code: # Copyright 2019 The TensorFlow Authors. 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.
  # ==============================================================================
  """Library to process data for Coqa 1.1 and Coqa 2.0."""
  
  # pylint: disable=g-bad-import-order
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import copy
  import json
  import math
  import six
  
  from absl import logging
  import tensorflow as tf
  
  from bert import tokenization
  
  class CoqaExample(object):
    """A single training/test example for simple sequence classification.
  
       For examples without an answer, the start and end position are -1.
    """
  
    def __init__(self,
                 story_id,
                 turn_id,
                 question_text,
                 doc_tokens,
                 gold_answer_text=None,
                 orig_answer_text=None,
                 start_position=None,
                 end_position=None,
                 qa_history_text = None):
      self.story_id = story_id
      self.turn_id=turn_id
      self.question_text = question_text
      self.doc_tokens = doc_tokens
      self.gold_answer_text=gold_answer_text
      self.orig_answer_text = orig_answer_text
      self.start_position = start_position
      self.end_position = end_position
      self.qa_history_text = qa_history_text
  
    def __str__(self):
      return self.__repr__()
  
    def __repr__(self):
      s = ""
      s += "\nstory_id: %s" % (tokenization.printable_text(self.story_id))
      s +="\nturn id: %s" % (self.turn_id)
      s += "\nquestion_text: %s" % (
          tokenization.printable_text(self.question_text))
      s +="\ngold_answer_text: %s" % (self.gold_answer_text)
      s +="\noriginal text: %s" % (self.orig_answer_text)
      s += "\ndoc_tokens: [%s]" % (" ".join(self.doc_tokens))
      s += "\nqa_history_text: [%s]" %  (self.qa_history_text)
      if self.start_position:
        s += "\nstart_position: %d" % (self.start_position)
      if self.start_position:
        s += "\nend_position: %d" % (self.end_position)
      return s
  
  
  class InputFeatures(object):
    """A single set of features of data."""
  
    def __init__(self,
                 unique_id,
                 example_index,
                 doc_span_index,
                 tokens,
                 token_to_orig_map,
                 token_is_max_context,
                 input_ids,
                 input_mask,
                 segment_ids,
                 answer_ids=None,
                 start_position=None,
                 end_position=None ):
      self.unique_id = unique_id
      self.example_index = example_index
      self.doc_span_index = doc_span_index
      self.tokens = tokens
      self.token_to_orig_map = token_to_orig_map
      self.token_is_max_context = token_is_max_context
      self.input_ids = input_ids
      self.input_mask = input_mask
      self.segment_ids = segment_ids
      self.answer_ids=answer_ids
      self.start_position = start_position
      self.end_position = end_position
  
  class FeatureWriter(object):
      """Writes InputFeature to TF example file."""
  
      def __init__(self, filename, is_training):
          self.filename = filename
          self.is_training = is_training
          self.num_features = 0
          self._writer = tf.compat.v1.python_io.TFRecordWriter(filename)
  
      def process_feature(self, feature):
          """Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
          self.num_features += 1
  
          def create_int_feature(values):
              feature = tf.train.Feature(
                  int64_list=tf.train.Int64List(value=list(values)))
              return feature
  
          features = collections.OrderedDict()
          features["unique_ids"] = create_int_feature([feature.unique_id])
          features["input_ids"] = create_int_feature(feature.input_ids)
          features["input_mask"] = create_int_feature(feature.input_mask)
          features["segment_ids"] = create_int_feature(feature.segment_ids)
  
          if self.is_training:
              features["start_positions"] = create_int_feature([feature.start_position])
              features["end_positions"] = create_int_feature([feature.end_position])
              features["answer_ids"] = create_int_feature(feature.answer_ids)
  
          tf_example = tf.train.Example(features=tf.train.Features(feature=features))
          self._writer.write(tf_example.SerializeToString())
  
      def close(self):
          self._writer.close()
  
  
  
  def  read_coqa_examples(input_file, is_training):
      """Read a CoQA json file into a list of SquadExample."""
  
      with tf.io.gfile.GFile(input_file, "r") as reader:
        input_data = json.load(reader)["data"]
  
      def is_whitespace(c):
          if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
              return True
          return False
  
      examples = []
  
