


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


UpperCAmelCase     :   Optional[int]    =					{
    """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__":
   UpperCAmelCase     :   Union[str, Any]    =					"""hopper-medium-v2"""
   UpperCAmelCase     :   int    =					gym.make(env_name)

   UpperCAmelCase     :   str    =					ValueGuidedRLPipeline.from_pretrained(
       """bglick13/hopper-medium-v2-value-function-hor32""",
       env=env,
   )

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

         # execute action in environment
         UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase     :   Any    =					env.step(denorm_actions)
         UpperCAmelCase     :   List[str]    =					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[str]    =					next_observation
   except KeyboardInterrupt:
      pass

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



from collections import namedtuple

UpperCAmelCase    =					namedtuple("""from_to""", """from_ to""")

UpperCAmelCase    =					{
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 1000),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00_454, 264.172),
    """cubicyard""": from_to(0.76_455, 1.30_795),
    """cubicfoot""": from_to(0.028, 35.3_147),
    """cup""": from_to(0.000_236_588, 4_226.75),
}






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      if from_type not in METRIC_CONVERSION:
            raise ValueError(
                f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
                + ''', '''.join(SCREAMING_SNAKE_CASE      )      )
      if to_type not in METRIC_CONVERSION:
            raise ValueError(
                f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
                + ''', '''.join(SCREAMING_SNAKE_CASE      )      )
      return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
   import doctest

   doctest.testmod()