





import argparse
import json

from tqdm import tqdm

def       A():
		lowerCAmelCase_												=				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"						,   )
		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(lowerCamelCase_				)
				for dpr_record in tqdm(lowerCamelCase_				):
						lowerCAmelCase_												=				dpr_record['question']
						lowerCAmelCase_												=				[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()







def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Optional[Any]  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[Any]					  =      [0 for i in range(r + 1  )]
			# nc0 = 1
			SCREAMING_SNAKE_CASE_   : Dict					  =      1
			for i in range(1  ,							n + 1  ):
						# to compute current row from previous row.
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      min(lowerCamelCase_  ,							lowerCamelCase_  )
						while j > 0:
									c[j] += c[j - 1]
									j -= 1
			return c[r]




print(binomial_coefficient(n=10, r=5))