      #for entry in input_data:
      for entry in input_data:
          paragraph_text = entry["story"]
          story_id = entry["id"]
          # print(story_id)
          doc_tokens = []
          char_to_word_offset = []
          prev_is_whitespace = True
  
          for c in paragraph_text:
              if is_whitespace(c):
                  prev_is_whitespace = True
              else:
                  if prev_is_whitespace:
                      doc_tokens.append(c)
                  else:
                      doc_tokens[-1] += c
                  prev_is_whitespace = False
              char_to_word_offset.append(len(doc_tokens) - 1)
  
          history_qas = []
          for i in range(len(entry["questions"])):
              que = entry["questions"][i]
              turn_id = int(que["turn_id"])
              question_text = que["input_text"]
              # print('turn id:',turn_id)
              answer = entry['answers'][i]
              start_position = None
              end_position = None
              orig_answer_text = ""
              qa_history_text = ""
  
              orig_answer_text = answer["span_text"]
              gold_answer_text = answer["input_text"]
  
              answer_offset = answer["span_start"]
              answer_length = len(orig_answer_text)
              if answer_offset <0:  #here we have bad data, we don't want to generate wrong start/end positions
                  start_position = 0
                  end_position = 0
              else:
                  start_position = char_to_word_offset[answer_offset]
                  end_position = char_to_word_offset[answer_offset + answer_length - 1]
  
              history_qas.append('{} {}'.format(question_text, gold_answer_text))
  
  
              for j in range(i):
                  qa_history_text=qa_history_text + '.' + history_qas[j]
  
  
              if not is_training:
                  start_position = -1
                  end_position = -1
                  orig_answer_text = ""
                  gold_answer_text = ""
  
              example = CoqaExample(
                  story_id=story_id,
                  turn_id=turn_id,
                  question_text=question_text,
                  doc_tokens=doc_tokens,
                  gold_answer_text=gold_answer_text,
                  orig_answer_text=orig_answer_text,
                  start_position=start_position,
                  end_position=end_position,
                  qa_history_text=qa_history_text
              )
              examples.append(example)
  
      return examples
  
  
  def convert_examples_to_features(examples, tokenizer, max_seq_length,
                                   doc_stride, max_query_length, is_training,
                                   output_fn):
      """Loads a data file into a list of `InputBatch`s."""
  
      base_id = 1000000000
      unique_id = base_id
  
      for (example_index, example) in enumerate(examples):
          query_tokens = tokenizer.tokenize(example.question_text)
  
          if len(query_tokens) > max_query_length:
              query_tokens = query_tokens[0:max_query_length]
  
          max_qa_history = max_query_length - len(query_tokens)
          qa_history_tokens = tokenizer.tokenize(example.qa_history_text)
  
          qa_history_tokens = qa_history_tokens[ -1 * min(len(qa_history_tokens) , max_qa_history):]
          #fetch lastest QA up to the maximum qa history length
  
  
  
          tok_to_orig_index = []
          orig_to_tok_index = []
          all_doc_tokens = []
          for (i, token) in enumerate(example.doc_tokens):
              orig_to_tok_index.append(len(all_doc_tokens))
              sub_tokens = tokenizer.tokenize(token)
              for sub_token in sub_tokens:
                  tok_to_orig_index.append(i)
                  all_doc_tokens.append(sub_token)
  
          tok_start_position = None
          tok_end_position = None
          answer_tokens=[]
          if is_training:
  
              answer_tokens = tokenizer.tokenize(example.gold_answer_text)
              tok_start_position = orig_to_tok_index[example.start_position]
              if example.end_position < len(example.doc_tokens) - 1:
                  tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
              else:
                  tok_end_position = len(all_doc_tokens) - 1
  
              (tok_start_position, tok_end_position) = _improve_answer_span(
                  all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
                  example.orig_answer_text)
  
          # The -3 accounts for [CLS], [SEP] and [SEP]
          max_tokens_for_doc = max_seq_length - len(query_tokens) - len(qa_history_tokens)- 3
  
