






import os





def 						__UpperCAmelCase      (					):
							snake_case_           =							os.path.dirname(os.path.realpath(a_))
							snake_case_           =							os.path.join(a_							,					'triangle.txt')

							with open(a_) as f:
														snake_case_           =							f.readlines()

							snake_case_           =							[[int(a_) for y in x.rstrip('\r\n').split(' ')] for x in triangle]

							for i in range(1							,					len(a_)):
														for j in range(len(a[i])):
																					snake_case_           =							a[i - 1][j] if j != len(a[i - 1]) else 0
																					snake_case_           =							a[i - 1][j - 1] if j > 0 else 0
																					a[i][j] += max(a_							,					a_)
							return max(a[-1])


if __name__ == "__main__":
	print(solution())













import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 			UpperCamelCase_				(      utils.Parser    ):



	'''simple docstring'''




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






def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_      ,		snake_case_           =							utils.load_model(a_							,					a_							,					a_							,					epoch=a_							,					device=a_)
							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__":
	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
	)





