


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel





class     __a     (       utils.Parser ):
 __lowercase					:				str  	=					"halfcheetah-medium-expert-v2"
 __lowercase					:				str  	=					"config.offline"





def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case	)    ->							Tuple:
       lowercase__     ,		lowercase__:  List[str]         =  utils.load_model(snake_case     ,				snake_case     ,				snake_case     ,				epoch=snake_case     ,				device=snake_case	)
       lowercase__:  int         =  TrajectoryTransformerModel(gpt.config	)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()	)
       lowercase__:  Optional[Any]         =  gpt.pos_emb
       trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()	)
       trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()	)
       trajectory_transformer.head.load_state_dict(gpt.head.state_dict()	)

       for i, block in enumerate(gpt.blocks	):
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()	)
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()	)
              trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()	)

              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()	)
              trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()	)
              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()	)
              trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()	)

       torch.save(trajectory_transformer.state_dict()     ,				'pytorch_model.bin'	)


if __name__ == "__main__":
 __lowerCAmelCase									=				Parser().parse_args('''plan''')
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )






class     __a     :
 def __init__(  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  str         =  []
        lowercase__:  Any         =  0
        lowercase__:  int         =  0
 def __str__(  self					)   ->							Any:




        '''simple docstring'''
        lowercase__:  str         =  '<' + str(self.entries					)[1:-1] + '>'
        return printed
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__					)   ->							List[str]:




        '''simple docstring'''
        self.entries.append(lowerCAmelCase__					)
        lowercase__:  List[str]         =  self.length + 1
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  Any         =  self.length - 1
        lowercase__:  Optional[int]         =  self.entries[self.front]
        # self.front-=1
        # self.entries = self.entries[self.front:]
        lowercase__:  int         =  self.entries[1:]
        return dequeued
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__					)   ->							List[str]:




        '''simple docstring'''
        for _ in range(lowerCAmelCase__					):
               self.put(self.get()					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''simple docstring'''
        return self.entries[0]
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        return self.length


