




'''simple docstring'''






import argparse
import json
import re
import string
import sys
from collections import Counter




def 		a_( lowerCamelCase      )				->str:






       '''simple docstring'''




       def remove_articles(lowerCamelCase      ):
              return re.sub(r'''\b(a|an|the)\b'''   ,				''' '''   ,				lowerCamelCase      )

       def white_space_fix(lowerCamelCase      ):
              return " ".join(text.split()      )

       def remove_punc(lowerCamelCase      ):
              lowercase  :		List[str]						=      set(string.punctuation      )
              return "".join(ch for ch in text if ch not in exclude      )

       def lower(lowerCamelCase      ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase      )      )      )      )




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->Any:






       '''simple docstring'''




       lowercase  :		Any						=      normalize_answer(lowerCamelCase      ).split()
       lowercase  :		List[Any]						=      normalize_answer(lowerCamelCase      ).split()
       lowercase  :		Dict						=      Counter(lowerCamelCase      ) & Counter(lowerCamelCase      )
       lowercase  :		Any						=      sum(common.values()      )
       if num_same == 0:
              return 0
       lowercase  :		Dict						=      1.0 * num_same / len(lowerCamelCase      )
       lowercase  :		List[str]						=      1.0 * num_same / len(lowerCamelCase      )
       lowercase  :		Optional[Any]						=      (2 * precision * recall) / (precision + recall)
       return fa




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->Any:






       '''simple docstring'''




       return normalize_answer(lowerCamelCase      ) == normalize_answer(lowerCamelCase      )




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Dict:






       '''simple docstring'''




       lowercase  :		Optional[int]						=      []
       for ground_truth in ground_truths:
              lowercase  :		str						=      metric_fn(lowerCamelCase   ,				lowerCamelCase      )
              scores_for_ground_truths.append(lowerCamelCase      )
       return max(lowerCamelCase      )




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->Union[str, Any]:






       '''simple docstring'''




       lowercase  :		List[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  :		int						=      '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
                                   print(lowerCamelCase   ,				file=sys.stderr      )
                                   continue
                            lowercase  :		Optional[Any]						=      [x['''text'''] for x in qa['''answers''']]
                            lowercase  :		List[str]						=      predictions[qa['''id''']]
                            exact_match += metric_max_over_ground_truths(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )
                            fa += metric_max_over_ground_truths(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )

       lowercase  :		Tuple						=      1_00.0 * exact_match / total
       lowercase  :		str						=      1_00.0 * fa / total

       return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
      lowercase	:      Optional[Any]				=       '1.1'
      lowercase	:      Any				=       argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
      parser.add_argument('''dataset_file''', help='''Dataset file''')
      parser.add_argument('''prediction_file''', help='''Prediction File''')
      lowercase	:      int				=       parser.parse_args()
      with open(args.dataset_file) as dataset_file:
            lowercase	:      List[str]				=       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,
                  )
            lowercase	:      List[str]				=       dataset_json['data']
      with open(args.prediction_file) as prediction_file:
            lowercase	:      str				=       json.load(prediction_file)
      print(json.dumps(evaluate(dataset, predictions)))










'''simple docstring'''






class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      [0, 1]





 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        if (difference := index - (len(self.sequence ) - 2)) >= 1:
               for _ in range(A ):
                      self.sequence.append(self.sequence[-1] + self.sequence[-2] )
        return self.sequence[:index]






def 		a_( )				->None:






       '''simple docstring'''




       print(
           '''Fibonacci Series Using Dynamic Programming\n'''   ,				'''Enter the index of the Fibonacci number you want to calculate '''   ,				'''in the prompt below. (To exit enter exit or Ctrl-C)\n'''   ,				sep=''''''   ,				)
       lowercase  :		Optional[int]						=      Fibonacci()

       while True:
              lowercase  :		str						=      input('''>> '''      )
              if prompt in {"exit", "quit"}:
                     break

              try:
                     lowercase  :		int						=      int(lowerCamelCase      )
              except ValueError:
                     print('''Enter a number or \'exit\''''      )
                     continue

              print(fibonacci.get(lowerCamelCase      )      )


if __name__ == "__main__":
      main()




