def SCREAMING_SNAKE_CASE			(       lowercase_:		float			):
       return -num if num < 0 else num


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]			):
       if len(lowercase_			) == 0:
              raise ValueError('abs_min() arg is an empty sequence'			)
       _UpperCamelCase       :    str					  =					x[0]
       for i in x:
              if abs_val(lowercase_			) < abs_val(lowercase_			):
                     _UpperCamelCase       :    List[str]					  =					i
       return j


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]			):
       if len(lowercase_			) == 0:
              raise ValueError('abs_max() arg is an empty sequence'			)
       _UpperCamelCase       :    Dict					  =					x[0]
       for i in x:
              if abs(lowercase_			) > abs(lowercase_			):
                     _UpperCamelCase       :    Union[str, Any]					  =					i
       return j


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]			):
       if len(lowercase_			) == 0:
              raise ValueError('abs_max_sort() arg is an empty sequence'			)
       return sorted(lowercase_						,  key=lowercase_			)[-1]


def SCREAMING_SNAKE_CASE			(       ):
       assert abs_val(0			) == 0
       assert abs_val(34			) == 34
       assert abs_val(-100000000000			) == 100000000000

       _UpperCamelCase       :    str					  =					[-3, -1, 2, -11]
       assert abs_max(lowercase_			) == -11
       assert abs_max_sort(lowercase_			) == -11
       assert abs_min(lowercase_			) == -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 test_abs_val()
 print(abs_val(-34))  # --> 34




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    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__":
 UpperCAmelCase     						=			Parser().parse_args('''plan''')
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )



