Delete squad_lib.py

squad_lib.py

@@ -1,975 +0,0 @@
-# Copyright 2024 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 SQuAD 1.1 and SQuAD 2.0."""
-# pylint: disable=g-bad-import-order
-import collections
-import copy
-import json
-import math
-import os
-
-import six
-
-from absl import logging
-import tensorflow as tf, tf_keras
-
-from official.nlp.tools import tokenization
-
-
-class SquadExample(object):
-  """A single training/test example for simple sequence classification.
-
-  For examples without an answer, the start and end position are -1.
-
-  Attributes:
-    qas_id: ID of the question-answer pair.
-    question_text: Original text for the question.
-    doc_tokens: The list of tokens in the context obtained by splitting on
-      whitespace only.
-    orig_answer_text: Original text for the answer.
-    start_position: Starting index of the answer in `doc_tokens`.
-    end_position: Ending index of the answer in `doc_tokens`.
-    is_impossible: Whether the question is impossible to answer given the
-      context. Only used in SQuAD 2.0.
-  """
-
-  def __init__(self,
-               qas_id,
-               question_text,
-               doc_tokens,
-               orig_answer_text=None,
-               start_position=None,
-               end_position=None,
-               is_impossible=False):
-    self.qas_id = qas_id
-    self.question_text = question_text
-    self.doc_tokens = doc_tokens
-    self.orig_answer_text = orig_answer_text
-    self.start_position = start_position
-    self.end_position = end_position
-    self.is_impossible = is_impossible
-
-  def __str__(self):
-    return self.__repr__()
-
-  def __repr__(self):
-    s = ""
-    s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
-    s += ", question_text: %s" % (
-        tokenization.printable_text(self.question_text))
-    s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
-    if self.start_position:
-      s += ", start_position: %d" % (self.start_position)
-    if self.start_position:
-      s += ", end_position: %d" % (self.end_position)
-    if self.start_position:
-      s += ", is_impossible: %r" % (self.is_impossible)
-    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,
-               paragraph_mask=None,
-               class_index=None,
-               start_position=None,
-               end_position=None,
-               is_impossible=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.start_position = start_position
-    self.end_position = end_position
-    self.is_impossible = is_impossible
-    self.paragraph_mask = paragraph_mask
-    self.class_index = class_index
-
-
-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
-    tf.io.gfile.makedirs(os.path.dirname(filename))
-    self._writer = tf.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 feature.paragraph_mask is not None:
-      features["paragraph_mask"] = create_int_feature(feature.paragraph_mask)
-    if feature.class_index is not None:
-      features["class_index"] = create_int_feature([feature.class_index])
-
-    if self.is_training:
-      features["start_positions"] = create_int_feature([feature.start_position])
-      features["end_positions"] = create_int_feature([feature.end_position])
-      impossible = 0
-      if feature.is_impossible:
-        impossible = 1
-      features["is_impossible"] = create_int_feature([impossible])
-
-    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_squad_examples(input_file, is_training,
-                        version_2_with_negative,
-                        translated_input_folder=None):
-  """Read a SQuAD json file into a list of SquadExample."""
-  with tf.io.gfile.GFile(input_file, "r") as reader:
-    input_data = json.load(reader)["data"]
-
-  if translated_input_folder is not None:
-    translated_files = tf.io.gfile.glob(
-        os.path.join(translated_input_folder, "*.json"))
-    for file in translated_files:
-      with tf.io.gfile.GFile(file, "r") as reader:
-        input_data.extend(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 paragraph in entry["paragraphs"]:
-      paragraph_text = paragraph["context"]
-      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)
-
-      for qa in paragraph["qas"]:
-        qas_id = qa["id"]
-        question_text = qa["question"]
-        start_position = None
-        end_position = None
-        orig_answer_text = None
-        is_impossible = False
-        if is_training:
-
-          if version_2_with_negative:
-            is_impossible = qa["is_impossible"]
-          if (len(qa["answers"]) != 1) and (not is_impossible):
-            raise ValueError(
-                "For training, each question should have exactly 1 answer.")
