





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


_lowerCAmelCase							:					Any		    =		{
    "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							:					Dict		    =		"hopper-medium-v2"
 _lowerCAmelCase							:					int		    =		gym.make(env_name)

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

 env.seed(0)
 _lowerCAmelCase							:					List[str]		    =		env.reset()
 _lowerCAmelCase							:					Dict		    =		0
 _lowerCAmelCase							:					Optional[int]		    =		0
 _lowerCAmelCase							:					Any		    =		1_000
 _lowerCAmelCase							:					int		    =		[obs.copy()]
 try:
  for t in tqdm.tqdm(range(T)):
   # call the policy
   _lowerCAmelCase							:					Dict		    =		pipeline(obs, planning_horizon=32)

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

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







from __future__ import annotations

from collections import namedtuple





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				namedtuple('''result'''			,			'''name value''' )
			if (voltage, current, power).count(0 ) != 1:
						raise ValueError('''Only one argument must be 0''' )
			elif power < 0:
						raise ValueError(
						    '''Power cannot be negative in any electrical/electronics system''' )
			elif voltage == 0:
						return result('''voltage'''			,			power / current )
			elif current == 0:
						return result('''current'''			,			power / voltage )
			elif power == 0:
						return result('''power'''			,			float(round(abs(voltage * current )			,			2 ) ) )
			else:
						raise ValueError('''Exactly one argument must be 0''' )


if __name__ == "__main__":
				import doctest

				doctest.testmod()



