



import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : int       =						CustomConfig


   def __init__(    self:     List[str]  ,lowerCamelCase_:     Dict							)	->      Tuple:
       super().__init__(lowerCamelCase_							)
       UpperCAmelCase_     :       List[str]  		=					torch.nn.Linear(config.hidden_size  ,config.hidden_size							)


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Union[str, Any]							)	->      str:
       return self.linear(lowerCamelCase_							)






   def       A__    (    self:     int  ,lowerCamelCase_:     Dict							)	->      str:
       pass







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : str       =						NoSuperInitConfig


   def __init__(    self:     Any  ,lowerCamelCase_:     int							)	->      List[str]:
       super().__init__(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					torch.nn.Linear(config.attribute  ,config.attribute							)


   def       A__    (    self:     int  ,lowerCamelCase_:     int							)	->      List[str]:
       return self.linear(lowerCamelCase_							)






   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     List[str]							)	->      str:
       pass




def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			)->			set:
				assert isinstance(lowerCAmelCase_			, lowerCAmelCase_			), f"""The input value of [set_a={set_a}] is not a set"""
				assert isinstance(lowerCAmelCase_			, lowerCAmelCase_			), f"""The input value of [set_b={set_b}] is not a set"""

				return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
						from doctest import testmod

						testmod()