





import argparse
import json

from tqdm import tqdm






def 	_SCREAMING_SNAKE_CASE		(      )      ->   Tuple:
 __lowerCAmelCase					:	Dict      =       argparse.ArgumentParser()

 # Required parameters
 parser.add_argument(
     """--src_path"""   ,   type=SCREAMING_SNAKE_CASE   ,   default="""biencoder-nq-dev.json"""   ,   help="""Path to raw DPR training data"""   ,   )
 parser.add_argument(
     """--evaluation_set"""   ,   type=SCREAMING_SNAKE_CASE   ,   help="""where to store parsed evaluation_set file"""   ,   )
 parser.add_argument(
     """--gold_data_path"""   ,   type=SCREAMING_SNAKE_CASE   ,   help="""where to store parsed gold_data_path file"""   ,   )
 __lowerCAmelCase					:	Union[str, Any]      =       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					:	Optional[Any]      =       json.load(SCREAMING_SNAKE_CASE   )
  for dpr_record in tqdm(SCREAMING_SNAKE_CASE   ):
   __lowerCAmelCase					:	Union[str, Any]      =       dpr_record["""question"""]
   __lowerCAmelCase					:	Dict      =       [context["""title"""] for context in dpr_record["""positive_ctxs"""]]
   eval_file.write(question + """\n"""   )
   gold_file.write("""\t""".join(SCREAMING_SNAKE_CASE   ) + """\n"""   )


if __name__ == "__main__":
      main()






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

 # invalid input
 if index >= len(SCREAMING_SNAKE_CASE   ) or index < 0:
  return None

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # index is the pivot
 if m <= index < m + count:
  return pivot
 # must be in smaller
 elif m > index:
  return quick_select(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 # must be in larger
 else:
  return quick_select(SCREAMING_SNAKE_CASE   ,   index - (m + count)   )