pyBERT-multilabel.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "BERT for Multi-label Sequence Classification",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "meO7ZaISZfZ1",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/bert_finetuning_with_cloud_tpus.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyD16g4Zdr5Z",
        "colab_type": "code",
        "outputId": "cfe07736-bae2-4bf8-8ad8-b9785530fb5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        }
      },
      "source": [
        "import sys\n",
        "import os\n",
        "import pprint\n",
        "import torch\n",
        "\n",
        "torch.cuda.is_available()\n",
        "\n",
        "!test -d bert_repo || git clone https://github.com/google-research/bert bert_repo\n",
        "!pip install pytorch-pretrained-bert\n",
        "  \n",
        "if not 'bert_repo' in sys.path:\n",
        "  sys.path += ['bert_repo']\n",
        "  \n",
        "!test -d data || mkdir data mkdir data/twitter; mkdir data/twitter/tmp\n",
        " \n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'bert_repo'...\n",
            "remote: Enumerating objects: 325, done.\u001b[K\n",
            "remote: Total 325 (delta 0), reused 0 (delta 0), pack-reused 325\u001b[K\n",
            "Receiving objects: 100% (325/325), 234.47 KiB | 845.00 KiB/s, done.\n",
            "Resolving deltas: 100% (186/186), done.\n",
            "Collecting pytorch-pretrained-bert\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d7/e0/c08d5553b89973d9a240605b9c12404bcf8227590de62bae27acbcfe076b/pytorch_pretrained_bert-0.6.2-py3-none-any.whl (123kB)\n",
            "\u001b[K     |████████████████████████████████| 133kB 9.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from pytorch-pretrained-bert) (2.21.0)\n",
            "Requirement already satisfied: torch>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from pytorch-pretrained-bert) (1.1.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from pytorch-pretrained-bert) (4.28.1)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from pytorch-pretrained-bert) (2018.1.10)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pytorch-pretrained-bert) (1.16.3)\n",
            "Requirement already satisfied: boto3 in /usr/local/lib/python3.6/dist-packages (from pytorch-pretrained-bert) (1.9.139)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch-pretrained-bert) (2019.3.9)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch-pretrained-bert) (1.24.2)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch-pretrained-bert) (2.8)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->pytorch-pretrained-bert) (3.0.4)\n",
            "Requirement already satisfied: botocore<1.13.0,>=1.12.139 in /usr/local/lib/python3.6/dist-packages (from boto3->pytorch-pretrained-bert) (1.12.139)\n",
            "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->pytorch-pretrained-bert) (0.9.4)\n",
            "Requirement already satisfied: s3transfer<0.3.0,>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from boto3->pytorch-pretrained-bert) (0.2.0)\n",
            "Requirement already satisfied: docutils>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.13.0,>=1.12.139->boto3->pytorch-pretrained-bert) (0.14)\n",
            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1; python_version >= \"2.7\" in /usr/local/lib/python3.6/dist-packages (from botocore<1.13.0,>=1.12.139->boto3->pytorch-pretrained-bert) (2.5.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil<3.0.0,>=2.1; python_version >= \"2.7\"->botocore<1.13.0,>=1.12.139->boto3->pytorch-pretrained-bert) (1.12.0)\n",
            "Installing collected packages: pytorch-pretrained-bert\n",
            "Successfully installed pytorch-pretrained-bert-0.6.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZGIYKTOQe6Ow",
        "colab_type": "text"
      },
      "source": [
        "# This takes a while dont run if you wont use fp16"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ms2cQQ9Ee3s1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! test -d ./apex || git clone https://github.com/NVIDIA/apex \n",
        "! pip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" ./apex\n",
        "\n",
        "if not 'apex' in sys.path:\n",
        "  sys.path += ['apex']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HUBP35oCDmbF",
        "colab_type": "text"
      },
      "source": [
        "### Prepare and import BERT and other modules\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7wzwke0sxS6W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pytorch_pretrained_bert.tokenization import BertTokenizer, WordpieceTokenizer\n",
        "from pytorch_pretrained_bert.modeling import BertForPreTraining, BertPreTrainedModel, BertModel, BertConfig, BertForMaskedLM, BertForSequenceClassification\n",
        "from pathlib import Path\n",
        "import torch\n",
        "import re\n",
        "from torch import Tensor\n",
        "from torch.nn import BCEWithLogitsLoss\n",
        "from fastai.text import Tokenizer, Vocab\n",
        "import pandas as pd\n",
        "import collections\n",
        "import os\n",
        "import pdb\n",
        "from tqdm import tqdm, trange\n",
        "import sys\n",
        "import random\n",
        "import numpy as np\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "import apex\n",
        "from sklearn.model_selection import train_test_split\n",
        "module_path = os.path.abspath(os.path.join('..'))\n",
        "if module_path not in sys.path:\n",
        "    sys.path.append(module_path)\n",
        "\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "from torch.utils.data.distributed import DistributedSampler\n",
        "from pytorch_pretrained_bert.optimization import BertAdam\n",
        "import logging\n",
        "logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s -   %(message)s',\n",
        "                    datefmt='%m/%d/%Y %H:%M:%S',\n",
        "                    level=logging.INFO)\n",
        "logger = logging.getLogger(__name__)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHTo0-wYV-cz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class BertForMultiLabelClassification(BertPreTrainedModel):\n",
        "  \"\"\"BERT model for classification.\n",
        "  This module is composed of the BERT model with a linear layer on top of\n",
        "  the pooled output.\n",
        "  Params:\n",
        "      `config`: a BertConfig class instance with the configuration to build a new model.\n",
        "      `num_labels`: the number of classes for the classifier. Default = 2.\n",
        "  Inputs:\n",
        "      `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]\n",
        "          with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts\n",
        "          `extract_features.py`, `run_classifier.py` and `run_squad.py`)\n",
        "      `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token\n",
        "          types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to\n",
        "          a `sentence B` token (see BERT paper for more details).\n",
        "      `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices\n",
        "          selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max\n",
        "          input sequence length in the current batch. It's the mask that we typically use for attention when\n",
        "          a batch has varying length sentences.\n",
        "      `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]\n",
        "          with indices selected in [0, ..., num_labels].\n",
        "  Outputs:\n",
        "      if `labels` is not `None`:\n",
        "          Outputs the CrossEntropy classification loss of the output with the labels.\n",
        "      if `labels` is `None`:\n",
        "          Outputs the classification logits of shape [batch_size, num_labels].\n",
        "  Example usage:\n",
        "  ```python\n",
        "  # Already been converted into WordPiece token ids\n",
        "  input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])\n",
        "  input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])\n",
        "  token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])\n",
        "  config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,\n",
        "      num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)\n",
        "  num_labels = 2\n",
        "  model = BertForSequenceClassification(config, num_labels)\n",
        "  logits = model(input_ids, token_type_ids, input_mask)\n",
        "  ```\n",
        "  \"\"\"\n",
        "  def __init__(self, config, num_labels=4):\n",
        "    super(BertForMultiLabelClassification, self).__init__(config)\n",
        "    self.num_labels = num_labels\n",
        "    self.bert = BertModel(config)\n",
        "    self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)\n",
        "    self.classifier = torch.nn.Linear(config.hidden_size, num_labels)\n",
        "    self.apply(self.init_bert_weights)\n",
        "\n",
        "  def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):\n",
        "    _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)\n",
        "    pooled_output = self.dropout(pooled_output)\n",
        "    logits = self.classifier(pooled_output)\n",
        "\n",
        "    if labels is not None:\n",
        "      loss_fct = BCEWithLogitsLoss()\n",
        "      loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1, self.num_labels))\n",
        "      return loss\n",
        "    else:\n",
        "      return logits\n",
        "\n",
        "  def freeze_bert_encoder(self):\n",
        "    for param in self.bert.parameters():\n",
        "      param.requires_grad = False\n",
        "\n",
        "  def unfreeze_bert_encoder(self):\n",
        "    for param in self.bert.parameters():\n",
        "      param.requires_grad = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "872USawseROx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"The following code overwrites some core BERT functionality\"\"\"\n",
        "\n",
        "class InputExample(object):\n",
        "  \"\"\"A single training/test example for simple sequence classification.\"\"\"\n",
        "\n",
        "  def __init__(self, guid, text_a, text_b=None, label=None):\n",
        "    \"\"\"Constructs a InputExample.\n",
        "\n",
        "    Args:\n",
        "      guid: Unique id for the example.\n",
        "      text_a: string. The untokenized text of the first sequence. For single\n",
        "      sequence tasks, only this sequence must be specified.\n",
        "      text_b: (Optional) string. The untokenized text of the second sequence.\n",
        "      Only must be specified for sequence pair tasks.\n",
        "      labels: (Optional) [string]. The label of the example. This should be\n",
        "      specified for train and dev examples, but not for test examples.\n",
        "    \"\"\"\n",
        "    self.guid = guid\n",
        "    self.text_a = text_a\n",
        "    self.text_b = text_b\n",
        "    self.label = label\n",
        "\n",
        "\n",
        "class InputFeatures(object):\n",
        "  \"\"\"A single set of features of data.\"\"\"\n",
        "\n",
        "  def __init__(self, input_ids, input_mask, segment_ids, label_id):\n",
        "    self.input_ids = input_ids\n",
        "    self.input_mask = input_mask\n",
        "    self.segment_ids = segment_ids\n",
