





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__							(    UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							int   ,				UpperCamelCase__:							Optional[Any]   ,				UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							Optional[int]   ,				UpperCamelCase__:							Union[str, Any]      ):
		# we don't care about the past rewards in this model

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

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

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

		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= 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							=     1000
_lowerCamelCase							=     'cuda'
_lowerCamelCase							=     1000.0  # normalization for rewards/returns
_lowerCamelCase							=     3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCamelCase							=     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,
    ]
)
_lowerCamelCase							=     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,
    ]
)
_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






from typing import Callable






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Callable      ):

		def wrapper(UpperCamelCase__:							List[Any]      ):
				SCREAMING_SNAKE_CASE__								= function_with_docstring.__doc__
				return target_function

		return wrapper