


"""simple docstring"""


import string
from math import logaa



def   snake_case						(				_a:     str		,   _a:     str							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		document.translate(
          str.maketrans(''		,   ''		,   string.punctuation							)							).replace('\n'		,   ''							)
      lowerCamelCase__           =		document_without_punctuation.split(' '							)  # word tokenization
      return len([word for word in tokenize_document if word.lower() == term.lower()]							)



def   snake_case						(				_a:     str		,   _a:     str							)->		tuple[int, int]:
      '''simple docstring'''


      lowerCamelCase__           =		corpus.lower().translate(
          str.maketrans(''		,   ''		,   string.punctuation							)							)  # strip all punctuation and replace it with ''
      lowerCamelCase__           =		corpus_without_punctuation.split('\n'							)
      lowerCamelCase__           =		term.lower()
      return (len([doc for doc in docs if term in doc]							), len(_lowercase							))



def   snake_case						(				_a:     int		,   _a:     int		,   _a:     Union[str, Any]=False							)->		float:
      '''simple docstring'''


      if smoothing:
            if n == 0:
                  raise ValueError('log10(0) is undefined.'							)
            return round(1 + logaa(n / (1 + df)							)		,   3							)

      if df == 0:
            raise ZeroDivisionError('df must be > 0'							)
      elif n == 0:
            raise ValueError('log10(0) is undefined.'							)
      return round(logaa(n / df							)		,   3							)






def   snake_case						(				_a:     int		,   _a:     int							)->		float:
      '''simple docstring'''


      return round(tf * idf		,   3							)







"""simple docstring"""


import argparse
import json

from tqdm import tqdm



def   snake_case						(				)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		argparse.ArgumentParser()

      # Required parameters
      parser.add_argument(
          '--src_path'		,   type=_a		,   default='biencoder-nq-dev.json'		,   help='Path to raw DPR training data'		,   )
      parser.add_argument(
          '--evaluation_set'		,   type=_a		,   help='where to store parsed evaluation_set file'		,   )
      parser.add_argument(
          '--gold_data_path'		,   type=_a		,   help='where to store parsed gold_data_path file'		,   )
      lowerCamelCase__           =		parser.parse_args()

      with open(args.src_path		,   'r'							) as src_file, open(args.evaluation_set		,   'w'							) as eval_file, open(
          args.gold_data_path		,   'w'							) as gold_file:
            lowerCamelCase__           =		json.load(_a							)
            for dpr_record in tqdm(_a							):
                  lowerCamelCase__           =		dpr_record['question']
                  lowerCamelCase__           =		[context['title'] for context in dpr_record['positive_ctxs']]
                  eval_file.write(question + '\n'							)
                  gold_file.write('\t'.join(_a							) + '\n'							)


if __name__ == "__main__":
   main()