        "    self.label_id = label_id\n",
        "\n",
        "class DataProcessor(object):\n",
        "  \"\"\"Base class for data converters for sequence classification data sets.\"\"\"\n",
        "\n",
        "  def get_train_examples(self, data_dir):\n",
        "    \"\"\"Gets a collection of `InputExample`s for the train set.\"\"\"\n",
        "    raise NotImplementedError()\n",
        "\n",
        "  def get_dev_examples(self, data_dir):\n",
        "    \"\"\"Gets a collection of `InputExample`s for the dev set.\"\"\"\n",
        "    raise NotImplementedError()\n",
        "\n",
        "  def get_test_examples(self, data_dir, data_file_name, size=-1):\n",
        "    \"\"\"Gets a collection of `InputExample`s for the dev set.\"\"\"\n",
        "    raise NotImplementedError() \n",
        "\n",
        "  def get_labels(self):\n",
        "    \"\"\"Gets the list of labels for this data set.\"\"\"\n",
        "    raise NotImplementedError()\n",
        "        \n",
        "class MultilabelProcessor(DataProcessor):\n",
        "  \"\"\"Custom Processor for the Myers-Briggs Data.\n",
        "\n",
        "   Assumes input data as follows:\n",
        "\n",
        "    id at index 0,text at index 1, labels at [2:]\n",
        "\n",
        "   Example for our problem:\n",
        "      0,sometext,0,1,1,1\n",
        "      1,moretext,1,0,0,1\n",
        "    OR\n",
        "      ENFJ,sometext,0,1,1,1\n",
        "      ENFJ,moretext,0,1,1,1\n",
        "\n",
        "   Meaning - id does not need to be unique, it can be used as a label\n",
        "   to try multi-class solution of the same task.\n",
        "  \"\"\"\n",
        "  \n",
        "  \n",
        "  def __init__(self, labels=None):\n",
        "    \"\"\"Constructs a MultilabelProcessor\n",
        "    \n",
        "    Args:\n",
        "      labels: array-like or list. Columns under which labels are stored in \n",
        "      the input dataframes. Default is None: assumes labels are [2:]\n",
        "    \"\"\"\n",
        "    \n",
        "    self.labels = labels\n",
        "\n",
        "  def get_train_examples(self, data_dir):\n",
        "    \"\"\"Pass in train data by specifying it's directory. Looks for train.csv\"\"\"\n",
        "    path = Path(data_dir, \"train.csv\")\n",
        "    logger.info(\"LOOKING AT {}\".format(path))\n",
        "    return self._create_examples(pd.read_csv(path), \"train\")\n",
        "\n",
        "  def get_dev_examples(self, data_dir):\n",
        "    \"\"\"Pass in val (development) data by specifying it's directory. Looks for val.csv.\"\"\"\n",
        "    path = Path(data_dir, \"val.csv\")\n",
        "    logger.info(\"LOOKING AT {}\".format(path))\n",
        "    return self._create_examples(pd.read_csv(path), \"val\")\n",
        "\n",
        "  def get_test_examples(self, data_dir, data_file_name, size=-1):\n",
        "    \"\"\"See base class.\"\"\"\n",
        "    data_df = pd.read_csv(os.path.join(data_dir, data_file_name))\n",
        "\n",
        "    if size == -1:\n",
        "      return self._create_examples(data_df, \"test\")\n",
        "    else:\n",
        "      return self._create_examples(data_df.sample(size), \"val\")\n",
        "\n",
        "  def get_labels(self):\n",
        "    \"\"\"4 binary labels: IE, NS, FT, JP. 1 means I,N,F,J; 0 means E,S,T,P\"\"\"\n",
        "    return self.labels\n",
        "\n",
        "  def _create_examples(self, df, set_type, labels_available=True):\n",
        "    \"\"\"Creates examples for the train, val and test sets.\"\"\"\n",
        "    if len(df.columns) <= 2:\n",
        "      raise IndexError(\"Invalid dataframe format. Must have guid, text, labels\")\n",
        "    if self.labels is None:\n",
        "      self.labels = df.columns[2:]\n",
        "      \n",
        "    examples = []\n",
        "\n",
        "    for i, row in enumerate(df.values):\n",
        "      guid = \"%s-%s-%s\" % (i, set_type, row[0])\n",
        "      text_a = row[1]\n",
        "      if labels_available:\n",
        "        labels = row[2:]\n",
        "      else:\n",
        "        labels = []\n",
        "      examples.append(\n",
        "        InputExample(guid=guid, text_a=text_a, label=labels))\n",
        "    return examples\n",
        "  \n",
        "def convert_examples_to_features(examples, label_list, max_seq_length,\n",
        "                                 tokenizer, output_mode=\"classification\"):\n",
        "  \"\"\"Loads a data file into a list of `InputBatch`s.\"\"\"\n",
        "\n",
        "  features = []\n",
        "  for (ex_index, example) in enumerate(examples):\n",
        "    if ex_index % 10000 == 0:\n",
        "      logger.info(\"Writing example %d of %d\" % (ex_index, len(examples)))\n",
        "\n",
        "    tokens_a = tokenizer.tokenize(example.text_a)\n",
        "    tokens_b = None\n",
        "    \n",
        "    if example.text_b:\n",
        "      tokens_b = tokenizer.tokenize(example.text_b)\n",
        "      # Modifies `tokens_a` and `tokens_b` in place so that the total\n",
        "      # length is less than the specified length.\n",
        "      # Account for [CLS], [SEP], [SEP] with \"- 3\"\n",
        "      _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)\n",
        "    else:\n",
        "      # Account for [CLS] and [SEP] with \"- 2\"\n",
        "      if len(tokens_a) > max_seq_length - 2:\n",
        "        tokens_a = tokens_a[:(max_seq_length - 2)]\n",
        "\n",
        "    # The convention in BERT is:\n",
        "    # (a) For sequence pairs:\n",
        "    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\n",
        "    #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1\n",
        "    # (b) For single sequences:\n",
        "    #  tokens:   [CLS] the dog is hairy . [SEP]\n",
        "    #  type_ids: 0   0   0   0  0     0 0\n",
        "    #\n",
        "    # Where \"type_ids\" are used to indicate whether this is the first\n",
        "    # sequence or the second sequence. The embedding vectors for `type=0` and\n",
        "    # `type=1` were learned during pre-training and are added to the wordpiece\n",
        "    # embedding vector (and position vector). This is not *strictly* necessary\n",
        "    # since the [SEP] token unambigiously separates the sequences, but it makes\n",
        "    # it easier for the model to learn the concept of sequences.\n",
        "    #\n",
        "    # For classification tasks, the first vector (corresponding to [CLS]) is\n",
        "    # used as as the \"sentence vector\". Note that this only makes sense because\n",
        "    # the entire model is fine-tuned.\n",
        "    tokens = [\"[CLS]\"] + tokens_a + [\"[SEP]\"]\n",
        "    segment_ids = [0] * len(tokens)\n",
        "\n",
        "    if tokens_b:\n",
        "      tokens += tokens_b + [\"[SEP]\"]\n",
        "      segment_ids += [1] * (len(tokens_b) + 1)\n",
        "\n",
        "    input_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "    # The mask has 1 for real tokens and 0 for padding tokens. Only real\n",
        "    # tokens are attended to.\n",
        "    input_mask = [1] * len(input_ids)\n",
        "\n",
        "    # Zero-pad up to the sequence length.\n",
        "    padding = [0] * (max_seq_length - len(input_ids))\n",
        "    input_ids += padding\n",
        "    input_mask += padding\n",
        "    segment_ids += padding\n",
        "\n",
        "    assert len(input_ids) == max_seq_length\n",
        "    assert len(input_mask) == max_seq_length\n",
        "    assert len(segment_ids) == max_seq_length\n",
        "\n",
        "    labels_ids = []\n",
        "    for label in example.label:\n",
        "      labels_ids.append(int(label))\n",
        "\n",
        "    if ex_index < 1:\n",
        "      logger.info(\"*** Example ***\")\n",
        "      logger.info(\"guid: %s\" % (example.guid))\n",
        "      logger.info(\"tokens: %s\" % \" \".join(\n",
        "              [str(x) for x in tokens]))\n",
        "      logger.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\n",
        "      logger.info(\"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\n",
        "      logger.info(\n",
        "              \"segment_ids: %s\" % \" \".join([str(x) for x in segment_ids]))\n",
        "      logger.info(\"label: %s (id = %s)\" % (example.label, labels_ids))\n",
        "\n",
        "    features.append(InputFeatures(input_ids=input_ids,\n",
        "                                  input_mask=input_mask,\n",
        "                                  segment_ids=segment_ids,\n",
        "                                  label_id=labels_ids,\n",
        "                                 ))\n",
        "  return features\n",
        "\n",
        "def _truncate_seq_pair(tokens_a, tokens_b, max_length):\n",
        "  \"\"\"Truncates a sequence pair in place to the maximum length.\"\"\"\n",
        "\n",
        "  # This is a simple heuristic which will always truncate the longer sequence\n",
        "  # one token at a time. This makes more sense than truncating an equal percent\n",
        "  # of tokens from each, since if one sequence is very short then each token\n",
        "  # that's truncated likely contains more information than a longer sequence.\n",
        "  while True:\n",
        "    total_length = len(tokens_a) + len(tokens_b)\n",
        "    if total_length <= max_length:\n",
        "      break\n",
        "    if len(tokens_a) > len(tokens_b):\n",
        "      tokens_a.pop()\n",
        "    else:\n",
        "      tokens_b.pop()\n",
        "\n",
        "def accuracy(y_pred:Tensor, y_true:Tensor):\n",
        "  \"\"\"Simple accuracy. A bad metric for multilabel.\"\"\"\n",
        "  return (y_pred == y_true).sum() / len(y_true)\n",
        "\n",
        "def accuracy_thresh(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True):\n",
        "  \"\"\"Compute accuracy when `y_pred` and `y_true` are the same size.\"\"\"\n",
        "  if sigmoid: \n",
        "    y_pred = y_pred.sigmoid()\n",
        "  return np.mean(((y_pred>thresh)==y_true.byte()).float().cpu().numpy(), axis=1).sum()\n",
        "  \n",
        "def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float=0.2, beta:float=2, \n",
        "                    eps:float=1e-9, sigmoid:bool=True):\n",
        "  \"\"\"Computes the f_beta between `preds` and `targets`.\"\n",
        "    F1-metric version, `beta` controls importantce of precision vs recall,\n",
        "    where beta=1 is equivalent to F1.\n",
        "  \"\"\"\n",
        "  beta2 = beta ** 2\n",
        "  if sigmoid: y_pred = y_pred.sigmoid()\n",
        "  y_pred = (y_pred>thresh).float()\n",
        "  y_true = y_true.float()\n",
        "  TP = (y_pred*y_true).sum(dim=1)\n",
        "  prec = TP/(y_pred.sum(dim=1)+eps)\n",
        "  rec = TP/(y_true.sum(dim=1)+eps)\n",
        "  res = (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)\n",
        "  return res.mean().item()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TC7hHzQjRfJy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! cp test.csv data/twitter/tmp ; cp train.csv data/twitter/tmp ; cp val.csv data/twitter/tmp"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0WJgiuwV3rr",
        "colab_type": "code",
