --md-cyan-800: #00838F;\n  --md-cyan-900: #006064;\n  --md-cyan-A100: #84FFFF;\n  --md-cyan-A200: #18FFFF;\n  --md-cyan-A400: #00E5FF;\n  --md-cyan-A700: #00B8D4;\n\n  --md-teal-50: #E0F2F1;\n  --md-teal-100: #B2DFDB;\n  --md-teal-200: #80CBC4;\n  --md-teal-300: #4DB6AC;\n  --md-teal-400: #26A69A;\n  --md-teal-500: #009688;\n  --md-teal-600: #00897B;\n  --md-teal-700: #00796B;\n  --md-teal-800: #00695C;\n  --md-teal-900: #004D40;\n  --md-teal-A100: #A7FFEB;\n  --md-teal-A200: #64FFDA;\n  --md-teal-A400: #1DE9B6;\n  --md-teal-A700: #00BFA5;\n\n  --md-green-50: #E8F5E9;\n  --md-green-100: #C8E6C9;\n  --md-green-200: #A5D6A7;\n  --md-green-300: #81C784;\n  --md-green-400: #66BB6A;\n  --md-green-500: #4CAF50;\n  --md-green-600: #43A047;\n  --md-green-700: #388E3C;\n  --md-green-800: #2E7D32;\n  --md-green-900: #1B5E20;\n  --md-green-A100: #B9F6CA;\n  --md-green-A200: #69F0AE;\n  --md-green-A400: #00E676;\n  --md-green-A700: #00C853;\n\n  --md-light-green-50: #F1F8E9;\n  --md-light-green-100: #DCEDC8;\n  --md-light-green-200: #C5E1A5;\n  --md-light-green-300: #AED581;\n  --md-light-green-400: #9CCC65;\n  --md-light-green-500: #8BC34A;\n  --md-light-green-600: #7CB342;\n  --md-light-green-700: #689F38;\n  --md-light-green-800: #558B2F;\n  --md-light-green-900: #33691E;\n  --md-light-green-A100: #CCFF90;\n  --md-light-green-A200: #B2FF59;\n  --md-light-green-A400: #76FF03;\n  --md-light-green-A700: #64DD17;\n\n  --md-lime-50: #F9FBE7;\n  --md-lime-100: #F0F4C3;\n  --md-lime-200: #E6EE9C;\n  --md-lime-300: #DCE775;\n  --md-lime-400: #D4E157;\n  --md-lime-500: #CDDC39;\n  --md-lime-600: #C0CA33;\n  --md-lime-700: #AFB42B;\n  --md-lime-800: #9E9D24;\n  --md-lime-900: #827717;\n  --md-lime-A100: #F4FF81;\n  --md-lime-A200: #EEFF41;\n  --md-lime-A400: #C6FF00;\n  --md-lime-A700: #AEEA00;\n\n  --md-yellow-50: #FFFDE7;\n  --md-yellow-100: #FFF9C4;\n  --md-yellow-200: #FFF59D;\n  --md-yellow-300: #FFF176;\n  --md-yellow-400: #FFEE58;\n  --md-yellow-500: #FFEB3B;\n  --md-yellow-600: #FDD835;\n  --md-yellow-700: #FBC02D;\n  --md-yellow-800: #F9A825;\n  --md-yellow-900: #F57F17;\n  --md-yellow-A100: #FFFF8D;\n  --md-yellow-A200: #FFFF00;\n  --md-yellow-A400: #FFEA00;\n  --md-yellow-A700: #FFD600;\n\n  --md-amber-50: #FFF8E1;\n  --md-amber-100: #FFECB3;\n  --md-amber-200: #FFE082;\n  --md-amber-300: #FFD54F;\n  --md-amber-400: #FFCA28;\n  --md-amber-500: #FFC107;\n  --md-amber-600: #FFB300;\n  --md-amber-700: #FFA000;\n  --md-amber-800: #FF8F00;\n  --md-amber-900: #FF6F00;\n  --md-amber-A100: #FFE57F;\n  --md-amber-A200: #FFD740;\n  --md-amber-A400: #FFC400;\n  --md-amber-A700: #FFAB00;\n\n  --md-orange-50: #FFF3E0;\n  --md-orange-100: #FFE0B2;\n  --md-orange-200: #FFCC80;\n  --md-orange-300: #FFB74D;\n  --md-orange-400: #FFA726;\n  --md-orange-500: #FF9800;\n  --md-orange-600: #FB8C00;\n  --md-orange-700: #F57C00;\n  --md-orange-800: #EF6C00;\n  --md-orange-900: #E65100;\n  --md-orange-A100: #FFD180;\n  --md-orange-A200: #FFAB40;\n  --md-orange-A400: #FF9100;\n  --md-orange-A700: #FF6D00;\n\n  --md-deep-orange-50: #FBE9E7;\n  --md-deep-orange-100: #FFCCBC;\n  --md-deep-orange-200: #FFAB91;\n  --md-deep-orange-300: #FF8A65;\n  --md-deep-orange-400: #FF7043;\n  --md-deep-orange-500: #FF5722;\n  --md-deep-orange-600: #F4511E;\n  --md-deep-orange-700: #E64A19;\n  --md-deep-orange-800: #D84315;\n  --md-deep-orange-900: #BF360C;\n  --md-deep-orange-A100: #FF9E80;\n  --md-deep-orange-A200: #FF6E40;\n  --md-deep-orange-A400: #FF3D00;\n  --md-deep-orange-A700: #DD2C00;\n\n  --md-brown-50: #EFEBE9;\n  --md-brown-100: #D7CCC8;\n  --md-brown-200: #BCAAA4;\n  --md-brown-300: #A1887F;\n  --md-brown-400: #8D6E63;\n  --md-brown-500: #795548;\n  --md-brown-600: #6D4C41;\n  --md-brown-700: #5D4037;\n  --md-brown-800: #4E342E;\n  --md-brown-900: #3E2723;\n\n  --md-grey-50: #FAFAFA;\n  --md-grey-100: #F5F5F5;\n  --md-grey-200: #EEEEEE;\n  --md-grey-300: #E0E0E0;\n  --md-grey-400: #BDBDBD;\n  --md-grey-500: #9E9E9E;\n  --md-grey-600: #757575;\n  --md-grey-700: #616161;\n  --md-grey-800: #424242;\n  --md-grey-900: #212121;\n\n  --md-blue-grey-50: #ECEFF1;\n  --md-blue-grey-100: #CFD8DC;\n  --md-blue-grey-200: #B0BEC5;\n  --md-blue-grey-300: #90A4AE;\n  --md-blue-grey-400: #78909C;\n  --md-blue-grey-500: #607D8B;\n  --md-blue-grey-600: #546E7A;\n  --md-blue-grey-700: #455A64;\n  --md-blue-grey-800: #37474F;\n  --md-blue-grey-900: #263238;\n}","/* Copyright (c) Jupyter Development Team.\n * Distributed under the terms of the Modified BSD License.\n */\n\n/*\n * We assume that the CSS variables in\n * https://github.com/jupyterlab/jupyterlab/blob/master/src/default-theme/variables.css\n * have been defined.\n */\n\n@import \"./phosphor.css\";\n\n:root {\n    --jp-widgets-color: var(--jp-content-font-color1);\n    --jp-widgets-label-color: var(--jp-widgets-color);\n    --jp-widgets-readout-color: var(--jp-widgets-color);\n    --jp-widgets-font-size: var(--jp-ui-font-size1);\n    --jp-widgets-margin: 2px;\n    --jp-widgets-inline-height: 28px;\n    --jp-widgets-inline-width: 300px;\n    --jp-widgets-inline-width-short: calc(var(--jp-widgets-inline-width) / 2 - var(--jp-widgets-margin));\n    --jp-widgets-inline-width-tiny: calc(var(--jp-widgets-inline-width-short) / 2 - var(--jp-widgets-margin));\n    --jp-widgets-inline-margin: 4px; /* margin between inline elements */\n    --jp-widgets-inline-label-width: 80px;\n    --jp-widgets-border-width: var(--jp-border-width);\n    --jp-widgets-vertical-height: 200px;\n    --jp-widgets-horizontal-tab-height: 24px;\n    --jp-widgets-horizontal-tab-width: 144px;\n    --jp-widgets-horizontal-tab-top-border: 2px;\n    --jp-widgets-progress-thickness: 20px;\n    --jp-widgets-container-padding: 15px;\n    --jp-widgets-input-padding: 4px;\n    --jp-widgets-radio-item-height-adjustment: 8px;\n    --jp-widgets-radio-item-height: calc(var(--jp-widgets-inline-height) - var(--jp-widgets-radio-item-height-adjustment));\n    --jp-widgets-slider-track-thickness: 4px;\n    --jp-widgets-slider-border-width: var(--jp-widgets-border-width);\n    --jp-widgets-slider-handle-size: 16px;\n    --jp-widgets-slider-handle-border-color: var(--jp-border-color1);\n    --jp-widgets-slider-handle-background-color: var(--jp-layout-color1);\n    --jp-widgets-slider-active-handle-color: var(--jp-brand-color1);\n    --jp-widgets-menu-item-height: 24px;\n    --jp-widgets-dropdown-arrow: url(\"data:image/svg+xml;base64,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\");\n    --jp-widgets-input-color: var(--jp-ui-font-color1);\n    --jp-widgets-input-background-color: var(--jp-layout-color1);\n    --jp-widgets-input-border-color: var(--jp-border-color1);\n    --jp-widgets-input-focus-border-color: var(--jp-brand-color2);\n    --jp-widgets-input-border-width: var(--jp-widgets-border-width);\n    --jp-widgets-disabled-opacity: 0.6;\n\n    /* From Material Design Lite */\n    --md-shadow-key-umbra-opacity: 0.2;\n    --md-shadow-key-penumbra-opacity: 0.14;\n    --md-shadow-ambient-shadow-opacity: 0.12;\n}\n\n.jupyter-widgets {\n    margin: var(--jp-widgets-margin);\n    box-sizing: border-box;\n    color: var(--jp-widgets-color);\n    overflow: visible;\n}\n\n.jupyter-widgets.jupyter-widgets-disconnected::before {\n    line-height: var(--jp-widgets-inline-height);\n    height: var(--jp-widgets-inline-height);\n}\n\n.jp-Output-result > .jupyter-widgets {\n    margin-left: 0;\n    margin-right: 0;\n}\n\n/* vbox and hbox */\n\n.widget-inline-hbox {\n    /* Horizontal widgets */\n    box-sizing: border-box;\n    display: flex;\n    flex-direction: row;\n    align-items: baseline;\n}\n\n.widget-inline-vbox {\n    /* Vertical Widgets */\n    box-sizing: border-box;\n    display: flex;\n    flex-direction: column;\n    align-items: center;\n}\n\n.widget-box {\n    box-sizing: border-box;\n    display: flex;\n    margin: 0;\n    overflow: auto;\n}\n\n.widget-gridbox {\n    box-sizing: border-box;\n    display: grid;\n    margin: 0;\n    overflow: auto;\n}\n\n.widget-hbox {\n    flex-direction: row;\n}\n\n.widget-vbox {\n    flex-direction: column;\n}\n\n/* General Button Styling */\n\n.jupyter-button {\n    padding-left: 10px;\n    padding-right: 10px;\n    padding-top: 0px;\n    padding-bottom: 0px;\n    display: inline-block;\n    white-space: nowrap;\n    overflow: hidden;\n    text-overflow: ellipsis;\n    text-align: center;\n    font-size: var(--jp-widgets-font-size);\n    cursor: pointer;\n\n    height: var(--jp-widgets-inline-height);\n    border: 0px solid;\n    line-height: var(--jp-widgets-inline-height);\n    box-shadow: none;\n\n    color: var(--jp-ui-font-color1);\n    background-color: var(--jp-layout-color2);\n    border-color: var(--jp-border-color2);\n    border: none;\n}\n\n.jupyter-button i.fa {\n    margin-right: var(--jp-widgets-inline-margin);\n    pointer-events: none;\n}\n\n.jupyter-button:empty:before {\n    content: \"\\200b\"; /* zero-width space */\n}\n\n.jupyter-widgets.jupyter-button:disabled {\n    opacity: var(--jp-widgets-disabled-opacity);\n}\n\n.jupyter-button i.fa.center {\n    margin-right: 0;\n}\n\n.jupyter-button:hover:enabled, .jupyter-button:focus:enabled {\n    /* MD Lite 2dp shadow */\n    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, var(--md-shadow-key-penumbra-opacity)),\n                0 3px 1px -2px rgba(0, 0, 0, var(--md-shadow-key-umbra-opacity)),\n                0 1px 5px 0 rgba(0, 0, 0, var(--md-shadow-ambient-shadow-opacity));\n}\n\n.jupyter-button:active, .jupyter-button.mod-active {\n    /* MD Lite 4dp shadow */\n    box-shadow: 0 4px 5px 0 rgba(0, 0, 0, var(--md-shadow-key-penumbra-opacity)),\n                0 1px 10px 0 rgba(0, 0, 0, var(--md-shadow-ambient-shadow-opacity)),\n                0 2px 4px -1px rgba(0, 0, 0, var(--md-shadow-key-umbra-opacity));\n    color: var(--jp-ui-font-color1);\n    background-color: var(--jp-layout-color3);\n}\n\n.jupyter-button:focus:enabled {\n    outline: 1px solid var(--jp-widgets-input-focus-border-color);\n}\n\n/* Button \"Primary\" Styling */\n\n.jupyter-button.mod-primary {\n    color: var(--jp-inverse-ui-font-color1);\n    background-color: var(--jp-brand-color1);\n}\n\n.jupyter-button.mod-primary.mod-active {\n    color: var(--jp-inverse-ui-font-color0);\n    background-color: var(--jp-brand-color0);\n}\n\n.jupyter-button.mod-primary:active {\n    color: var(--jp-inverse-ui-font-color0);\n    background-color: var(--jp-brand-color0);\n}\n\n/* Button \"Success\" Styling */\n\n.jupyter-button.mod-success {\n    color: var(--jp-inverse-ui-font-color1);\n    background-color: var(--jp-success-color1);\n}\n\n.jupyter-button.mod-success.mod-active {\n    color: var(--jp-inverse-ui-font-color0);\n    background-color: var(--jp-success-color0);\n }\n\n.jupyter-button.mod-success:active {\n    color: var(--jp-inverse-ui-font-color0);\n    background-color: var(--jp-success-color0);\n }\n\n /* Button \"Info\" Styling */\n\n.jupyter-button.mod-info {\n    color: var(--jp-inverse-ui-font-color1);\n    background-color: var(--jp-info-color1);\n}\n\n.jupyter-button.mod-info.mod-active {\n    color: var(--jp-inverse-ui-font-color0);\n    background-color: var(--jp-info-color0);\n}\n\n.jupyter-button.mod-info:active {\n    color: var(--jp-inverse-ui-font-color0);\n    background-color: var(--jp-info-color0);\n}\n\n/* Button \"Warning\" Styling */\n\n.jupyter-button.mod-warning {\n    color: var(--jp-inverse-ui-font-color1);\n    background-color: var(--jp-warn-color1);\n}\n\n.jupyter-button.mod-warning.mod-active {\n    color: var(--jp-inverse-ui-font-color0);\n    background-color: var(--jp-warn-color0);\n}\n\n.jupyter-button.mod-warning:active {\n    color: var(--jp-inverse-ui-font-color0);\n    background-color: var(--jp-warn-color0);\n}\n\n/* Button \"Danger\" Styling */\n\n.jupyter-button.mod-danger {\n    color: var(--jp-inverse-ui-font-color1);\n    background-color: var(--jp-error-color1);\n}\n\n.jupyter-button.mod-danger.mod-active {\n    color: var(--jp-inverse-ui-font-color0);\n    background-color: var(--jp-error-color0);\n}\n\n.jupyter-button.mod-danger:active {\n    color: var(--jp-inverse-ui-font-color0);\n    background-color: var(--jp-error-color0);\n}\n\n/* Widget Button*/\n\n.widget-button, .widget-toggle-button {\n    width: var(--jp-widgets-inline-width-short);\n}\n\n/* Widget Label Styling */\n\n/* Override Bootstrap label css */\n.jupyter-widgets label {\n    margin-bottom: initial;\n}\n\n.widget-label-basic {\n    /* Basic Label */\n    color: var(--jp-widgets-label-color);\n    font-size: var(--jp-widgets-font-size);\n    overflow: hidden;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n    line-height: var(--jp-widgets-inline-height);\n}\n\n.widget-label {\n    /* Label */\n    color: var(--jp-widgets-label-color);\n    font-size: var(--jp-widgets-font-size);\n    overflow: hidden;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n    line-height: var(--jp-widgets-inline-height);\n}\n\n.widget-inline-hbox .widget-label {\n    /* Horizontal Widget Label */\n    color: var(--jp-widgets-label-color);\n    text-align: right;\n    margin-right: calc( var(--jp-widgets-inline-margin) * 2 );\n    width: var(--jp-widgets-inline-label-width);\n    flex-shrink: 0;\n}\n\n.widget-inline-vbox .widget-label {\n    /* Vertical Widget Label */\n    color: var(--jp-widgets-label-color);\n    text-align: center;\n    line-height: var(--jp-widgets-inline-height);\n}\n\n/* Widget Readout Styling */\n\n.widget-readout {\n    color: var(--jp-widgets-readout-color);\n    font-size: var(--jp-widgets-font-size);\n    height: var(--jp-widgets-inline-height);\n    line-height: var(--jp-widgets-inline-height);\n    overflow: hidden;\n    white-space: nowrap;\n    text-align: center;\n}\n\n.widget-readout.overflow {\n    /* Overflowing Readout */\n\n    /* From Material Design Lite\n        shadow-key-umbra-opacity: 0.2;\n        shadow-key-penumbra-opacity: 0.14;\n        shadow-ambient-shadow-opacity: 0.12;\n     */\n    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.2),\n                        0 3px 1px -2px rgba(0, 0, 0, 0.14),\n                        0 1px 5px 0 rgba(0, 0, 0, 0.12);\n\n    -moz-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.2),\n                     0 3px 1px -2px rgba(0, 0, 0, 0.14),\n                     0 1px 5px 0 rgba(0, 0, 0, 0.12);\n\n    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.2),\n                0 3px 1px -2px rgba(0, 0, 0, 0.14),\n                0 1px 5px 0 rgba(0, 0, 0, 0.12);\n}\n\n.widget-inline-hbox .widget-readout {\n    /* Horizontal Readout */\n    text-align: center;\n    max-width: var(--jp-widgets-inline-width-short);\n    min-width: var(--jp-widgets-inline-width-tiny);\n    margin-left: var(--jp-widgets-inline-margin);\n}\n\n.widget-inline-vbox .widget-readout {\n    /* Vertical Readout */\n    margin-top: var(--jp-widgets-inline-margin);\n    /* as wide as the widget */\n    width: inherit;\n}\n\n/* Widget Checkbox Styling */\n\n.widget-checkbox {\n    width: var(--jp-widgets-inline-width);\n    height: var(--jp-widgets-inline-height);\n    line-height: var(--jp-widgets-inline-height);\n}\n\n.widget-checkbox input[type=\"checkbox\"] {\n    margin: 0px calc( var(--jp-widgets-inline-margin) * 2 ) 0px 0px;\n    line-height: var(--jp-widgets-inline-height);\n    font-size: large;\n    flex-grow: 1;\n    flex-shrink: 0;\n    align-self: center;\n}\n\n/* Widget Valid Styling */\n\n.widget-valid {\n    height: var(--jp-widgets-inline-height);\n    line-height: var(--jp-widgets-inline-height);\n    width: var(--jp-widgets-inline-width-short);\n    font-size: var(--jp-widgets-font-size);\n}\n\n.widget-valid i:before {\n    line-height: var(--jp-widgets-inline-height);\n    margin-right: var(--jp-widgets-inline-margin);\n    margin-left: var(--jp-widgets-inline-margin);\n\n    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */\n    display: inline-block;\n    font: normal normal normal 14px/1 FontAwesome;\n    font-size: inherit;\n    text-rendering: auto;\n    -webkit-font-smoothing: antialiased;\n    -moz-osx-font-smoothing: grayscale;\n}\n\n.widget-valid.mod-valid i:before {\n    content: \"\\f00c\";\n    color: green;\n}\n\n.widget-valid.mod-invalid i:before {\n    content: \"\\f00d\";\n    color: red;\n}\n\n.widget-valid.mod-valid .widget-valid-readout {\n    display: none;\n}\n\n/* Widget Text and TextArea Stying */\n\n.widget-textarea, .widget-text {\n    width: var(--jp-widgets-inline-width);\n}\n\n.widget-text input[type=\"text\"], .widget-text input[type=\"number\"]{\n    height: var(--jp-widgets-inline-height);\n    line-height: var(--jp-widgets-inline-height);\n}\n\n.widget-text input[type=\"text\"]:disabled, .widget-text input[type=\"number\"]:disabled, .widget-textarea textarea:disabled {\n    opacity: var(--jp-widgets-disabled-opacity);\n}\n\n.widget-text input[type=\"text\"], .widget-text input[type=\"number\"], .widget-textarea textarea {\n    box-sizing: border-box;\n    border: var(--jp-widgets-input-border-width) solid var(--jp-widgets-input-border-color);\n    background-color: var(--jp-widgets-input-background-color);\n    color: var(--jp-widgets-input-color);\n    font-size: var(--jp-widgets-font-size);\n    padding: var(--jp-widgets-input-padding) calc( var(--jp-widgets-input-padding) *  2 );\n    flex-grow: 1;\n    min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n    flex-shrink: 1;\n    outline: none !important;\n}\n\n.widget-textarea textarea {\n    height: inherit;\n    width: inherit;\n}\n\n.widget-text input:focus, .widget-textarea textarea:focus {\n    border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n/* Widget Slider */\n\n.widget-slider .ui-slider {\n    /* Slider Track */\n    border: var(--jp-widgets-slider-border-width) solid var(--jp-layout-color3);\n    background: var(--jp-layout-color3);\n    box-sizing: border-box;\n    position: relative;\n    border-radius: 0px;\n}\n\n.widget-slider .ui-slider .ui-slider-handle {\n    /* Slider Handle */\n    outline: none !important; /* focused slider handles are colored - see below */\n    position: absolute;\n    background-color: var(--jp-widgets-slider-handle-background-color);\n    border: var(--jp-widgets-slider-border-width) solid var(--jp-widgets-slider-handle-border-color);\n    box-sizing: border-box;\n    z-index: 1;\n    background-image: none; /* Override jquery-ui */\n}\n\n/* Override jquery-ui */\n.widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {\n    background-color: var(--jp-widgets-slider-active-handle-color);\n    border: var(--jp-widgets-slider-border-width) solid var(--jp-widgets-slider-active-handle-color);\n}\n\n.widget-slider .ui-slider .ui-slider-handle:active {\n    background-color: var(--jp-widgets-slider-active-handle-color);\n    border-color: var(--jp-widgets-slider-active-handle-color);\n    z-index: 2;\n    transform: scale(1.2);\n}\n\n.widget-slider  .ui-slider .ui-slider-range {\n    /* Interval between the two specified value of a double slider */\n    position: absolute;\n    background: var(--jp-widgets-slider-active-handle-color);\n    z-index: 0;\n}\n\n/* Shapes of Slider Handles */\n\n.widget-hslider .ui-slider .ui-slider-handle {\n    width: var(--jp-widgets-slider-handle-size);\n    height: var(--jp-widgets-slider-handle-size);\n    margin-top: calc((var(--jp-widgets-slider-track-thickness) - var(--jp-widgets-slider-handle-size)) / 2 - var(--jp-widgets-slider-border-width));\n    margin-left: calc(var(--jp-widgets-slider-handle-size) / -2 + var(--jp-widgets-slider-border-width));\n    border-radius: 50%;\n    top: 0;\n}\n\n.widget-vslider .ui-slider .ui-slider-handle {\n    width: var(--jp-widgets-slider-handle-size);\n    height: var(--jp-widgets-slider-handle-size);\n    margin-bottom: calc(var(--jp-widgets-slider-handle-size) / -2 + var(--jp-widgets-slider-border-width));\n    margin-left: calc((var(--jp-widgets-slider-track-thickness) - var(--jp-widgets-slider-handle-size)) / 2 - var(--jp-widgets-slider-border-width));\n    border-radius: 50%;\n    left: 0;\n}\n\n.widget-hslider .ui-slider .ui-slider-range {\n    height: calc( var(--jp-widgets-slider-track-thickness) * 2 );\n    margin-top: calc((var(--jp-widgets-slider-track-thickness) - var(--jp-widgets-slider-track-thickness) * 2 ) / 2 - var(--jp-widgets-slider-border-width));\n}\n\n.widget-vslider .ui-slider .ui-slider-range {\n    width: calc( var(--jp-widgets-slider-track-thickness) * 2 );\n    margin-left: calc((var(--jp-widgets-slider-track-thickness) - var(--jp-widgets-slider-track-thickness) * 2 ) / 2 - var(--jp-widgets-slider-border-width));\n}\n\n/* Horizontal Slider */\n\n.widget-hslider {\n    width: var(--jp-widgets-inline-width);\n    height: var(--jp-widgets-inline-height);\n    line-height: var(--jp-widgets-inline-height);\n\n    /* Override the align-items baseline. This way, the description and readout\n    