


"""simple docstring"""



import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel







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




def     UpperCAmelCase    (					_lowercase   :      Union[str, Any] ,      _lowercase   :      Any ,      _lowercase   :      List[str] ,      _lowercase   :      Dict ,      _lowercase   :      Optional[int]  )	->   Tuple:


	"""simple docstring"""




	lowerCAmelCase_       ,  lowerCAmelCase_			       = utils.load_model(_lowercase ,      _lowercase ,      _lowercase ,      epoch=_lowercase ,      device=_lowercase  )
	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__":
		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
		)



"""simple docstring"""



import doctest

import projectq
from projectq.ops import H, Measure
def     UpperCAmelCase    (					_lowercase   :      projectq.cengines._main.MainEngine  )	->   int:


	"""simple docstring"""




	lowerCAmelCase_			       = quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(_lowercase  )


if __name__ == "__main__":
		doctest.testmod()

		# initialises a new quantum backend
		lowercase_							 =       projectq.MainEngine()

		# Generate a list of 10 random numbers
		lowercase_							 =       [get_random_number(quantum_engine) for _ in range(10)]

		# Flushes the quantum engine from memory
		quantum_engine.flush()

		print('Random numbers', random_numbers_list)