        "outputId": "a9e489c9-5463-4419-d558-4dd5369fe5ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\"\"\"Google's tensorflow training parameters\"\"\"\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "TRAIN_BATCH_SIZE = 32\n",
        "EVAL_BATCH_SIZE = 8\n",
        "PREDICT_BATCH_SIZE = 8\n",
        "LEARNING_RATE = 2e-5\n",
        "NUM_TRAIN_EPOCHS = 3.0\n",
        "MAX_SEQ_LENGTH = 128\n",
        "# Warmup is a period of time where hte learning rate \n",
        "# is small and gradually increases--usually helps training.\n",
        "WARMUP_PROPORTION = 0.1\n",
        "# Model configs\n",
        "SAVE_CHECKPOINTS_STEPS = 1000\n",
        "SAVE_SUMMARY_STEPS = 500\n",
        "\n",
        "\n",
        "# Compute number of train and warmup steps from batch size\n",
        "train_examples = processor.get_train_examples(TASK_DATA_DIR)\n",
        "num_train_steps = int(len(train_examples) / TRAIN_BATCH_SIZE * NUM_TRAIN_EPOCHS)\n",
        "num_warmup_steps = int(num_train_steps * WARMUP_PROPORTION)\n",
        "\n",
        "# Setup TPU related config\n",
        "\n",
        "\n",
        "def get_run_config(output_dir):\n",
        "  return tf.contrib.tpu.RunConfig(\n",
        "    cluster=tpu_cluster_resolver,\n",
        "    model_dir=output_dir,\n",
        "    save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS,\n",
        "    tpu_config=tf.contrib.tpu.TPUConfig(\n",
        "      iterations_per_loop=ITERATIONS_PER_LOOP,\n",
        "      num_shards=NUM_TPU_CORES,\n",
        "      per_host_input_for_training=tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2))\n",
        "\"\"\"\n",
        "\n",
        "\"\"\"Set pytorch parameters\"\"\"\n",
        "\n",
        "LABEL_COLS=['I','N','F','J']\n",
        "\n",
        "DATA=Path('./data')\n",
        "DATA.mkdir(exist_ok=True)\n",
        "\n",
        "DATA_PATH=DATA/'twitter'\n",
        "DATA_PATH.mkdir(exist_ok=True)\n",
        "BERT_MODEL = 'uncased_L-12_H-768_A-12'\n",
        "PATH=DATA_PATH/'tmp'\n",
        "PATH.mkdir(exist_ok=True)\n",
        "\n",
        "CLAS_DATA_PATH=PATH/'class'\n",
        "CLAS_DATA_PATH.mkdir(exist_ok=True)\n",
        "\n",
        "model_state_dict = None\n",
        "\n",
        "args = {\n",
        "  \"train_size\": -1,\n",
        "  \"val_size\": -1,\n",
        "  \"full_data_dir\": DATA_PATH,\n",
        "  \"data_dir\": PATH,\n",
        "  \"task_name\": \"twitter\",\n",
        "  \"labels\": LABEL_COLS,\n",
        "  \"no_cuda\": False,\n",
        "  \"bert_model\": 'bert-base-uncased',\n",
        "  \"output_dir\": CLAS_DATA_PATH/'output',\n",
        "  \"max_seq_length\": 128,\n",
        "  \"do_train\": True,\n",
        "  \"do_eval\": True,\n",
        "  \"do_lower_case\": True,\n",
        "  \"train_batch_size\": 16,\n",
        "  \"eval_batch_size\": 8,\n",
        "  \"learning_rate\": 3e-5,\n",
        "  \"num_train_epochs\": 4.0,\n",
        "  \"warmup_proportion\": 0.1,\n",
        "  \"local_rank\": -1,\n",
        "  \"seed\": 42,\n",
        "  \"gradient_accumulation_steps\": 1,\n",
        "  \"optimize_on_cpu\": False,\n",
        "  \"fp16\": False,\n",
        "  \"loss_scale\": 128,\n",
        "  \n",
        "}\n",
        "\n",
        "\n",
        "#\n",
        "if args[\"local_rank\"] == -1 or args[\"no_cuda\"]:\n",
        "  device = torch.device(\"cuda\" if torch.cuda.is_available() and not args[\"no_cuda\"] else \"cpu\")\n",
        "  n_gpu = torch.cuda.device_count()\n",
        "#     n_gpu = 1\n",
        "else:\n",
        "  torch.cuda.set_device(args['local_rank'])\n",
        "  device = torch.device(\"cuda\", args['local_rank'])\n",
        "  n_gpu = 1\n",
        "  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs\n",
        "  torch.distributed.init_process_group(backend='nccl')\n",
        "logger.info(\"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}\".format(\n",
        "        device, n_gpu, bool(args['local_rank'] != -1), args['fp16']))\n",
        "\n",
        "processors = {\n",
        "  \"twitter\": MultilabelProcessor\n",
        "}\n",
        "\n",
        "args['train_batch_size'] = int(args['train_batch_size'] / args['gradient_accumulation_steps'])\n",
        "\n",
        "random.seed(args['seed'])\n",
        "np.random.seed(args['seed'])\n",
        "torch.manual_seed(args['seed'])\n",
        "task_name = args['task_name'].lower()\n",
        "\n",
        "if task_name not in processors:\n",
        "  raise ValueError(\"Task not found: %s\" % (task_name))\n",
        "\n",
        "label_list=args['labels']\n",
        "processor = processors[task_name](label_list)\n",
        "num_labels = len(args['labels'])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "04/29/2019 09:51:12 - INFO - __main__ -   device: cuda n_gpu: 1, distributed training: False, 16-bits training: False\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9W1U0sMOv-k",
        "colab_type": "code",
        "outputId": "d3d28ac1-3954-4283-86c7-5db5e2a611b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(args['bert_model'], do_lower_case=args['do_lower_case'])\n",
        "\n",
        "logger.info(\"***** Loading data *****\")\n",
        "\n",
        "train_examples = None\n",
        "num_train_steps = None\n",
        "if args['do_train']:\n",
        "  train_examples = processor.get_train_examples(args['full_data_dir'])\n",
        "  num_train_steps = int(\n",
        "      len(train_examples)/args['train_batch_size']/args['gradient_accumulation_steps']*args['num_train_epochs']\n",
        "  )\n",
        "eval_examples = processor.get_dev_examples(args['data_dir'])\n",
        "train_features = convert_examples_to_features(train_examples, args['labels'], args['max_seq_length'], tokenizer)\n",
        "\n",
        "\n",
        "logger.info(\"  Num examples = %d\", len(train_examples))\n",
        "logger.info(\"  Batch size = %d\", args['train_batch_size'])\n",
        "logger.info(\"  Num steps = %d\", num_train_steps)\n",
        "all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)\n",
        "all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)\n",
        "all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)\n",
        "all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)\n",
        "train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)\n",
        "if args['local_rank'] == -1:\n",
        "  train_sampler = RandomSampler(train_data)\n",
        "else:\n",
        "  train_sampler = DistributedSampler(train_data)\n",
        "train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args['train_batch_size'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "04/29/2019 09:51:12 - INFO - pytorch_pretrained_bert.tokenization -   loading vocabulary file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /root/.pytorch_pretrained_bert/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n",
            "04/29/2019 09:51:12 - INFO - __main__ -   ***** Loading data *****\n",
            "04/29/2019 09:51:12 - INFO - __main__ -   LOOKING AT data/twitter/train.csv\n",
            "04/29/2019 09:51:12 - INFO - __main__ -   LOOKING AT data/twitter/tmp/val.csv\n",
            "04/29/2019 09:51:12 - INFO - __main__ -   Writing example 0 of 2963\n",
            "04/29/2019 09:51:12 - INFO - __main__ -   *** Example ***\n",
            "04/29/2019 09:51:12 - INFO - __main__ -   guid: 0-train-0\n",
            "04/29/2019 09:51:12 - INFO - __main__ -   tokens: [CLS] i ' m attending @ user guide to a better blog ebook launch party for a chance to win $ 300 gift card ! @ hash ##tag @ ur ##l / 9 ##n ##15 ##b ##v ##lt ##ep rs ##v ##p to the @ user guide to a better blog twitter party for a chance to win a $ 300 gift card ! @ hash ##tag @ ur ##l / 9 ##n ##15 ##b ##v ##lt ##ep joined a fitness challenge on match ##up ! @ ur ##l / ct ##st ##v ##f ##df ##te @ hash ##tag @ hash ##tag @ hash ##tag accessories : meet mrs . grant , a @ hash ##tag with a purpose @ user @ hash ##tag @ hash ##tag [SEP]\n",
            "04/29/2019 09:51:12 - INFO - __main__ -   input_ids: 101 1045 1005 1049 7052 1030 5310 5009 2000 1037 2488 9927 26885 4888 2283 2005 1037 3382 2000 2663 1002 3998 5592 4003 999 1030 23325 15900 1030 24471 2140 1013 1023 2078 16068 2497 2615 7096 13699 12667 2615 2361 2000 1996 1030 5310 5009 2000 1037 2488 9927 10474 2283 2005 1037 3382 2000 2663 1037 1002 3998 5592 4003 999 1030 23325 15900 1030 24471 2140 1013 1023 2078 16068 2497 2615 7096 13699 2587 1037 10516 4119 2006 2674 6279 999 1030 24471 2140 1013 14931 3367 2615 2546 20952 2618 1030 23325 15900 1030 23325 15900 1030 23325 15900 16611 1024 3113 3680 1012 3946 1010 1037 1030 23325 15900 2007 1037 3800 1030 5310 1030 23325 15900 1030 23325 15900 102\n",
            "04/29/2019 09:51:12 - INFO - __main__ -   input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            "04/29/2019 09:51:12 - INFO - __main__ -   segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "04/29/2019 09:51:12 - INFO - __main__ -   label: [0 1 1 1] (id = [0, 1, 1, 1])\n",
            "04/29/2019 09:51:24 - INFO - __main__ -     Num examples = 2963\n",
            "04/29/2019 09:51:24 - INFO - __main__ -     Batch size = 16\n",
            "04/29/2019 09:51:24 - INFO - __main__ -     Num steps = 740\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJvK45ZkSpmg",
        "colab_type": "code",
        "outputId": "f5f0ce71-7a29-43ce-f84e-704886336cf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 5392
        }
      },
      "source": [
        "def get_model():\n",
        "  model = BertForMultiLabelClassification.from_pretrained(args['bert_model'], num_labels = num_labels)\n",
        "  return model\n",
        "\n",
        "model = get_model()\n",
        "\n",
        "\n",
        "\n",
        "if args['fp16']:\n",
        "  model.half()\n",
        "model.to(device)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "04/29/2019 09:51:24 - INFO - pytorch_pretrained_bert.modeling -   loading archive file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz from cache at /root/.pytorch_pretrained_bert/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6e241983c6f0.ae3cef932725ca7a30cdcb93fc6e09150a55e2a130ec7af63975a16c153ae2ba\n",
            "04/29/2019 09:51:24 - INFO - pytorch_pretrained_bert.modeling -   extracting archive file /root/.pytorch_pretrained_bert/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6e241983c6f0.ae3cef932725ca7a30cdcb93fc6e09150a55e2a130ec7af63975a16c153ae2ba to temp dir /tmp/tmpqjmsyyq8\n",
            "04/29/2019 09:51:28 - INFO - pytorch_pretrained_bert.modeling -   Model config {\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "04/29/2019 09:51:32 - INFO - pytorch_pretrained_bert.modeling -   Weights of BertForMultiLabelClassification not initialized from pretrained model: ['classifier.weight', 'classifier.bias']\n",
            "04/29/2019 09:51:32 - INFO - pytorch_pretrained_bert.modeling -   Weights from pretrained model not used in BertForMultiLabelClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForMultiLabelClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "        (1): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "        (2): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "        (3): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "        (4): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "        (5): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "        (6): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "        (7): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "        (8): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "        (9): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "        (10): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "        (11): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): FusedLayerNorm(torch.Size([768]), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1)\n",