still seem to align their baseline properly, and we don't have to have\n    align-self: stretch in the .slider-container. */\n    align-items: center;\n}\n\n.widgets-slider .slider-container {\n    overflow: visible;\n}\n\n.widget-hslider .slider-container {\n    height: var(--jp-widgets-inline-height);\n    margin-left: calc(var(--jp-widgets-slider-handle-size) / 2 - 2 * var(--jp-widgets-slider-border-width));\n    margin-right: calc(var(--jp-widgets-slider-handle-size) / 2 - 2 * var(--jp-widgets-slider-border-width));\n    flex: 1 1 var(--jp-widgets-inline-width-short);\n}\n\n.widget-hslider .ui-slider {\n    /* Inner, invisible slide div */\n    height: var(--jp-widgets-slider-track-thickness);\n    margin-top: calc((var(--jp-widgets-inline-height) - var(--jp-widgets-slider-track-thickness)) / 2);\n    width: 100%;\n}\n\n/* Vertical Slider */\n\n.widget-vbox .widget-label {\n    height: var(--jp-widgets-inline-height);\n    line-height: var(--jp-widgets-inline-height);\n}\n\n.widget-vslider {\n    /* Vertical Slider */\n    height: var(--jp-widgets-vertical-height);\n    width: var(--jp-widgets-inline-width-tiny);\n}\n\n.widget-vslider .slider-container {\n    flex: 1 1 var(--jp-widgets-inline-width-short);\n    margin-left: auto;\n    margin-right: auto;\n    margin-bottom: calc(var(--jp-widgets-slider-handle-size) / 2 - 2 * var(--jp-widgets-slider-border-width));\n    margin-top: calc(var(--jp-widgets-slider-handle-size) / 2 - 2 * var(--jp-widgets-slider-border-width));\n    display: flex;\n    flex-direction: column;\n}\n\n.widget-vslider .ui-slider-vertical {\n    /* Inner, invisible slide div */\n    width: var(--jp-widgets-slider-track-thickness);\n    flex-grow: 1;\n    margin-left: auto;\n    margin-right: auto;\n}\n\n/* Widget Progress Styling */\n\n.progress-bar {\n    -webkit-transition: none;\n    -moz-transition: none;\n    -ms-transition: none;\n    -o-transition: none;\n    transition: none;\n}\n\n.progress-bar {\n    height: var(--jp-widgets-inline-height);\n}\n\n.progress-bar {\n    background-color: var(--jp-brand-color1);\n}\n\n.progress-bar-success {\n    background-color: var(--jp-success-color1);\n}\n\n.progress-bar-info {\n    background-color: var(--jp-info-color1);\n}\n\n.progress-bar-warning {\n    background-color: var(--jp-warn-color1);\n}\n\n.progress-bar-danger {\n    background-color: var(--jp-error-color1);\n}\n\n.progress {\n    background-color: var(--jp-layout-color2);\n    border: none;\n    box-shadow: none;\n}\n\n/* Horisontal Progress */\n\n.widget-hprogress {\n    /* Progress Bar */\n    height: var(--jp-widgets-inline-height);\n    line-height: var(--jp-widgets-inline-height);\n    width: var(--jp-widgets-inline-width);\n    align-items: center;\n\n}\n\n.widget-hprogress .progress {\n    flex-grow: 1;\n    margin-top: var(--jp-widgets-input-padding);\n    margin-bottom: var(--jp-widgets-input-padding);\n    align-self: stretch;\n    /* Override bootstrap style */\n    height: initial;\n}\n\n/* Vertical Progress */\n\n.widget-vprogress {\n    height: var(--jp-widgets-vertical-height);\n    width: var(--jp-widgets-inline-width-tiny);\n}\n\n.widget-vprogress .progress {\n    flex-grow: 1;\n    width: var(--jp-widgets-progress-thickness);\n    margin-left: auto;\n    margin-right: auto;\n    margin-bottom: 0;\n}\n\n/* Select Widget Styling */\n\n.widget-dropdown {\n    height: var(--jp-widgets-inline-height);\n    width: var(--jp-widgets-inline-width);\n    line-height: var(--jp-widgets-inline-height);\n}\n\n.widget-dropdown > select {\n    padding-right: 20px;\n    border: var(--jp-widgets-input-border-width) solid var(--jp-widgets-input-border-color);\n    border-radius: 0;\n    height: inherit;\n    flex: 1 1 var(--jp-widgets-inline-width-short);\n    min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n    box-sizing: border-box;\n    outline: none !important;\n    box-shadow: none;\n    background-color: var(--jp-widgets-input-background-color);\n    color: var(--jp-widgets-input-color);\n    font-size: var(--jp-widgets-font-size);\n    vertical-align: top;\n    padding-left: calc( var(--jp-widgets-input-padding) * 2);\n\tappearance: none;\n\t-webkit-appearance: none;\n\t-moz-appearance: none;\n    background-repeat: no-repeat;\n\tbackground-size: 20px;\n\tbackground-position: right center;\n    background-image: var(--jp-widgets-dropdown-arrow);\n}\n.widget-dropdown > select:focus {\n    border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n.widget-dropdown > select:disabled {\n    opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* To disable the dotted border in Firefox around select controls.\n   See http://stackoverflow.com/a/18853002 */\n.widget-dropdown > select:-moz-focusring {\n    color: transparent;\n    text-shadow: 0 0 0 #000;\n}\n\n/* Select and SelectMultiple */\n\n.widget-select {\n    width: var(--jp-widgets-inline-width);\n    line-height: var(--jp-widgets-inline-height);\n\n    /* Because Firefox defines the baseline of a select as the bottom of the\n    control, we align the entire control to the top and add padding to the\n    select to get an approximate first line baseline alignment. */\n    align-items: flex-start;\n}\n\n.widget-select > select {\n    border: var(--jp-widgets-input-border-width) solid var(--jp-widgets-input-border-color);\n    background-color: var(--jp-widgets-input-background-color);\n    color: var(--jp-widgets-input-color);\n    font-size: var(--jp-widgets-font-size);\n    flex: 1 1 var(--jp-widgets-inline-width-short);\n    outline: none !important;\n    overflow: auto;\n    height: inherit;\n\n    /* Because Firefox defines the baseline of a select as the bottom of the\n    control, we align the entire control to the top and add padding to the\n    select to get an approximate first line baseline alignment. */\n    padding-top: 5px;\n}\n\n.widget-select > select:focus {\n    border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n.wiget-select > select > option {\n    padding-left: var(--jp-widgets-input-padding);\n    line-height: var(--jp-widgets-inline-height);\n    /* line-height doesn't work on some browsers for select options */\n    padding-top: calc(var(--jp-widgets-inline-height)-var(--jp-widgets-font-size)/2);\n    padding-bottom: calc(var(--jp-widgets-inline-height)-var(--jp-widgets-font-size)/2);\n}\n\n\n\n/* Toggle Buttons Styling */\n\n.widget-toggle-buttons {\n    line-height: var(--jp-widgets-inline-height);\n}\n\n.widget-toggle-buttons .widget-toggle-button {\n    margin-left: var(--jp-widgets-margin);\n    margin-right: var(--jp-widgets-margin);\n}\n\n.widget-toggle-buttons .jupyter-button:disabled {\n    opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Radio Buttons Styling */\n\n.widget-radio {\n    width: var(--jp-widgets-inline-width);\n    line-height: var(--jp-widgets-inline-height);\n}\n\n.widget-radio-box {\n    display: flex;\n    flex-direction: column;\n    align-items: stretch;\n    box-sizing: border-box;\n    flex-grow: 1;\n    margin-bottom: var(--jp-widgets-radio-item-height-adjustment);\n}\n\n.widget-radio-box label {\n    height: var(--jp-widgets-radio-item-height);\n    line-height: var(--jp-widgets-radio-item-height);\n    font-size: var(--jp-widgets-font-size);\n}\n\n.widget-radio-box input {\n    height: var(--jp-widgets-radio-item-height);\n    line-height: var(--jp-widgets-radio-item-height);\n    margin: 0 calc( var(--jp-widgets-input-padding) * 2 ) 0 1px;\n    float: left;\n}\n\n/* Color Picker Styling */\n\n.widget-colorpicker {\n    width: var(--jp-widgets-inline-width);\n    height: var(--jp-widgets-inline-height);\n    line-height: var(--jp-widgets-inline-height);\n}\n\n.widget-colorpicker > .widget-colorpicker-input {\n    flex-grow: 1;\n    flex-shrink: 1;\n    min-width: var(--jp-widgets-inline-width-tiny);\n}\n\n.widget-colorpicker input[type=\"color\"] {\n    width: var(--jp-widgets-inline-height);\n    height: var(--jp-widgets-inline-height);\n    padding: 0 2px; /* make the color square actually square on Chrome on OS X */\n    background: var(--jp-widgets-input-background-color);\n    color: var(--jp-widgets-input-color);\n    border: var(--jp-widgets-input-border-width) solid var(--jp-widgets-input-border-color);\n    border-left: none;\n    flex-grow: 0;\n    flex-shrink: 0;\n    box-sizing: border-box;\n    align-self: stretch;\n    outline: none !important;\n}\n\n.widget-colorpicker.concise input[type=\"color\"] {\n    border-left: var(--jp-widgets-input-border-width) solid var(--jp-widgets-input-border-color);\n}\n\n.widget-colorpicker input[type=\"color\"]:focus, .widget-colorpicker input[type=\"text\"]:focus {\n    border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n.widget-colorpicker input[type=\"text\"] {\n    flex-grow: 1;\n    outline: none !important;\n    height: var(--jp-widgets-inline-height);\n    line-height: var(--jp-widgets-inline-height);\n    background: var(--jp-widgets-input-background-color);\n    color: var(--jp-widgets-input-color);\n    border: var(--jp-widgets-input-border-width) solid var(--jp-widgets-input-border-color);\n    font-size: var(--jp-widgets-font-size);\n    padding: var(--jp-widgets-input-padding) calc( var(--jp-widgets-input-padding) *  2 );\n    min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n    flex-shrink: 1;\n    box-sizing: border-box;\n}\n\n.widget-colorpicker input[type=\"text\"]:disabled {\n    opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Date Picker Styling */\n\n.widget-datepicker {\n    width: var(--jp-widgets-inline-width);\n    height: var(--jp-widgets-inline-height);\n    line-height: var(--jp-widgets-inline-height);\n}\n\n.widget-datepicker input[type=\"date\"] {\n    flex-grow: 1;\n    flex-shrink: 1;\n    min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n    outline: none !important;\n    height: var(--jp-widgets-inline-height);\n    border: var(--jp-widgets-input-border-width) solid var(--jp-widgets-input-border-color);\n    background-color: var(--jp-widgets-input-background-color);\n    color: var(--jp-widgets-input-color);\n    font-size: var(--jp-widgets-font-size);\n    padding: var(--jp-widgets-input-padding) calc( var(--jp-widgets-input-padding) *  2 );\n    box-sizing: border-box;\n}\n\n.widget-datepicker input[type=\"date\"]:focus {\n    border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n.widget-datepicker input[type=\"date\"]:invalid {\n    