




"""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 							lowercase   (snake_case__				:	Tuple ,    snake_case__				:	List[Any] ,    snake_case__				:	Optional[int] ,    snake_case__				:	Union[str, Any] ,    snake_case__				:	Optional[int] ,    snake_case__				:	Optional[int]	)       ->					int:


				'''simple docstring'''






				lowerCAmelCase		=			states.reshape(1 ,    -1 ,    model.config.state_dim	)
				lowerCAmelCase		=			actions.reshape(1 ,    -1 ,    model.config.act_dim	)
				lowerCAmelCase		=			returns_to_go.reshape(1 ,    -1 ,    1	)
				lowerCAmelCase		=			timesteps.reshape(1 ,    -1	)

				if model.config.max_length is not None:
								lowerCAmelCase		=			states[:, -model.config.max_length :]
								lowerCAmelCase		=			actions[:, -model.config.max_length :]
								lowerCAmelCase		=			returns_to_go[:, -model.config.max_length :]
								lowerCAmelCase		=			timesteps[:, -model.config.max_length :]

								# pad all tokens to sequence length
								lowerCAmelCase		=			torch.cat(
								    [torch.zeros(model.config.max_length - states.shape[1]	), torch.ones(states.shape[1]	)]	)
								lowerCAmelCase		=			attention_mask.to(dtype=torch.long ,    device=states.device	).reshape(1 ,    -1	)
								lowerCAmelCase		=			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	)
								lowerCAmelCase		=			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	)
								lowerCAmelCase		=			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	)
								lowerCAmelCase		=			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:
								lowerCAmelCase		=			None

				lowerCAmelCase	,  lowerCAmelCase	,  lowerCAmelCase		=			model(
				    states=snake_case__ ,    actions=snake_case__ ,    rewards=snake_case__ ,    returns_to_go=snake_case__ ,    timesteps=snake_case__ ,    attention_mask=snake_case__ ,    return_dict=snake_case__ ,    )

				return action_preds[0, -1]


# build the environment

a								=							gym.make('Hopper-v3')
a								=							env.observation_space.shape[0]
a								=							env.action_space.shape[0]
a								=							1_0_0_0
a								=							'cuda'
a								=							1000.0  # normalization for rewards/returns
a								=							3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a								=							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,
    ]
)
a								=							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,
    ]
)
a								=							torch.from_numpy(state_mean).to(device=device)
a								=							torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
		a				,    a								=							0, 0
		a								=							env.reset()
		a								=							torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
		a								=							torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
		a								=							torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
		a								=							torch.zeros(0, device=device, dtype=torch.floataa)

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

				a								=							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),
				)
				a								=							action
				a								=							action.detach().cpu().numpy()

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

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

				a								=							target_return[0, -1] - (reward / scale)
				a								=							torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
				a								=							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











"""simple docstring"""







def 							lowercase   (snake_case__				:	list	)       ->					bool:


				'''simple docstring'''






				if not isinstance(snake_case__ ,    snake_case__	):
								raise ValueError("""Input series is not valid, valid series - [2, 4, 8]"""	)
				if len(snake_case__	) == 0:
								raise ValueError("""Input list must be a non empty list"""	)
				if len(snake_case__	) == 1:
								return True
				try:
								lowerCAmelCase		=			series[1] / series[0]
								for index in range(len(snake_case__	) - 1	):
												if series[index + 1] / series[index] != common_ratio:
																return False
				except ZeroDivisionError:
								return False
				return True





def 							lowercase   (snake_case__				:	list	)       ->					float:


				'''simple docstring'''






				if not isinstance(snake_case__ ,    snake_case__	):
								raise ValueError("""Input series is not valid, valid series - [2, 4, 8]"""	)
				if len(snake_case__	) == 0:
								raise ValueError("""Input list must be a non empty list"""	)
				lowerCAmelCase		=			1
				for value in series:
								answer *= value
				return pow(snake_case__ ,    1 / len(snake_case__	)	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