              "  (classifier): Linear(in_features=768, out_features=4, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAVIfyBiUspe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.optim.lr_scheduler import _LRScheduler, Optimizer\n",
        "\n",
        "def warmup_linear(x, warmup=0.002):\n",
        "  \"\"\"Warmup for how long?\"\"\"\n",
        "  if x < warmup:\n",
        "      return x/warmup\n",
        "  return 1.0 - x\n",
        "\n",
        "class CyclicLR(object):\n",
        "  \"\"\"Sets the learning rate of each parameter group according to\n",
        "  cyclical learning rate policy (CLR). The policy cycles the learning\n",
        "  rate between two boundaries with a constant frequency, as detailed in\n",
        "  the paper `Cyclical Learning Rates for Training Neural Networks`_.\n",
        "  The distance between the two boundaries can be scaled on a per-iteration\n",
        "  or per-cycle basis.\n",
        "  Cyclical learning rate policy changes the learning rate after every batch.\n",
        "  `batch_step` should be called after a batch has been used for training.\n",
        "  To resume training, save `last_batch_iteration` and use it to instantiate `CycleLR`.\n",
        "  This class has three built-in policies, as put forth in the paper:\n",
        "  \"triangular\":\n",
        "      A basic triangular cycle w/ no amplitude scaling.\n",
        "  \"triangular2\":\n",
        "      A basic triangular cycle that scales initial amplitude by half each cycle.\n",
        "  \"exp_range\":\n",
        "      A cycle that scales initial amplitude by gamma**(cycle iterations) at each\n",
        "      cycle iteration.\n",
        "  This implementation was adapted from the github repo: `bckenstler/CLR`_\n",
        "  Args:\n",
        "      optimizer (Optimizer): Wrapped optimizer.\n",
        "      base_lr (float or list): Initial learning rate which is the\n",
        "          lower boundary in the cycle for eachparam groups.\n",
        "          Default: 0.001\n",
        "      max_lr (float or list): Upper boundaries in the cycle for\n",
        "          each parameter group. Functionally,\n",
        "          it defines the cycle amplitude (max_lr - base_lr).\n",
        "          The lr at any cycle is the sum of base_lr\n",
        "          and some scaling of the amplitude; therefore\n",
        "          max_lr may not actually be reached depending on\n",
        "          scaling function. Default: 0.006\n",
        "      step_size (int): Number of training iterations per\n",
        "          half cycle. Authors suggest setting step_size\n",
        "          2-8 x training iterations in epoch. Default: 2000\n",
        "      mode (str): One of {triangular, triangular2, exp_range}.\n",
        "          Values correspond to policies detailed above.\n",
        "          If scale_fn is not None, this argument is ignored.\n",
        "          Default: 'triangular'\n",
        "      gamma (float): Constant in 'exp_range' scaling function:\n",
        "          gamma**(cycle iterations)\n",
        "          Default: 1.0\n",
        "      scale_fn (function): Custom scaling policy defined by a single\n",
        "          argument lambda function, where\n",
        "          0 <= scale_fn(x) <= 1 for all x >= 0.\n",
        "          mode paramater is ignored\n",
        "          Default: None\n",
        "      scale_mode (str): {'cycle', 'iterations'}.\n",
        "          Defines whether scale_fn is evaluated on\n",
        "          cycle number or cycle iterations (training\n",
        "          iterations since start of cycle).\n",
        "          Default: 'cycle'\n",
        "      last_batch_iteration (int): The index of the last batch. Default: -1\n",
        "  Example:\n",
        "      >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)\n",
        "      >>> scheduler = torch.optim.CyclicLR(optimizer)\n",
        "      >>> data_loader = torch.utils.data.DataLoader(...)\n",
        "      >>> for epoch in range(10):\n",
        "      >>>     for batch in data_loader:\n",
        "      >>>         scheduler.batch_step()\n",
        "      >>>         train_batch(...)\n",
        "  .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186\n",
        "  .. _bckenstler/CLR: https://github.com/bckenstler/CLR\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self,\n",
        "               optimizer,\n",
        "               base_lr=1e-3,\n",
        "               max_lr=6e-3,\n",
        "               step_size=2000,\n",
        "               mode='triangular',\n",
        "               gamma=1.,\n",
        "               scale_fn=None,\n",
        "               scale_mode='cycle',\n",
        "               last_batch_iteration=-1,\n",
        "              ):\n",
        "    \n",
        "    self.optimizer = optimizer\n",
        "\n",
        "    if isinstance(base_lr, list) or isinstance(base_lr, tuple):\n",
        "      if len(base_lr) != len(optimizer.param_groups):\n",
        "        raise ValueError(\"expected {} base_lr, got {}\".format(\n",
        "          len(optimizer.param_groups), len(base_lr)))\n",
        "      self.base_lrs = list(base_lr)\n",
        "    else:\n",
        "      self.base_lrs = [base_lr] * len(optimizer.param_groups)\n",
        "\n",
        "    if isinstance(max_lr, list) or isinstance(max_lr, tuple):\n",
        "      if len(max_lr) != len(optimizer.param_groups):\n",
        "        raise ValueError(\"expected {} max_lr, got {}\".format(\n",
        "          len(optimizer.param_groups), len(max_lr)))\n",
        "      self.max_lrs = list(max_lr)\n",
        "    else:\n",
        "      self.max_lrs = [max_lr] * len(optimizer.param_groups)\n",
        "\n",
        "    self.step_size = step_size\n",
        "\n",
        "    if mode not in ['triangular', 'triangular2', 'exp_range'] and scale_fn is None:\n",
        "       raise ValueError('mode is invalid and scale_fn is None')\n",
        "\n",
        "    self.mode = mode\n",
        "    self.gamma = gamma\n",
        "\n",
        "    if scale_fn is None:\n",
        "      if self.mode == 'triangular':\n",
        "        self.scale_fn = self._triangular_scale_fn\n",
        "        self.scale_mode = 'cycle'\n",
        "      elif self.mode == 'triangular2':\n",
        "        self.scale_fn = self._triangular2_scale_fn\n",
        "        self.scale_mode = 'cycle'\n",
        "      elif self.mode == 'exp_range':\n",
        "        self.scale_fn = self._exp_range_scale_fn\n",
        "        self.scale_mode = 'iterations'\n",
        "    else:\n",
        "      self.scale_fn = scale_fn\n",
        "      self.scale_mode = scale_mode\n",
        "\n",
        "    self.batch_step(last_batch_iteration + 1)\n",
        "    self.last_batch_iteration = last_batch_iteration\n",
        "\n",
        "  def batch_step(self, batch_iteration=None):\n",
        "    if batch_iteration is None:\n",
        "      batch_iteration = self.last_batch_iteration + 1\n",
        "    self.last_batch_iteration = batch_iteration\n",
        "    for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n",
        "      param_group['lr'] = lr\n",
        "\n",
        "  def _triangular_scale_fn(self, x):\n",
        "    return 1.\n",
        "\n",
        "  def _triangular2_scale_fn(self, x):\n",
        "    return 1 / (2. ** (x - 1))\n",
        "\n",
        "  def _exp_range_scale_fn(self, x):\n",
        "    return self.gamma**(x)\n",
        "\n",
        "  def get_lr(self):\n",
        "    step_size = float(self.step_size)\n",
        "    cycle = np.floor(1 + self.last_batch_iteration / (2 * step_size))\n",
        "    x = np.abs(self.last_batch_iteration / step_size - 2 * cycle + 1)\n",
        "\n",
        "    lrs = []\n",
        "    param_lrs = zip(self.optimizer.param_groups, self.base_lrs, self.max_lrs)\n",
        "    for param_group, base_lr, max_lr in param_lrs:\n",
        "      base_height = (max_lr - base_lr) * np.maximum(0, (1 - x))\n",
        "      if self.scale_mode == 'cycle':\n",
        "        lr = base_lr + base_height * self.scale_fn(cycle)\n",
        "      else:\n",
        "        lr = base_lr + base_height * self.scale_fn(self.last_batch_iteration)\n",
        "      lrs.append(lr)\n",
        "    return lrs\n",
        "  \n",
        "# Prepare optimizer\n",
        "param_optimizer = list(model.named_parameters())\n",
        "no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
        "\n",
        "optimizer_grouped_parameters = [\n",
        "    {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0},\n",
        "  ]\n",
        "\n",
        "t_total = num_train_steps\n",
        "\n",
        "if args['local_rank'] != -1:\n",
        "  t_total = t_total // torch.distributed.get_world_size()\n",
        "\n",
        "bert_adam=BertAdam(optimizer_grouped_parameters,lr=args['learning_rate'],warmup=args['warmup_proportion'],t_total=t_total)\n",
        "\n",
        "scheduler = CyclicLR(optimizer=bert_adam, base_lr=2e-5, max_lr=5e-5, step_size=2500, last_batch_iteration=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0l12eQkOFSV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Prepare optimizer\n",
        "param_optimizer = list(model.named_parameters())\n",
        "no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
        "optimizer_grouped_parameters = [\n",
        "    {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "    ]\n",
        "t_total = num_train_steps\n",
        "if args['local_rank'] != -1:\n",
        "    t_total = t_total // torch.distributed.get_world_size()\n",
        "if args['fp16']:\n",
        "    try:\n",
        "        from apex.optimizers import FP16_Optimizer\n",
        "        from apex.optimizers import FusedAdam\n",
        "    except ImportError:\n",
        "        raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.\")\n",
        "\n",
        "    optimizer = FusedAdam(optimizer_grouped_parameters,\n",
        "                          lr=args['learning_rate'],\n",
        "                          bias_correction=False,\n",
        "                          max_grad_norm=1.0)\n",
        "    if args['loss_scale'] == 0:\n",
        "        optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)\n",
        "    else:\n",
        "        optimizer = FP16_Optimizer(optimizer, static_loss_scale=args['loss_scale'])\n",
        "\n",
        "else:\n",
        "    optimizer = BertAdam(optimizer_grouped_parameters,\n",
        "                         lr=args['learning_rate'],\n",
        "                         warmup=args['warmup_proportion'],\n",
        "                         t_total=t_total)\n",
        "\n",
        "scheduler = CyclicLR(optimizer, base_lr=2e-5, max_lr=5e-5, step_size=2500, last_batch_iteration=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hyfO_jQgwkB-",
        "colab_type": "text"
      },
      "source": [
