






import torch

from transformers import AutoTokenizer



class 							_A (    UpperCamelCase							):
    """simple docstring"""

    def __init__(							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]			)			->       Dict:
           __UpperCAmelCase    							=AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       Tuple:

           if isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			) and all(isinstance(_x   ,    __SCREAMING_SNAKE_CASE			) for _x in x			):
                  __UpperCAmelCase    							=None
                  for l in x:
                         __UpperCAmelCase    							=self.tokenizer(
                             __SCREAMING_SNAKE_CASE   ,    padding="""max_length"""   ,    max_length=384   ,    truncation=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""   ,    )
                         __UpperCAmelCase    							=torch.tensor([len(__SCREAMING_SNAKE_CASE			)]			)
                         if d is not None:
                                for k in d.keys():
                                       __UpperCAmelCase    							=torch.cat((d[k], t[k])   ,    0			)
                         else:
                                __UpperCAmelCase    							=t

                  __UpperCAmelCase    							=torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""			)			)
                  __UpperCAmelCase    							=torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""			)			)

           elif isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			) and all(isinstance(_x   ,    __SCREAMING_SNAKE_CASE			) for _x in x			):
                  __UpperCAmelCase    							=self.tokenizer(
                      __SCREAMING_SNAKE_CASE   ,    padding="""max_length"""   ,    max_length=384   ,    truncation=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""   ,    )

           else:
                  raise Exception(
                      """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
                      """ strings` for supports are supported."""			)

           return d



    def _a       (							self			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    str=0.70			)			->       Optional[int]:
           __UpperCAmelCase    							=[]
           for idx in range(len(W_query["""input_ids"""]			)			):
                  __UpperCAmelCase    							=__UpperCAmelCase    							=range(p_start.shape[1]			)

                  __UpperCAmelCase    							=[]
                  for start_id in start_indexes:
                         for end_id in end_indexes:
                                if start_id < end_id:
                                       output.append(
                                           (
                                               start_id,
                                               end_id,
                                               p_start[idx][start_id].item(),
                                               p_end[idx][end_id].item(),
                                           )			)

                  output.sort(key=lambda __SCREAMING_SNAKE_CASE			: (tup[2] * tup[3])   ,    reverse=__SCREAMING_SNAKE_CASE			)
                  __UpperCAmelCase    							=[]
                  for k in range(len(__SCREAMING_SNAKE_CASE			)			):
                         if output[k][2] * output[k][3] >= thresh:
                                __UpperCAmelCase							,   __UpperCAmelCase    							=output[k][0], output[k][1]
                                __UpperCAmelCase    							=self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]			)
                                temp.append((decoded, output[k][2] * output[k][3])			)

                  final_outputs.append(__SCREAMING_SNAKE_CASE			)

           return final_outputs












from collections import defaultdict







def 			lowercase__  (				A_:	int = 5		)  ->							int:







       """simple docstring"""






       __UpperCAmelCase    							=defaultdict(A_		)
       __UpperCAmelCase    							=0

       while True:
              __UpperCAmelCase    							=get_digits(A_		)
              freqs[digits].append(A_		)

              if len(freqs[digits]		) == max_base:
                     __UpperCAmelCase    							=freqs[digits][0] ** 3
                     return base

              num += 1







def 			lowercase__  (				A_:	int		)  ->							str:







       """simple docstring"""






       return "".join(sorted(str(num**3		)		)		)


if __name__ == "__main__":
   print(F"""{solution() = }""")




