
'''simple docstring'''





import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  Dict				,	lowerCamelCase_:  Tuple				,	lowerCamelCase_:  int				,	lowerCamelCase_:  List[Any]				,	lowerCamelCase_:  Dict		):



				'''simple docstring'''







				A     :  Optional[int]   =				states.reshape(1				,	-1				,	model.config.state_dim		)
				A     :  Any   =				actions.reshape(1				,	-1				,	model.config.act_dim		)
				A     :  Optional[int]   =				returns_to_go.reshape(1				,	-1				,	1		)
				A     :  Dict   =				timesteps.reshape(1				,	-1		)

				if model.config.max_length is not None:
								A     :  List[Any]   =				states[:, -model.config.max_length :]
								A     :  Optional[int]   =				actions[:, -model.config.max_length :]
								A     :  Union[str, Any]   =				returns_to_go[:, -model.config.max_length :]
								A     :  List[Any]   =				timesteps[:, -model.config.max_length :]

								# pad all tokens to sequence length
								A     :  Optional[Any]   =				torch.cat(
								    [torch.zeros(model.config.max_length - states.shape[1]		), torch.ones(states.shape[1]		)]		)
								A     :  Tuple   =				attention_mask.to(dtype=torch.long				,	device=states.device		).reshape(1				,	-1		)
								A     :  int   =				torch.cat(
								    [
								        torch.zeros(
								            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)				,	device=states.device				,	),
								        states,
								    ]				,	dim=1				,	).to(dtype=torch.floataa		)
								A     :  List[str]   =				torch.cat(
								    [
								        torch.zeros(
								            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)				,	device=actions.device				,	),
								        actions,
								    ]				,	dim=1				,	).to(dtype=torch.floataa		)
								A     :  Union[str, Any]   =				torch.cat(
								    [
								        torch.zeros(
								            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)				,	device=returns_to_go.device				,	),
								        returns_to_go,
								    ]				,	dim=1				,	).to(dtype=torch.floataa		)
								A     :  str   =				torch.cat(
								    [
								        torch.zeros(
								            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])				,	device=timesteps.device		),
								        timesteps,
								    ]				,	dim=1				,	).to(dtype=torch.long		)
				else:
								A     :  Union[str, Any]   =				None

				A     :  Optional[int]   =				model(
				    states=lowerCamelCase_				,	actions=lowerCamelCase_				,	rewards=lowerCamelCase_				,	returns_to_go=lowerCamelCase_				,	timesteps=lowerCamelCase_				,	attention_mask=lowerCamelCase_				,	return_dict=lowerCamelCase_				,	)

				return action_preds[0, -1]


# build the environment

UpperCamelCase_  =	gym.make("Hopper-v3")
UpperCamelCase_  =	env.observation_space.shape[0]
UpperCamelCase_  =	env.action_space.shape[0]
UpperCamelCase_  =	10_00
UpperCamelCase_  =	"cuda"
UpperCamelCase_  =	1000.0  # normalization for rewards/returns
UpperCamelCase_  =	36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase_  =	np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
UpperCamelCase_  =	np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
UpperCamelCase_  =	torch.from_numpy(state_mean).to(device=device)
UpperCamelCase_  =	torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
UpperCamelCase_  =	DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
UpperCamelCase_  =	model.to(device)
model.eval()

for ep in range(10):
					UpperCamelCase_ ,		UpperCamelCase_  =	0, 0
					UpperCamelCase_  =	env.reset()
					UpperCamelCase_  =	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
					UpperCamelCase_  =	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
					UpperCamelCase_  =	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
					UpperCamelCase_  =	torch.zeros(0, device=device, dtype=torch.floataa)

					UpperCamelCase_  =	torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
					for t in range(max_ep_len):
										env.render()
										# add padding
										UpperCamelCase_  =	torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
										UpperCamelCase_  =	torch.cat([rewards, torch.zeros(1, device=device)])

										UpperCamelCase_  =	get_action(
										    model,
										    (states.to(dtype=torch.floataa) - state_mean) / state_std,
										    actions.to(dtype=torch.floataa),
										    rewards.to(dtype=torch.floataa),
										    target_return.to(dtype=torch.floataa),
										    timesteps.to(dtype=torch.long),
										)
										UpperCamelCase_  =	action
										UpperCamelCase_  =	action.detach().cpu().numpy()

										UpperCamelCase_ ,		UpperCamelCase_ ,		UpperCamelCase_ ,		UpperCamelCase_  =	env.step(action)

										UpperCamelCase_  =	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
										UpperCamelCase_  =	torch.cat([states, cur_state], dim=0)
										UpperCamelCase_  =	reward

										UpperCamelCase_  =	target_return[0, -1] - (reward / scale)
										UpperCamelCase_  =	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
										UpperCamelCase_  =	torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

										episode_return += reward
										episode_length += 1

										if done:
															break

UpperCamelCase_  =	10_00





def 							_lowerCamelCase			(					lowerCamelCase_:  float		):



				'''simple docstring'''







				return x * x





def 							_lowerCamelCase			(					lowerCamelCase_:  Tuple				,	lowerCamelCase_:  float				,	lowerCamelCase_:  float				,	lowerCamelCase_:  int = 4		):



				'''simple docstring'''







				assert callable(
				    lowerCamelCase_		), f"""the function(object) passed should be callable your input : {function}"""
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""a should be float or integer your input : {a}"""
				assert isinstance(function(lowerCamelCase_		)				,	(float, int)		), (
				    "the function should return integer or float return type of your function, "
				    f"""{type(lowerCamelCase_		)}"""
				)
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""b should be float or integer your input : {b}"""
				assert (
				    isinstance(lowerCamelCase_				,	lowerCamelCase_		) and precision > 0
				), f"""precision should be positive integer your input : {precision}"""

				# just applying the formula of simpson for approximate integration written in
				# mentioned article in first comment of this file and above this function

				A     :  Tuple   =				(b - a) / N_STEPS
				A     :  List[str]   =				function(lowerCamelCase_		) + function(lowerCamelCase_		)

				for i in range(1				,	lowerCamelCase_		):
								A     :  int   =				a + h * i
								result += function(lowerCamelCase_		) * (4 if i % 2 else 2)

				result *= h / 3
				return round(lowerCamelCase_				,	lowerCamelCase_		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()