'''simple docstring'''

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 		SCREAMING_SNAKE_CASE_				(utils.Parser			):



			"""simple docstring"""






			__magic_name__   :							str             =					"halfcheetah-medium-expert-v2"
			__magic_name__   :							str             =					"config.offline"







def 					UpperCAmelCase__      (							_lowerCAmelCase						: Optional[int],			_lowerCAmelCase						: Union[str, Any],			_lowerCAmelCase						: List[Any],			_lowerCAmelCase						: str,			_lowerCAmelCase						: Any)						->		Optional[int]:



						"""simple docstring"""

						snake_case							,				snake_case   :					str							=						utils.load_model(_lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase,			epoch=_lowerCAmelCase,			device=_lowerCAmelCase)
						snake_case   :					int							=						TrajectoryTransformerModel(gpt.config)

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict())
						snake_case   :					Union[str, 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__":
				A_        =   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 math


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5,			int(math.sqrt(_lowerCAmelCase) + 1),			6):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int = 200_0000)						->		int:



						"""simple docstring"""

						return sum(num for num in range(3,			_lowerCAmelCase,			2) if is_prime(_lowerCAmelCase)) + 2 if n > 2 else 0


if __name__ == "__main__":
						print(f"{solution() = }")





