





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


_lowerCamelCase :    Any  							= {
    '''n_samples''': 6_4,
    '''horizon''': 3_2,
    '''num_inference_steps''': 2_0,
    '''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 :    Any  							= gym.make(env_name)

		_lowerCamelCase :    List[Any]  							= ValueGuidedRLPipeline.from_pretrained(
		    '''bglick13/hopper-medium-v2-value-function-hor32''',
		    env=env,
		)

		env.seed(0)
		_lowerCamelCase :    List[Any]  							= env.reset()
		_lowerCamelCase :    Tuple  							= 0
		_lowerCamelCase :    Any  							= 0
		_lowerCamelCase :    Optional[Any]  							= 1_0_0_0
		_lowerCamelCase :    Tuple  							= [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						_lowerCamelCase :    Tuple  							= pipeline(obs, planning_horizon=3_2)

						# execute action in environment
						_lowerCamelCase :    Dict  							= env.step(denorm_actions)
						_lowerCamelCase :    Union[str, 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 :    Optional[int]  							= next_observation
		except KeyboardInterrupt:
				pass

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













def _a (  SCREAMING_SNAKE_CASE__      :  int = 50_00_00_00       )	->    int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						set()
							SCREAMING_SNAKE_CASE__       :      Dict     =						int((limit - 24) ** (1 / 2)       )

							SCREAMING_SNAKE_CASE__       :      Any     =						set(range(3			,				prime_square_limit + 1			,				2       )       )
							primes.add(2       )
							for p in range(3			,				prime_square_limit + 1			,				2       ):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p			,				prime_square_limit + 1			,				SCREAMING_SNAKE_CASE__       )       )       )

							for primea in primes:
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						primea * primea
														for primea in primes:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						primea * primea * primea
																					if square + cube >= limit - 16:
																												break
																					for primea in primes:
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						primea * primea * primea * primea
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						square + cube + tetr
																												if total >= limit:
																																			break
																												ret.add(SCREAMING_SNAKE_CASE__       )

							return len(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
		print(f"{solution() = }")






