

"""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 		A_	(   __UpperCamelCase     :				List[Any]       ,	__UpperCamelCase     :				int       ,	__UpperCamelCase     :				str       ,	__UpperCamelCase     :				List[Any]       ,	__UpperCamelCase     :				Optional[int]       ,	__UpperCamelCase     :				Tuple				):
		# we don't care about the past rewards in this model

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

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

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

		lowercase					,  lowercase					,  lowercase										=							model(
		    states=__UpperCamelCase       ,	actions=__UpperCamelCase       ,	rewards=__UpperCamelCase       ,	returns_to_go=__UpperCamelCase       ,	timesteps=__UpperCamelCase       ,	attention_mask=__UpperCamelCase       ,	return_dict=__UpperCamelCase       ,	)

		return action_preds[0, -1]


# build the environment

__lowerCAmelCase				      =   gym.make('''Hopper-v3''')
__lowerCAmelCase				      =   env.observation_space.shape[0]
__lowerCAmelCase				      =   env.action_space.shape[0]
__lowerCAmelCase				      =   1_000
__lowerCAmelCase				      =   '''cuda'''
__lowerCAmelCase				      =   1000.0  # normalization for rewards/returns
__lowerCAmelCase				      =   3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__lowerCAmelCase				      =   np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
__lowerCAmelCase				      =   np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
__lowerCAmelCase				      =   torch.from_numpy(state_mean).to(device=device)
__lowerCAmelCase				      =   torch.from_numpy(state_std).to(device=device)

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

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

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

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

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

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

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






from typing import Callable
def 		A_	(   __UpperCamelCase     :				Callable				):

		def wrapper(__UpperCamelCase     :				Optional[Any]				):
				lowercase										=							function_with_docstring.__doc__
				return target_function

		return wrapper