
"""simple docstring"""




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


UpperCAmelCase__      :    List[str]    							= {
    'n_samples': 6_4,
    'horizon': 3_2,
    'num_inference_steps': 2_0,
    '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__      :    Any    							= 'hopper-medium-v2'
					UpperCAmelCase__      :    Union[str, Any]    							= gym.make(env_name)

					UpperCAmelCase__      :    List[str]    							= ValueGuidedRLPipeline.from_pretrained(
					    'bglick13/hopper-medium-v2-value-function-hor32',
					    env=env,
					)

					env.seed(0)
					UpperCAmelCase__      :    str    							= env.reset()
					UpperCAmelCase__      :    List[Any]    							= 0
					UpperCAmelCase__      :    Optional[int]    							= 0
					UpperCAmelCase__      :    Optional[Any]    							= 1_0_0_0
					UpperCAmelCase__      :    Union[str, Any]    							= [obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															UpperCAmelCase__      :    Optional[Any]    							= pipeline(obs, planning_horizon=3_2)

															# execute action in environment
															UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__      :    Any    							= 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__      :    List[Any]    							= next_observation
					except KeyboardInterrupt:
										pass

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






def a__    (       A_					):






  '''simple docstring'''





  if not isinstance(A_,     A_					):
    raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""					)
  if len(A_					) == 0:
    raise ValueError("""Input list must be a non empty list"""					)
  if len(A_					) == 1:
    return True
  __magic_name__				     =       series[1] - series[0]
  for index in range(len(A_					) - 1					):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True

def a__    (       A_					):






  '''simple docstring'''





  if not isinstance(A_,     A_					):
    raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""					)
  if len(A_					) == 0:
    raise ValueError("""Input list must be a non empty list"""					)
  __magic_name__				     =       0
  for val in series:
    answer += val
  return answer / len(A_					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

