



class 	__snake_case :







							'''simple docstring'''






							def __init__(							self						):
											a__			 =						[0]
											a__			 =						0


							def _a (							self					,					a_						):
											a__			 =						self.__heap[i]
											while i // 2 > 0:
															if self.__heap[i] > self.__heap[i // 2]:
																			a__			 =						self.__heap[i // 2]
																			a__			 =						temporary
															i //= 2


							def _a (							self					,					a_						):
											self.__heap.append(a_						)
											self.__size += 1
											self.__swap_up(self.__size						)


							def _a (							self					,					a_						):
											while self.__size >= 2 * i:
															if 2 * i + 1 > self.__size:
																			a__			 =						2 * i
															else:
																			if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																							a__			 =						2 * i
																			else:
																							a__			 =						2 * i + 1
															a__			 =						self.__heap[i]
															if self.__heap[i] < self.__heap[bigger_child]:
																			a__			 =						self.__heap[bigger_child]
																			a__			 =						temporary
															a__			 =						bigger_child


							def _a (							self						):
											a__			 =						self.__heap[1]
											a__			 =						self.__heap[self.__size]
											self.__size -= 1
											self.__heap.pop()
											self.__swap_down(1						)
											return max_value


							@property
							def _a (							self						):
											return self.__heap[1:]







							def __len__(							self						):
											return self.__size


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# create an instance of BinaryHeap
				UpperCAmelCase								=   BinaryHeap()
				binary_heap.insert(6)
				binary_heap.insert(10)
				binary_heap.insert(15)
				binary_heap.insert(12)
				# pop root(max-values because it is max heap)
				print(binary_heap.pop())  # 15
				print(binary_heap.pop())  # 12
				# get the list and size after operations
				print(binary_heap.get_list)
				print(len(binary_heap))






import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 	__snake_case (				utils.Parser):







							'''simple docstring'''






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




def      A_ (		__a       :			List[Any]	,						__a       :			Union[str, Any]	,						__a       :			Optional[Any]	,						__a       :			Dict	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__ ,     a__			 =						utils.load_model(__a	,						__a	,						__a	,						epoch=__a	,						device=__a						)
				a__			 =						TrajectoryTransformerModel(gpt.config						)

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

