





"""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__						= 1000
  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"""


import warnings


warnings.warn(
    "memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
    "`from accelerate import find_executable_batch_size` to avoid this warning.",
    FutureWarning,
)
