


"""simple docstring"""


import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class   _a      (      SCREAMING_SNAKE_CASE_ ):
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					str				):

          with open(SCREAMING_SNAKE_CASE__							,					encoding='utf-8'				) as input_file:
                lowerCamelCase__           =		re.compile(R'(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'				)
                lowerCamelCase__           =		input_file.read()
                lowerCamelCase__           =		regexp.search(SCREAMING_SNAKE_CASE__				)

          return match
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					str				):

          with open(SCREAMING_SNAKE_CASE__							,					encoding='utf-8'				) as input_file:
                lowerCamelCase__           =		re.compile(R'#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'							,					re.DOTALL				)
                lowerCamelCase__           =		input_file.read()
                # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
                lowerCamelCase__           =		regexp.finditer(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		[match for match in matches if match is not None and match.group(1				) is not None]
          return matches[0] if matches else None
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		Path('./datasets'				)
          lowerCamelCase__           =		list(dataset_paths.absolute().glob('**/*.py'				)				)

          for dataset in dataset_files:
                if self._no_encoding_on_file_open(str(SCREAMING_SNAKE_CASE__				)				):
                      raise AssertionError(F'open(...) must use utf-8 encoding in {dataset}'				)


    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		Path('./datasets'				)
          lowerCamelCase__           =		list(dataset_paths.absolute().glob('**/*.py'				)				)

          for dataset in dataset_files:
                if self._no_print_statements(str(SCREAMING_SNAKE_CASE__				)				):
                      raise AssertionError(F'print statement found in {dataset}. Use datasets.logger/logging instead.'				)







"""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()