          # We can have documents that are longer than the maximum sequence length.
          # To deal with this we do a sliding window approach, where we take chunks
          # of the up to our max length with a stride of `doc_stride`.
          _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
              "DocSpan", ["start", "length"])
          doc_spans = []
          start_offset = 0
          while start_offset < len(all_doc_tokens):
              length = len(all_doc_tokens) - start_offset
              if length > max_tokens_for_doc:
                  length = max_tokens_for_doc
              doc_spans.append(_DocSpan(start=start_offset, length=length))
              if start_offset + length == len(all_doc_tokens):
                  break
              start_offset += min(length, doc_stride)
  
          for (doc_span_index, doc_span) in enumerate(doc_spans):
              tokens = []
              token_to_orig_map = {}
              token_is_max_context = {}
              segment_ids = []
              tokens.append("[CLS]")
              segment_ids.append(0)
  
  
              for token in query_tokens:
                  tokens.append(token)
                  segment_ids.append(0)
  
              tokens.append("[SEP]")
              segment_ids.append(0)
  
              for i in range(doc_span.length):
                  split_token_index = doc_span.start + i
                  token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
  
                  is_max_context = _check_is_max_context(doc_spans, doc_span_index,
                                                         split_token_index)
                  token_is_max_context[len(tokens)] = is_max_context
                  tokens.append(all_doc_tokens[split_token_index])
                  segment_ids.append(1)
  
              for token in qa_history_tokens:
                  tokens.append(token)
                  segment_ids.append(1)
  
              tokens.append("[SEP]")
              segment_ids.append(1)
  
              input_ids = tokenizer.convert_tokens_to_ids(tokens)
              answer_ids=tokenizer.convert_tokens_to_ids(answer_tokens)
  
              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              input_mask = [1] * len(input_ids)
  
              # Zero-pad up to the sequence length.
              while len(input_ids) < max_seq_length:
                  input_ids.append(0)
                  input_mask.append(0)
                  segment_ids.append(0)
              assert len(input_ids) == max_seq_length
              assert len(input_mask) == max_seq_length
              assert len(segment_ids) == max_seq_length
  
              start_position = None
              end_position = None
  
  
              if is_training:
  
                  # For training, if our document chunk does not contain an annotation
                  # we throw it out, since there is nothing to predict.
                  doc_start = doc_span.start
                  doc_end = doc_span.start + doc_span.length - 1
                  out_of_span = False
                  if not (tok_start_position >= doc_start and
                          tok_end_position <= doc_end):
                      out_of_span = True
                  if out_of_span:
                      start_position = 0
                      end_position = 0
                  else:
                      doc_offset = len(query_tokens)  + 2
                      start_position = tok_start_position - doc_start + doc_offset
                      end_position = tok_end_position - doc_start + doc_offset
                  if end_position <0 or start_position<0:
                      end_position=0
                      start_position=0
  
              if example_index < 20:
                  logging.info("*** Example ***")
                  logging.info("unique_id: %s" % (unique_id))
                  logging.info("example_index: %s" % (example_index))
                  logging.info("doc_span_index: %s" % (doc_span_index))
                  logging.info("tokens: %s" % " ".join(
                      [tokenization.printable_text(x) for x in tokens]))
                  logging.info("token_to_orig_map: %s" % " ".join(
                      ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
                  logging.info("token_is_max_context: %s" % " ".join([
                      "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
                  ]))
                  logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
                  logging.info(
                      "input_mask: %s" % " ".join([str(x) for x in input_mask]))
                  logging.info(
                      "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
  
                  if is_training:
                      answer_text = " ".join(tokens[start_position:(end_position + 1)])
                      logging.info("start_position: %d" % (start_position))
                      logging.info("end_position: %d" % (end_position))
                      logging.info(
                          "rationale: %s" % (tokenization.printable_text(answer_text)))
                      logging.info(
                          "span text: %s" % (tokenization.printable_text(example.orig_answer_text)))
  
                      logging.info(
                          "answer: %s" % (tokenization.printable_text(example.gold_answer_text)))
                      logging.info(
                          "answer_ids: %s" % " ".join([str(x) for x in answer_ids]))
  
              feature = InputFeatures(
                  unique_id=unique_id,
                  example_index=example_index,
                  doc_span_index=doc_span_index,
                  tokens=tokens,
                  token_to_orig_map=token_to_orig_map,
                  token_is_max_context=token_is_max_context,
                  input_ids=input_ids,
                  input_mask=input_mask,
                  segment_ids=segment_ids,
                  answer_ids=answer_ids,
                  start_position=start_position,
                  end_position=end_position)
  
              # Run callback
              output_fn(feature)
  
              unique_id += 1
      return unique_id - base_id
  
  def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
                           orig_answer_text):
    """Returns tokenized answer spans that better match the annotated answer."""
  
