

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


_snake_case         =  {
    '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__":
    _snake_case         =  'hopper-medium-v2'
    _snake_case         =  gym.make(env_name)

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

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

            # execute action in environment
            _snake_case  ,     _snake_case  ,     _snake_case  ,     _snake_case         =  env.step(denorm_actions)
            _snake_case         =  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())

            _snake_case         =  next_observation
    except KeyboardInterrupt:
        pass

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









'''simple docstring'''
from __future__ import annotations







def  _A		(     snake_case		,							snake_case = None		,							snake_case = None					)      ->     None:
   if start is None:
      _lowercase      :				Dict      					=       0

   if end is None:
      _lowercase      :				List[Any]      					=       len(snake_case					) - 1

   if start >= end:
      return

   _lowercase      :				int      					=       (start + end) // 2

   slowsort(snake_case		,							snake_case		,							snake_case					)
   slowsort(snake_case		,							mid + 1		,							snake_case					)

   if sequence[end] < sequence[mid]:
      _lowercase					,				_lowercase      :				Any      					=       sequence[mid], sequence[end]

   slowsort(snake_case		,							snake_case		,							end - 1					)


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

    testmod()






