import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig



class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		CustomConfig



							def __init__(						self      ,				lowercase_				):
													super().__init__(lowercase_				)
													_snake_case						:     Dict				=    torch.nn.Linear(config.hidden_size      ,				config.hidden_size				)



							def        UpperCamelCase       (						self      ,				lowercase_				):
													return self.linear(lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_				):
													pass




class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		NoSuperInitConfig



							def __init__(						self      ,				lowercase_				):
													super().__init__(lowercase_				)
													_snake_case						:     Union[str, Any]				=    torch.nn.Linear(config.attribute      ,				config.attribute				)



							def        UpperCamelCase       (						self      ,				lowercase_				):
													return self.linear(lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_				):
													pass
def 					snake_case						(__lowercase						)       ->       int:

						'''simple docstring'''



						if not isinstance(__lowercase					,			__lowercase						) or number < 0:
												raise ValueError("Input must be a non-negative integer"						)

						_snake_case						:     List[Any]				=    number & ~(number - 1)
						_snake_case						:     Union[str, Any]				=    0
						while intermediate:
												intermediate >>= 1
												index += 1
						return index - 1


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)