        "## Validation function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Qk2E8YCvniS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def eval():\n",
        "  args['output_dir'].mkdir(exist_ok=True)\n",
        "\n",
        "  eval_features = convert_examples_to_features(eval_examples, args['labels'], args['max_seq_length'], tokenizer)\n",
        "  logger.info(\"***** Running evaluation *****\")\n",
        "  logger.info(\"  Num examples = %d\", len(eval_examples))\n",
        "  logger.info(\"  Batch size = %d\", args['eval_batch_size'])\n",
        "  all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)\n",
        "  all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)\n",
        "  all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)\n",
        "  all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float)\n",
        "  eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)\n",
        "  # Run prediction for full data\n",
        "  eval_sampler = SequentialSampler(eval_data)\n",
        "  eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args['eval_batch_size'])\n",
        "\n",
        "  all_logits = None\n",
        "  all_labels = None\n",
        "\n",
        "  model.eval()\n",
        "  eval_loss, eval_accuracy = 0, 0\n",
        "  nb_eval_steps, nb_eval_examples = 0, 0\n",
        "  for input_ids, input_mask, segment_ids, label_id in eval_dataloader:\n",
        "    input_ids = input_ids.to(device)\n",
        "    input_mask = input_mask.to(device)\n",
        "    segment_ids = segment_ids.to(device)\n",
        "    label_id = label_id.to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "      tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_id)\n",
        "      logits = model(input_ids, segment_ids, input_mask)\n",
        "\n",
        "\n",
        "    tmp_eval_accuracy = accuracy_thresh(logits, label_id)\n",
        "    if all_logits is None:\n",
        "      all_logits = logits.detach().cpu().numpy()\n",
        "    else:\n",
        "      all_logits = np.concatenate((all_logits, logits.detach().cpu().numpy()), axis=0)\n",
        "\n",
        "    if all_labels is None:\n",
        "      all_labels = label_id.detach().cpu().numpy()\n",
        "    else:    \n",
        "      all_labels = np.concatenate((all_labels, label_id.detach().cpu().numpy()), axis=0)\n",
        "\n",
        "\n",
        "  eval_loss += tmp_eval_loss.mean().item()\n",
        "  eval_accuracy += tmp_eval_accuracy\n",
        "\n",
        "  nb_eval_examples += input_ids.size(0)\n",
        "  nb_eval_steps += 1\n",
        "\n",
        "  eval_loss = eval_loss / nb_eval_steps\n",
        "  eval_accuracy = eval_accuracy / nb_eval_examples\n",
        "\n",
        "  # Compute ROC curve and ROC area for each class\n",
        "  fpr = dict()\n",
        "  tpr = dict()\n",
        "  roc_auc = dict()\n",
        "\n",
        "  for i in range(num_labels):\n",
        "    fpr[i], tpr[i], _ = roc_curve(all_labels[:, i], all_logits[:, i])\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "  # Compute micro-average ROC curve and ROC area\n",
        "  fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(all_labels.ravel(), all_logits.ravel())\n",
        "  roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
        "\n",
        "  result = {'eval_loss': eval_loss,\n",
        "            'eval_accuracy': eval_accuracy,\n",
        "            'roc_auc': roc_auc  }\n",
        "\n",
        "  output_eval_file = os.path.join(args['output_dir'], \"eval_results.txt\")\n",
        "  with open(output_eval_file, \"w\") as writer:\n",
        "    logger.info(\"***** Eval results *****\")\n",
        "    for key in sorted(result.keys()):\n",
        "      logger.info(\"  %s = %s\", key, str(result[key]))\n",
        "  return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7uOAu1lA0EuH",
        "colab_type": "text"
      },
      "source": [
        "## Train model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4YTbMxsP0H9I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def fit(num_epocs=args['num_train_epochs']):\n",
        "  global_step = 0\n",
        "  model.train()\n",
        "  for i_ in tqdm(range(int(num_epocs)), desc=\"Epoch\"):\n",
        "\n",
        "    tr_loss = 0\n",
        "    nb_tr_examples, nb_tr_steps = 0, 0\n",
        "    for step, batch in enumerate(tqdm(train_dataloader, desc=\"Iteration\")):\n",
        "\n",
        "      batch = tuple(t.to(device) for t in batch)\n",
        "      input_ids, input_mask, segment_ids, label_id = batch\n",
        "      loss = model(input_ids, segment_ids, input_mask, label_id)\n",
        "      if n_gpu > 1:\n",
        "        loss = loss.mean() # mean() to average on multi-gpu.\n",
        "      if args['gradient_accumulation_steps'] > 1:\n",
        "        loss = loss / args['gradient_accumulation_steps']\n",
        "\n",
        "      if args['fp16']:\n",
        "        optimizer.backward(loss)\n",
        "      else:\n",
        "        loss.backward()\n",
        "\n",
        "      tr_loss += loss.item()\n",
        "      nb_tr_examples += input_ids.size(0)\n",
        "      nb_tr_steps += 1\n",
        "      if (step + 1) % args['gradient_accumulation_steps'] == 0:\n",
        "        # modify learning rate with special warm up BERT uses\n",
        "        lr_this_step = args['learning_rate'] * warmup_linear(global_step/t_total, args['warmup_proportion'])\n",
        "        for param_group in optimizer.param_groups:\n",
        "          param_group['lr'] = lr_this_step\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        global_step += 1\n",
        "\n",
        "    logger.info('Loss after epoc {}'.format(tr_loss / nb_tr_steps))\n",
        "    logger.info('Eval after epoc {}'.format(i_+1))\n",
        "    eval()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sfQeTnCu0t2z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.unfreeze_bert_encoder()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qPcBRUib0xUy",
        "colab_type": "code",
        "outputId": "37d34b1b-dafc-4845-bff9-35fddcce9f05",
        "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,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");
}

 .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,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 1421
        }
      },
      "source": [
        "fit()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "25053ff4baad4e71b1b46224cc1f26c4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Epoch', max=4, style=ProgressStyle(description_width='initial…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e2a42d27373f4d27bbab7b387ef94a95",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Iteration', max=186, style=ProgressStyle(description_width='i…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "04/29/2019 09:53:03 - INFO - __main__ -   Loss after epoc 0.6897769262713771\n",
            "04/29/2019 09:53:03 - INFO - __main__ -   Eval after epoc 1\n",
            "04/29/2019 09:53:03 - INFO - __main__ -   Writing example 0 of 329\n",
            "04/29/2019 09:53:03 - INFO - __main__ -   *** Example ***\n",
            "04/29/2019 09:53:03 - INFO - __main__ -   guid: 0-val-1823\n",
            "04/29/2019 09:53:03 - INFO - __main__ -   tokens: [CLS] at it . @ user what scenery / because its a desert ha ##ha ##ha ##ha / get it ? / cu ##z its just dirt and rocks ha ##ha . . ah . . ha @ hash ##tag spending my morning waiting to get screwed by com ##cast @ user id ##k ##lo ##l i feel like ask ##in someone to do free any ##thi ##g for ya is a lil rude . \" hey man can you fix my transmission for free lo ##l ty \" @ user but in the same breath its like , why we ma ##kin a ps ##a outta \" free art = bad and you are terrible so stop \" is kinda dumb and funny . @ user [SEP]\n",
            "04/29/2019 09:53:03 - INFO - __main__ -   input_ids: 101 2012 2009 1012 1030 5310 2054 17363 1013 2138 2049 1037 5532 5292 3270 3270 3270 1013 2131 2009 1029 1013 12731 2480 2049 2074 6900 1998 5749 5292 3270 1012 1012 6289 1012 1012 5292 1030 23325 15900 5938 2026 2851 3403 2000 2131 14180 2011 4012 10526 1030 5310 8909 2243 4135 2140 1045 2514 2066 3198 2378 2619 2000 2079 2489 2151 15222 2290 2005 8038 2003 1037 13451 12726 1012 1000 4931 2158 2064 2017 8081 2026 6726 2005 2489 8840 2140 5939 1000 1030 5310 2021 1999 1996 2168 3052 2049 2066 1010 2339 2057 5003 4939 1037 8827 2050 24955 1000 2489 2396 1027 2919 1998 2017 2024 6659 2061 2644 1000 2003 17704 12873 1998 6057 1012 1030 5310 102\n",
            "04/29/2019 09:53:03 - INFO - __main__ -   input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            "04/29/2019 09:53:03 - INFO - __main__ -   segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "04/29/2019 09:53:03 - INFO - __main__ -   label: [0 0 0 1] (id = [0, 0, 0, 1])\n",
            "04/29/2019 09:53:04 - INFO - __main__ -   ***** Running evaluation *****\n",
            "04/29/2019 09:53:04 - INFO - __main__ -     Num examples = 329\n",
            "04/29/2019 09:53:04 - INFO - __main__ -     Batch size = 8\n",
            "04/29/2019 09:53:10 - INFO - __main__ -   ***** Eval results *****\n",
            "04/29/2019 09:53:10 - INFO - __main__ -     eval_accuracy = 0.5\n",
            "04/29/2019 09:53:10 - INFO - __main__ -     eval_loss = 0.7157965898513794\n",
            "04/29/2019 09:53:10 - INFO - __main__ -     roc_auc = {0: 0.5845269016697588, 1: 0.6056510146644942, 2: 0.5684708666075126, 3: 0.6433104375093325, 'micro': 0.5593464913802074}\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "21c8e3fb6acc4a858d9e7b8c99c26855",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Iteration', max=186, style=ProgressStyle(description_width='i…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "04/29/2019 09:54:35 - INFO - __main__ -   Loss after epoc 0.6547121242169411\n",
            "04/29/2019 09:54:35 - INFO - __main__ -   Eval after epoc 2\n",
            "04/29/2019 09:54:35 - INFO - __main__ -   Writing example 0 of 329\n",
            "04/29/2019 09:54:35 - INFO - __main__ -   *** Example ***\n",
            "04/29/2019 09:54:35 - INFO - __main__ -   guid: 0-val-1823\n",
            "04/29/2019 09:54:35 - INFO - __main__ -   tokens: [CLS] at it . @ user what scenery / because its a desert ha ##ha ##ha ##ha / get it ? / cu ##z its just dirt and rocks ha ##ha . . ah . . ha @ hash ##tag spending my morning waiting to get screwed by com ##cast @ user id ##k ##lo ##l i feel like ask ##in someone to do free any ##thi ##g for ya is a lil rude . \" hey man can you fix my transmission for free lo ##l ty \" @ user but in the same breath its like , why we ma ##kin a ps ##a outta \" free art = bad and you are terrible so stop \" is kinda dumb and funny . @ user [SEP]\n",
            "04/29/2019 09:54:35 - INFO - __main__ -   input_ids: 101 2012 2009 1012 1030 5310 2054 17363 1013 2138 2049 1037 5532 5292 3270 3270 3270 1013 2131 2009 1029 1013 12731 2480 2049 2074 6900 1998 5749 5292 3270 1012 1012 6289 1012 1012 5292 1030 23325 15900 5938 2026 2851 3403 2000 2131 14180 2011 4012 10526 1030 5310 8909 2243 4135 2140 1045 2514 2066 3198 2378 2619 2000 2079 2489 2151 15222 2290 2005 8038 2003 1037 13451 12726 1012 1000 4931 2158 2064 2017 8081 2026 6726 2005 2489 8840 2140 5939 1000 1030 5310 2021 1999 1996 2168 3052 2049 2066 1010 2339 2057 5003 4939 1037 8827 2050 24955 1000 2489 2396 1027 2919 1998 2017 2024 6659 2061 2644 1000 2003 17704 12873 1998 6057 1012 1030 5310 102\n",
