





"""simple docstring"""
import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowerCamelCase_       =		{
    "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__":
  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_0_0_0
  lowerCamelCase_       =		[obs.copy()]
  try:
    for t in tqdm.tqdm(range(T)):
      # call the policy
      lowerCamelCase_       =		pipeline(obs, planning_horizon=3_2)

      # 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"""
def 					__lowerCamelCase       (							a_					:		str							)      ->				list:
      return [
          txt[:a] + txt[a].upper() + txt[a + 1 :]
          for a in range(len(a_							)							)
          if txt[a].isalpha()
      ]


if __name__ == "__main__":
  __import__("doctest").testmod()