





'''simple docstring'''


import copy
import random

from transformers import CLIPTokenizer






class       UpperCamelCase__  (		_UpperCAmelCase     ):







 def __init__(				self			:   Union[str, Any] , *lowerCamelCase			:   Optional[Any] , **lowerCamelCase			:   str		):




    '''simple docstring'''
    super().__init__(*A_ , **A_		)
    a__     = {}







 def 						__a   (				self			:   Dict , lowerCamelCase			:   List[str] , *lowerCamelCase			:   int , **lowerCamelCase			:   Optional[Any]		):




    '''simple docstring'''
    a__     = super().add_tokens(A_ , *A_ , **A_		)
    if num_added_tokens == 0:
       raise ValueError(
           F'''The tokenizer already contains the token {placeholder_token}. Please pass a different'''
           " `placeholder_token` that is not already in the tokenizer."		)







 def 						__a   (				self			:   Dict , lowerCamelCase			:   int , *lowerCamelCase			:   List[Any] , lowerCamelCase			:   List[str]=1 , **lowerCamelCase			:   Union[str, Any]		):




    '''simple docstring'''
    a__     = []
    if num_vec_per_token == 1:
       self.try_adding_tokens(A_ , *A_ , **A_		)
       output.append(A_		)
    else:
       a__     = []
       for i in range(A_		):
          a__     = placeholder_token + F'''_{i}'''
          self.try_adding_tokens(A_ , *A_ , **A_		)
          output.append(A_		)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
    for token in self.token_map:
       if token in placeholder_token:
          raise ValueError(
              F'''The tokenizer already has placeholder token {token} that can get confused with'''
              F''' {placeholder_token}keep placeholder tokens independent'''		)
    a__     = output







 def 						__a   (				self			:   Optional[Any] , lowerCamelCase			:   Dict , lowerCamelCase			:   Tuple=False , lowerCamelCase			:   Optional[Any]=1.0		):




    '''simple docstring'''
    if isinstance(A_ , A_		):
       a__     = []
       for i in range(len(A_		)		):
          output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=A_		)		)
       return output
    for placeholder_token in self.token_map:
       if placeholder_token in text:
          a__     = self.token_map[placeholder_token]
          a__     = tokens[: 1 + int(len(A_		) * prop_tokens_to_load		)]
          if vector_shuffle:
             a__     = copy.copy(A_		)
             random.shuffle(A_		)
          a__     = text.replace(A_ , " ".join(A_		)		)
    return text







 def __call__(				self			:   Optional[Any] , lowerCamelCase			:   List[str] , *lowerCamelCase			:   int , lowerCamelCase			:   Tuple=False , lowerCamelCase			:   List[str]=1.0 , **lowerCamelCase			:   str		):




    '''simple docstring'''
    return super().__call__(
        self.replace_placeholder_tokens_in_text(
            A_ , vector_shuffle=A_ , prop_tokens_to_load=A_		) , *A_ , **A_ , )







 def 						__a   (				self			:   List[Any] , lowerCamelCase			:   List[Any] , *lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Optional[Any]=False , lowerCamelCase			:   Dict=1.0 , **lowerCamelCase			:   Tuple		):




    '''simple docstring'''
    return super().encode(
        self.replace_placeholder_tokens_in_text(
            A_ , vector_shuffle=A_ , prop_tokens_to_load=A_		) , *A_ , **A_ , )










'''simple docstring'''


import argparse
import json

from tqdm import tqdm







def    _lowerCamelCase  ()   ->    Any:
			a__     = 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"					,  )
			a__     = 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__     = json.load(__lowerCamelCase     )
						for dpr_record in tqdm(__lowerCamelCase     ):
									a__     = dpr_record["question"]
									a__     = [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()



