






def 						__UpperCAmelCase      (					a_							,					a_):
							# "Simpson Rule"
							# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
							snake_case_           =							(boundary[1] - boundary[0]) / steps
							snake_case_           =							boundary[0]
							snake_case_           =							boundary[1]
							snake_case_           =							make_points(a_							,					a_							,					a_)
							snake_case_           =							0.0
							y += (h / 3.0) * f(a_)
							snake_case_           =							2
							for i in x_i:
														y += (h / 3) * (4 - 2 * (cnt % 2)) * f(a_)
														cnt += 1
							y += (h / 3.0) * f(a_)
							return y





def 						__UpperCAmelCase      (					a_							,					a_							,					a_):
							snake_case_           =							a + h
							while x < (b - h):
														yield x
														snake_case_           =							x + h





def 						__UpperCAmelCase      (					a_):  # enter your function here
							snake_case_           =							(x - 0) * (x - 0)
							return y





def 						__UpperCAmelCase      (					):
							snake_case_           =							0.0  # Lower bound of integration
							snake_case_           =							1.0  # Upper bound of integration
							snake_case_           =							10.0  # define number of steps or resolution
							snake_case_           =							[a, b]  # define boundary of integration
							snake_case_           =							method_a(a_							,					a_)
							print(f'''y = {y}''')


if __name__ == "__main__":
	main()













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
	)





