




"""simple docstring"""





import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class  __lowercase    (     utils.Parser		):


				'''simple docstring'''


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







def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   int							     =   utils.load_model(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     epoch=lowerCamelCase ,     device=lowerCamelCase						)
						snake_case__    :   str							     =   TrajectoryTransformerModel(gpt.config						)

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()						)
						snake_case__    :   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__":
							_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"""





from __future__ import annotations

import math
import random

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Tuple							     =   d * e - 1
						snake_case__    :   Optional[int]							     =   0
						snake_case__    :   Tuple							     =   0
						while p == 0:
												snake_case__    :   Optional[int]							     =   random.randint(2 ,     n - 1						)
												snake_case__    :   Tuple							     =   k
												while True:
																		if t % 2 == 0:
																								snake_case__    :   List[Any]							     =   t // 2
																								snake_case__    :   Union[str, Any]							     =   (g**t) % n
																								snake_case__    :   Dict							     =   math.gcd(x - 1 ,     lowerCamelCase						)
																								if x > 1 and y > 1:
																														snake_case__    :   Union[str, Any]							     =   y
																														snake_case__    :   str							     =   n // y
																														break  # find the correct factors
																		else:
																								break  # t is not divisible by 2, break and choose another g
						return sorted([p, q]						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()


