


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


lowerCAmelCase__  :Tuple   			=							{
    '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__  :List[str]   			=							'hopper-medium-v2'
		lowerCAmelCase__  :Union[str, Any]   			=							gym.make(env_name)

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

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

						# execute action in environment
						lowerCAmelCase__  :Tuple   			=							env.step(denorm_actions)
						lowerCAmelCase__  :Optional[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__  :List[str]   			=							next_observation
		except KeyboardInterrupt:
				pass

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


def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				__A   			= [0] * len(a_						)
				__A   			= []
				__A   			= [1] * len(a_						)

				for values in graph.values():
								for i in values:
												indegree[i] += 1

				for i in range(len(a_						)						):
								if indegree[i] == 0:
												queue.append(a_						)

				while queue:
								__A   			= queue.pop(0						)
								for x in graph[vertex]:
												indegree[x] -= 1

												if long_dist[vertex] + 1 > long_dist[x]:
																__A   			= long_dist[vertex] + 1

												if indegree[x] == 0:
																queue.append(a_						)

				print(max(a_						)						)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE							:List[Any]							 =				{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
