



def 			UpperCamelCase(  __UpperCamelCase							:		list[int]     ,__UpperCamelCase							:		list[int]     ,__UpperCamelCase							:		int ):
		return not any(
		    neighbour == 1 and colored_vertices[i] == color
		    for i, neighbour in enumerate(__UpperCamelCase ) )


def 			UpperCamelCase(  __UpperCamelCase							:		list[list[int]]     ,__UpperCamelCase							:		int     ,__UpperCamelCase							:		list[int]     ,__UpperCamelCase							:		int ):

		# Base Case
		if index == len(__UpperCamelCase ):
				return True

		# Recursive Step
		for i in range(__UpperCamelCase ):
				if valid_coloring(graph[index]     ,__UpperCamelCase     ,__UpperCamelCase ):
						# Color current vertex
						lowerCAmelCase_			:     Dict									= i
						# Validate coloring
						if util_color(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,index + 1 ):
								return True
						# Backtrack
						lowerCAmelCase_			:     Optional[Any]									= -1
		return False





def 			UpperCamelCase(  __UpperCamelCase							:		list[list[int]]     ,__UpperCamelCase							:		int ):
		lowerCAmelCase_			:     Dict									= [-1] * len(__UpperCamelCase )

		if util_color(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,0 ):
				return colored_vertices

		return []










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


A__  :  Union[str, Any]								=					{
    '''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__":
						A__  :  Optional[int]								=					'''hopper-medium-v2'''
						A__  :  int								=					gym.make(env_name)

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

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

																		# execute action in environment
																		A__					,     A__					,     A__					,     A__  :  str								=					env.step(denorm_actions)
																		A__  :  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())

																		A__  :  List[str]								=					next_observation
						except KeyboardInterrupt:
												pass

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





