code: 
  
  
  
  
  
  
  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
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  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() = }""")
  
  
  
  
  