


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							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Union[str, Any]:
						# we don't care about the past rewards in this model

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

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

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

						_a ,	_a ,	_a     =			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

lowercase_								=    gym.make('Hopper-v3')
lowercase_								=    env.observation_space.shape[0]
lowercase_								=    env.action_space.shape[0]
lowercase_								=    10_00
lowercase_								=    'cuda'
lowercase_								=    1000.0  # normalization for rewards/returns
lowercase_								=    36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
lowercase_								=    np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
lowercase_								=    np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
lowercase_								=    torch.from_numpy(state_mean).to(device=device)
lowercase_								=    torch.from_numpy(state_std).to(device=device)

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

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

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

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

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

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

										lowercase_								=    target_return[0, -1] - (reward / scale)
										lowercase_								=    torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
										lowercase_								=    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 math import pi


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
						return degree / (180 / pi)


if __name__ == "__main__":
		from doctest import testmod

		testmod()


