




"""simple docstring"""







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


SCREAMING_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__":
  SCREAMING_SNAKE_CASE_	     =       'hopper-medium-v2'
  SCREAMING_SNAKE_CASE_	     =       gym.make(env_name)

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

  env.seed(0)
  SCREAMING_SNAKE_CASE_	     =       env.reset()
  SCREAMING_SNAKE_CASE_	     =       0
  SCREAMING_SNAKE_CASE_	     =       0
  SCREAMING_SNAKE_CASE_	     =       1000
  SCREAMING_SNAKE_CASE_	     =       [obs.copy()]
  try:
    for t in tqdm.tqdm(range(T)):
      # call the policy
      SCREAMING_SNAKE_CASE_	     =       pipeline(obs, planning_horizon=32)

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

      SCREAMING_SNAKE_CASE_	     =       next_observation
  except KeyboardInterrupt:
    pass

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


'''simple docstring'''

import os
from collections.abc import Iterator

def 					__UpperCamelCase				(						lowercase__    :	str = "."			):






     '''simple docstring'''





     for dir_path, dir_names, filenames in os.walk(lowercase__			):
          __lowercase      		=[d for d in dir_names if d != 'scripts' and d[0] not in '._']
          for filename in filenames:
               if filename == "__init__.py":
                    continue
               if os.path.splitext(lowercase__			)[1] in (".py", ".ipynb"):
                    yield os.path.join(lowercase__,       lowercase__			).lstrip('./'			)

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     return F'''{i * "  "}*''' if i else "\n##"

def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=old_path.split(os.sep			)
     for i, new_part in enumerate(new_path.split(os.sep			)			):
          if (i + 1 > len(lowercase__			) or old_parts[i] != new_part) and new_part:
               print(F'''{md_prefix(lowercase__			)} {new_part.replace("_",       " "			).title()}'''			)
     return new_path

def 					__UpperCamelCase				(						lowercase__    :	str = "."			):






     '''simple docstring'''





     __lowercase      		=''
     for filepath in sorted(good_file_paths(lowercase__			)			):
          __lowercase		,  __lowercase      		=os.path.split(lowercase__			)
          if filepath != old_path:
               __lowercase      		=print_path(lowercase__,       lowercase__			)
          __lowercase      		=(filepath.count(os.sep			) + 1) if filepath else 0
          __lowercase      		=F'''{filepath}/{filename}'''.replace(' ',       '%20'			)
          __lowercase      		=os.path.splitext(filename.replace('_',       ' '			).title()			)[0]
          print(F'''{md_prefix(lowercase__			)} [{filename}]({url})'''			)


if __name__ == "__main__":
       print_directory_md('''.''')