border-color: var(--jp-warn-color1);\n}\n\n.widget-datepicker input[type=\"date\"]:disabled {\n    opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Play Widget */\n\n.widget-play {\n    width: var(--jp-widgets-inline-width-short);\n    display: flex;\n    align-items: stretch;\n}\n\n.widget-play .jupyter-button {\n    flex-grow: 1;\n    height: auto;\n}\n\n.widget-play .jupyter-button:disabled {\n    opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Tab Widget */\n\n.jupyter-widgets.widget-tab {\n    display: flex;\n    flex-direction: column;\n}\n\n.jupyter-widgets.widget-tab > .p-TabBar {\n    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */\n    overflow-x: visible;\n    overflow-y: visible;\n}\n\n.jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {\n    /* Make sure that the tab grows from bottom up */\n    align-items: flex-end;\n    min-width: 0;\n    min-height: 0;\n}\n\n.jupyter-widgets.widget-tab > .widget-tab-contents {\n    width: 100%;\n    box-sizing: border-box;\n    margin: 0;\n    background: var(--jp-layout-color1);\n    color: var(--jp-ui-font-color1);\n    border: var(--jp-border-width) solid var(--jp-border-color1);\n    padding: var(--jp-widgets-container-padding);\n    flex-grow: 1;\n    overflow: auto;\n}\n\n.jupyter-widgets.widget-tab > .p-TabBar {\n    font: var(--jp-widgets-font-size) Helvetica, Arial, sans-serif;\n    min-height: calc(var(--jp-widgets-horizontal-tab-height) + var(--jp-border-width));\n}\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {\n    flex: 0 1 var(--jp-widgets-horizontal-tab-width);\n    min-width: 35px;\n    min-height: calc(var(--jp-widgets-horizontal-tab-height) + var(--jp-border-width));\n    line-height: var(--jp-widgets-horizontal-tab-height);\n    margin-left: calc(-1 * var(--jp-border-width));\n    padding: 0px 10px;\n    background: var(--jp-layout-color2);\n    color: var(--jp-ui-font-color2);\n    border: var(--jp-border-width) solid var(--jp-border-color1);\n    border-bottom: none;\n    position: relative;\n}\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {\n    color: var(--jp-ui-font-color0);\n    /* We want the background to match the tab content background */\n    background: var(--jp-layout-color1);\n    min-height: calc(var(--jp-widgets-horizontal-tab-height) + 2 * var(--jp-border-width));\n    transform: translateY(var(--jp-border-width));\n    overflow: visible;\n}\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {\n    position: absolute;\n    top: calc(-1 * var(--jp-border-width));\n    left: calc(-1 * var(--jp-border-width));\n    content: '';\n    height: var(--jp-widgets-horizontal-tab-top-border);\n    width: calc(100% + 2 * var(--jp-border-width));\n    background: var(--jp-brand-color1);\n}\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {\n    margin-left: 0;\n}\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {\n    background: var(--jp-layout-color1);\n    color: var(--jp-ui-font-color1);\n}\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {\n    margin-left: 4px;\n}\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {\n    font-family: FontAwesome;\n    content: '\\f00d'; /* close */\n}\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {\n    line-height: var(--jp-widgets-horizontal-tab-height);\n}\n\n/* Accordion Widget */\n\n.p-Collapse {\n    display: flex;\n    flex-direction: column;\n    align-items: stretch;\n}\n\n.p-Collapse-header {\n    padding: var(--jp-widgets-input-padding);\n    cursor: pointer;\n    color: var(--jp-ui-font-color2);\n    background-color: var(--jp-layout-color2);\n    border: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n    padding: calc(var(--jp-widgets-container-padding) * 2 / 3) var(--jp-widgets-container-padding);\n    font-weight: bold;\n}\n\n.p-Collapse-header:hover {\n    background-color: var(--jp-layout-color1);\n    color: var(--jp-ui-font-color1);\n}\n\n.p-Collapse-open > .p-Collapse-header {\n    background-color: var(--jp-layout-color1);\n    color: var(--jp-ui-font-color0);\n    cursor: default;\n    border-bottom: none;\n}\n\n.p-Collapse .p-Collapse-header::before {\n    content: '\\f0da\\00A0';  /* caret-right, non-breaking space */\n    display: inline-block;\n    font: normal normal normal 14px/1 FontAwesome;\n    font-size: inherit;\n    text-rendering: auto;\n    -webkit-font-smoothing: antialiased;\n    -moz-osx-font-smoothing: grayscale;\n}\n\n.p-Collapse-open > .p-Collapse-header::before {\n    content: '\\f0d7\\00A0'; /* caret-down, non-breaking space */\n}\n\n.p-Collapse-contents {\n    padding: var(--jp-widgets-container-padding);\n    background-color: var(--jp-layout-color1);\n    color: var(--jp-ui-font-color1);\n    border-left: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n    border-right: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n    border-bottom: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n    overflow: auto;\n}\n\n.p-Accordion {\n    display: flex;\n    flex-direction: column;\n    align-items: stretch;\n}\n\n.p-Accordion .p-Collapse {\n    margin-bottom: 0;\n}\n\n.p-Accordion .p-Collapse + .p-Collapse {\n    margin-top: 4px;\n}\n\n\n\n/* HTML widget */\n\n.widget-html, .widget-htmlmath {\n    font-size: var(--jp-widgets-font-size);\n}\n\n.widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {\n    /* Fill out the area in the HTML widget */\n    align-self: stretch;\n    flex-grow: 1;\n    flex-shrink: 1;\n    /* Makes sure the baseline is still aligned with other elements */\n    line-height: var(--jp-widgets-inline-height);\n    /* Make it possible to have absolutely-positioned elements in the html */\n    position: relative;\n}\n","/* This file has code derived from PhosphorJS CSS files, as noted below. The license for this PhosphorJS code is:\n\nCopyright (c) 2014-2017, PhosphorJS Contributors\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n  list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n  this list of conditions and the following disclaimer in the documentation\n  and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n  contributors may be used to endorse or promote products derived from\n  this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n*/\n\n/*\n * The following section is derived from https://github.com/phosphorjs/phosphor/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css \n * We've scoped the rules so that they are consistent with exactly our code.\n */\n\n.jupyter-widgets.widget-tab > .p-TabBar {\n  display: flex;\n  -webkit-user-select: none;\n  -moz-user-select: none;\n  -ms-user-select: none;\n  user-select: none;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] {\n  flex-direction: row;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] {\n  flex-direction: column;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {\n  margin: 0;\n  padding: 0;\n  display: flex;\n  flex: 1 1 auto;\n  list-style-type: none;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] > .p-TabBar-content {\n  flex-direction: row;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] > .p-TabBar-content {\n  flex-direction: column;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {\n  display: flex;\n  flex-direction: row;\n  box-sizing: border-box;\n  overflow: hidden;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {\n  flex: 0 0 auto;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel {\n  flex: 1 1 auto;\n  overflow: hidden;\n  white-space: nowrap;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden {\n  display: none !important;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab {\n  position: relative;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab {\n  left: 0;\n  transition: left 150ms ease;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab {\n  top: 0;\n  transition: top 150ms ease;\n}\n\n\n.jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging {\n  transition: none;\n}\n\n/* End tabbar.css */\n"]} */",
       "ok": true,
       "headers": [
        [
         "content-type",
         "text/css"
        ]
       ],
       "status": 200.0,
       "status_text": ""
      }
     },
     "base_uri": "https://localhost:8080/",
     "height": 1000.0
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0626 00:35:36.399345 140703702083456 dataset_builder.py:540] Using custom data configuration zh-en\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n",
      "\u001b[1mDownloading and preparing dataset wmt_translate (?? GiB) to /root/tensorflow_datasets/wmt_translate/zh-en/0.0.3...\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Completed...', max=1, style=ProgressStyl…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Size...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Extraction completed...', max=1, style=Prog…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Extraction completed...', max=1, style=Prog…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Shuffling...', max=10, style=ProgressStyle(description_width=…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0626 00:36:15.390593 140703702083456 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/file_format_adapter.py:247: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use eager execution and: \n",
      "`tf.data.TFRecordDataset(path)`\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Writing...', max=25278, style=ProgressStyle(description_width…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Writing...', max=25278, style=ProgressStyle(description_width…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Writing...', max=25278, style=ProgressStyle(description_width…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Writing...', max=25278, style=ProgressStyle(description_width…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Writing...', max=25278, style=ProgressStyle(description_width…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Writing...', max=25278, style=ProgressStyle(description_width…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Writing...', max=25278, style=ProgressStyle(description_width…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Writing...', max=25277, style=ProgressStyle(description_width…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Writing...', max=25277, style=ProgressStyle(description_width…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Writing...', max=25277, style=ProgressStyle(description_width…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Shuffling...', max=1, style=ProgressStyle(description_width='…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Writing...', max=2002, style=ProgressStyle(description_width=…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Computing statistics...', max=2, style=ProgressStyle(descript…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0626 00:36:18.650468 140703702083456 dataset_builder.py:727] Statistics not present in the dataset. TFDS is not able to load the total number of examples, so using the subsplit API may not provide precise subsplits.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0626 00:37:34.540203 140703702083456 dataset_builder.py:727] Statistics not present in the dataset. TFDS is not able to load the total number of examples, so using the subsplit API may not provide precise subsplits.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1mDataset wmt_translate downloaded and prepared to /root/tensorflow_datasets/wmt_translate/zh-en/0.0.3. Subsequent calls will reuse this data.\u001b[0m\n",
