Delete squad_lib_sp.py

squad_lib_sp.py

@@ -1,976 +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.
-
-"""Run ALBERT on SQuAD 1.1 and SQuAD 2.0 using sentence piece tokenization.
-
-The file is forked from:
-
-https://github.com/google-research/ALBERT/blob/master/run_squad_sp.py
-"""
-import collections
-import copy
-import json
-import math
-import os
-
-from absl import logging
-import numpy as np
-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.
-  """
-
-  def __init__(self,
-               qas_id,
-               question_text,
-               paragraph_text,
-               orig_answer_text=None,
-               start_position=None,
-               end_position=None,
-               is_impossible=False):
-    self.qas_id = qas_id
-    self.question_text = question_text
-    self.paragraph_text = paragraph_text
-    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 += ", paragraph_text: [%s]" % (" ".join(self.paragraph_text))
-    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,
-               tok_start_to_orig_index,
-               tok_end_to_orig_index,
-               token_is_max_context,
-               tokens,
-               input_ids,
-               input_mask,
-               segment_ids,
-               paragraph_len,
-               class_index=None,
-               paragraph_mask=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.tok_start_to_orig_index = tok_start_to_orig_index
-    self.tok_end_to_orig_index = tok_end_to_orig_index
-    self.token_is_max_context = token_is_max_context
-    self.tokens = tokens
-    self.input_ids = input_ids
-    self.input_mask = input_mask
-    self.paragraph_mask = paragraph_mask
-    self.segment_ids = segment_ids
-    self.paragraph_len = paragraph_len
-    self.class_index = class_index
-    self.start_position = start_position
-    self.end_position = end_position
-    self.is_impossible = is_impossible
-
-
-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."""
-  del version_2_with_negative
-  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"])
-
-  examples = []
-  for entry in input_data:
-    for paragraph in entry["paragraphs"]:
-      paragraph_text = paragraph["context"]
-
-      for qa in paragraph["qas"]:
-        qas_id = qa["id"]
-        question_text = qa["question"]
-        start_position = None
-        orig_answer_text = None
-        is_impossible = False
-
-        if is_training:
-          is_impossible = qa.get("is_impossible", False)
-          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"]
-            start_position = answer["answer_start"]
-          else:
-            start_position = -1
-            orig_answer_text = ""
-
-        example = SquadExample(
-            qas_id=qas_id,
-            question_text=question_text,
-            paragraph_text=paragraph_text,
-            orig_answer_text=orig_answer_text,
-            start_position=start_position,
-            is_impossible=is_impossible)
-        examples.append(example)
-
-  return examples
-
-
-def _convert_index(index, pos, m=None, is_start=True):
-  """Converts index."""
-  if index[pos] is not None:
-    return index[pos]
-  n = len(index)
-  rear = pos
-  while rear < n - 1 and index[rear] is None:
-    rear += 1
-  front = pos
-  while front > 0 and index[front] is None:
-    front -= 1
-  assert index[front] is not None or index[rear] is not None
-  if index[front] is None:
-    if index[rear] >= 1:  # pytype: disable=unsupported-operands
-      if is_start:
-        return 0
-      else:
-        return index[rear] - 1
-    return index[rear]
-  if index[rear] is None:
-    if m is not None and index[front] < m - 1:
-      if is_start:
-        return index[front] + 1
-      else:
-        return m - 1
-    return index[front]
-  if is_start:
-    if index[rear] > index[front] + 1:
-      return index[front] + 1
-    else:
-      return index[rear]
-  else:
-    if index[rear] > index[front] + 1:
-      return index[rear] - 1
-    else:
-      return index[front]
-
-
-def convert_examples_to_features(examples,
-                                 tokenizer,
-                                 max_seq_length,
-                                 doc_stride,
-                                 max_query_length,
-                                 is_training,
-                                 output_fn,
-                                 do_lower_case,
-                                 xlnet_format=False,
-                                 batch_size=None):
-  """Loads a data file into a list of `InputBatch`s."""
