"""simple docstring"""


def       lowerCamelCase_			( __lowerCAmelCase = 30    )							-> Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__ =exponent + 2
						lowerCamelCase__ =(1 + 5**0.5) / 2
						lowerCamelCase__ =(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

						return int(_a    )


if __name__ == "__main__":
	print(F'''{solution() = }''')


"""simple docstring"""


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel






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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						lowerCamelCase__ ,				lowerCamelCase__ =utils.load_model(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   epoch=__lowerCAmelCase       ,   device=__lowerCAmelCase    )
						lowerCamelCase__ =TrajectoryTransformerModel(gpt.config    )

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()    )
						lowerCamelCase__ =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							=Parser().parse_args('plan')
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)


