code: 
  
  import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       def remove_articles(SCREAMING_SNAKE_CASE_	:					List[str]    ):
            return re.sub(R'\b(a|an|the)\b'			,  ' '			,  SCREAMING_SNAKE_CASE_    )
  
       def white_space_fix(SCREAMING_SNAKE_CASE_	:					List[str]    ):
            return " ".join(text.split()    )
  
       def remove_punc(SCREAMING_SNAKE_CASE_	:					Any    ):
            lowercase__					:     Optional[int]   = set(string.punctuation    )
            return "".join(ch for ch in text if ch not in exclude    )
  
       def lower(SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
            return text.lower()
  
       return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_    )    )    )    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[int]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     List[str]   = normalize_answer(SCREAMING_SNAKE_CASE_    ).split()
       lowercase__					:     Any   = normalize_answer(SCREAMING_SNAKE_CASE_    ).split()
       lowercase__					:     Optional[int]   = Counter(SCREAMING_SNAKE_CASE_    ) & Counter(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Optional[int]   = sum(common.values()    )
       if num_same == 0:
            return 0
       lowercase__					:     Optional[Any]   = 1.0 * num_same / len(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Any   = 1.0 * num_same / len(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     int   = (2 * precision * recall) / (precision + recall)
       return fa
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Optional[int]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       return normalize_answer(SCREAMING_SNAKE_CASE_    ) == normalize_answer(SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					List[Any]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Union[str, Any]   = []
       for ground_truth in ground_truths:
            lowercase__					:     Dict   = metric_fn(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
            scores_for_ground_truths.append(SCREAMING_SNAKE_CASE_    )
       return max(SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					str    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Any   = 0
       for article in dataset:
            for paragraph in article["paragraphs"]:
                 for qa in paragraph["qas"]:
                      total += 1
                      if qa["id"] not in predictions:
                           lowercase__					:     Any   = 'Unanswered question ' + qa['id'] + ' will receive score 0.'
                           print(SCREAMING_SNAKE_CASE_			,  file=sys.stderr    )
                           continue
                      lowercase__					:     str   = [x['text'] for x in qa['answers']]
                      lowercase__					:     Tuple   = predictions[qa['id']]
                      exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
                      fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       lowercase__					:     List[str]   = 100.0 * exact_match / total
       lowercase__					:     str   = 100.0 * fa / total
  
       return {"exact_match": exact_match, "f1": fa}
  
  
  if __name__ == "__main__":
         snake_case_    						=				'''1.1'''
         snake_case_    						=				argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
         parser.add_argument('''dataset_file''', help='''Dataset file''')
         parser.add_argument('''prediction_file''', help='''Prediction File''')
         snake_case_    						=				parser.parse_args()
         with open(args.dataset_file) as dataset_file:
                snake_case_    						=				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,
                       )
                snake_case_    						=				dataset_json['''data''']
         with open(args.prediction_file) as prediction_file:
                snake_case_    						=				json.load(prediction_file)
         print(json.dumps(evaluate(dataset, predictions)))
  
  
style_context: 
  
  from transformers.models.cpm.tokenization_cpm import CpmTokenizer
  from transformers.testing_utils import custom_tokenizers
  
  from ..xlnet.test_modeling_xlnet import XLNetModelTest
  
  
  @custom_tokenizers
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a):
           return True
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Tuple   = CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate')
           lowercase__					:     str   = 'Hugging Face大法好，谁用谁知道。'
           lowercase__					:     Optional[int]   = 'Hugging Face大法好,谁用谁知道。<unk>'
           lowercase__					:     Any   = '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()
  
           lowercase__					:     Any   = tokenizer.tokenize(a)
           self.assertListEqual(a		,     a)
  
           lowercase__					:     List[Any]   = tokens + [tokenizer.unk_token]
  
           lowercase__					:     Any   = [1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)
  
           lowercase__					:     Optional[Any]   = tokenizer.decode(a)
           self.assertEqual(a		,     a)
  
  