-  cnt_pos, cnt_neg = 0, 0
-  base_id = 1000000000
-  unique_id = base_id
-  max_n, max_m = 1024, 1024
-  f = np.zeros((max_n, max_m), dtype=np.float32)
-
-  for (example_index, example) in enumerate(examples):
-
-    if example_index % 100 == 0:
-      logging.info("Converting %d/%d pos %d neg %d", example_index,
-                   len(examples), cnt_pos, cnt_neg)
-
-    query_tokens = tokenization.encode_ids(
-        tokenizer.sp_model,
-        tokenization.preprocess_text(
-            example.question_text, lower=do_lower_case))
-
-    if len(query_tokens) > max_query_length:
-      query_tokens = query_tokens[0:max_query_length]
-
-    paragraph_text = example.paragraph_text
-    para_tokens = tokenization.encode_pieces(
-        tokenizer.sp_model,
-        tokenization.preprocess_text(
-            example.paragraph_text, lower=do_lower_case))
-
-    chartok_to_tok_index = []
-    tok_start_to_chartok_index = []
-    tok_end_to_chartok_index = []
-    char_cnt = 0
-    for i, token in enumerate(para_tokens):
-      new_token = token.replace(tokenization.SPIECE_UNDERLINE, " ")
-      chartok_to_tok_index.extend([i] * len(new_token))
-      tok_start_to_chartok_index.append(char_cnt)
-      char_cnt += len(new_token)
-      tok_end_to_chartok_index.append(char_cnt - 1)
-
-    tok_cat_text = "".join(para_tokens).replace(tokenization.SPIECE_UNDERLINE,
-                                                " ")
-    n, m = len(paragraph_text), len(tok_cat_text)
-
-    if n > max_n or m > max_m:
-      max_n = max(n, max_n)
-      max_m = max(m, max_m)
-      f = np.zeros((max_n, max_m), dtype=np.float32)
-
-    g = {}
-
-    # pylint: disable=cell-var-from-loop
-    def _lcs_match(max_dist, n=n, m=m):
-      """Longest-common-substring algorithm."""
-      f.fill(0)
-      g.clear()
-
-      ### longest common sub sequence
-      # f[i, j] = max(f[i - 1, j], f[i, j - 1], f[i - 1, j - 1] + match(i, j))
-      for i in range(n):
-
-        # unlike standard LCS, this is specifically optimized for the setting
-        # because the mismatch between sentence pieces and original text will
-        # be small
-        for j in range(i - max_dist, i + max_dist):
-          if j >= m or j < 0:
-            continue
-
-          if i > 0:
-            g[(i, j)] = 0
-            f[i, j] = f[i - 1, j]
-
-          if j > 0 and f[i, j - 1] > f[i, j]:
-            g[(i, j)] = 1
-            f[i, j] = f[i, j - 1]
-
-          f_prev = f[i - 1, j - 1] if i > 0 and j > 0 else 0
-          if (tokenization.preprocess_text(
-              paragraph_text[i], lower=do_lower_case,
-              remove_space=False) == tok_cat_text[j] and f_prev + 1 > f[i, j]):
-            g[(i, j)] = 2
-            f[i, j] = f_prev + 1
-
-    # pylint: enable=cell-var-from-loop
-
-    max_dist = abs(n - m) + 5
-    for _ in range(2):
-      _lcs_match(max_dist)
-      if f[n - 1, m - 1] > 0.8 * n:
-        break
-      max_dist *= 2
-
-    orig_to_chartok_index = [None] * n
-    chartok_to_orig_index = [None] * m
-    i, j = n - 1, m - 1
-    while i >= 0 and j >= 0:
-      if (i, j) not in g:
-        break
-      if g[(i, j)] == 2:
-        orig_to_chartok_index[i] = j
-        chartok_to_orig_index[j] = i
-        i, j = i - 1, j - 1
-      elif g[(i, j)] == 1:
-        j = j - 1
-      else:
-        i = i - 1
-
-    if (all(v is None for v in orig_to_chartok_index) or
-        f[n - 1, m - 1] < 0.8 * n):
-      logging.info("MISMATCH DETECTED!")
-      continue
-
-    tok_start_to_orig_index = []
-    tok_end_to_orig_index = []
-    for i in range(len(para_tokens)):
-      start_chartok_pos = tok_start_to_chartok_index[i]
-      end_chartok_pos = tok_end_to_chartok_index[i]
-      start_orig_pos = _convert_index(
-          chartok_to_orig_index, start_chartok_pos, n, is_start=True)
-      end_orig_pos = _convert_index(
-          chartok_to_orig_index, end_chartok_pos, n, is_start=False)
-
-      tok_start_to_orig_index.append(start_orig_pos)
-      tok_end_to_orig_index.append(end_orig_pos)
-
-    if not is_training:
-      tok_start_position = tok_end_position = None
-
-    if is_training and example.is_impossible:
-      tok_start_position = 0
-      tok_end_position = 0
-
-    if is_training and not example.is_impossible:
-      start_position = example.start_position
-      end_position = start_position + len(example.orig_answer_text) - 1
-
-      start_chartok_pos = _convert_index(
-          orig_to_chartok_index, start_position, is_start=True)
-      tok_start_position = chartok_to_tok_index[start_chartok_pos]
-
-      end_chartok_pos = _convert_index(
-          orig_to_chartok_index, end_position, is_start=False)
-      tok_end_position = chartok_to_tok_index[end_chartok_pos]
-      assert tok_start_position <= tok_end_position
-
-    def _piece_to_id(x):
-      return tokenizer.sp_model.PieceToId(x)
-
-    all_doc_tokens = list(map(_piece_to_id, para_tokens))
-
-    # 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_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 = []
-
-      cur_tok_start_to_orig_index = []
-      cur_tok_end_to_orig_index = []
-
-      # 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(tokenizer.sp_model.PieceToId("[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
-
-          cur_tok_start_to_orig_index.append(
-              tok_start_to_orig_index[split_token_index])
-          cur_tok_end_to_orig_index.append(
-              tok_end_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(tokenizer.sp_model.PieceToId("[SEP]"))
-        segment_ids.append(seg_p)
-        paragraph_mask.append(0)
-        return len(tokens)
-
-      def process_class(seg_class):
-        class_index = len(segment_ids)
-        tokens.append(tokenizer.sp_model.PieceToId("[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
-        paragraph_len = 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)
