




"""simple docstring"""

import math
class      A__   :




	def __init__( self	:						Any   ,     lowercase	:						Optional[int]						)       ->			List[str]:
						__snake_case        =						len(lowercase						)
						__snake_case        =						[0] * (
						    4 * self.N
						)  # approximate the overall size of segment tree with array N
						self.build(1   ,     0   ,     self.N - 1						)




	def 				a_		( self	:						Union[str, Any]   ,     lowercase	:						Optional[Any]						)       ->			List[Any]:
						return idx * 2




	def 				a_		( self	:						str   ,     lowercase	:						Optional[Any]						)       ->			int:
						return idx * 2 + 1




	def 				a_		( self	:						List[str]   ,     lowercase	:						str   ,     lowercase	:						str   ,     lowercase	:						Dict						)       ->			List[Any]:  # noqa: E741
						if l == r:
											__snake_case        =						A[l]
						else:
											__snake_case        =						(l + r) // 2
											self.build(self.left(lowercase						)   ,     lowercase   ,     lowercase						)
											self.build(self.right(lowercase						)   ,     mid + 1   ,     lowercase						)
											__snake_case        =						max(self.st[self.left(lowercase						)]   ,     self.st[self.right(lowercase						)]						)




	def 				a_		( self	:						List[Any]   ,     lowercase	:						Optional[Any]   ,     lowercase	:						List[str]   ,     lowercase	:						Optional[Any]						)       ->			List[Any]:
						return self.update_recursive(1   ,     0   ,     self.N - 1   ,     a - 1   ,     b - 1   ,     lowercase						)




	def 				a_		( self	:						Dict   ,     lowercase	:						Any   ,     lowercase	:						Optional[Any]   ,     lowercase	:						Dict   ,     lowercase	:						Any   ,     lowercase	:						Union[str, Any]   ,     lowercase	:						Optional[int]						)       ->			Optional[Any]:  # noqa: E741
						if r < a or l > b:
											return True
						if l == r:
											__snake_case        =						val
											return True
						__snake_case        =						(l + r) // 2
						self.update_recursive(self.left(lowercase						)   ,     lowercase   ,     lowercase   ,     lowercase   ,     lowercase   ,     lowercase						)
						self.update_recursive(self.right(lowercase						)   ,     mid + 1   ,     lowercase   ,     lowercase   ,     lowercase   ,     lowercase						)
						__snake_case        =						max(self.st[self.left(lowercase						)]   ,     self.st[self.right(lowercase						)]						)
						return True




	def 				a_		( self	:						List[Any]   ,     lowercase	:						str   ,     lowercase	:						List[str]						)       ->			Tuple:
						return self.query_recursive(1   ,     0   ,     self.N - 1   ,     a - 1   ,     b - 1						)




	def 				a_		( self	:						List[str]   ,     lowercase	:						List[Any]   ,     lowercase	:						Tuple   ,     lowercase	:						str   ,     lowercase	:						int   ,     lowercase	:						Tuple						)       ->			List[str]:  # noqa: E741
						if r < a or l > b:
											return -math.inf
						if l >= a and r <= b:
											return self.st[idx]
						__snake_case        =						(l + r) // 2
						__snake_case        =						self.query_recursive(self.left(lowercase						)   ,     lowercase   ,     lowercase   ,     lowercase   ,     lowercase						)
						__snake_case        =						self.query_recursive(self.right(lowercase						)   ,     mid + 1   ,     lowercase   ,     lowercase   ,     lowercase						)
						return max(lowercase   ,     lowercase						)




	def 				a_		( self	:						int						)       ->			int:
						__snake_case        =						[]
						for i in range(1   ,     N + 1						):
											show_list += [self.query(lowercase   ,     lowercase						)]
						print(lowercase						)


if __name__ == "__main__":
		UpperCamelCase__			:     Dict			       =				[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
		UpperCamelCase__			:     Optional[int]			       =				15
		UpperCamelCase__			:     Any			       =				SegmentTree(A)
		print(segt.query(4, 6))
		print(segt.query(7, 11))
		print(segt.query(7, 12))
		segt.update(1, 3, 1_11)
		print(segt.query(1, 15))
		segt.update(7, 8, 2_35)
		segt.show_data()








"""simple docstring"""

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel
class      A__   (   utils.Parser  ):
	A_				:					str      =   "halfcheetah-medium-expert-v2"
	A_				:					str      =   "config.offline"




def    snake_case__						(							lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]		):
					__snake_case   ,   __snake_case        =						utils.load_model(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase					,       epoch=lowerCAmelCase					,       device=lowerCAmelCase		)
					__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__":
		UpperCamelCase__			:     int			       =				Parser().parse_args("plan")
		convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
		    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
		)


