



def       A_				(   _UpperCAmelCase   ):  # noqa: E741
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	len(__lowerCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	0
						SCREAMING_SNAKE_CASE_:			Tuple  		=	[0] * n
						SCREAMING_SNAKE_CASE_:			str  		=	[False] * n
						SCREAMING_SNAKE_CASE_:			Tuple  		=	[False] * n

						def dfs(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
												if parent == root:
																		out_edge_count += 1
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	True
												SCREAMING_SNAKE_CASE_:			int  		=	at

												for to in l[at]:
																		if to == parent:
																								pass
																		elif not visited[to]:
																								SCREAMING_SNAKE_CASE_:			List[Any]  		=	dfs(__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase   )
																								SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	min(low[at]    ,     low[to]   )

																								# AP found via bridge
																								if at < low[to]:
																														SCREAMING_SNAKE_CASE_:			List[str]  		=	True
																								# AP found via cycle
																								if at == low[to]:
																														SCREAMING_SNAKE_CASE_:			Optional[int]  		=	True
																		else:
																								SCREAMING_SNAKE_CASE_:			Optional[int]  		=	min(low[at]    ,     __lowerCAmelCase   )
												return out_edge_count

						for i in range(__lowerCAmelCase   ):
												if not visited[i]:
																		SCREAMING_SNAKE_CASE_:			int  		=	0
																		SCREAMING_SNAKE_CASE_:			Optional[int]  		=	dfs(__lowerCAmelCase    ,     __lowerCAmelCase    ,     -1    ,     __lowerCAmelCase   )
																		SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	out_edge_count > 1

						for x in range(len(__lowerCAmelCase   )   ):
												if is_art[x] is True:
																		print(__lowerCAmelCase   )


# Adjacency list of graph
lowerCAmelCase							:  Tuple            =						{
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)









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


lowerCAmelCase							:  Tuple            =						{
    """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							:  Optional[int]            =						"""hopper-medium-v2"""
						lowerCAmelCase							:  Optional[int]            =						gym.make(env_name)

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

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

																		# execute action in environment
																		lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase							:  Dict            =						env.step(denorm_actions)
																		lowerCAmelCase							:  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())

																		lowerCAmelCase							:  Tuple            =						next_observation
						except KeyboardInterrupt:
												pass

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






