text: """ Official evaluation script for v1.1 of the SQuAD dataset. """
  from __future__ import print_function
  from collections import Counter
  import string
  import re
  import argparse
  import json
  import sys
  import os
  import pandas as pd
  
  
  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,total):
      scores_for_ground_truths = []
      for ground_truth in ground_truths:
          score = metric_fn(prediction, ground_truth)
          scores_for_ground_truths.append(score)
      return (scores_for_ground_truths)
  
  
  def evaluate(dataset, predictions):
      f1 = exact_match = total = 0
      same = same0 = 0
      for article in dataset:
          for paragraph in article['paragraphs']:
              for qa in paragraph['qas']:
                  total += 1
                  one_answer=[total]
                  if qa['id'] not in predictions:
                      message = 'Unanswered question ' + qa['id'] + \
                                ' will receive score 0.'
                      print(message, file=sys.stderr)
                      continue
                  ground_truths = list(map(lambda x: x['text'], qa['answers'])) #dev答案
                  prediction = predictions[qa['id']]  #预测答案
                  exact_match_list = metric_max_over_ground_truths(
                      exact_match_score, prediction, ground_truths,total)
                  f1_list= metric_max_over_ground_truths(
                      f1_score, prediction, ground_truths,total)
                  print('f1score: ')
                  print(f1_list)
                  print('\n')
                  if(f1_list[0]>f1_list[1]):
                      Category=1
                  if(f1_list[0]<f1_list[1]):
                      Category=2
                  if(f1_list[0]==f1_list[1]):
                      Category=1
                      if(f1_list[0]==0):
                          same0+=1
                      else:
                          same+=1
                  one_answer.append(f1_list[0])    #使用f1score
                  one_answer.append(f1_list[1])  
                  answer.append(one_answer)
  
                  one_answer=[total]
                  one_answer.append(Category)
                  answer0.append(one_answer)
  
                  if(f1_list[0]==f1_list[1]):
                      Category=2
                  one_answer=[total]
                  one_answer.append(Category)
                  answer1.append(one_answer)
  
                  
                  exact_match +=max(exact_match_list)
                  f1+=max(f1_list)
  
      exact_match = 100.0 * exact_match / total
      f1 = 100.0 * f1 / total
      print({'same0':same0,'same':same})
  
  
      return {'exact_match': exact_match, 'f1': f1}
  
  
  if __name__ == '__main__':
      answer=[]  #[[1,0.1,0.2],[2,0.2,0.8],...]
      answer0=[]
      answer1=[]
  
      expected_version = '1.1'
      parser = argparse.ArgumentParser(
          description='Evaluation for SQuAD ' + expected_version)
      parser.add_argument('dataset_file', help='Dataset file')
      parser.add_argument('prediction_file', help='Prediction File')
      args = parser.parse_args()
      with open(args.dataset_file,'r', encoding='UTF-8') as dataset_file:
          dataset_json = json.load(dataset_file)
          if (dataset_json['version'] != expected_version):
              print('Evaluation expects v-' + expected_version +
                    ', but got dataset with v-' + dataset_json['version'],
                    file=sys.stderr)
          dataset = dataset_json['data']
      with open(args.prediction_file,'r', encoding='UTF-8') as prediction_file:
          predictions = json.load(prediction_file)
      print(json.dumps(evaluate(dataset, predictions)))
      myans=pd.DataFrame(columns=['Id','Ans1','Ans2'],data=answer)
      myans.to_csv('ydc_first_try.csv')
  
  
      ans0=pd.DataFrame(columns=['Id','Category'],data=answer0)
      ans0[['Id','Category']].to_csv('ydc_first_try_0.csv',index=0)
      ans1=pd.DataFrame(columns=['Id','Category'],data=answer1)
      ans1[['Id','Category']].to_csv('ydc_first_try_1.csv',index=0)