


'''simple docstring'''







from bisect import bisect
from itertools import accumulate




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       Any:
     """simple docstring"""




     __a   			= sorted(zip(UpperCamelCase__					,	UpperCamelCase__		)					,	key=lambda __SCREAMING_SNAKE_CASE		: x[0] / x[1]					,	reverse=UpperCamelCase__		)
     __a			,    __a   			= [i[0] for i in r], [i[1] for i in r]
     __a   			= list(accumulate(UpperCamelCase__		)		)
     __a   			= bisect(UpperCamelCase__					,	UpperCamelCase__		)
     return (
         0
         if k == 0
         else sum(vl[:k]		) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
         if k != n
         else sum(vl[:k]		)
     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()









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


_UpperCAmelCase						:  List[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__":
					_UpperCAmelCase						:  List[str]									=			"""hopper-medium-v2"""
					_UpperCAmelCase						:  Any									=			gym.make(env_name)

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

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

															# execute action in environment
															_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase						:  str									=			env.step(denorm_actions)
															_UpperCAmelCase						:  Tuple									=			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						:  Dict									=			next_observation
					except KeyboardInterrupt:
										pass

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