    # The CoQA annotations are character based. We first project them to
    # whitespace-tokenized words. But then after WordPiece tokenization, we can
    # often find a "better match". For example:
    #
    #   Question: What year was John Smith born?
    #   Context: The leader was John Smith (1895-1943).
    #   Answer: 1895
    #
    # The original whitespace-tokenized answer will be "(1895-1943).". However
    # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
    # the exact answer, 1895.
    #
    # However, this is not always possible. Consider the following:
    #
    #   Question: What country is the top exporter of electornics?
    #   Context: The Japanese electronics industry is the lagest in the world.
    #   Answer: Japan
    #
    # In this case, the annotator chose "Japan" as a character sub-span of
    # the word "Japanese". Since our WordPiece tokenizer does not split
    # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
    # in CoQA, but does happen.
    tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
  
    for new_start in range(input_start, input_end + 1):
      for new_end in range(input_end, new_start - 1, -1):
        text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
        if text_span == tok_answer_text:
          return (new_start, new_end)
  
    return (input_start, input_end)
  
  def _check_is_max_context(doc_spans, cur_span_index, position):
    """Check if this is the 'max context' doc span for the token."""
  
    # Because of the sliding window approach taken to scoring documents, a single
    # token can appear in multiple documents. E.g.
    #  Doc: the man went to the store and bought a gallon of milk
    #  Span A: the man went to the
    #  Span B: to the store and bought
    #  Span C: and bought a gallon of
    #  ...
    #
    # Now the word 'bought' will have two scores from spans B and C. We only
    # want to consider the score with "maximum context", which we define as
    # the *minimum* of its left and right context (the *sum* of left and
    # right context will always be the same, of course).
    #
    # In the example the maximum context for 'bought' would be span C since
    # it has 1 left context and 3 right context, while span B has 4 left context
    # and 0 right context.
    best_score = None
    best_span_index = None
    for (span_index, doc_span) in enumerate(doc_spans):
      end = doc_span.start + doc_span.length - 1
      if position < doc_span.start:
        continue
      if position > end:
        continue
      num_left_context = position - doc_span.start
      num_right_context = end - position
      score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
      if best_score is None or score > best_score:
        best_score = score
        best_span_index = span_index
  
    return cur_span_index == best_span_index
  
  
  RawResult = collections.namedtuple("RawResult",
                                     ["unique_id", "start_logits", "end_logits"])
   
  
  
  
  def _get_best_indexes(logits, n_best_size):
    """Get the n-best logits from a list."""
    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
  
    best_indexes = []
    for i in range(len(index_and_score)):  # pylint: disable=consider-using-enumerate
      if i >= n_best_size:
        break
      best_indexes.append(index_and_score[i][0])
    return best_indexes
  
  
  def _compute_softmax(scores):
    """Compute softmax probability over raw logits."""
    if not scores:
      return []
  
    max_score = None
    for score in scores:
      if max_score is None or score > max_score:
        max_score = score
  
    exp_scores = []
    total_sum = 0.0
    for score in scores:
      x = math.exp(score - max_score)
      exp_scores.append(x)
      total_sum += x
  
    probs = []
    for score in exp_scores:
      probs.append(score / total_sum)
    return probs
  
  
  def generate_tf_record_from_json_file(input_file_path,
                                        vocab_file_path,
                                        output_path,
                                        max_seq_length=384,
                                        do_lower_case=True,
                                        max_query_length=128,
                                        doc_stride=128 ):
    """Generates and saves training data into a tf record file."""
    train_examples = read_coqa_examples(
        input_file=input_file_path,
        is_training=True )
    tokenizer = tokenization.FullTokenizer(
        vocab_file=vocab_file_path, do_lower_case=do_lower_case)
    train_writer = FeatureWriter(filename=output_path, is_training=True)
    number_of_examples = convert_examples_to_features(
        examples=train_examples,
        tokenizer=tokenizer,
        max_seq_length=max_seq_length,
        doc_stride=doc_stride,
        max_query_length=max_query_length,
        is_training=True,
        output_fn=train_writer.process_feature
      )
    train_writer.close()
  
