



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


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

				__snake_case     :Optional[int]			       =ValueGuidedRLPipeline.from_pretrained(
				    'bglick13/hopper-medium-v2-value-function-hor32',
				    env=env,
				)

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

												# execute action in environment
												__snake_case       ,				__snake_case       ,				__snake_case       ,				__snake_case     :Union[str, Any]			       =env.step(denorm_actions)
												__snake_case     :Dict			       =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())

												__snake_case     :Dict			       =next_observation
				except KeyboardInterrupt:
								pass

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




'''simple docstring'''
def A__       (	UpperCAmelCase_ = 6_0_0_8_5_1_4_7_5_1_4_3				):

						try:
												_UpperCamelCase   :						str												=  int(UpperCAmelCase_				)
						except (TypeError, ValueError):
												raise TypeError('Parameter n must be int or castable to int.'				)
						if n <= 0:
												raise ValueError('Parameter n must be greater than or equal to one.'				)
						_UpperCamelCase   :						int												=  1
						_UpperCamelCase   :						Optional[Any]												=  2
						while i * i <= n:
												while n % i == 0:
																		_UpperCamelCase   :						Union[str, Any]												=  i
																		n //= i
												i += 1
						if n > 1:
												_UpperCamelCase   :						Dict												=  n
						return int(UpperCAmelCase_				)


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


