




import argparse
import json

from tqdm import tqdm





def        __UpperCAmelCase						(							)       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      argparse.ArgumentParser()

			# Required parameters
			parser.add_argument(
			    '--src_path'  ,							type=lowerCamelCase_  ,							default='biencoder-nq-dev.json'  ,							help='Path to raw DPR training data'  ,							)
			parser.add_argument(
			    '--evaluation_set'  ,							type=lowerCamelCase_  ,							help='where to store parsed evaluation_set file'  ,							)
			parser.add_argument(
			    '--gold_data_path'  ,							type=lowerCamelCase_  ,							help='where to store parsed gold_data_path file'  ,							)
			SCREAMING_SNAKE_CASE_   : List[str]					  =      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:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      json.load(lowerCamelCase_  )
						for dpr_record in tqdm(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      dpr_record['question']
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      [context['title'] for context in dpr_record['positive_ctxs']]
									eval_file.write(question + '\n'  )
									gold_file.write('\t'.join(lowerCamelCase_  ) + '\n'  )


if __name__ == "__main__":
						main()





lowerCamelCase__     :		List[str]        = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCamelCase__     :		List[Any]        = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCamelCase__     :		int        = {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}






