text: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ Named entity recognition fine-tuning: utilities to work with CoNLL-2003 task. """
  
  from __future__ import absolute_import, division, print_function
  
  import logging
  import os
  from io import open
  from multiprocessing import Pool, cpu_count
  from tqdm.auto import tqdm
  import pandas as pd
  
  
  class InputExample(object):
      """A single training/test example for token classification."""
  
      def __init__(self, guid, words, labels):
          """Constructs a InputExample.
          Args:
              guid: Unique id for the example.
              words: list. The words of the sequence.
              labels: (Optional) list. The labels for each word of the sequence. This should be
              specified for train and dev examples, but not for test examples.
          """
          self.guid = guid
          self.words = words
          self.labels = labels
  
  
  class InputFeatures(object):
      """A single set of features of data."""
  
      def __init__(self, input_ids, input_mask, segment_ids, label_ids):
          self.input_ids = input_ids
          self.input_mask = input_mask
          self.segment_ids = segment_ids
          self.label_ids = label_ids
  
  
  def read_examples_from_file(data_file, mode):
      file_path = data_file
      guid_index = 1
      examples = []
      with open(file_path, encoding="utf-8") as f:
          words = []
          labels = []
          for line in f:
              if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                  if words:
                      examples.append(InputExample(guid="{}-{}".format(mode, guid_index),
                                                   words=words,
                                                   labels=labels))
                      guid_index += 1
                      words = []
                      labels = []
              else:
                  splits = line.split(" ")
                  words.append(splits[0])
                  if len(splits) > 1:
                      labels.append(splits[-1].replace("\n", ""))
                  else:
                      # Examples could have no label for mode = "test"
                      labels.append("O")
          if words:
              examples.append(InputExample(guid="%s-%d".format(mode, guid_index),
                                           words=words,
                                           labels=labels))
      return examples
  
  
  def get_examples_from_df(data):
      return [InputExample(guid=sentence_id, words=sentence_df['words'].tolist(), labels=sentence_df['labels'].tolist()) for sentence_id, sentence_df in data.groupby(['sentence_id'])]
  
  
  def convert_example_to_feature(example_row):
      example, label_map, max_seq_length, tokenizer, cls_token_at_end, cls_token, cls_token_segment_id, sep_token, sep_token_extra, pad_on_left, pad_token, pad_token_segment_id, pad_token_label_id, sequence_a_segment_id, mask_padding_with_zero = example_row
  
      tokens = []
      label_ids = []
      for word, label in zip(example.words, example.labels):
          word_tokens = tokenizer.tokenize(word)
          tokens.extend(word_tokens)
          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))
  
      # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
      special_tokens_count = 3 if sep_token_extra else 2
      if len(tokens) > max_seq_length - special_tokens_count:
          tokens = tokens[:(max_seq_length - special_tokens_count)]
          label_ids = label_ids[:(max_seq_length - special_tokens_count)]
  
      # The convention in BERT is:
      # (a) For sequence pairs:
      #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
      #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
      # (b) For single sequences:
      #  tokens:   [CLS] the dog is hairy . [SEP]
      #  type_ids:   0   0   0   0  0     0   0
      #
      # Where "type_ids" are used to indicate whether this is the first
      # sequence or the second sequence. The embedding vectors for `type=0` and
      # `type=1` were learned during pre-training and are added to the wordpiece
      # embedding vector (and position vector). This is not *strictly* necessary
      # since the [SEP] token unambiguously separates the sequences, but it makes
      # it easier for the model to learn the concept of sequences.
      #
      # For classification tasks, the first vector (corresponding to [CLS]) is
      # used as as the "sentence vector". Note that this only makes sense because
      # the entire model is fine-tuned.
      tokens += [sep_token]
      label_ids += [pad_token_label_id]
      if sep_token_extra:
          # roberta uses an extra separator b/w pairs of sentences
          tokens += [sep_token]
          label_ids += [pad_token_label_id]
      segment_ids = [sequence_a_segment_id] * len(tokens)
  
      if cls_token_at_end:
          tokens += [cls_token]
          label_ids += [pad_token_label_id]
          segment_ids += [cls_token_segment_id]
      else:
          tokens = [cls_token] + tokens
          label_ids = [pad_token_label_id] + label_ids
          segment_ids = [cls_token_segment_id] + segment_ids
  
      input_ids = tokenizer.convert_tokens_to_ids(tokens)
  
      # The mask has 1 for real tokens and 0 for padding tokens. Only real
      # tokens are attended to.
      input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
  
      # Zero-pad up to the sequence length.
      padding_length = max_seq_length - len(input_ids)
      if pad_on_left:
          input_ids = ([pad_token] * padding_length) + input_ids
          input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
          segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
          label_ids = ([pad_token_label_id] * padding_length) + label_ids
      else:
          input_ids += ([pad_token] * padding_length)
          input_mask += ([0 if mask_padding_with_zero else 1] * padding_length)
          segment_ids += ([pad_token_segment_id] * padding_length)
          label_ids += ([pad_token_label_id] * padding_length)
  
      assert len(input_ids) == max_seq_length
      assert len(input_mask) == max_seq_length
      assert len(segment_ids) == max_seq_length
      assert len(label_ids) == max_seq_length
  
      return InputFeatures(
          input_ids=input_ids,
          input_mask=input_mask,
          segment_ids=segment_ids,
          label_ids=label_ids
      )
  
  def convert_examples_to_features(
          examples,
          label_list,
          max_seq_length,
          tokenizer,
          cls_token_at_end=False,
          cls_token="[CLS]",
          cls_token_segment_id=1,
          sep_token="[SEP]",
          sep_token_extra=False,
          pad_on_left=False,
          pad_token=0,
          pad_token_segment_id=0,
          pad_token_label_id=-1,
          sequence_a_segment_id=0,
          mask_padding_with_zero=True,
          process_count=cpu_count() - 2,
          chunksize=500,
          silent=False,
          use_multiprocessing=True
      ):
      """ Loads a data file into a list of `InputBatch`s
          `cls_token_at_end` define the location of the CLS token:
              - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
              - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
          `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
      """
  
      label_map = {label: i for i, label in enumerate(label_list)}
  
      examples = [(
          example, 
          label_map, 
          max_seq_length,
          tokenizer,
          cls_token_at_end,
          cls_token,
          cls_token_segment_id,
          sep_token,
          sep_token_extra,
          pad_on_left,
          pad_token,
          pad_token_segment_id,
          pad_token_label_id,
          sequence_a_segment_id,
          mask_padding_with_zero) for example in examples]
  
      if use_multiprocessing:
          with Pool(process_count) as p:
                  features = list(tqdm(p.imap(convert_example_to_feature, examples, chunksize=chunksize), total=len(examples), disable=silent))
      else:
          features = []
          for example in tqdm(examples):
              features.append(convert_example_to_feature(example))
      return features
  
  
  def get_labels(path):
      if path:
          with open(path, "r") as f:
              labels = f.read().splitlines()
          if "O" not in labels:
              labels = ["O"] + labels
          return labels
      else:
          return ["O", "B-MISC", "I-MISC",  "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]