-          if not is_impossible:
-            answer = qa["answers"][0]
-            orig_answer_text = answer["text"]
-            answer_offset = answer["answer_start"]
-            answer_length = len(orig_answer_text)
-            start_position = char_to_word_offset[answer_offset]
-            end_position = char_to_word_offset[answer_offset + answer_length -
-                                               1]
-            # Only add answers where the text can be exactly recovered from the
-            # document. If this CAN'T happen it's likely due to weird Unicode
-            # stuff so we will just skip the example.
-            #
-            # Note that this means for training mode, every example is NOT
-            # guaranteed to be preserved.
-            actual_text = " ".join(doc_tokens[start_position:(end_position +
-                                                              1)])
-            cleaned_answer_text = " ".join(
-                tokenization.whitespace_tokenize(orig_answer_text))
-            if actual_text.find(cleaned_answer_text) == -1:
-              logging.warning("Could not find answer: '%s' vs. '%s'",
-                              actual_text, cleaned_answer_text)
-              continue
-          else:
-            start_position = -1
-            end_position = -1
-            orig_answer_text = ""
-
-        example = SquadExample(
-            qas_id=qas_id,
-            question_text=question_text,
-            doc_tokens=doc_tokens,
-            orig_answer_text=orig_answer_text,
-            start_position=start_position,
-            end_position=end_position,
-            is_impossible=is_impossible)
-        examples.append(example)
-
-  return examples
-
-
-def convert_examples_to_features(examples,
-                                 tokenizer,
-                                 max_seq_length,
-                                 doc_stride,
-                                 max_query_length,
-                                 is_training,
-                                 output_fn,
-                                 xlnet_format=False,
-                                 batch_size=None):
-  """Loads a data file into a list of `InputBatch`s."""
-
-  base_id = 1000000000
-  unique_id = base_id
-  feature = None
-  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]
-
-    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
-    if is_training and example.is_impossible:
-      tok_start_position = -1
-      tok_end_position = -1
-    if is_training and not example.is_impossible:
-      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) - 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 = []
-
-      # Paragraph mask used in XLNet.
-      # 1 represents paragraph and class tokens.
-      # 0 represents query and other special tokens.
-      paragraph_mask = []
-
-      # pylint: disable=cell-var-from-loop
-      def process_query(seg_q):
-        for token in query_tokens:
-          tokens.append(token)
-          segment_ids.append(seg_q)
-          paragraph_mask.append(0)
-        tokens.append("[SEP]")
-        segment_ids.append(seg_q)
-        paragraph_mask.append(0)
-
-      def process_paragraph(seg_p):
-        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(seg_p)
-          paragraph_mask.append(1)
-        tokens.append("[SEP]")
-        segment_ids.append(seg_p)
-        paragraph_mask.append(0)
-
-      def process_class(seg_class):
-        class_index = len(segment_ids)
-        tokens.append("[CLS]")
-        segment_ids.append(seg_class)
-        paragraph_mask.append(1)
-        return class_index
-
-      if xlnet_format:
-        seg_p, seg_q, seg_class, seg_pad = 0, 1, 2, 3
-        process_paragraph(seg_p)
-        process_query(seg_q)
-        class_index = process_class(seg_class)
-      else:
-        seg_p, seg_q, seg_class, seg_pad = 1, 0, 0, 0
-        class_index = process_class(seg_class)
-        process_query(seg_q)
-        process_paragraph(seg_p)
-
-      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] * 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(seg_pad)
-        paragraph_mask.append(0)
-
-      assert len(input_ids) == max_seq_length
-      assert len(input_mask) == max_seq_length
-      assert len(segment_ids) == max_seq_length
-      assert len(paragraph_mask) == max_seq_length
-
-      start_position = 0
-      end_position = 0
-      span_contains_answer = False
-
-      if is_training and not example.is_impossible:
-        # 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
-        span_contains_answer = (tok_start_position >= doc_start and
-                                tok_end_position <= doc_end)
-        if span_contains_answer:
-          doc_offset = 0 if xlnet_format else len(query_tokens) + 2
-          start_position = tok_start_position - doc_start + doc_offset
-          end_position = tok_end_position - doc_start + doc_offset
-
-      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]))
-        logging.info("paragraph_mask: %s", " ".join(
-            [str(x) for x in paragraph_mask]))
-        logging.info("class_index: %d", class_index)
-        if is_training:
-          if span_contains_answer:
-            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("answer: %s", tokenization.printable_text(answer_text))
-          else:
-            logging.info("document span doesn't contain answer")
-
-      feature = InputFeatures(
-          unique_id=unique_id,
-          example_index=example_index,
-          doc_span_index=doc_span_index,
-          tokens=tokens,
-          paragraph_mask=paragraph_mask,
-          class_index=class_index,
-          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,
-          start_position=start_position,
-          end_position=end_position,
-          is_impossible=not span_contains_answer)
-
-      # Run callback
-      if is_training:
-        output_fn(feature)
-      else:
-        output_fn(feature, is_padding=False)
-
-      unique_id += 1
-
-  if not is_training and feature:
-    assert batch_size
-    num_padding = 0
-    num_examples = unique_id - base_id
-    if unique_id % batch_size != 0:
-      num_padding = batch_size - (num_examples % batch_size)
-    logging.info("Adding padding examples to make sure no partial batch.")
