text: # coding=utf-8
  
  """Official evaluation script for the MRQA Workshop Shared Task.
  Adapted fromt the SQuAD v1.1 official evaluation script.
  Modified slightly for the ELECTRA codebase.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import string
  import re
  import json
  import tensorflow.compat.v1 as tf
  from collections import Counter
  
  import configure_finetuning
  
  
  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 read_predictions(prediction_file):
    with tf.io.gfile.GFile(prediction_file) as f:
      predictions = json.load(f)
    return predictions
  
  
  def read_answers(gold_file):
    answers = {}
    with tf.io.gfile.GFile(gold_file, 'r') as f:
      for i, line in enumerate(f):
        example = json.loads(line)
        if i == 0 and 'header' in example:
          continue
        for qa in example['qas']:
          answers[qa['qid']] = qa['answers']
    return answers
  
  
  def evaluate(answers, predictions, skip_no_answer=False):
    f1 = exact_match = total = 0
    for qid, ground_truths in answers.items():
      if qid not in predictions:
        if not skip_no_answer:
          message = 'Unanswered question %s will receive score 0.' % qid
          print(message)
          total += 1
        continue
      total += 1
      prediction = predictions[qid]
      exact_match += metric_max_over_ground_truths(
          exact_match_score, prediction, ground_truths)
      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}
  
  
  def main(config: configure_finetuning.FinetuningConfig, split, task_name):
    answers = read_answers(os.path.join(config.raw_data_dir(task_name), split + ".jsonl"))
    predictions = read_predictions(config.qa_preds_file(task_name))
    return evaluate(answers, predictions, True)
  