

'''simple docstring'''

from typing import Dict

from .base import GenericTensor, Pipeline





class 							_snake_case (       _a						):
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      str=None     ,SCREAMING_SNAKE_CASE__			:      int=None     ,SCREAMING_SNAKE_CASE__			:      int=None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     if tokenize_kwargs is None:
       SCREAMING_SNAKE_CASE:int						      =					{}

     if truncation is not None:
       if "truncation" in tokenize_kwargs:
         raise ValueError(
             "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					truncation

     SCREAMING_SNAKE_CASE:Tuple						      =					tokenize_kwargs

     SCREAMING_SNAKE_CASE:Tuple						      =					{}
     if return_tensors is not None:
       SCREAMING_SNAKE_CASE:int						      =					return_tensors

     return preprocess_params, {}, postprocess_params
   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      int							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.framework
     SCREAMING_SNAKE_CASE:List[str]						      =					self.tokenizer(SCREAMING_SNAKE_CASE__     ,return_tensors=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     return model_inputs
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:str						      =					self.model(**SCREAMING_SNAKE_CASE__							)
     return model_outputs
   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      str=False							):
     # [0] is the first available tensor, logits or last_hidden_state.
     if return_tensors:
       return model_outputs[0]
     if self.framework == "pt":
       return model_outputs[0].tolist()
     elif self.framework == "tf":
       return model_outputs[0].numpy().tolist()







   def __call__( self			:      Any     ,*SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      List[str]							):
     return super().__call__(*SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)









'''simple docstring'''

def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Dict						      =					(1 + 24 * n) ** 0.5
  return ((1 + root) / 6) % 1 == 0





def    A_  ( snake_case = 5000 ):
  SCREAMING_SNAKE_CASE:int						      =					[(i * (3 * i - 1)) // 2 for i in range(1      ,       snake_case )]
  for i, pentagonal_i in enumerate(snake_case ):
    for j in range(snake_case      ,       len(snake_case ) ):
      SCREAMING_SNAKE_CASE:int						      =					pentagonal_nums[j]
      SCREAMING_SNAKE_CASE:Any						      =					pentagonal_i + pentagonal_j
      SCREAMING_SNAKE_CASE:int						      =					pentagonal_j - pentagonal_i
      if is_pentagonal(snake_case ) and is_pentagonal(snake_case ):
        return b

  return -1


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