-    logging.info("Adds %d padding examples for inference.", num_padding)
-    dummy_feature = copy.deepcopy(feature)
-    for _ in range(num_padding):
-      dummy_feature.unique_id = unique_id
-
-      # Run callback
-      output_fn(feature, is_padding=True)
-      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 SQuAD 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 electronics?
-  #   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 SQuAD, 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
-
-
-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,
-                      version_2_with_negative=False,
-                      null_score_diff_threshold=0.0,
-                      verbose=False):
-  """Write final predictions to the json file and log-odds of null if needed."""
-  logging.info("Writing predictions to: %s", (output_prediction_file))
-  logging.info("Writing nbest to: %s", (output_nbest_file))
-
-  all_predictions, all_nbest_json, scores_diff_json = (
-      postprocess_output(
-          all_examples=all_examples,
-          all_features=all_features,
-          all_results=all_results,
-          n_best_size=n_best_size,
-          max_answer_length=max_answer_length,
-          do_lower_case=do_lower_case,
-          version_2_with_negative=version_2_with_negative,
-          null_score_diff_threshold=null_score_diff_threshold,
-          verbose=verbose))
-
-  write_to_json_files(all_predictions, output_prediction_file)
-  write_to_json_files(all_nbest_json, output_nbest_file)
-  if version_2_with_negative:
-    write_to_json_files(scores_diff_json, output_null_log_odds_file)
-
-
-def postprocess_output(all_examples,
-                       all_features,
-                       all_results,
-                       n_best_size,
-                       max_answer_length,
-                       do_lower_case,
-                       version_2_with_negative=False,
-                       null_score_diff_threshold=0.0,
-                       xlnet_format=False,
-                       verbose=False):
-  """Postprocess model output, to form predicton results."""
-
-  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"])
-
-  all_predictions = collections.OrderedDict()
-  all_nbest_json = 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("Skip eval example %s, not in pred.", feature.unique_id)
-        continue
-      result = unique_id_to_result[feature.unique_id]
-
-      # if we could have irrelevant answers, get the min score of irrelevant
-      if version_2_with_negative:
-        if xlnet_format:
-          feature_null_score = result.class_logits
-        else:
-          feature_null_score = result.start_logits[0] + result.end_logits[0]
-        if feature_null_score < score_null:
-          score_null = feature_null_score
-          min_null_feature_index = feature_index
-          null_start_logit = result.start_logits[0]
-          null_end_logit = result.end_logits[0]
-      for (start_index, start_logit,
-           end_index, end_logit) in _get_best_indexes_and_logits(
-               result=result,
-               n_best_size=n_best_size,
-               xlnet_format=xlnet_format):
-        # 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=start_logit,
-                end_logit=end_logit))
-
-    if version_2_with_negative and not xlnet_format:
-      prelim_predictions.append(
-          _PrelimPrediction(
-              feature_index=min_null_feature_index,
-              start_index=0,
-              end_index=0,
-              start_logit=null_start_logit,
-              end_logit=null_end_logit))
-    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 or xlnet_format:  # 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, verbose=verbose)
-        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))
-
-    # if we didn't include the empty option in the n-best, include it
-    if version_2_with_negative and not xlnet_format:
-      if "" not in seen_predictions:
-        nbest.append(
-            _NbestPrediction(
-                text="", start_logit=null_start_logit,
-                end_logit=null_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
-
-    if not version_2_with_negative:
-      all_predictions[example.qas_id] = nbest_json[0]["text"]
-    else:
-      # pytype: disable=attribute-error
