
import argparse
import json

from tqdm import tqdm







def        A__					(						)  ->	Optional[Any]:
				UpperCamelCase_:							int	=						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"""     ,    )
				UpperCamelCase_:							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:
								UpperCamelCase_:							Optional[Any]	=						json.load(lowerCamelCase_						)
								for dpr_record in tqdm(lowerCamelCase_						):
												UpperCamelCase_:							Optional[Any]	=						dpr_record['''question''']
												UpperCamelCase_:							Dict	=						[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_						: Optional[Any]         =  """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCamelCase_						: Union[str, Any]         =  [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCamelCase_						: Optional[Any]         =  {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}






