





'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


__SCREAMING_SNAKE_CASE     :List[Any]       =				logging.get_logger(__name__)







class   A_	(  lowerCAmelCase_			):




 def __init__(					self       :    str	,			*snake_case_       :    List[str]	,			**snake_case_       :    int    ):
      warnings.warn(
          "The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use LayoutLMv2ImageProcessor instead."	,			snake_case_	,			)
      super().__init__(*snake_case_	,			**snake_case_    )











import argparse
import json

from tqdm import tqdm







def  _SCREAMING_SNAKE_CASE				(   )      ->      List[Any]:
					__A	:						Tuple       =				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'					,						)
					__A	:						Optional[int]       =				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:
										__A	:						List[Any]       =				json.load(a  )
										for dpr_record in tqdm(a  ):
															__A	:						Dict       =				dpr_record['question']
															__A	:						Any       =				[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()




