


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 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Any   ,    lowercase__	:Tuple   ,    lowercase__	:Tuple   ,    lowercase__	:Optional[Any]   ,    lowercase__	:Optional[int]   ,    lowercase__	:int					)							->	List[Any]:
						# we don't care about the past rewards in this model

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

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

												# pad all tokens to sequence length
												__magic_name__					:				List[Any]				=   torch.cat(
												    [torch.zeros(model.config.max_length - states.shape[1]					), torch.ones(states.shape[1]					)]					)
												__magic_name__					:				int				=   attention_mask.to(dtype=torch.long   ,    device=states.device					).reshape(1   ,    -1					)
												__magic_name__					:				Union[str, Any]				=   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					)
												__magic_name__					:				Tuple				=   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					)
												__magic_name__					:				Tuple				=   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					)
												__magic_name__					:				Optional[Any]				=   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:
												__magic_name__					:				Optional[Any]				=   None

						__magic_name__					:				Dict				=   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

snake_case__  				=			gym.make('Hopper-v3')
snake_case__  				=			env.observation_space.shape[0]
snake_case__  				=			env.action_space.shape[0]
snake_case__  				=			1000
snake_case__  				=			"""cuda"""
snake_case__  				=			1000.0  # normalization for rewards/returns
snake_case__  				=			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case__  				=			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,
    ]
)
snake_case__  				=			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,
    ]
)
snake_case__  				=			torch.from_numpy(state_mean).to(device=device)
snake_case__  				=			torch.from_numpy(state_std).to(device=device)

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

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

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

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

						snake_case__  				=			env.step(action)

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

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






def       lowerCAmelCase			(		lowerCAmelCase_			)->			int:
				lowerCAmelCase_			:   Optional[int]     					=					len(lowerCAmelCase_			)
				if n in (0, 1):
								return 0
				return sum(query * (n - i - 1) for i, query in enumerate(sorted(lowerCAmelCase_			)			)			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()