import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



class        lowercase_  (  utils.Parser						):



	"""simple docstring"""





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

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__							)					->     Tuple:
							lowerCAmelCase     		=		utils.load_model(_lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  epoch=_lowerCamelCase       ,  device=_lowerCamelCase							)
							lowerCAmelCase     		=		TrajectoryTransformerModel(gpt.config							)

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




import random




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->None:
								lowerCAmelCase     		=		x
								lowerCAmelCase     		=		y


	def       SCREAMING_SNAKE_CASE_    (							self    )						->bool:
								return (self.x**2 + self.y**2) <= 1




	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Tuple:
								return cls(x=random.random()		,      y=random.random()    )

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     float:
							if number_of_simulations < 1:
														raise ValueError('''At least one simulation is necessary to estimate PI.'''							)

							lowerCAmelCase     		=		0
							for _ in range(snake_case__							):
														lowerCAmelCase     		=		Point.random_unit_square()

														if random_point.is_in_unit_circle():
																					number_in_unit_circle += 1

							return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
	# import doctest

	# doctest.testmod()
	from math import pi

	lowercase__      :    int          =     '''Please enter the desired number of Monte Carlo simulations: '''
	lowercase__      :    Dict          =     estimate_pi(int(input(prompt).strip()))
	print(f'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')