    meta_data = {
        "task_type": "bert_coqa",
        "train_data_size": number_of_examples,
        "max_seq_length": max_seq_length,
        "max_query_length": max_query_length,
        "doc_stride": doc_stride,
    }
  
    return meta_data
  
  
  def write_predictions(all_examples, all_features, all_results, n_best_size,
                        max_answer_length, do_lower_case, output_prediction_file,
                        output_nbest_file, output_null_log_odds_file):
    """Write final predictions to the json file and log-odds of null if needed."""
    logging.info("Writing predictions to: %s" % (output_prediction_file))
  
    example_index_to_features = collections.defaultdict(list)
    for feature in all_features:
      example_index_to_features[feature.example_index].append(feature)
  
    unique_id_to_result = {}
    for result in all_results:
      unique_id_to_result[result.unique_id] = result
  
    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
        "PrelimPrediction",
        ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
  
    _Prediction = collections.namedtuple('Prediction',['id','turn_id','answer'])
  
    all_predictions = [] #collections.OrderedDict()
  
    scores_diff_json = collections.OrderedDict()
  
    for (example_index, example) in enumerate(all_examples):
      features = example_index_to_features[example_index]
  
      prelim_predictions = []
      # keep track of the minimum score of null start+end of position 0
      score_null = 1000000  # large and positive
      min_null_feature_index = 0  # the paragraph slice with min mull score
      null_start_logit = 0  # the start logit at the slice with min null score
      null_end_logit = 0  # the end logit at the slice with min null score
      for (feature_index, feature) in enumerate(features):
          if feature.unique_id not in unique_id_to_result:
              logging.info('%s not found.' % (feature.unique_id))
          if feature.unique_id in unique_id_to_result:
            result = unique_id_to_result[feature.unique_id]
            start_indexes = _get_best_indexes(result.start_logits, n_best_size)
            end_indexes = _get_best_indexes(result.end_logits, n_best_size)
            # if we could have irrelevant answers, get the min score of irrelevant
  
            for start_index in start_indexes:
              for end_index in end_indexes:
                # We could hypothetically create invalid predictions, e.g., predict
                # that the start of the span is in the question. We throw out all
                # invalid predictions.
                if start_index >= len(feature.tokens):
                  continue
                if end_index >= len(feature.tokens):
                  continue
                if start_index not in feature.token_to_orig_map:
                  continue
                if end_index not in feature.token_to_orig_map:
                  continue
                if not feature.token_is_max_context.get(start_index, False):
                  continue
                if end_index < start_index:
                  continue
                length = end_index - start_index + 1
                if length > max_answer_length:
                  continue
                prelim_predictions.append(
                    _PrelimPrediction(
                        feature_index=feature_index,
                        start_index=start_index,
                        end_index=end_index,
                        start_logit=result.start_logits[start_index],
                        end_logit=result.end_logits[end_index]))
  
  
      prelim_predictions = sorted(
          prelim_predictions,
          key=lambda x: (x.start_logit + x.end_logit),
          reverse=True)
  
      _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
          "NbestPrediction", ["text", "start_logit", "end_logit"])
  
      seen_predictions = {}
      nbest = []
      for pred in prelim_predictions:
        if len(nbest) >= n_best_size:
          break
        feature = features[pred.feature_index]
        if pred.start_index > 0:  # this is a non-null prediction
          tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
          orig_doc_start = feature.token_to_orig_map[pred.start_index]
          orig_doc_end = feature.token_to_orig_map[pred.end_index]
          orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
          tok_text = " ".join(tok_tokens)
  
          # De-tokenize WordPieces that have been split off.
          tok_text = tok_text.replace(" ##", "")
          tok_text = tok_text.replace("##", "")
  
          # Clean whitespace
          tok_text = tok_text.strip()
          tok_text = " ".join(tok_text.split())
          orig_text = " ".join(orig_tokens)
  
          final_text = get_final_text(tok_text, orig_text, do_lower_case)
          if final_text in seen_predictions:
            continue
  
          seen_predictions[final_text] = True
        else:
          final_text = ""
          seen_predictions[final_text] = True
  
        nbest.append(
            _NbestPrediction(
                text=final_text,
                start_logit=pred.start_logit,
                end_logit=pred.end_logit))
  
