text: """
  Official evaluation script for ReCoRD v1.0.
  (Some functions are adopted from the SQuAD evaluation script.)
  """
  
  from __future__ import print_function
  from collections import Counter
  import string
  import re
  import argparse
  import json
  import sys
  from ...utils import get_logger
  
  logger=get_logger()
  
  __all__=['normalize_answer', 'evaluate']
  
  
  def normalize_answer(s):
      """Lower text and remove punctuation, articles and extra whitespace."""
      def remove_articles(text):
          return re.sub(r'\b(a|an|the)\b', ' ', text)
  
      def white_space_fix(text):
          return ' '.join(text.split())
  
      def remove_punc(text):
          exclude = set(string.punctuation)
          return ''.join(ch for ch in text if ch not in exclude)
  
      def lower(text):
          return text.lower()
  
      return white_space_fix(remove_articles(remove_punc(lower(s))))
  
  
  def f1_score(prediction, ground_truth):
      prediction_tokens = normalize_answer(prediction).split()
      ground_truth_tokens = normalize_answer(ground_truth).split()
      common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
      num_same = sum(common.values())
      if num_same == 0:
          return 0
      precision = 1.0 * num_same / len(prediction_tokens)
      recall = 1.0 * num_same / len(ground_truth_tokens)
      f1 = (2 * precision * recall) / (precision + recall)
      return f1
  
  
  def exact_match_score(prediction, ground_truth):
      return normalize_answer(prediction) == normalize_answer(ground_truth)
  
  
  def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
      scores_for_ground_truths = []
      for ground_truth in ground_truths:
          score = metric_fn(prediction, ground_truth)
          scores_for_ground_truths.append(score)
      return max(scores_for_ground_truths)
  
  
  def evaluate(answers, predictions):
      f1 = exact_match = total = 0
      correct_ids = []
      for qid in answers:
          total += 1
          if qid not in predictions:
              message = 'Unanswered question {} will receive score 0.'.format(qid)
              logger.warning(message)
              continue
  
          ground_truths = list(map(lambda x: x['text'], answers[qid]))
          prediction = predictions[qid]
  
          _exact_match = metric_max_over_ground_truths(exact_match_score, prediction, ground_truths)
          if int(_exact_match) == 1:
              correct_ids.append(qid)
          exact_match += _exact_match
  
          f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths)
  
      exact_match = 100.0 * exact_match / total
      f1 = 100.0 * f1 / total
  
      return {'exact_match': exact_match, 'f1': f1}
  