






"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   list[int]  )    ->     bool:

							# 1. Validate that path exists between current and next vertices
							if graph[path[curr_ind - 1]][next_ver] == 0:
														return False

							# 2. Validate that next vertex is not already in path
							return not any(vertex == next_ver for vertex in path  )

def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   list[int]    ,						lowerCamelCase__     :   int  )    ->     bool:

							# Base Case
							if curr_ind == len(lowerCamelCase__  ):
														# return whether path exists between current and starting vertices
														return graph[path[curr_ind - 1]][path[0]] == 1

							# Recursive Step
							for next_ver in range(0    ,						len(lowerCamelCase__  )  ):
														if valid_connection(lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__  ):
																					# Insert current vertex  into path as next transition
																					lowerCamelCase_	:							Tuple									=next_ver
																					# Validate created path
																					if util_hamilton_cycle(lowerCamelCase__    ,						lowerCamelCase__    ,						curr_ind + 1  ):
																												return True
																					# Backtrack
																					lowerCamelCase_	:							int									=-1
							return False

def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   int = 0  )    ->     list[int]:
							lowerCamelCase_	:							Optional[Any]									=[-1] * (len(lowerCamelCase__  ) + 1)
							# initialize start and end of path with starting index
							lowerCamelCase_	:							Optional[int]									=start_index
							# evaluate and if we find answer return path either return empty array
							return path if util_hamilton_cycle(lowerCamelCase__    ,						lowerCamelCase__    ,						1  ) else []











"""simple docstring"""



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


A__	:    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__":
						A__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

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

						env.seed(0)
						A__	:    Dict			 = env.reset()
						A__	:    Optional[int]			 = 0
						A__	:    str			 = 0
						A__	:    List[Any]			 = 1_000
						A__	:    Tuple			 = [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__	:    Any			 = env.step(denorm_actions)
																		A__	:    List[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())

																		A__	:    Optional[Any]			 = next_observation
						except KeyboardInterrupt:
												pass

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



