



"""simple docstring"""




class     __snake_case						:




							def __init__(	self					:	Union[str, Any]):
									lowerCAmelCase_      :					List[str]     =			[0, 1]







							def        UpperCAmelCase__      (	self					:	Tuple    ,    A_					:	int):
									if (difference := index - (len(self.sequence) - 2)) >= 1:
											for _ in range(A_):
													self.sequence.append(self.sequence[-1] + self.sequence[-2])
									return self.sequence[:index]





def       UpperCamelCase(  ):
		print(
		    '''Fibonacci Series Using Dynamic Programming\n'''					,'''Enter the index of the Fibonacci number you want to calculate '''					,'''in the prompt below. (To exit enter exit or Ctrl-C)\n'''					,sep=''''''					,)
		lowerCAmelCase_      :					Dict     =			Fibonacci()

		while True:
				lowerCAmelCase_      :					str     =			input('''>> '''			)
				if prompt in {"exit", "quit"}:
						break

				try:
						lowerCAmelCase_      :					int     =			int(__UpperCamelCase			)
				except ValueError:
						print('''Enter a number or \'exit\''''			)
						continue

				print(fibonacci.get(__UpperCamelCase			)			)


if __name__ == "__main__":
						main()










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
						)





