code: 
  
  
  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
  
  
  
style_context: 
  
  
  from math import pi
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
  						return degree / (180 / pi)
  
  
  if __name__ == "__main__":
  		from doctest import testmod
  
  		testmod()
  
  
  