      "datasets is {'train': <_OptionsDataset shapes: ((), ()), types: (tf.string, tf.string)>, 'validation': <_OptionsDataset shapes: ((), ()), types: (tf.string, tf.string)>}\n"
     ]
    }
   ],
   "source": [
    "config = tfds.translate.wmt.WmtConfig(\n",
    "    description=\"WMT 2019 translation task dataset.\",\n",
    "    version=\"0.0.3\",\n",
    "    language_pair=(\"zh\", \"en\"),\n",
    "    subsets={\n",
    "        tfds.Split.TRAIN: [\"newscommentary_v13\"],\n",
    "        tfds.Split.VALIDATION: [\"newsdev2017\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "builder = tfds.builder(\"wmt_translate\", config=config)\n",
    "print(builder.info.splits)\n",
    "builder.download_and_prepare()\n",
    "datasets = builder.as_dataset(as_supervised=True)\n",
    "print('datasets is {}'.format(datasets))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "g5n8epgrGeW7",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "train_examples = datasets['train']\n",
    "val_examples = datasets['validation']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "lumMBmcKoTZ8",
    "colab_type": "code",
    "outputId": "47ae3ca8-c309-47cc-d1df-466c28a7ca11",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561509456152E12,
     "user_tz": -480.0,
     "elapsed": 194283.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "虽然继承了祖荫，但朴槿惠已经证明了自己是个机敏而老练的政治家——她历经20年才爬上韩国大国家党最高领导层并成为全国知名人物。\n",
      "While Park derives some of her power from her family pedigree, she has proven to be an astute and seasoned politician –&nbsp;one who climbed the Grand National Party’s leadership ladder over the last two decades to emerge as a national figure.\n"
     ]
    }
   ],
   "source": [
    "for zh, en in train_examples.take(1):\n",
    "  print(tf.compat.as_text(zh.numpy()))\n",
    "  print(tf.compat.as_text(en.numpy()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3u8SkhL6GeW9",
    "colab_type": "text"
   },
   "source": [
    "Create a custom subwords tokenizer from the training dataset for the decoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "QaaeAPebGeW-",
    "colab_type": "code",
    "outputId": "17b7891e-09df-4e95-bd41-084c8222ec9f",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561509663673E12,
     "user_tz": -480.0,
     "elapsed": 401601.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2877 ----> Trans\n",
      "466 ----> former \n",
      "9 ----> is \n",
      "3502 ----> aw\n",
      "157 ----> es\n",
      "1805 ----> ome\n",
      "8013 ----> .\n"
     ]
    }
   ],
   "source": [
    "vocab_file = 'vocab_en'\n",
    "if os.path.isfile(vocab_file + '.subwords'):\n",
    "  tokenizer_en = tfds.features.text.SubwordTextEncoder.load_from_file(vocab_file)\n",
    "else: \n",
    "  tokenizer_en = tfds.features.text.SubwordTextEncoder.build_from_corpus(\n",
    "      (en.numpy() for zh, en in train_examples), target_vocab_size=2 ** 13)\n",
    "  tokenizer_en.save_to_file('vocab_en')\n",
    "  \n",
    "sample_string = 'Transformer is awesome.'\n",
    "tokenized_string = tokenizer_en.encode(sample_string)\n",
    "for ts in tokenized_string:\n",
    "  print ('{} ----> {}'.format(ts, tokenizer_en.decode([ts])))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dH6tm4WpGeXD",
    "colab_type": "text"
   },
   "source": [
    "The encoder uses BERT tokenizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "IeVWQ0oCGeXE",
    "colab_type": "code",
    "outputId": "cee2cbd1-ef63-4761-9630-80c6f835db57",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561509663674E12,
     "user_tz": -480.0,
     "elapsed": 401402.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[101, 1812, 7384, 185, 7716, 3172, 1046, 113, 10245, 8224, 11677, 8998, 114, 1158, 2456, 749, 4294, 3172, 2861, 102]\n",
      "['[CLS]', '埃', '隆', '·', '马', '斯', '克', '(', 'el', '##on', 'mu', '##sk', ')', '创', '建', '了', '特', '斯', '拉', '[SEP]']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "tokenizer_zh = FullTokenizer(\n",
    "        vocab_file='chinese_L-12_H-768_A-12/vocab.txt', do_lower_case=True)\n",
    "\n",
    "test_tokens = tokenizer_zh.tokenize('埃隆·马斯克(Elon Musk)创建了特斯拉')\n",
    "test_ids = tokenizer_zh.convert_tokens_to_ids(['[CLS]'] + test_tokens + ['[SEP]'])\n",
    "print(test_ids)\n",
    "print(tokenizer_zh.convert_ids_to_tokens(test_ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hDloKnWoGeXG",
    "colab_type": "text"
   },
   "source": [
    "Add a start and end token to the input and target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "fbOUAit_GeXH",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "MAX_SEQ_LENGTH = 128\n",
    "\n",
    "\n",
    "def encode(zh, en, seq_length=MAX_SEQ_LENGTH):\n",
    "    tokens_zh = tokenizer_zh.tokenize(tf.compat.as_text(zh.numpy()))\n",
    "    lang1 = tokenizer_zh.convert_tokens_to_ids(['[CLS]'] + tokens_zh + ['[SEP]'])\n",
    "    if len(lang1) < seq_length:\n",
    "      lang1 = lang1 + list(np.zeros(seq_length - len(lang1), 'int32'))\n",
    "      \n",
    "    lang2 = [tokenizer_en.vocab_size] + tokenizer_en.encode(\n",
    "        tf.compat.as_text(en.numpy())) + [tokenizer_en.vocab_size + 1]\n",
    "    if len(lang2) < seq_length:\n",
    "      lang2 = lang2 + list(np.zeros(seq_length - len(lang2), 'int32'))\n",
    "      \n",
    "    return lang1, lang2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "RRClaq3Cw5i-",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def filter_max_length(x, y, max_length=MAX_SEQ_LENGTH):\n",
    "  return tf.logical_and(tf.size(x) <= max_length,\n",
    "                        tf.size(y) <= max_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oNe7zbSkGeXJ",
    "colab_type": "text"
   },
   "source": [
    "Encode texts into integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "ifgR-DCoGeXK",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "BUFFER_SIZE = 50000\n",
    "BATCH_SIZE = 64\n",
    "\n",
    "train_dataset = train_examples.map(\n",
    "    lambda zh, en: tf.py_function(encode, [zh, en], [tf.int32, tf.int32]))\n",
    "train_dataset = train_dataset.filter(filter_max_length)\n",
    "\n",
    "# cache the dataset to memory to get a speedup while reading from it.\n",
    "train_dataset = train_dataset.cache()\n",
    "train_dataset = train_dataset.shuffle(BUFFER_SIZE).padded_batch(\n",
    "    BATCH_SIZE, padded_shapes=([-1], [-1]), drop_remainder=True)\n",
    "train_dataset = train_dataset.prefetch(tf.data.experimental.AUTOTUNE)\n",
    "\n",
    "val_dataset = val_examples.map(\n",
    "    lambda zh, en: tf.py_function(encode, [zh, en], [tf.int32, tf.int32]))\n",
    "val_dataset = val_dataset.filter(filter_max_length)\n",
    "val_dataset = val_dataset.padded_batch(BATCH_SIZE, padded_shapes=([-1], [-1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DY_pVCZmGeXO",
    "colab_type": "text"
   },
   "source": [
    "## Positional encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LfmeGsmGGeXP",
    "colab_type": "text"
   },
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "_c8ztTc4GeXQ",
    "colab_type": "code",
    "colab": {}
   },
   "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "GQlzAsGEGeXR",
    "colab_type": "code",
    "colab": {}
   },
   "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",
    "    sines = np.sin(angle_rads[:, 0::2])\n",
    "\n",
    "    # apply cos to odd indices in the array; 2i+1\n",
    "    cosines = np.cos(angle_rads[:, 1::2])\n",
    "\n",
    "    pos_encoding = np.concatenate([sines, cosines], axis=-1)\n",
    "\n",
    "    pos_encoding = pos_encoding[np.newaxis, ...]\n",
    "\n",
    "    return tf.cast(pos_encoding, dtype=tf.float32)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cIvZZNLdGeXV",
    "colab_type": "text"
   },
   "source": [
    "## Masking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_LQ2K-KnGeXW",
    "colab_type": "text"
   },
   "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": 0,
   "metadata": {
    "id": "zrfWBXwSGeXX",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def create_padding_mask(seq):\n",
    "  seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\n",
    "  \n",
    "  # add extra dimensions so that we can add the padding\n",
    "  # to the attention logits.\n",
    "  return seq[:, tf.newaxis, tf.newaxis, :]  # (batch_size, 1, 1, seq_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LDBeunerGeXZ",
    "colab_type": "text"
   },
   "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": 0,
   "metadata": {
    "id": "HkMb_oR7GeXZ",
    "colab_type": "code",
    "colab": {}
   },
   "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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8COn99QiGeXc",
    "colab_type": "text"
   },
   "source": [
    "## Scaled dot product attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "iFWhCq4xGeXd",
    "colab_type": "code",
    "colab": {}
   },
   "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mn1jMs9-GeXg",
    "colab_type": "text"
   },
   "source": [
    "## Multi-head attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "ne5w002OGeXh",
    "colab_type": "code",
    "colab": {}
   },
   "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,\n",
