






import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowercase  							= 2 << 20  # 2mb
lowercase  							= get_logger(__name__)




class 			UpperCamelCase_				(      snake_case_    ):



	'''simple docstring'''




	def 							_UpperCamelCase      (	self )	->   Tuple:
								snake_case_           =							self._options.get_all_options().get('runner' )
								return runner in [None, "DirectRunner", "PortableRunner"]






def 						__UpperCAmelCase      (					a_							,					a_							,					a_=False):
							snake_case_           =							FileSystems
							if fs.exists(a_):
														if force_upload:
																					logger.info(f'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.''')
														else:
																					logger.info(f'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.''')
																					return
							with fs.create(a_) as remote_file:
														with open(a_							,					'rb') as local_file:
																					snake_case_           =							local_file.read(a_)
																					while chunk:
																												remote_file.write(a_)
																												snake_case_           =							local_file.read(a_)





def 						__UpperCAmelCase      (					a_							,					a_							,					a_=False):
							snake_case_           =							FileSystems
							if os.path.exists(a_):
														if force_download:
																					logger.info(f'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.''')
														else:
																					logger.info(f'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.''')
																					return
							with fs.open(a_) as remote_file:
														with open(a_							,					'wb') as local_file:
																					snake_case_           =							remote_file.read(a_)
																					while chunk:
																												local_file.write(a_)
																												snake_case_           =							remote_file.read(a_)













import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 			UpperCamelCase_				(      utils.Parser    ):



	'''simple docstring'''




	lowerCAmelCase													=			"halfcheetah-medium-expert-v2"
	lowerCAmelCase													=			"config.offline"






def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_      ,		snake_case_           =							utils.load_model(a_							,					a_							,					a_							,					epoch=a_							,					device=a_)
							snake_case_           =							TrajectoryTransformerModel(gpt.config)

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





