





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


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

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

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

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

															UpperCAmelCase_ 							=       next_observation
					except KeyboardInterrupt:
										pass

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








import random





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    float    ,      UpperCamelCase__     :    bool = False  )     ->	dict:



					'''simple docstring'''




					_snake_case													=			{i: [] for i in range(UpperCamelCase__  )}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(UpperCamelCase__  )
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(UpperCamelCase__  ):
										for j in range(i + 1    ,      UpperCamelCase__  ):
															if random.random() < probability:
																				graph[i].append(UpperCamelCase__  )
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(UpperCamelCase__  )
					return graph





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	dict:



					'''simple docstring'''




					return {
					    i: [j for j in range(UpperCamelCase__  ) if i != j] for i in range(UpperCamelCase__  )
					}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



