squad_util.py

"""
Copyright 2020 The OneFlow 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.
"""

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 BERT on SQuAD 1.1 and SQuAD 2.0."""

import collections
import json
import math
import os
import random
#import modeling
#import optimization
import tokenization
import six
import numpy as np


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,
               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,
               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


def read_squad_examples(input_file, is_training):
  """Read a SQuAD json file into a list of SquadExample."""
  #with tf.gfile.Open(input_file, "r") as reader:
  with open(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 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 FLAGS.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:
              print("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):
  """Loads a data file into a list of `InputBatch`s."""

  unique_id = 1000000000

  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 = []
      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)
      tokens.append("[SEP]")
      segment_ids.append(1)

      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(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 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
        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 is_training and example.is_impossible:
        start_position = 0
        end_position = 0

      if 0:#example_index < 20:
        print("*** Example ***")
        print("unique_id: %s" % (unique_id))
        print("example_index: %s" % (example_index))
        print("doc_span_index: %s" % (doc_span_index))
        print("tokens: %s" % " ".join(
            [tokenization.printable_text(x) for x in tokens]))
        print("token_to_orig_map: %s" % " ".join(
            ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
        print("token_is_max_context: %s" % " ".join([
            "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
        ]))
        print("input_ids: %s" % " ".join([str(x) for x in input_ids]))
        print(
            "input_mask: %s" % " ".join([str(x) for x in input_mask]))
        print(
            "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
        if is_training and example.is_impossible:
          print("impossible example")
        if is_training and not example.is_impossible:
          answer_text = " ".join(tokens[start_position:(end_position + 1)])
          print("start_position: %d" % (start_position))
          print("end_position: %d" % (end_position))
          print(
              "answer: %s" % (tokenization.printable_text(answer_text)))

      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,
          start_position=start_position,
          end_position=end_position,
          is_impossible=example.is_impossible)

      # Run callback
      output_fn(feature)

      unique_id += 1


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 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 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


RawResult = collections.namedtuple("RawResult",
                                   ["unique_id", "start_logits", "end_logits"])


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, FLAGS):
  """Write final predictions to the json file and log-odds of null if needed."""
  print("Writing predictions to: %s" % (output_prediction_file))
  print("Writing nbest to: %s" % (output_nbest_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"])

  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):
      try:
        result = unique_id_to_result[feature.unique_id]
      except KeyError as error:
        print(error)
        continue
      
      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
      if FLAGS.version_2_with_negative:
        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 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]))

    if FLAGS.version_2_with_negative:
      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:  # 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, FLAGS)
        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 inlude the empty option in the n-best, inlcude it
    if FLAGS.version_2_with_negative:
      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 FLAGS.version_2_with_negative:
      all_predictions[example.qas_id] = nbest_json[0]["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 > FLAGS.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

  #with tf.gfile.GFile(output_prediction_file, "w") as writer:
  with open(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, FLAGS):
  """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-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:
    if FLAGS.verbose_logging:
      print(
          "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 FLAGS.verbose_logging:
      print("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 FLAGS.verbose_logging:
      print("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 FLAGS.verbose_logging:
      print("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(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)):
    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 gen_eval_predict_json(FLAGS, all_results):
  os.makedirs(FLAGS.output_dir, exist_ok=True)

  tokenizer = tokenization.FullTokenizer(
      vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)

  eval_examples = read_squad_examples(
      input_file=FLAGS.predict_file, is_training=False)

  # eval_writer = FeatureWriter(
  #     filename=os.path.join(FLAGS.output_dir, "eval.tf_record"),
  #     is_training=False)
  eval_features = []

  def append_feature(feature):
    eval_features.append(feature)
    # eval_writer.process_feature(feature)

  convert_examples_to_features(
      examples=eval_examples,
      tokenizer=tokenizer,
      max_seq_length=FLAGS.max_seq_length,
      doc_stride=FLAGS.doc_stride,
      max_query_length=FLAGS.max_query_length,
      is_training=False,
      output_fn=append_feature)
  #eval_writer.close()

  print("***** Running predictions *****")
  print("  Num orig examples = %d", len(eval_examples))
  print("  Num split examples = %d", len(eval_features))
  print("  Batch size = %d", FLAGS.predict_batch_size)

  output_prediction_file = os.path.join(FLAGS.output_dir, "predictions.json")
  output_nbest_file = os.path.join(FLAGS.output_dir, "nbest_predictions.json")
  output_null_log_odds_file = os.path.join(FLAGS.output_dir, "null_odds.json")

  write_predictions(eval_examples, eval_features, all_results,
                    FLAGS.n_best_size, FLAGS.max_answer_length,
                    FLAGS.do_lower_case, output_prediction_file,
                    output_nbest_file, output_null_log_odds_file, FLAGS)