            "04/29/2019 09:54:35 - INFO - __main__ -   input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            "04/29/2019 09:54:35 - INFO - __main__ -   segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "04/29/2019 09:54:35 - INFO - __main__ -   label: [0 0 0 1] (id = [0, 0, 0, 1])\n",
            "04/29/2019 09:54:36 - INFO - __main__ -   ***** Running evaluation *****\n",
            "04/29/2019 09:54:36 - INFO - __main__ -     Num examples = 329\n",
            "04/29/2019 09:54:36 - INFO - __main__ -     Batch size = 8\n",
            "04/29/2019 09:54:42 - INFO - __main__ -   ***** Eval results *****\n",
            "04/29/2019 09:54:42 - INFO - __main__ -     eval_accuracy = 0.75\n",
            "04/29/2019 09:54:42 - INFO - __main__ -     eval_loss = 0.6294651627540588\n",
            "04/29/2019 09:54:42 - INFO - __main__ -     roc_auc = {0: 0.6385899814471243, 1: 0.6611242778847578, 2: 0.6324312333629104, 3: 0.6477527251007914, 'micro': 0.6407367581254987}\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d0611d84596d420290022ab1f2f479ae",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Iteration', max=186, style=ProgressStyle(description_width='i…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "04/29/2019 09:56:08 - INFO - __main__ -   Loss after epoc 0.5166947707053153\n",
            "04/29/2019 09:56:08 - INFO - __main__ -   Eval after epoc 3\n",
            "04/29/2019 09:56:08 - INFO - __main__ -   Writing example 0 of 329\n",
            "04/29/2019 09:56:08 - INFO - __main__ -   *** Example ***\n",
            "04/29/2019 09:56:08 - INFO - __main__ -   guid: 0-val-1823\n",
            "04/29/2019 09:56:08 - INFO - __main__ -   tokens: [CLS] at it . @ user what scenery / because its a desert ha ##ha ##ha ##ha / get it ? / cu ##z its just dirt and rocks ha ##ha . . ah . . ha @ hash ##tag spending my morning waiting to get screwed by com ##cast @ user id ##k ##lo ##l i feel like ask ##in someone to do free any ##thi ##g for ya is a lil rude . \" hey man can you fix my transmission for free lo ##l ty \" @ user but in the same breath its like , why we ma ##kin a ps ##a outta \" free art = bad and you are terrible so stop \" is kinda dumb and funny . @ user [SEP]\n",
            "04/29/2019 09:56:08 - INFO - __main__ -   input_ids: 101 2012 2009 1012 1030 5310 2054 17363 1013 2138 2049 1037 5532 5292 3270 3270 3270 1013 2131 2009 1029 1013 12731 2480 2049 2074 6900 1998 5749 5292 3270 1012 1012 6289 1012 1012 5292 1030 23325 15900 5938 2026 2851 3403 2000 2131 14180 2011 4012 10526 1030 5310 8909 2243 4135 2140 1045 2514 2066 3198 2378 2619 2000 2079 2489 2151 15222 2290 2005 8038 2003 1037 13451 12726 1012 1000 4931 2158 2064 2017 8081 2026 6726 2005 2489 8840 2140 5939 1000 1030 5310 2021 1999 1996 2168 3052 2049 2066 1010 2339 2057 5003 4939 1037 8827 2050 24955 1000 2489 2396 1027 2919 1998 2017 2024 6659 2061 2644 1000 2003 17704 12873 1998 6057 1012 1030 5310 102\n",
            "04/29/2019 09:56:08 - INFO - __main__ -   input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            "04/29/2019 09:56:08 - INFO - __main__ -   segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "04/29/2019 09:56:08 - INFO - __main__ -   label: [0 0 0 1] (id = [0, 0, 0, 1])\n",
            "04/29/2019 09:56:09 - INFO - __main__ -   ***** Running evaluation *****\n",
            "04/29/2019 09:56:09 - INFO - __main__ -     Num examples = 329\n",
            "04/29/2019 09:56:09 - INFO - __main__ -     Batch size = 8\n",
            "04/29/2019 09:56:15 - INFO - __main__ -   ***** Eval results *****\n",
            "04/29/2019 09:56:15 - INFO - __main__ -     eval_accuracy = 0.75\n",
            "04/29/2019 09:56:15 - INFO - __main__ -     eval_loss = 0.5402817726135254\n",
            "04/29/2019 09:56:15 - INFO - __main__ -     roc_auc = {0: 0.6630055658627086, 1: 0.6764923715005183, 2: 0.6504732327713694, 3: 0.6822831118411229, 'micro': 0.662633690612462}\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "39dc598555e04bbdb3e3622cb560098b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Iteration', max=186, style=ProgressStyle(description_width='i…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "04/29/2019 09:57:40 - WARNING - pytorch_pretrained_bert.optimization -   Training beyond specified 't_total'. Learning rate multiplier set to 0.0. Please set 't_total' of WarmupLinearSchedule correctly.\n",
            "04/29/2019 09:57:40 - WARNING - pytorch_pretrained_bert.optimization -   Training beyond specified 't_total'. Learning rate multiplier set to 0.0. Please set 't_total' of WarmupLinearSchedule correctly.\n",
            "04/29/2019 09:57:41 - WARNING - pytorch_pretrained_bert.optimization -   Training beyond specified 't_total'. Learning rate multiplier set to 0.0. Please set 't_total' of WarmupLinearSchedule correctly.\n",
            "04/29/2019 09:57:41 - INFO - __main__ -   Loss after epoc 0.3919578013561105\n",
            "04/29/2019 09:57:41 - INFO - __main__ -   Eval after epoc 4\n",
            "04/29/2019 09:57:41 - INFO - __main__ -   Writing example 0 of 329\n",
            "04/29/2019 09:57:41 - INFO - __main__ -   *** Example ***\n",
            "04/29/2019 09:57:41 - INFO - __main__ -   guid: 0-val-1823\n",
            "04/29/2019 09:57:41 - INFO - __main__ -   tokens: [CLS] at it . @ user what scenery / because its a desert ha ##ha ##ha ##ha / get it ? / cu ##z its just dirt and rocks ha ##ha . . ah . . ha @ hash ##tag spending my morning waiting to get screwed by com ##cast @ user id ##k ##lo ##l i feel like ask ##in someone to do free any ##thi ##g for ya is a lil rude . \" hey man can you fix my transmission for free lo ##l ty \" @ user but in the same breath its like , why we ma ##kin a ps ##a outta \" free art = bad and you are terrible so stop \" is kinda dumb and funny . @ user [SEP]\n",
            "04/29/2019 09:57:41 - INFO - __main__ -   input_ids: 101 2012 2009 1012 1030 5310 2054 17363 1013 2138 2049 1037 5532 5292 3270 3270 3270 1013 2131 2009 1029 1013 12731 2480 2049 2074 6900 1998 5749 5292 3270 1012 1012 6289 1012 1012 5292 1030 23325 15900 5938 2026 2851 3403 2000 2131 14180 2011 4012 10526 1030 5310 8909 2243 4135 2140 1045 2514 2066 3198 2378 2619 2000 2079 2489 2151 15222 2290 2005 8038 2003 1037 13451 12726 1012 1000 4931 2158 2064 2017 8081 2026 6726 2005 2489 8840 2140 5939 1000 1030 5310 2021 1999 1996 2168 3052 2049 2066 1010 2339 2057 5003 4939 1037 8827 2050 24955 1000 2489 2396 1027 2919 1998 2017 2024 6659 2061 2644 1000 2003 17704 12873 1998 6057 1012 1030 5310 102\n",
            "04/29/2019 09:57:41 - INFO - __main__ -   input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            "04/29/2019 09:57:41 - INFO - __main__ -   segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "04/29/2019 09:57:41 - INFO - __main__ -   label: [0 0 0 1] (id = [0, 0, 0, 1])\n",
            "04/29/2019 09:57:42 - INFO - __main__ -   ***** Running evaluation *****\n",
            "04/29/2019 09:57:42 - INFO - __main__ -     Num examples = 329\n",
            "04/29/2019 09:57:42 - INFO - __main__ -     Batch size = 8\n",
            "04/29/2019 09:57:48 - INFO - __main__ -   ***** Eval results *****\n",
            "04/29/2019 09:57:48 - INFO - __main__ -     eval_accuracy = 0.75\n",
            "04/29/2019 09:57:48 - INFO - __main__ -     eval_loss = 0.5105093121528625\n",
            "04/29/2019 09:57:48 - INFO - __main__ -     roc_auc = {0: 0.658961038961039, 1: 0.6796400533254334, 2: 0.6489574090505768, 3: 0.6777661639540092, 'micro': 0.6636812468925967}\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cx_8BAUH1HWn",
        "colab_type": "text"
      },
      "source": [
        "## Save pretrained model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVH6k5cJiWOG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! mkdir data/twitter/models\n",
        "\n",
        "PYTORCH_PRETRAINED_BERT_CACHE=Path('./data/twitter/models')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cP9SO7fI02uW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save a trained model\n",
        "model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self\n",
        "output_model_file = os.path.join(PYTORCH_PRETRAINED_BERT_CACHE, \"finetuned_pytorch_model.bin\")\n",
        "torch.save(model_to_save.state_dict(), output_model_file)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lkUNQumj1NWl",
        "colab_type": "text"
      },
      "source": [
        "## Reload it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8RbMBpg1PCx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load a trained model that you have fine-tuned\n",
        "model_state_dict = torch.load(output_model_file)\n",
        "model = BertForMultiLabelClassification.from_pretrained(args['bert_model'], num_labels = num_labels, state_dict=model_state_dict)\n",
        "model.to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUuIsGgP1TQZ",
        "colab_type": "text"
      },
      "source": [
        "## Run on val"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3fvFaHFR1WeS",
        "colab_type": "code",
        "outputId": "2900937f-e8f6-47c5-e942-e86f00a80533",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "eval()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "04/29/2019 10:25:15 - INFO - __main__ -   Writing example 0 of 329\n",
            "04/29/2019 10:25:15 - INFO - __main__ -   *** Example ***\n",
            "04/29/2019 10:25:15 - INFO - __main__ -   guid: 0-val-1823\n",
            "04/29/2019 10:25:15 - INFO - __main__ -   tokens: [CLS] at it . @ user what scenery / because its a desert ha ##ha ##ha ##ha / get it ? / cu ##z its just dirt and rocks ha ##ha . . ah . . ha @ hash ##tag spending my morning waiting to get screwed by com ##cast @ user id ##k ##lo ##l i feel like ask ##in someone to do free any ##thi ##g for ya is a lil rude . \" hey man can you fix my transmission for free lo ##l ty \" @ user but in the same breath its like , why we ma ##kin a ps ##a outta \" free art = bad and you are terrible so stop \" is kinda dumb and funny . @ user [SEP]\n",
            "04/29/2019 10:25:15 - INFO - __main__ -   input_ids: 101 2012 2009 1012 1030 5310 2054 17363 1013 2138 2049 1037 5532 5292 3270 3270 3270 1013 2131 2009 1029 1013 12731 2480 2049 2074 6900 1998 5749 5292 3270 1012 1012 6289 1012 1012 5292 1030 23325 15900 5938 2026 2851 3403 2000 2131 14180 2011 4012 10526 1030 5310 8909 2243 4135 2140 1045 2514 2066 3198 2378 2619 2000 2079 2489 2151 15222 2290 2005 8038 2003 1037 13451 12726 1012 1000 4931 2158 2064 2017 8081 2026 6726 2005 2489 8840 2140 5939 1000 1030 5310 2021 1999 1996 2168 3052 2049 2066 1010 2339 2057 5003 4939 1037 8827 2050 24955 1000 2489 2396 1027 2919 1998 2017 2024 6659 2061 2644 1000 2003 17704 12873 1998 6057 1012 1030 5310 102\n",
            "04/29/2019 10:25:15 - INFO - __main__ -   input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            "04/29/2019 10:25:15 - INFO - __main__ -   segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "04/29/2019 10:25:15 - INFO - __main__ -   label: [0 0 0 1] (id = [0, 0, 0, 1])\n",
            "04/29/2019 10:25:16 - INFO - __main__ -   ***** Running evaluation *****\n",
            "04/29/2019 10:25:16 - INFO - __main__ -     Num examples = 329\n",
            "04/29/2019 10:25:16 - INFO - __main__ -     Batch size = 8\n",
            "04/29/2019 10:25:22 - INFO - __main__ -   ***** Eval results *****\n",
