



"""simple docstring"""




def       UpperCamelCase(  __UpperCamelCase							:		int			):
		if not isinstance(lowerCamelCase_					,lowerCamelCase_			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
		if len(lowerCamelCase_			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		if len(lowerCamelCase_			) == 1:
				return True
		try:
				lowerCAmelCase_      :					Optional[Any]     =			series[1] / series[0]
				for index in range(len(lowerCamelCase_			) - 1			):
						if series[index + 1] / series[index] != common_ratio:
								return False
		except ZeroDivisionError:
				return False
		return True


def       UpperCamelCase(  __UpperCamelCase							:		List[str]			):
		if not isinstance(lowerCamelCase_					,lowerCamelCase_			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
		if len(lowerCamelCase_			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		lowerCAmelCase_      :					Tuple     =			1
		for value in series:
				answer *= value
		return pow(lowerCamelCase_					,1 / len(lowerCamelCase_			)			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()










import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



class     __snake_case						(					utils.Parser						):
							_a      				=			"halfcheetah-medium-expert-v2"
							_a      				=			"config.offline"





def       UpperCamelCase(  __UpperCamelCase							:		List[Any]					,__UpperCamelCase							:		Any					,__UpperCamelCase							:		Tuple					,__UpperCamelCase							:		Optional[Any]					,__UpperCamelCase							:		List[Any]			):
		lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[Any]     =			utils.load_model(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase					,epoch=__UpperCamelCase					,device=__UpperCamelCase			)
		lowerCAmelCase_      :					List[str]     =			TrajectoryTransformerModel(gpt.config			)

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





