






import math
from timeit import timeit





def 						__UpperCAmelCase      (					a_):
							snake_case_           =							0
							snake_case_           =							abs(a_)
							while True:
														snake_case_           =							n // 10
														digits += 1
														if n == 0:
																					break
							return digits





def 						__UpperCAmelCase      (					a_):
							return 1 if n == 0 else math.floor(math.log(abs(a_)							,					10) + 1)





def 						__UpperCAmelCase      (					a_):
							return len(str(abs(a_)))





def 						__UpperCAmelCase      (					):
							from collections.abc import Callable

							def benchmark_a_function(a_							,					a_) -> None:
														snake_case_           =							f'''{func.__name__}({value})'''
														snake_case_           =							timeit(f'''__main__.{call}'''							,					setup='import __main__')
														print(f'''{call}: {func(a_)} -- {timing} seconds''')

							for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
														for func in (num_digits, num_digits_fast, num_digits_faster):
																					benchmark_a_function(a_							,					a_)
														print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()













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
	)