            "04/29/2019 10:25:22 - INFO - __main__ -     eval_accuracy = tensor(0, device='cuda:0')\n",
            "04/29/2019 10:25:22 - INFO - __main__ -     eval_accuracy_thresh = 0.75\n",
            "04/29/2019 10:25:22 - INFO - __main__ -     eval_loss = 0.5105093121528625\n",
            "04/29/2019 10:25:22 - INFO - __main__ -     roc_auc = {0: 0.658961038961039, 1: 0.6796400533254334, 2: 0.6489574090505768, 3: 0.6777661639540092, 'micro': 0.6636812468925967}\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_accuracy': tensor(0, device='cuda:0'),\n",
              " 'eval_accuracy_thresh': 0.75,\n",
              " 'eval_loss': 0.5105093121528625,\n",
              " 'roc_auc': {0: 0.658961038961039,\n",
              "  1: 0.6796400533254334,\n",
              "  2: 0.6489574090505768,\n",
              "  3: 0.6777661639540092,\n",
              "  'micro': 0.6636812468925967}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eOBavSrp1YWd",
        "colab_type": "text"
      },
      "source": [
        "## Run on test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qV_NYfsV1i6q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict(model, path, test_filename='test.csv'):\n",
        "  predict_processor = MultilabelProcessor(path)\n",
        "  test_examples = predict_processor.get_test_examples(path, test_filename, size=-1)\n",
        "\n",
        "  # Hold input data for returning it \n",
        "  input_data = [{ 'id': input_example.guid, 'comment_text': input_example.text_a } for input_example in test_examples]\n",
        "\n",
        "  test_features = convert_examples_to_features(test_examples, label_list, args['max_seq_length'], tokenizer)\n",
        "\n",
        "  logger.info(\"***** Running prediction *****\")\n",
        "  logger.info(\"  Num examples = %d\", len(test_examples))\n",
        "  logger.info(\"  Batch size = %d\", args['eval_batch_size'])\n",
        "\n",
        "  all_input_ids = torch.tensor([f.input_ids for f in test_features], dtype=torch.long)\n",
        "  all_input_mask = torch.tensor([f.input_mask for f in test_features], dtype=torch.long)\n",
        "  all_segment_ids = torch.tensor([f.segment_ids for f in test_features], dtype=torch.long)\n",
        "\n",
        "  test_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids)\n",
        "\n",
        "  # Run prediction for full data\n",
        "  test_sampler = SequentialSampler(test_data)\n",
        "  test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=args['eval_batch_size'])\n",
        "\n",
        "  all_logits = None\n",
        "\n",
        "  model.eval()\n",
        "  eval_loss, eval_accuracy = 0, 0\n",
        "  nb_eval_steps, nb_eval_examples = 0, 0\n",
        "  for step, batch in enumerate(tqdm(test_dataloader, desc=\"Prediction Iteration\")):\n",
        "    input_ids, input_mask, segment_ids = batch\n",
        "    input_ids = input_ids.to(device)\n",
        "    input_mask = input_mask.to(device)\n",
        "    segment_ids = segment_ids.to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "      logits = model(input_ids, segment_ids, input_mask)\n",
        "      logits = logits.sigmoid()\n",
        "\n",
        "    if all_logits is None:\n",
        "      all_logits = logits.detach().cpu().numpy()\n",
        "    else:\n",
        "      all_logits = np.concatenate((all_logits, logits.detach().cpu().numpy()), axis=0)\n",
        "\n",
        "    nb_eval_examples += input_ids.size(0)\n",
        "    nb_eval_steps += 1\n",
        "\n",
        "  return pd.merge(pd.DataFrame(input_data), pd.DataFrame(all_logits, columns=label_list), left_index=True, right_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJ5X7vRs1vdi",
        "colab_type": "code",
        "outputId": "05e78963-53cc-4afb-f6f9-b063968584e1",
        "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,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");
}

 .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,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "result = predict(model, DATA_PATH)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "04/29/2019 10:27:09 - INFO - __main__ -   Writing example 0 of 296\n",
            "04/29/2019 10:27:09 - INFO - __main__ -   *** Example ***\n",
            "04/29/2019 10:27:09 - INFO - __main__ -   guid: 0-test-0\n",
            "04/29/2019 10:27:09 - INFO - __main__ -   tokens: [CLS] @ user @ user @ user would be great or @ user or law students for rep ##ro justice could be great fits in an airport and deeply sad i ' m not able to be standing with protesters in nyc . sending every io ##ta of @ ur ##l / p ##bt ##d ##w ##bb 9 ##ez justice department to open civil rights investigation in eric garner case rt usa @ ur ##l / rs ##0 ##h ##vn ##nh ##e ##q @ hash ##tag @ hash ##tag @ user mine too , it was so beneath human dignity how they treated him . his family deserves justice . we all do . members of congress do hands up ! gesture in pro ##ts ##et on [SEP]\n",
            "04/29/2019 10:27:09 - INFO - __main__ -   input_ids: 101 1030 5310 1030 5310 1030 5310 2052 2022 2307 2030 1030 5310 2030 2375 2493 2005 16360 3217 3425 2071 2022 2307 16142 1999 2019 3199 1998 6171 6517 1045 1005 1049 2025 2583 2000 2022 3061 2007 13337 1999 16392 1012 6016 2296 22834 2696 1997 1030 24471 2140 1013 1052 19279 2094 2860 10322 1023 9351 3425 2533 2000 2330 2942 2916 4812 1999 4388 18661 2553 19387 3915 1030 24471 2140 1013 12667 2692 2232 16022 25311 2063 4160 1030 23325 15900 1030 23325 15900 1030 5310 3067 2205 1010 2009 2001 2061 4218 2529 13372 2129 2027 5845 2032 1012 2010 2155 17210 3425 1012 2057 2035 2079 1012 2372 1997 3519 2079 2398 2039 999 9218 1999 4013 3215 3388 2006 102\n",
            "04/29/2019 10:27:09 - INFO - __main__ -   input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            "04/29/2019 10:27:09 - INFO - __main__ -   segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "04/29/2019 10:27:09 - INFO - __main__ -   label: [0 1 1 1] (id = [0, 1, 1, 1])\n",
            "04/29/2019 10:27:10 - INFO - __main__ -   ***** Running prediction *****\n",
            "04/29/2019 10:27:10 - INFO - __main__ -     Num examples = 296\n",
            "04/29/2019 10:27:10 - INFO - __main__ -     Batch size = 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "84e8123efc8747a9a9df9d2dbccbe009",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Prediction Iteration', max=37, style=ProgressStyle(descriptio…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_xYq-RF1xtP",
        "colab_type": "code",
        "outputId": "608d068b-7858-4102-a76e-51df3a14ffcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "result.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(296, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4j4YyYj-2NdZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result[LABEL_COLS].to_csv(DATA_PATH/'classification_result.csv', index=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Q4MNbRAorri",
        "colab_type": "code",
        "outputId": "7d81765a-ff15-46d7-908d-35ddf1cfcd27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1969
        }
      },
      "source": [
        "result[LABEL_COLS]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>I</th>\n",
              "      <th>N</th>\n",
              "      <th>F</th>\n",
              "      <th>J</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.221900</td>\n",
              "      <td>0.738083</td>\n",
              "      <td>0.281688</td>\n",
              "      <td>0.172758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.162330</td>\n",
              "      <td>0.410585</td>\n",
              "      <td>0.735914</td>\n",
              "      <td>0.898665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.693617</td>\n",
              "      <td>0.456062</td>\n",
              "      <td>0.570172</td>\n",
              "      <td>0.779696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.335347</td>\n",
              "      <td>0.526126</td>\n",
              "      <td>0.250279</td>\n",
              "      <td>0.333264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.847586</td>\n",
              "      <td>0.493204</td>\n",
              "      <td>0.298861</td>\n",
              "      <td>0.151132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.597096</td>\n",
              "      <td>0.479653</td>\n",
              "      <td>0.744491</td>\n",
              "      <td>0.740229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.918423</td>\n",
              "      <td>0.607985</td>\n",
              "      <td>0.590625</td>\n",
              "      <td>0.202568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.711891</td>\n",
              "      <td>0.784681</td>\n",
              "      <td>0.689125</td>\n",
              "      <td>0.845608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.941358</td>\n",
              "      <td>0.458784</td>\n",
              "      <td>0.648079</td>\n",
              "      <td>0.649091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.388873</td>\n",
              "      <td>0.866237</td>\n",
              "      <td>0.760528</td>\n",
              "      <td>0.733217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.176462</td>\n",
              "      <td>0.358163</td>\n",
              "      <td>0.696575</td>\n",
              "      <td>0.694639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.927088</td>\n",
              "      <td>0.609165</td>\n",
              "      <td>0.836254</td>\n",
              "      <td>0.767375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.156097</td>\n",
              "      <td>0.592323</td>\n",
              "      <td>0.804315</td>\n",
              "      <td>0.860682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.882530</td>\n",
              "      <td>0.452126</td>\n",
              "      <td>0.642132</td>\n",
              "      <td>0.840855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.897776</td>\n",
              "      <td>0.216959</td>\n",
              "      <td>0.453055</td>\n",
              "      <td>0.297808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.721995</td>\n",
              "      <td>0.133667</td>\n",
              "      <td>0.439164</td>\n",
              "      <td>0.064382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.123051</td>\n",
              "      <td>0.669605</td>\n",
              "      <td>0.502974</td>\n",
              "      <td>0.656946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.491684</td>\n",
              "      <td>0.878971</td>\n",
              "      <td>0.212693</td>\n",
              "      <td>0.507798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.340636</td>\n",
              "      <td>0.838359</td>\n",
              "      <td>0.371663</td>\n",
              "      <td>0.355584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.882755</td>\n",
              "      <td>0.800359</td>\n",
              "      <td>0.814970</td>\n",
              "      <td>0.850706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.692887</td>\n",
              "      <td>0.847527</td>\n",
              "      <td>0.727396</td>\n",
              "      <td>0.528457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.659120</td>\n",
              "      <td>0.498536</td>\n",
              "      <td>0.243783</td>\n",
              "      <td>0.254870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.252591</td>\n",
