
import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 				_UpperCamelCase (    utils.Parser     ):






			'''simple docstring'''

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







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		List[str]:
				UpperCamelCase_,      UpperCamelCase_:							Tuple		=  utils.load_model(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			epoch=lowerCamelCase   ,			device=lowerCamelCase  )
				UpperCamelCase_:							Optional[int]		=  TrajectoryTransformerModel(gpt.config  )

				trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()  )
				UpperCamelCase_:							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__":
							lowerCamelCase_  :       Optional[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 math import cos, sin







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_:							int		=  float(lowerCamelCase  )
				return [[scaling_factor * int(x == y  ) for x in range(2  )] for y in range(2  )]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Union[str, Any]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				return [[c, -s], [s, c]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Any		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  c * s
				return [[c * c, cs], [cs, s * s]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							List[str]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							int		=  c * s
				return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"""    {scaling(5) = }""")
print(F"""  {rotation(45) = }""")
print(F"""{projection(45) = }""")





print(F"""{reflection(45) = }""")