      # In very rare edge cases we could have no valid predictions. So we
      # just create a nonce prediction in this case to avoid failure.
      if not nbest:
        nbest.append(
            _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
  
      assert len(nbest) >= 1
  
      total_scores = []
      best_non_null_entry = None
      for entry in nbest:
        total_scores.append(entry.start_logit + entry.end_logit)
        if not best_non_null_entry:
          if entry.text:
            best_non_null_entry = entry
  
      probs = _compute_softmax(total_scores)
  
      nbest_json = []
      for (i, entry) in enumerate(nbest):
        output = collections.OrderedDict()
        output["text"] = entry.text
        output["probability"] = probs[i]
        output["start_logit"] = entry.start_logit
        output["end_logit"] = entry.end_logit
        nbest_json.append(output)
  
      assert len(nbest_json) >= 1
  
  
      all_predictions.append({"id":example.story_id,"turn_id":example.turn_id,"answer": nbest_json[0]["text"]})
  
  
    with tf.io.gfile.GFile(output_prediction_file, "w") as writer:
      writer.write(json.dumps(all_predictions, indent=4) + "\n")
  
  
  
  
  def get_final_text(pred_text, orig_text, do_lower_case):
    """Project the tokenized prediction back to the original text."""
  
    # When we created the data, we kept track of the alignment between original
    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
    # now `orig_text` contains the span of our original text corresponding to the
    # span that we predicted.
    #
    # However, `orig_text` may contain extra characters that we don't want in
    # our prediction.
    #
    # For example, let's say:
    #   pred_text = steve smith
    #   orig_text = Steve Smith's
    #
    # We don't want to return `orig_text` because it contains the extra "'s".
    #
    # We don't want to return `pred_text` because it's already been normalized
    # (the CoQA eval script also does punctuation stripping/lower casing but
    # our tokenizer does additional normalization like stripping accent
    # characters).
    #
    # What we really want to return is "Steve Smith".
    #
    # Therefore, we have to apply a semi-complicated alignment heruistic between
    # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
    # can fail in certain cases in which case we just return `orig_text`.
  
    def _strip_spaces(text):
      ns_chars = []
      ns_to_s_map = collections.OrderedDict()
      for (i, c) in enumerate(text):
        if c == " ":
          continue
        ns_to_s_map[len(ns_chars)] = i
        ns_chars.append(c)
      ns_text = "".join(ns_chars)
      return (ns_text, ns_to_s_map)
  
    # We first tokenize `orig_text`, strip whitespace from the result
    # and `pred_text`, and check if they are the same length. If they are
    # NOT the same length, the heuristic has failed. If they are the same
    # length, we assume the characters are one-to-one aligned.
    tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
  
    tok_text = " ".join(tokenizer.tokenize(orig_text))
  
    start_position = tok_text.find(pred_text)
    if start_position == -1:
       return orig_text
    end_position = start_position + len(pred_text) - 1
  
    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
  
    if len(orig_ns_text) != len(tok_ns_text):
      return orig_text
  
    # We then project the characters in `pred_text` back to `orig_text` using
    # the character-to-character alignment.
    tok_s_to_ns_map = {}
    for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
      tok_s_to_ns_map[tok_index] = i
  
    orig_start_position = None
    if start_position in tok_s_to_ns_map:
      ns_start_position = tok_s_to_ns_map[start_position]
      if ns_start_position in orig_ns_to_s_map:
        orig_start_position = orig_ns_to_s_map[ns_start_position]
  
    if orig_start_position is None:
      return orig_text
  
    orig_end_position = None
    if end_position in tok_s_to_ns_map:
      ns_end_position = tok_s_to_ns_map[end_position]
      if ns_end_position in orig_ns_to_s_map:
        orig_end_position = orig_ns_to_s_map[ns_end_position]
  
    if orig_end_position is None:
      return orig_text
  
    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
    return output_text
  
apis: tensorflow.compat.v1.python_io.TFRecordWriter tensorflow.train.Features tensorflow.io.gfile.GFile