              "      <td>0.548782</td>\n",
              "      <td>0.178011</td>\n",
              "      <td>0.531768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.787773</td>\n",
              "      <td>0.409385</td>\n",
              "      <td>0.662255</td>\n",
              "      <td>0.450042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.066457</td>\n",
              "      <td>0.452601</td>\n",
              "      <td>0.424565</td>\n",
              "      <td>0.836827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.547276</td>\n",
              "      <td>0.835930</td>\n",
              "      <td>0.604481</td>\n",
              "      <td>0.848558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.159584</td>\n",
              "      <td>0.882446</td>\n",
              "      <td>0.188859</td>\n",
              "      <td>0.484719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.664005</td>\n",
              "      <td>0.545373</td>\n",
              "      <td>0.398496</td>\n",
              "      <td>0.727621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.592154</td>\n",
              "      <td>0.555415</td>\n",
              "      <td>0.641811</td>\n",
              "      <td>0.709055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.175789</td>\n",
              "      <td>0.475877</td>\n",
              "      <td>0.461736</td>\n",
              "      <td>0.699295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>0.330043</td>\n",
              "      <td>0.326363</td>\n",
              "      <td>0.450156</td>\n",
              "      <td>0.668852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267</th>\n",
              "      <td>0.918689</td>\n",
              "      <td>0.394427</td>\n",
              "      <td>0.682854</td>\n",
              "      <td>0.753758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>0.518915</td>\n",
              "      <td>0.755863</td>\n",
              "      <td>0.682527</td>\n",
              "      <td>0.585953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>0.322267</td>\n",
              "      <td>0.685550</td>\n",
              "      <td>0.601228</td>\n",
              "      <td>0.278163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>0.554429</td>\n",
              "      <td>0.822029</td>\n",
              "      <td>0.884997</td>\n",
              "      <td>0.877406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>0.899612</td>\n",
              "      <td>0.664535</td>\n",
              "      <td>0.376969</td>\n",
              "      <td>0.463745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>0.713908</td>\n",
              "      <td>0.768050</td>\n",
              "      <td>0.627976</td>\n",
              "      <td>0.493828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273</th>\n",
              "      <td>0.428707</td>\n",
              "      <td>0.844607</td>\n",
              "      <td>0.805908</td>\n",
              "      <td>0.575076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>274</th>\n",
              "      <td>0.309204</td>\n",
              "      <td>0.620567</td>\n",
              "      <td>0.489081</td>\n",
              "      <td>0.357888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>0.599041</td>\n",
              "      <td>0.781659</td>\n",
              "      <td>0.707454</td>\n",
              "      <td>0.700340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>0.891129</td>\n",
              "      <td>0.454297</td>\n",
              "      <td>0.654368</td>\n",
              "      <td>0.590653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>0.341818</td>\n",
              "      <td>0.366493</td>\n",
              "      <td>0.843295</td>\n",
              "      <td>0.831230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>0.553684</td>\n",
              "      <td>0.137217</td>\n",
              "      <td>0.358682</td>\n",
              "      <td>0.118729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>0.857658</td>\n",
              "      <td>0.457503</td>\n",
              "      <td>0.363691</td>\n",
              "      <td>0.257450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>0.841841</td>\n",
              "      <td>0.491576</td>\n",
              "      <td>0.666359</td>\n",
              "      <td>0.789906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>0.964220</td>\n",
              "      <td>0.487134</td>\n",
              "      <td>0.511775</td>\n",
              "      <td>0.376888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>0.574984</td>\n",
              "      <td>0.850403</td>\n",
              "      <td>0.490231</td>\n",
              "      <td>0.326804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>0.347173</td>\n",
              "      <td>0.500672</td>\n",
              "      <td>0.369015</td>\n",
              "      <td>0.699770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284</th>\n",
              "      <td>0.460533</td>\n",
              "      <td>0.267079</td>\n",
              "      <td>0.526217</td>\n",
              "      <td>0.239052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>0.645411</td>\n",
              "      <td>0.642918</td>\n",
              "      <td>0.552594</td>\n",
              "      <td>0.655833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>286</th>\n",
              "      <td>0.796659</td>\n",
              "      <td>0.765055</td>\n",
              "      <td>0.655998</td>\n",
              "      <td>0.874620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>0.170682</td>\n",
              "      <td>0.461453</td>\n",
              "      <td>0.574921</td>\n",
              "      <td>0.703725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>288</th>\n",
              "      <td>0.921374</td>\n",
              "      <td>0.210217</td>\n",
              "      <td>0.442598</td>\n",
              "      <td>0.092145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>0.257889</td>\n",
              "      <td>0.176123</td>\n",
              "      <td>0.147567</td>\n",
              "      <td>0.835352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>290</th>\n",
              "      <td>0.270399</td>\n",
              "      <td>0.342860</td>\n",
              "      <td>0.312934</td>\n",
              "      <td>0.630246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>0.731272</td>\n",
              "      <td>0.806971</td>\n",
              "      <td>0.580962</td>\n",
              "      <td>0.296329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>0.807371</td>\n",
              "      <td>0.344912</td>\n",
              "      <td>0.646456</td>\n",
              "      <td>0.571138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>0.961831</td>\n",
              "      <td>0.800805</td>\n",
              "      <td>0.778128</td>\n",
              "      <td>0.590810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>0.510384</td>\n",
              "      <td>0.391445</td>\n",
              "      <td>0.309059</td>\n",
              "      <td>0.623155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>0.149192</td>\n",
              "      <td>0.606999</td>\n",
              "      <td>0.300915</td>\n",
              "      <td>0.176548</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>296 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            I         N         F         J\n",
              "0    0.221900  0.738083  0.281688  0.172758\n",
              "1    0.162330  0.410585  0.735914  0.898665\n",
              "2    0.693617  0.456062  0.570172  0.779696\n",
              "3    0.335347  0.526126  0.250279  0.333264\n",
              "4    0.847586  0.493204  0.298861  0.151132\n",
              "5    0.597096  0.479653  0.744491  0.740229\n",
              "6    0.918423  0.607985  0.590625  0.202568\n",
              "7    0.711891  0.784681  0.689125  0.845608\n",
              "8    0.941358  0.458784  0.648079  0.649091\n",
              "9    0.388873  0.866237  0.760528  0.733217\n",
              "10   0.176462  0.358163  0.696575  0.694639\n",
              "11   0.927088  0.609165  0.836254  0.767375\n",
              "12   0.156097  0.592323  0.804315  0.860682\n",
              "13   0.882530  0.452126  0.642132  0.840855\n",
              "14   0.897776  0.216959  0.453055  0.297808\n",
              "15   0.721995  0.133667  0.439164  0.064382\n",
              "16   0.123051  0.669605  0.502974  0.656946\n",
              "17   0.491684  0.878971  0.212693  0.507798\n",
              "18   0.340636  0.838359  0.371663  0.355584\n",
              "19   0.882755  0.800359  0.814970  0.850706\n",
              "20   0.692887  0.847527  0.727396  0.528457\n",
              "21   0.659120  0.498536  0.243783  0.254870\n",
              "22   0.252591  0.548782  0.178011  0.531768\n",
              "23   0.787773  0.409385  0.662255  0.450042\n",
              "24   0.066457  0.452601  0.424565  0.836827\n",
              "25   0.547276  0.835930  0.604481  0.848558\n",
              "26   0.159584  0.882446  0.188859  0.484719\n",
              "27   0.664005  0.545373  0.398496  0.727621\n",
              "28   0.592154  0.555415  0.641811  0.709055\n",
              "29   0.175789  0.475877  0.461736  0.699295\n",
              "..        ...       ...       ...       ...\n",
              "266  0.330043  0.326363  0.450156  0.668852\n",
              "267  0.918689  0.394427  0.682854  0.753758\n",
              "268  0.518915  0.755863  0.682527  0.585953\n",
              "269  0.322267  0.685550  0.601228  0.278163\n",
              "270  0.554429  0.822029  0.884997  0.877406\n",
              "271  0.899612  0.664535  0.376969  0.463745\n",
              "272  0.713908  0.768050  0.627976  0.493828\n",
              "273  0.428707  0.844607  0.805908  0.575076\n",
              "274  0.309204  0.620567  0.489081  0.357888\n",
              "275  0.599041  0.781659  0.707454  0.700340\n",
              "276  0.891129  0.454297  0.654368  0.590653\n",
              "277  0.341818  0.366493  0.843295  0.831230\n",
              "278  0.553684  0.137217  0.358682  0.118729\n",
              "279  0.857658  0.457503  0.363691  0.257450\n",
              "280  0.841841  0.491576  0.666359  0.789906\n",
              "281  0.964220  0.487134  0.511775  0.376888\n",
              "282  0.574984  0.850403  0.490231  0.326804\n",
              "283  0.347173  0.500672  0.369015  0.699770\n",
              "284  0.460533  0.267079  0.526217  0.239052\n",
              "285  0.645411  0.642918  0.552594  0.655833\n",
              "286  0.796659  0.765055  0.655998  0.874620\n",
              "287  0.170682  0.461453  0.574921  0.703725\n",
              "288  0.921374  0.210217  0.442598  0.092145\n",
              "289  0.257889  0.176123  0.147567  0.835352\n",
              "290  0.270399  0.342860  0.312934  0.630246\n",
              "291  0.731272  0.806971  0.580962  0.296329\n",
              "292  0.807371  0.344912  0.646456  0.571138\n",
              "293  0.961831  0.800805  0.778128  0.590810\n",
              "294  0.510384  0.391445  0.309059  0.623155\n",
              "295  0.149192  0.606999  0.300915  0.176548\n",
              "\n",
              "[296 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    }
  ]
}