    "                                        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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "CIC9xYPCGeXj",
    "colab_type": "code",
    "outputId": "bea0d26b-a920-4594-e988-cc36ae7a1408",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561509665108E12,
     "user_tz": -480.0,
     "elapsed": 402580.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35.0
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([1, 60, 512]), TensorShape([1, 8, 60, 60]))"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_mha = MultiHeadAttention(d_model=512, num_heads=8)\n",
    "y = tf.random.uniform((1, 60, 768))  # (batch_size, encoder_sequence, d_model)\n",
    "q = tf.random.uniform((1, 60, 512))  # (batch_size, encoder_sequence, d_model)\n",
    "out, attn = temp_mha(y, k=y, q=q, mask=None)\n",
    "out.shape, attn.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-qOdTWaxGeXl",
    "colab_type": "text"
   },
   "source": [
    "## Point wise feed forward network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "2lJmb-7DGeXl",
    "colab_type": "code",
    "colab": {}
   },
   "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",
    "    ])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1uG_HsM0GeXn",
    "colab_type": "text"
   },
   "source": [
    "## Encoder Layer and Encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lgoHtRikGeXn",
    "colab_type": "text"
   },
   "source": [
    "Use bert as encoder. The output shape is (batch_size, input_seq_len, d_model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "rFG2C0wkGeXo",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "\n",
    "def build_encoder(config_file):\n",
    "    with tf.io.gfile.GFile(config_file, \"r\") as reader:\n",
    "        stock_params = StockBertConfig.from_json_string(reader.read())\n",
    "        bert_params = stock_params.to_bert_model_layer_params()\n",
    "\n",
    "    return BertModelLayer.from_params(bert_params, name=\"bert\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zQCYcYThGeXr",
    "colab_type": "text"
   },
   "source": [
    "## Decoder Layer and Decoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SpcebMDZGeXr",
    "colab_type": "text"
   },
   "source": [
    "Each decoder layer consists of sublayers:\n",
    "\n",
    "1. Masked multi-head attention (with look ahead mask and padding mask)\n",
    "\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 sublaye\n",
    "\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "6wXkK9oRGeXr",
    "colab_type": "code",
    "colab": {}
   },
   "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "JvRhep_YGeXt",
    "colab_type": "code",
    "outputId": "8240b212-af36-4e44-ceb5-5a6c60b5299a",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561509665118E12,
     "user_tz": -480.0,
     "elapsed": 402373.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35.0
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([64, 50, 512])"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_decoder_layer = DecoderLayer(512, 8, 2048)\n",
    "sample_encoder_output = tf.random.uniform((64, 128, 768))\n",
    "\n",
    "sample_decoder_layer_output, _, _ = sample_decoder_layer(\n",
    "    tf.random.uniform((64, 50, 512)), sample_encoder_output,\n",
    "    False, None, None)\n",
    "\n",
    "sample_decoder_layer_output.shape  # (batch_size, target_seq_len, d_model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wtWp6FbaGeXu",
    "colab_type": "text"
   },
   "source": [
    "The Decoder consists of: 1. Output Embedding 2. Positional Encoding 3. N decoder layers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "rlaibr1MGeXv",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "class Decoder(tf.keras.layers.Layer):\n",
    "    def __init__(self, num_layers, d_model, num_heads, dff, target_vocab_size,\n",
    "                 rate=0.1):\n",
    "        super(Decoder, self).__init__()\n",
    "\n",
    "        self.d_model = d_model\n",
    "        self.num_layers = num_layers\n",
    "\n",
    "        self.embedding = tf.keras.layers.Embedding(target_vocab_size, d_model)\n",
    "        self.pos_encoding = positional_encoding(target_vocab_size, self.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",
    "        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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "JOZywXPjGeXw",
    "colab_type": "code",
    "outputId": "957fef61-7e0d-45b3-c057-618146ada9b2",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561509665651E12,
     "user_tz": -480.0,
     "elapsed": 402703.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35.0
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([64, 26, 512]), TensorShape([64, 8, 26, 128]))"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "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",
    "\n",
    "output, attn = sample_decoder(tf.random.uniform((64, 26)), \n",
    "                              enc_output=sample_encoder_output, \n",
    "                              training=False, look_ahead_mask=None, \n",
    "                              padding_mask=None)\n",
    "\n",
    "output.shape, attn['decoder_layer2_block2'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dsCztpCaGeXy",
    "colab_type": "text"
   },
   "source": [
    "## Create the Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RjJdYaqtGeXy",
    "colab_type": "text"
   },
   "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": 0,
   "metadata": {
    "id": "ToOkfzUifDgj",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "class Config(object):\n",
    "  def __init__(self, num_layers, d_model, dff, num_heads):\n",
    "    self.num_layers = num_layers\n",
    "    self.d_model = d_model\n",
    "    self.dff = dff\n",
    "    self.num_heads = num_heads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "kG9ZtpNCGeXz",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "from bert.loader import map_to_stock_variable_name\n",
    "\n",
    "\n",
    "class Transformer(tf.keras.Model):\n",
    "  def __init__(self, config,\n",
    "               target_vocab_size, \n",
    "               bert_config_file,\n",
    "               bert_training=False, \n",
    "               rate=0.1,\n",
    "               name='transformer'):\n",
    "      super(Transformer, self).__init__(name=name)\n",
    "\n",
    "      self.encoder = build_encoder(config_file=bert_config_file)\n",
    "      self.encoder.trainable = bert_training\n",
    "\n",
    "      self.decoder = Decoder(config.num_layers, config.d_model, \n",
    "                             config.num_heads, config.dff, target_vocab_size, rate)\n",
    "\n",
    "      self.final_layer = tf.keras.layers.Dense(target_vocab_size)\n",
    "\n",
    "\n",
    "  def load_stock_weights(self, bert: BertModelLayer, ckpt_file):\n",
    "      assert isinstance(bert, BertModelLayer), \"Expecting a BertModelLayer instance as first argument\"\n",
    "      assert tf.compat.v1.train.checkpoint_exists(ckpt_file), \"Checkpoint does not exist: {}\".format(ckpt_file)\n",
    "      ckpt_reader = tf.train.load_checkpoint(ckpt_file)\n",
    "\n",
    "      bert_prefix = 'transformer/bert'\n",
    "\n",
    "      weights = []\n",
    "      for weight in bert.weights:\n",
    "          stock_name = map_to_stock_variable_name(weight.name, bert_prefix)\n",
    "          if ckpt_reader.has_tensor(stock_name):\n",
    "              value = ckpt_reader.get_tensor(stock_name)\n",
    "              weights.append(value)\n",
    "          else:\n",
    "              raise ValueError(\"No value for:[{}], i.e.:[{}] in:[{}]\".format(\n",
    "                  weight.name, stock_name, ckpt_file))\n",
    "      bert.set_weights(weights)\n",
    "      print(\"Done loading {} BERT weights from: {} into {} (prefix:{})\".format(\n",
    "          len(weights), ckpt_file, bert, bert_prefix))\n",
    "\n",
    "  def restore_encoder(self, bert_ckpt_file):\n",
    "      # loading the original pre-trained weights into the BERT layer:\n",
    "      self.load_stock_weights(self.encoder, bert_ckpt_file)\n",
    "\n",
    "  def call(self, inp, tar, training, look_ahead_mask, dec_padding_mask):\n",
    "      enc_output = self.encoder(inp, training=self.encoder.trainable)  # (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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PSdmgqGMGeX2",
    "colab_type": "text"
   },
   "source": [
    "## Set hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t2qMwHTYGeX2",
    "colab_type": "text"
   },
   "source": [
    "To keep this example small and relatively fast, the values for num_layers, d_model, and dff have been reduced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "LJKlwYeuGeX3",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "target_vocab_size = tokenizer_en.vocab_size + 2\n",
    "dropout_rate = 0.1\n",
    "config = Config(num_layers=6, d_model=256, dff=1024, num_heads=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "_fXdBCXle0JB",
    "colab_type": "code",
    "outputId": "29f7976d-77f9-4fbb-d362-8825eb7a3955",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.56150966884E12,
     "user_tz": -480.0,
     "elapsed": 405677.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145.0
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0626 00:41:07.395809 140703702083456 deprecation.py:323] From <ipython-input-27-b176af2b20fd>:24: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 128)\n",
      "(64, 128, 8225)\n",
      "Done loading 196 BERT weights from: chinese_L-12_H-768_A-12/bert_model.ckpt into <bert.model.BertModelLayer object at 0x7ff75ca86e48> (prefix:transformer/bert)\n"
     ]
    }
   ],
   "source": [
    "MODEL_DIR = \"chinese_L-12_H-768_A-12\"\n",
    "bert_config_file = os.path.join(MODEL_DIR, \"bert_config.json\")\n",
    "bert_ckpt_file = os.path.join(MODEL_DIR, \"bert_model.ckpt\")\n",
    "\n",
    "transformer = Transformer(config=config,\n",
    "                          target_vocab_size=target_vocab_size,\n",
    "                          bert_config_file=bert_config_file)\n",
    "  \n",
    "inp = tf.random.uniform((BATCH_SIZE, MAX_SEQ_LENGTH))\n",
    "tar_inp = tf.random.uniform((BATCH_SIZE, MAX_SEQ_LENGTH))\n",
    "fn_out, _ = transformer(inp, tar_inp, \n",
    "                        True,\n",
