



'''simple docstring'''





import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig



class       snake_case__		(  _SCREAMING_SNAKE_CASE							):
 A__							=      CustomConfig
 def __init__(					self		:				List[Any]	,			__a		:				Union[str, Any]   )	->					Tuple:







       '''simple docstring'''
       super().__init__(__a   )
       __snake_case      :					List[Any]						=  torch.nn.Linear(config.hidden_size	,			config.hidden_size   )
 def A_					(					self		:				List[str]	,			__a		:				int   )	->					Dict:







       '''simple docstring'''
       return self.linear(__a   )





 def A_					(					self		:				Optional[int]	,			__a		:				Optional[int]   )	->					Optional[Any]:







       '''simple docstring'''
       pass



class       snake_case__		(  _SCREAMING_SNAKE_CASE							):
 A__							=      NoSuperInitConfig
 def __init__(					self		:				Tuple	,			__a		:				int   )	->					Dict:







       '''simple docstring'''
       super().__init__(__a   )
       __snake_case      :					Optional[int]						=  torch.nn.Linear(config.attribute	,			config.attribute   )
 def A_					(					self		:				Dict	,			__a		:				Any   )	->					Optional[int]:







       '''simple docstring'''
       return self.linear(__a   )





 def A_					(					self		:				Dict	,			__a		:				Tuple   )	->					Any:







       '''simple docstring'''
       pass









def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		bool:

    """simple docstring"""


    if not isinstance(lowercase_    ,   lowercase_					):
        __UpperCamelCase     						=  F"Input value of [number={number}] must be an integer"
        raise TypeError(lowercase_					)
    if number < 0 or number % 2 == 1:
        return False
    __UpperCamelCase     						=  int(number**0.5					)
    return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






