



"""simple docstring"""




from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

A__  :  int       = TypeVar('''T''', bound=bool)



class     __snake_case						(					Generic[T]						):




							def __init__(	self					:	List[Any]    ,    A_					:	str):
									lowerCAmelCase_      :					T     =			value
									lowerCAmelCase_      :					RandomizedHeapNode[T] | None     =			None
									lowerCAmelCase_      :					RandomizedHeapNode[T] | None     =			None




							@property
							def        UpperCAmelCase__      (	self					:	Dict):
									return self._value







							@staticmethod
							def        UpperCAmelCase__      (	A_					:	Optional[int]    ,    A_					:	Any):
									if not roota:
											return roota

									if not roota:
											return roota

									if roota.value > roota.value:
											lowerCAmelCase_      :					int     =			roota, roota

									if random.choice([True, False]):
											lowerCAmelCase_      :					Dict     =			roota.right, roota.left

									lowerCAmelCase_      :					Any     =			RandomizedHeapNode.merge(roota.left    ,    _UpperCAmelCase)

									return roota





class     __snake_case						(					Generic[T]						):




							def __init__(	self					:	int    ,    A_					:	Optional[Any] = ()):
									lowerCAmelCase_      :					RandomizedHeapNode[T] | None     =			None

									if data:
											for item in data:
													self.insert(_UpperCAmelCase)




							def        UpperCAmelCase__      (	self					:	List[str]    ,    A_					:	List[str]):
									lowerCAmelCase_      :					str     =			RandomizedHeapNode.merge(self._root    ,    RandomizedHeapNode(_UpperCAmelCase))




							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									lowerCAmelCase_      :					Dict     =			self.top()

									if self._root is None:
											return None

									lowerCAmelCase_      :					List[str]     =			RandomizedHeapNode.merge(self._root.left    ,    self._root.right)

									return result




							def        UpperCAmelCase__      (	self					:	int):
									if not self._root:
											raise IndexError('''Can\'t get top element for the empty heap.''')
									return self._root.value




							def        UpperCAmelCase__      (	self					:	List[str]):
									lowerCAmelCase_      :					int     =			None




							def        UpperCAmelCase__      (	self					:	List[str]):
									lowerCAmelCase_      :					int     =			[]
									while self:
											result.append(self.pop())

									return result







							def __bool__(	self					:	Tuple):
									return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()










import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



class     __snake_case						(					utils.Parser						):
							_a      				=			"halfcheetah-medium-expert-v2"
							_a      				=			"config.offline"





def       UpperCamelCase(  __UpperCamelCase							:		List[Any]					,__UpperCamelCase							:		Any					,__UpperCamelCase							:		Tuple					,__UpperCamelCase							:		Optional[Any]					,__UpperCamelCase							:		List[Any]			):
		lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[Any]     =			utils.load_model(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase					,epoch=__UpperCamelCase					,device=__UpperCamelCase			)
		lowerCAmelCase_      :					List[str]     =			TrajectoryTransformerModel(gpt.config			)

		trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
		lowerCAmelCase_      :					str     =			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__  :  Any       = Parser().parse_args('''plan''')
						convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
						    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
						)