-        paragraph_len = process_paragraph(seg_p)
-
-      input_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
-
-      span_is_impossible = example.is_impossible
-      start_position = None
-      end_position = None
-      if is_training and not span_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
-        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:
-          # continue
-          start_position = 0
-          end_position = 0
-          span_is_impossible = True
-        else:
-          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 is_training and span_is_impossible:
-        start_position = class_index
-        end_position = class_index
-
-      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("tok_start_to_orig_index: %s",
-                     " ".join([str(x) for x in cur_tok_start_to_orig_index]))
-        logging.info("tok_end_to_orig_index: %s",
-                     " ".join([str(x) for x in cur_tok_end_to_orig_index]))
-        logging.info(
-            "token_is_max_context: %s", " ".join(
-                ["%d:%s" % (x, y) for (x, y) in token_is_max_context.items()]))
-        logging.info(
-            "input_pieces: %s",
-            " ".join([tokenizer.sp_model.IdToPiece(x) for x in tokens]))
-        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 and span_is_impossible:
-          logging.info("impossible example span")
-
-        if is_training and not span_is_impossible:
-          pieces = [
-              tokenizer.sp_model.IdToPiece(token)
-              for token in tokens[start_position:(end_position + 1)]
-          ]
-          answer_text = tokenizer.sp_model.DecodePieces(pieces)
-          logging.info("start_position: %d", (start_position))
-          logging.info("end_position: %d", (end_position))
-          logging.info("answer: %s", (tokenization.printable_text(answer_text)))
-
-          # With multi processing, the example_index is actually the index
-          # within the current process therefore we use example_index=None
-          # to avoid being used in the future.
-          # The current code does not use example_index of training data.
-      if is_training:
-        feat_example_index = None
-      else:
-        feat_example_index = example_index
-
-      feature = InputFeatures(
-          unique_id=unique_id,
-          example_index=feat_example_index,
-          doc_span_index=doc_span_index,
-          tok_start_to_orig_index=cur_tok_start_to_orig_index,
-          tok_end_to_orig_index=cur_tok_end_to_orig_index,
-          token_is_max_context=token_is_max_context,
-          tokens=[tokenizer.sp_model.IdToPiece(x) for x in tokens],
-          input_ids=input_ids,
-          input_mask=input_mask,
-          paragraph_mask=paragraph_mask,
-          segment_ids=segment_ids,
-          paragraph_len=paragraph_len,
-          class_index=class_index,
-          start_position=start_position,
-          end_position=end_position,
-          is_impossible=span_is_impossible)
-
-      # Run callback
-      if is_training:
-        output_fn(feature)
-      else:
-        output_fn(feature, is_padding=False)
-
-      unique_id += 1
-      if span_is_impossible:
-        cnt_neg += 1
-      else:
-        cnt_pos += 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)
-    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
-
-  logging.info("Total number of instances: %d = pos %d neg %d",
-               cnt_pos + cnt_neg, cnt_pos, cnt_neg)
-  return unique_id - base_id
-
-
-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."""
-
-  del do_lower_case, verbose
-  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]
-
-      doc_offset = 0 if xlnet_format else feature.tokens.index("[SEP]") + 1
-
-      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 - doc_offset >= len(feature.tok_start_to_orig_index):
-          continue
-        if end_index - doc_offset >= len(feature.tok_end_to_orig_index):
-          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 - doc_offset,
-                end_index=end_index - doc_offset,
-                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=-1,
-              end_index=-1,
-              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_start_to_orig_index = feature.tok_start_to_orig_index
-        tok_end_to_orig_index = feature.tok_end_to_orig_index
-        start_orig_pos = tok_start_to_orig_index[pred.start_index]
-        end_orig_pos = tok_end_to_orig_index[pred.end_index]
-
-        paragraph_text = example.paragraph_text
-        final_text = paragraph_text[start_orig_pos:end_orig_pos + 1].strip()
-        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:
-      assert 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:
-        # predict "" iff the null score - the score of best non-null > threshold
-        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
-
-    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_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
-
-
-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 generate_tf_record_from_json_file(input_file_path,
-                                      sp_model_file,
-                                      output_path,
-                                      translated_input_folder=None,
-                                      max_seq_length=384,
-                                      do_lower_case=True,
-                                      max_query_length=64,
-                                      doc_stride=128,
-                                      xlnet_format=False,
-                                      version_2_with_negative=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.FullSentencePieceTokenizer(
-      sp_model_file=sp_model_file)
-  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,
-      do_lower_case=do_lower_case)
-  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