-      # predict "" iff the null score - the score of best non-null > threshold
-      if best_non_null_entry is not None:
-        if xlnet_format:
-          score_diff = score_null
-          scores_diff_json[example.qas_id] = score_diff
-          all_predictions[example.qas_id] = best_non_null_entry.text
-        else:
-          score_diff = score_null - best_non_null_entry.start_logit - (
-              best_non_null_entry.end_logit)
-          scores_diff_json[example.qas_id] = score_diff
-          if score_diff > null_score_diff_threshold:
-            all_predictions[example.qas_id] = ""
-          else:
-            all_predictions[example.qas_id] = best_non_null_entry.text
-      else:
-        logging.warning("best_non_null_entry is None")
-        scores_diff_json[example.qas_id] = score_null
-        all_predictions[example.qas_id] = ""
-      # pytype: enable=attribute-error
-
-    all_nbest_json[example.qas_id] = nbest_json
-
-  return all_predictions, all_nbest_json, scores_diff_json
-
-
-def write_to_json_files(json_records, json_file):
-  with tf.io.gfile.GFile(json_file, "w") as writer:
-    writer.write(json.dumps(json_records, indent=4) + "\n")
-
-
-def get_final_text(pred_text, orig_text, do_lower_case, verbose=False):
-  """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 SQuAD 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-character 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:
-    if verbose:
-      logging.info("Unable to find text: '%s' in '%s'", pred_text, orig_text)
-    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):
-    if verbose:
-      logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
-                   orig_ns_text, 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:
-    if verbose:
-      logging.info("Couldn't map start position")
-    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:
-    if verbose:
-      logging.info("Couldn't map end position")
-    return orig_text
-
-  output_text = orig_text[orig_start_position:(orig_end_position + 1)]
-  return output_text
-
-
-def _get_best_indexes_and_logits(result,
-                                 n_best_size,
-                                 xlnet_format=False):
-  """Generates the n-best indexes and logits from a list."""
-  if xlnet_format:
-    for i in range(n_best_size):
-      for j in range(n_best_size):
-        j_index = i * n_best_size + j
-        yield (result.start_indexes[i], result.start_logits[i],
-               result.end_indexes[j_index], result.end_logits[j_index])
-  else:
-    start_index_and_score = sorted(enumerate(result.start_logits),
-                                   key=lambda x: x[1], reverse=True)
-    end_index_and_score = sorted(enumerate(result.end_logits),
-                                 key=lambda x: x[1], reverse=True)
-    for i in range(len(start_index_and_score)):
-      if i >= n_best_size:
-        break
-      for j in range(len(end_index_and_score)):
-        if j >= n_best_size:
-          break
-        yield (start_index_and_score[i][0], start_index_and_score[i][1],
-               end_index_and_score[j][0], end_index_and_score[j][1])
-
-
-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,
-                                      translated_input_folder=None,
-                                      max_seq_length=384,
-                                      do_lower_case=True,
-                                      max_query_length=64,
-                                      doc_stride=128,
-                                      version_2_with_negative=False,
-                                      xlnet_format=False):
-  """Generates and saves training data into a tf record file."""
-  train_examples = read_squad_examples(
-      input_file=input_file_path,
-      is_training=True,
-      version_2_with_negative=version_2_with_negative,
-      translated_input_folder=translated_input_folder)
-  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,
-      xlnet_format=xlnet_format)
-  train_writer.close()
-
-  meta_data = {
-      "task_type": "bert_squad",
-      "train_data_size": number_of_examples,
-      "max_seq_length": max_seq_length,
-      "max_query_length": max_query_length,
-      "doc_stride": doc_stride,
-      "version_2_with_negative": version_2_with_negative,
-  }
-
-  return meta_data