


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


__UpperCAmelCase : str 			=				{
    "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__":
  __UpperCAmelCase : Optional[int] 			=				"hopper-medium-v2"
  __UpperCAmelCase : Optional[int] 			=				gym.make(env_name)

  __UpperCAmelCase : Optional[Any] 			=				ValueGuidedRLPipeline.from_pretrained(
      "bglick13/hopper-medium-v2-value-function-hor32",
      env=env,
  )

  env.seed(0)
  __UpperCAmelCase : Tuple 			=				env.reset()
  __UpperCAmelCase : Optional[int] 			=				0
  __UpperCAmelCase : List[str] 			=				0
  __UpperCAmelCase : List[str] 			=				1000
  __UpperCAmelCase : Any 			=				[obs.copy()]
  try:
    for t in tqdm.tqdm(range(T)):
      # call the policy
      __UpperCAmelCase : Optional[int] 			=				pipeline(obs, planning_horizon=32)

      # execute action in environment
      __UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase : List[str] 			=				env.step(denorm_actions)
      __UpperCAmelCase : Dict 			=				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())

      __UpperCAmelCase : str 			=				next_observation
  except KeyboardInterrupt:
    pass

  print(f'''Total reward: {total_reward}''')





from .imports import is_tqdm_available


if is_tqdm_available():
  from tqdm.auto import tqdm as _tqdm

from ..state import PartialState


def      a	(							SCREAMING_SNAKE_CASE_      :		bool = True							,			*SCREAMING_SNAKE_CASE_      :		List[str]							,			**SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





       if not is_tqdm_available():
              raise ImportError('''Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.'''       )
       UpperCamelCase    :	int   	=		False
       if main_process_only:
              UpperCamelCase    :	int   	=		PartialState().local_process_index == 0
       return _tqdm(*SCREAMING_SNAKE_CASE_							,			**SCREAMING_SNAKE_CASE_							,			disable=SCREAMING_SNAKE_CASE_       )

