





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							,     _lowerCamelCase							,     _lowerCamelCase							,     _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}")













import requests
from bsa import BeautifulSoup




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  dict       )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__			,				params=SCREAMING_SNAKE_CASE__       ).content			,				"html.parser"       )
							SCREAMING_SNAKE_CASE__       :      str     =						soup.find("div"			,				attrs={"class": "gs_ri"}       )
							SCREAMING_SNAKE_CASE__       :      int     =						div.find("div"			,				attrs={"class": "gs_fl"}       ).find_all("a"       )
							return anchors[2].get_text()


if __name__ == "__main__":
		_lowerCamelCase :    Any  							= {
		    '''title''': (
		        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
		        '''capacitance, volumetric capacitance, and energy density'''
		    ),
		    '''journal''': '''Chem. Mater.''',
		    '''volume''': 3_0,
		    '''pages''': '''3979-3990''',
		    '''year''': 2_0_1_8,
		    '''hl''': '''en''',
		}
		print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))






