

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel
class 		SCREAMING_SNAKE_CASE__					(		utils.Parser   ):







						"""simple docstring"""




						lowercase  :				str											=							"halfcheetah-medium-expert-v2"
						lowercase  :				str											=							"config.offline"







def  UpperCAmelCase_   (_lowerCAmelCase :		Optional[int]     ,			_lowerCAmelCase :		str     ,			_lowerCAmelCase :		Optional[Any]     ,			_lowerCAmelCase :		List[str]     ,			_lowerCAmelCase :		List[Any]		):
			__UpperCamelCase							,				__UpperCamelCase    :    str								=      utils.load_model(_lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			epoch=_lowerCAmelCase     ,			device=_lowerCAmelCase		)
			__UpperCamelCase    :    Union[str, Any]								=      TrajectoryTransformerModel(gpt.config		)

			trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()		)
			__UpperCamelCase    :    Optional[int]								=      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__":
					lowercase     :       int        =       Parser().parse_args("plan")
					convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
					    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
					)


from collections.abc import Callable

lowercase     :       Optional[Any]        =       Callable[[float], float]  # type alias for a real -> real function







def  UpperCAmelCase_   (_lowerCAmelCase :		RealFunc     ,			_lowerCAmelCase :		RealFunc     ,			_lowerCAmelCase :		int     ,			):
			__UpperCamelCase    :    Any								=      float(_lowerCAmelCase		)
			while True:
						try:
									__UpperCamelCase    :    List[Any]								=      prev_guess - function(_lowerCAmelCase		) / derivative(_lowerCAmelCase		)
						except ZeroDivisionError:
									raise ZeroDivisionError("Could not find root"		) from None
						if abs(prev_guess - next_guess		) < 10**-5:
									return next_guess
						__UpperCamelCase    :    Tuple								=      next_guess







def  UpperCAmelCase_   (_lowerCAmelCase :		float		):
			return (x**3) - (2 * x) - 5







def  UpperCAmelCase_   (_lowerCAmelCase :		float		):
			return 3 * (x**2) - 2


if __name__ == "__main__":
					print(newton(f, fa, 3))