



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


SCREAMING_SNAKE_CASE__   			=   {
    '''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__":
							SCREAMING_SNAKE_CASE__   			=   '''hopper-medium-v2'''
							SCREAMING_SNAKE_CASE__   			=   gym.make(env_name)

							SCREAMING_SNAKE_CASE__   			=   ValueGuidedRLPipeline.from_pretrained(
							    '''bglick13/hopper-medium-v2-value-function-hor32''',
							    env=env,
							)

							env.seed(0)
							SCREAMING_SNAKE_CASE__   			=   env.reset()
							SCREAMING_SNAKE_CASE__   			=   0
							SCREAMING_SNAKE_CASE__   			=   0
							SCREAMING_SNAKE_CASE__   			=   1_0_0_0
							SCREAMING_SNAKE_CASE__   			=   [obs.copy()]
							try:
														for t in tqdm.tqdm(range(T)):
																					# call the policy
																					SCREAMING_SNAKE_CASE__   			=   pipeline(obs, planning_horizon=3_2)

																					# execute action in environment
																					SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__   			=   env.step(denorm_actions)
																					SCREAMING_SNAKE_CASE__   			=   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())

																					SCREAMING_SNAKE_CASE__   			=   next_observation
							except KeyboardInterrupt:
														pass

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









SCREAMING_SNAKE_CASE__   			=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   list[str]:
			A__	  =     set()
			# keep track of all the paths to be checked
			A__	  =     [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						A__	  =     queue.pop(0       )
						# get the last node from the path
						A__	  =     path[-1]
						if node not in explored:
									A__	  =     graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												A__	  =     list(__UpperCamelCase       )
												new_path.append(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(__UpperCamelCase       )

    # in case there's no path between the 2 nodes
			return []


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			A__	  =     [start]
			A__	  =     set(__UpperCamelCase       )
			# Keep tab on distances from `start` node.
			A__	  =     {start: 0, target: -1}
			while queue:
						A__	  =     queue.pop(0       )
						if node == target:
									A__	  =     (
									    dist[node] if dist[target] == -1 else min(dist[target]			,						dist[node]       )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												A__	  =     dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4