    "                        look_ahead_mask=None,\n",
    "                        dec_padding_mask=None)\n",
    "print(tar_inp.shape) # (batch_size, tar_seq_len) \n",
    "print(fn_out.shape)  # (batch_size, tar_seq_len, target_vocab_size) \n",
    "\n",
    "# init bert pre-trained weights\n",
    "transformer.restore_encoder(bert_ckpt_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "VGAWc7sJQ-C4",
    "colab_type": "code",
    "outputId": "828b6ead-bfa4-44b8-afc4-95eef6d2479a",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561509668841E12,
     "user_tz": -480.0,
     "elapsed": 405478.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"transformer\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bert (BertModelLayer)        multiple                  101675520 \n",
      "_________________________________________________________________\n",
      "decoder_1 (Decoder)          multiple                  9999104   \n",
      "_________________________________________________________________\n",
      "dense_94 (Dense)             multiple                  2113825   \n",
      "=================================================================\n",
      "Total params: 113,788,449\n",
      "Trainable params: 12,112,929\n",
      "Non-trainable params: 101,675,520\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "transformer.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WlT1XVQ7GeX4",
    "colab_type": "text"
   },
   "source": [
    "## Optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TFeTO3p2GeX5",
    "colab_type": "text"
   },
   "source": [
    "Use the Adam optimizer with a custom learning rate scheduler according to the formula in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "WyJQLgNEGeX5",
    "colab_type": "code",
    "colab": {}
   },
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "9ZV-MS99GeX7",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "learning_rate = CustomSchedule(config.d_model)\n",
    "\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98,\n",
    "                                     epsilon=1e-9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "MoeoZUMUGeX9",
    "colab_type": "code",
    "outputId": "c0df3b37-bddd-433c-8518-9f873cdc425f",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561509668847E12,
     "user_tz": -480.0,
     "elapsed": 405272.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301.0
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Train Step')"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEKCAYAAAAvlUMdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmIa/6b5CMgEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = CustomSchedule(config.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": "eBcj8bbeGeX-",
    "colab_type": "text"
   },
   "source": [
    "## Loss and metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZRsfA0--GeX_",
    "colab_type": "text"
   },
   "source": [
    "Since the target sequences are padded, it is important to apply a padding mask when calculating the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "6rVFjCHhGeX_",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n",
    "    from_logits=True, reduction='none')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "AaTrk5guGeYA",
    "colab_type": "code",
    "colab": {}
   },
   "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_mean(loss_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "n9qHuK8oGeYC",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
    "train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(\n",
    "    name='train_accuracy')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EdCj8MwqGeYH",
    "colab_type": "text"
   },
   "source": [
    "## Training and checkpointing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nC8JvcInGeYK",
    "colab_type": "text"
   },
   "source": [
    "Create the checkpoint path and the checkpoint manager. This will be used to save checkpoints every n epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "ECbAGB3CGeYK",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "checkpoint_path = \"./checkpoints/train\"\n",
    "\n",
    "ckpt = tf.train.Checkpoint(transformer=transformer,\n",
    "                           optimizer=optimizer)\n",
    "\n",
    "ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)\n",
    "\n",
    "# if a checkpoint exists, restore the latest checkpoint.\n",
    "if ckpt_manager.latest_checkpoint:\n",
    "    ckpt.restore(ckpt_manager.latest_checkpoint)\n",
    "    print('Latest checkpoint restored!!')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tUIzaBHFGeYM",
    "colab_type": "text"
   },
   "source": [
    "During training this example uses teacher-forcing (like in the text generation tutorial). Teacher forcing is passing the true output to the next time step regardless of what the model predicts at the current time step.\n",
    "\n",
    "As the 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 peaking at the expected output the model uses a look-ahead mask.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "oAsfFtAB1G97",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def create_masks(inp, tar):\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 combined_mask, dec_padding_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "nwZrRyLVGeYQ",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_step(inp, tar):\n",
    "    tar_inp = tar[:, :-1]\n",
    "    tar_real = tar[:, 1:]\n",
    "\n",
    "    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",
    "                                     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(tar_real, predictions)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ocUsGWOIGeYR",
    "colab_type": "text"
   },
   "source": [
    "Chinese is used as the input language and English is the target language."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "Rh7PlNI7GeYS",
    "colab_type": "code",
    "outputId": "2de6c0eb-ee43-4423-f570-c7e3ba6ce6a8",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561523920702E12,
     "user_tz": -480.0,
     "elapsed": 2243066.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000.0
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0626 00:41:08.328056 140700424128256 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string\n",
      "W0626 00:41:08.330051 140700424128256 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string\n",
      "W0626 00:41:08.348146 140700432520960 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string\n",
      "W0626 00:41:08.350182 140700432520960 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string\n",
      "W0626 00:41:08.354208 140700432520960 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string\n",
      "W0626 00:42:36.146764 140703702083456 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1205: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 Batch 0 Loss 2.6266 Accuracy 0.0001\n",
      "Epoch 1 Batch 500 Loss 1.9671 Accuracy 0.0167\n",
      "Epoch 1 Batch 1000 Loss 1.7554 Accuracy 0.0311\n",
      "Epoch 1 Batch 1500 Loss 1.6164 Accuracy 0.0402\n",
      "Epoch 1 Batch 2000 Loss 1.5107 Accuracy 0.0474\n",
      "Epoch 1 Batch 2500 Loss 1.4223 Accuracy 0.0536\n",
      "Epoch 1 Batch 3000 Loss 1.3485 Accuracy 0.0590\n",
      "Epoch 1 Batch 3500 Loss 1.2875 Accuracy 0.0636\n",
      "Saving checkpoint for epoch 1 at ./checkpoints/train/ckpt-1\n",
      "Epoch 1 Loss 1.2419 Accuracy 0.0672\n",
      "Time taken for 1 epoch: 3596.885156393051 secs\n",
      "\n",
      "Epoch 2 Batch 0 Loss 0.9382 Accuracy 0.0941\n",
      "Epoch 2 Batch 500 Loss 0.8513 Accuracy 0.0995\n",
      "Epoch 2 Batch 1000 Loss 0.8361 Accuracy 0.1017\n",
      "Epoch 2 Batch 1500 Loss 0.8205 Accuracy 0.1037\n",
      "Epoch 2 Batch 2000 Loss 0.8060 Accuracy 0.1054\n",
      "Epoch 2 Batch 2500 Loss 0.7922 Accuracy 0.1070\n",
      "Epoch 2 Batch 3000 Loss 0.7813 Accuracy 0.1086\n",
      "Epoch 2 Batch 3500 Loss 0.7711 Accuracy 0.1100\n",
      "Saving checkpoint for epoch 2 at ./checkpoints/train/ckpt-2\n",
      "Epoch 2 Loss 0.7630 Accuracy 0.1112\n",
      "Time taken for 1 epoch: 3552.8913764953613 secs\n",
      "\n",
      "Epoch 3 Batch 0 Loss 0.6753 Accuracy 0.1172\n",
      "Epoch 3 Batch 500 Loss 0.6747 Accuracy 0.1225\n",
      "Epoch 3 Batch 1000 Loss 0.6712 Accuracy 0.1234\n",
      "Epoch 3 Batch 1500 Loss 0.6667 Accuracy 0.1242\n",
      "Epoch 3 Batch 2000 Loss 0.6620 Accuracy 0.1250\n",
      "Epoch 3 Batch 2500 Loss 0.6582 Accuracy 0.1257\n",
      "Epoch 3 Batch 3000 Loss 0.6540 Accuracy 0.1263\n",
      "Epoch 3 Batch 3500 Loss 0.6502 Accuracy 0.1269\n",
      "Saving checkpoint for epoch 3 at ./checkpoints/train/ckpt-3\n",
      "Epoch 3 Loss 0.6474 Accuracy 0.1274\n",
      "Time taken for 1 epoch: 3547.938088655472 secs\n",
      "\n",
      "Epoch 4 Batch 0 Loss 0.6691 Accuracy 0.1323\n",
      "Epoch 4 Batch 500 Loss 0.6059 Accuracy 0.1335\n",
      "Epoch 4 Batch 1000 Loss 0.6038 Accuracy 0.1335\n",
      "Epoch 4 Batch 1500 Loss 0.6016 Accuracy 0.1335\n",
      "Epoch 4 Batch 2000 Loss 0.6010 Accuracy 0.1342\n",
      "Epoch 4 Batch 2500 Loss 0.5993 Accuracy 0.1346\n",
      "Epoch 4 Batch 3000 Loss 0.5968 Accuracy 0.1350\n",
      "Epoch 4 Batch 3500 Loss 0.5955 Accuracy 0.1353\n",
      "Saving checkpoint for epoch 4 at ./checkpoints/train/ckpt-4\n",
      "Epoch 4 Loss 0.5936 Accuracy 0.1355\n",
      "Time taken for 1 epoch: 3553.9979977607727 secs\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "EPOCHS = 4\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "    start = time.time()\n",
    "\n",
    "    train_loss.reset_states()\n",
    "    train_accuracy.reset_states()\n",
    "\n",
    "    # inp -> chinese, tar -> english\n",
    "    for (batch, (inp, tar)) in enumerate(train_dataset):\n",
    "        train_step(inp, tar)\n",
    "\n",
    "        if batch % 500 == 0:\n",
    "            print('Epoch {} Batch {} Loss {:.4f} Accuracy {:.4f}'.format(\n",
    "                epoch + 1, batch, train_loss.result(), train_accuracy.result()))\n",
    "\n",
    "    if (epoch + 1) % 1 == 0:\n",
    "        ckpt_save_path = ckpt_manager.save()\n",
    "        print('Saving checkpoint for epoch {} at {}'.format(epoch + 1,\n",
    "                                                            ckpt_save_path))\n",
