





from torch import nn


class     __magic_name__     (							nn.Module							):





     """simple docstring"""



     def __init__(							self				:Any	,					snake_case				:Dict	,					snake_case				:Union[str, Any]     ):





        '''simple docstring'''




        super().__init__()
        A_    : List[Any]								=	class_size
        A_    : List[Any]								=	embed_size
        # self.mlp1 = nn.Linear(embed_size, embed_size)
        # self.mlp2 = (nn.Linear(embed_size, class_size))
        A_    : int								=	nn.Linear(snake_case	,					snake_case     )





     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:Any     ):





        '''simple docstring'''




        A_    : Dict								=	self.mlp(snake_case     )
        return logits

import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str  ,  **lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						Union[str, Any] =					AutoConfig.from_pretrained(lowerCamelCase  ,  **lowerCamelCase	)
 UpperCamelCase_					:						str =					AutoModelForSeqaSeqLM.from_config(lowerCamelCase	)
 model.save_pretrained(lowerCamelCase	)
 AutoTokenizer.from_pretrained(lowerCamelCase	).save_pretrained(lowerCamelCase	)
 return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)



