



"""simple docstring"""





import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowerCamelCase       	=						{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
  lowerCamelCase       	=						"""hopper-medium-v2"""
  lowerCamelCase       	=						gym.make(env_name)

  lowerCamelCase       	=						ValueGuidedRLPipeline.from_pretrained(
      """bglick13/hopper-medium-v2-value-function-hor32""",
      env=env,
  )

  env.seed(0)
  lowerCamelCase       	=						env.reset()
  lowerCamelCase       	=						0
  lowerCamelCase       	=						0
  lowerCamelCase       	=						1_000
  lowerCamelCase       	=						[obs.copy()]
  try:
    for t in tqdm.tqdm(range(T)):
      # call the policy
      lowerCamelCase       	=						pipeline(obs, planning_horizon=32)

      # execute action in environment
      lowerCamelCase	,						lowerCamelCase	,						lowerCamelCase	,						lowerCamelCase       	=						env.step(denorm_actions)
      lowerCamelCase       	=						env.get_normalized_score(total_reward)

      # update return
      total_reward += reward
      total_score += score
      print(
          F"Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"
          F" {total_score}"
      )

      # save observations for rendering
      rollout.append(next_observation.copy())

      lowerCamelCase       	=						next_observation
  except KeyboardInterrupt:
    pass

  print(F"Total reward: {total_reward}")









"""simple docstring"""





from collections.abc import Iterable
from typing import Generic, TypeVar

lowerCamelCase       	=						TypeVar("""_T""")
class 				lowercase__       (   Generic[_T]				):







       '''simple docstring'''


       def __init__(							self      :  int		,    _UpperCAmelCase      :  Iterable[_T] | None = None   )			->						None:


             '''simple docstring'''




             UpperCAmelCase_      			=							list(iterable or []   )
             UpperCAmelCase_      			=							[]



       def __len__(							self      :  Optional[int]   )			->						int:


             '''simple docstring'''




             return len(self._stacka   ) + len(self._stacka   )



       def __repr__(							self      :  Optional[Any]   )			->						str:


             '''simple docstring'''




             return F"""Queue({tuple(self._stacka[::-1] + self._stacka   )})"""



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  _T   )			->						None:


             '''simple docstring'''




             self._stacka.append(_UpperCAmelCase   )







       def 					lowercase__    (							self      :  Dict   )			->						_T:


             '''simple docstring'''




             UpperCAmelCase_      			=							self._stacka.pop
             UpperCAmelCase_      			=							self._stacka.append

             if not self._stacka:
                   while self._stacka:
                         stacka_append(stacka_pop()   )

             if not self._stacka:
                   raise IndexError("Queue is empty"   )
             return self._stacka.pop()


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

  testmod()




