





'''simple docstring'''







import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel


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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						List[str]:
						UpperCAmelCase_    ,							UpperCAmelCase_      :   Tuple      =	utils.load_model(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      epoch=__UpperCAmelCase,      device=__UpperCAmelCase )
						UpperCAmelCase_      :   Optional[int]      =	TrajectoryTransformerModel(gpt.config )

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict() )
						UpperCAmelCase_      :   List[Any]      =	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__":
					snake_case_  :							str            =	Parser().parse_args("plan")
					convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
					    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
					)










import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info







def 							A     (						__UpperCAmelCase      )			->			Dict:



						'''simple docstring'''






						class 				a_    :

												def __init__(       self      :Optional[int]  ,		_lowercase      :Any)						->   None:
																		assert isinstance(_lowercase  ,		(bytes, str))
																		UpperCAmelCase_					=	content




												def       __a			(       self      :List[str])						->   Tuple:
																		return json.loads(self.content)

						def mock_response(*__UpperCAmelCase				,   **__UpperCAmelCase      ):
												assert args[0] == AUTHENTICATED_USER_ENDPOINT
												assert "Authorization" in kwargs["headers"]
												assert kwargs["headers"]["Authorization"].startswith('''token '''      )
												assert "Accept" in kwargs["headers"]
												return FakeResponse(b'''{"login":"test","id":1}'''      )

						monkeypatch.setattr(__UpperCAmelCase				,   '''get'''				,   __UpperCAmelCase      )
						UpperCAmelCase_					=	fetch_github_info('''token'''      )
						assert result["login"] == "test"
						assert result["id"] == 1