    "\n",
    "    print('Epoch {} Loss {:.4f} Accuracy {:.4f}'.format(epoch + 1,\n",
    "                                                        train_loss.result(),\n",
    "                                                        train_accuracy.result()))\n",
    "\n",
    "    print('Time taken for 1 epoch: {} secs\\n'.format(time.time() - start))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QrA-OoDHGeYT",
    "colab_type": "text"
   },
   "source": [
    "## Evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yzz2_QpaGeYT",
    "colab_type": "text"
   },
   "source": [
    "The following steps are used for evaluation:\n",
    "\n",
    "- Encode the input sentence using the Portuguese tokenizer (tokenizer_pt). Moreover, add the start and end token so the input is equivalent to what the model is trained with. This is the encoder input.\n",
    "\n",
    "- The decoder input is the start token == tokenizer_en.vocab_size.\n",
    "\n",
    "- Calculate the padding masks and the look ahead masks.\n",
    "\n",
    "- The decoder then outputs the predictions by looking at the encoder output and its own output (self-attention).\n",
    "\n",
    "- Select the last word and calculate the argmax of that.\n",
    "\n",
    "- Concatentate the predicted word to the decoder input as pass it to the decoder.\n",
    "\n",
    "- In this approach, the decoder predicts the next word based on the previous words it predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "Fz1yAT3WGeYU",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def encode_zh(zh):\n",
    "    tokens_zh = tokenizer_zh.tokenize(zh)\n",
    "    lang1 = tokenizer_zh.convert_tokens_to_ids(['[CLS]'] + tokens_zh + ['[SEP]'])\n",
    "\n",
    "    return lang1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "dgP8UyKo5xVV",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35.0
    },
    "outputId": "5f8fb8f1-44f1-47e7-f0c4-b0fe102c4f16",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561523921821E12,
     "user_tz": -480.0,
     "elapsed": 1121.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[CLS]', '我', '爱', '你', '啊', '[SEP]']\n"
     ]
    }
   ],
   "source": [
    "sentence_ids = encode_zh(\"我爱你啊\")\n",
    "print(tokenizer_zh.convert_ids_to_tokens(sentence_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "T0xE9GOVGeYV",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def evaluate(transformer, inp_sentence):\n",
    "    # normalize input sentence\n",
    "    inp_sentence = encode_zh(inp_sentence)\n",
    "    encoder_input = tf.expand_dims(inp_sentence, 0)\n",
    "\n",
    "    # as the target is english, the first word to the transformer should be the\n",
    "    # english start token.\n",
    "    decoder_input = [tokenizer_en.vocab_size]\n",
    "    output = tf.expand_dims(decoder_input, 0)\n",
    "\n",
    "    for i in range(MAX_SEQ_LENGTH):\n",
    "        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",
    "                                                     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.cast(tf.argmax(predictions, axis=-1), tf.int32)\n",
    "\n",
    "        # return the result if the predicted_id is equal to the end token\n",
    "        if tf.equal(predicted_id, tokenizer_en.vocab_size + 1):\n",
    "            return tf.squeeze(output, axis=0), attention_weights\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 tf.squeeze(output, axis=0), attention_weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "h5X7F9WgGeYW",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def plot_attention_weights(attention, sentence, result, layer):\n",
    "    fig = plt.figure(figsize=(16, 8))\n",
    "\n",
    "    sentence_ids = encode_zh(sentence)\n",
    "\n",
    "    attention = tf.squeeze(attention[layer], axis=0)\n",
    "\n",
    "    for head in range(attention.shape[0]):\n",
    "        ax = fig.add_subplot(2, 4, head + 1)\n",
    "\n",
    "        # plot the attention weights\n",
    "        ax.matshow(attention[head][:-1, :], cmap='viridis')\n",
    "\n",
    "        fontdict = {'fontsize': 10, 'family' : 'DFKai-SB'}\n",
    "\n",
    "        ax.set_xticks(range(len(sentence_ids)))\n",
    "        ax.set_yticks(range(len(result)))\n",
    "\n",
    "        ax.set_ylim(len(result) - 1.5, -0.5)\n",
    "\n",
    "        ax.set_xticklabels(\n",
    "            tokenizer_zh.convert_ids_to_tokens(sentence_ids),\n",
    "            fontdict=fontdict, rotation=90)\n",
    "\n",
    "        ax.set_yticklabels([tokenizer_en.decode([i]) for i in result\n",
    "                            if i < tokenizer_en.vocab_size],\n",
    "                           fontdict=fontdict)\n",
    "\n",
    "        ax.set_xlabel('Head {}'.format(head + 1))\n",
    "\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "YIE5ZFM-GeYZ",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "def translate(transformer, sentence, plot=''):\n",
    "    result, attention_weights = evaluate(transformer, sentence)\n",
    "\n",
    "    predicted_sentence = tokenizer_en.decode([i for i in result\n",
    "                                              if i < tokenizer_en.vocab_size])\n",
    "\n",
    "    print('Input: {}'.format(sentence))\n",
    "    print('Predicted translation: {}'.format(predicted_sentence))\n",
    "\n",
    "    if plot:\n",
    "        plot_attention_weights(attention_weights, sentence, result, plot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "EqRUvh4q1p89",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 91.0
    },
    "outputId": "d1ecabd7-ca43-4890-ca95-ff1e7766d109",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.56152470806E12,
     "user_tz": -480.0,
     "elapsed": 43519.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input: 虽然继承了祖荫，但朴槿惠已经证明了自己是个机敏而老练的政治家——她历经20年才爬上韩国大国家党最高领导层并成为全国知名人物。\n",
      "Predicted translation: While inherited her father, Park has proven that she is a brave and al-keal – a politician who has been able to topple the country’s largest party and become a national leader.\n",
      "Real translation: While Park derives some of her power from her family pedigree, she has proven to be an astute and seasoned politician –&nbsp;one who climbed the Grand National Party’s leadership ladder over the last two decades to emerge as a national figure.\n"
     ]
    }
   ],
   "source": [
    "translate(transformer, '虽然继承了祖荫，但朴槿惠已经证明了自己是个机敏而老练的政治家——她历经20年才爬上韩国大国家党最高领导层并成为全国知名人物。')\n",
    "print('Real translation: While Park derives some of her power from her family pedigree, she has proven to be an astute and seasoned politician –&nbsp;one who climbed the Grand National Party’s leadership ladder over the last two decades to emerge as a national figure.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "X-51tfRT4NAt",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53.0
    },
    "outputId": "f47b29a0-7f43-451e-cb21-47bc3c385ab2",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561525027701E12,
     "user_tz": -480.0,
     "elapsed": 3648.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input: 我爱你是一件幸福的事情。\n",
      "Predicted translation: I love you are a blessing.\n"
     ]
    }
   ],
   "source": [
    "translate(transformer, \"我爱你是一件幸福的事情。\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I9luCwHewWjW",
    "colab_type": "text"
   },
   "source": [
    "## Save weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "jyi3Tndv9ov_",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "transformer.save_weights('bert_nmt_ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "CHOHMxSoe8bh",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73.0
    },
    "outputId": "3b391ed4-0ad3-4dcf-faf9-7d7b57ed644a",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.561523946283E12,
     "user_tz": -480.0,
     "elapsed": 11943.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input: 是的，从某种程度上来看，我们可以通过现代技术或者录像回放来审视有争议的判罚决定，以此来解决这一问题。\n",
      "Predicted translation: Yes, in some ways, we can use modern techniques or video video to review contentious decisions, so that it is possible to address the problem.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "new_transformer = Transformer(config=config,\n",
    "                          target_vocab_size=target_vocab_size,\n",
    "                          bert_config_file=bert_config_file)\n",
    "  \n",
    "fn_out, _ = new_transformer(inp, tar_inp, \n",
    "                        True,\n",
    "                        look_ahead_mask=None,\n",
    "                        dec_padding_mask=None)\n",
    "new_transformer.load_weights('bert_nmt_ckpt')\n",
    "\n",
    "translate(new_transformer, '我爱你')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dklXqOq8GeYa",
    "colab_type": "text"
   },
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p3kZK1veGeYa",
    "colab_type": "text"
   },
   "source": [
    "In this tutorial, you learned about positional encoding, multi-head attention, the importance of masking and how to create a transformer with BERT.\n",
    "\n",
    "Try using a different dataset to train the transformer. Futhermore, you can implement beam search to get better predictions.\n",
    "\n",
    "At last, you can copy pre-trained weights and relevant files to your drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "dAFjRESSGeYa",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 127.0
    },
    "outputId": "42ad92b1-afb4-433c-a482-d61673114539",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.56152479744E12,
     "user_tz": -480.0,
     "elapsed": 34006.0,
     "user": {
      "displayName": "hongyong li",
      "photoUrl": "",
      "userId": "12874942180838233111"
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "# 运行此单元格即可装载您的 Google 云端硬盘。\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "ELYJLONnPhS_",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "!cp bert_nmt_ckpt* \"./drive/My Drive/models/nmt/\"\n",
    "!cp vocab_en* \"./drive/My Drive/models/nmt/\""
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "nmt_with_transformer.ipynb",
   "version": "0.3.2",
   "provenance": [],
   "collapsed_sections": [
    "tan_kJ7RGeWy"
   ],
   "toc_visible": true
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "accelerator": "GPU"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}