


"""simple docstring"""




import heapq
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							list[list]		 =						[]

 # for each node and his adjacency list add them and the rank of the node to queue
 # using heapq module the queue will be filled like a Priority Queue
 # heapq works with a min priority queue, so I used -1*len(v) to build it
 for key, value in graph.items():
  # O(log(n))
  heapq.heappush(SCREAMING_SNAKE_CASE__      ,	[-1 * len(SCREAMING_SNAKE_CASE__						), (key, value)]						)

 # chosen_vertices = set of chosen vertices
 __lowerCamelCase		:							List[Any]		 =						set()

 # while queue isn't empty and there are still edges
 #   (queue[0][0] is the rank of the node with max rank)
 while queue and queue[0][0] != 0:
  # extract vertex with max rank from queue and add it to chosen_vertices
  __lowerCamelCase		:							Optional[int]		 =						heapq.heappop(SCREAMING_SNAKE_CASE__						)[1][0]
  chosen_vertices.add(SCREAMING_SNAKE_CASE__						)

  # Remove all arcs adjacent to argmax
  for elem in queue:
   # if v haven't adjacent node, skip
   if elem[0] == 0:
    continue
   # if argmax is reachable from elem
   # remove argmax from elem's adjacent list and update his rank
   if argmax in elem[1][1]:
    __lowerCamelCase		:							List[Any]		 =						elem[1][1].index(SCREAMING_SNAKE_CASE__						)
    del elem[1][1][index]
    elem[0] += 1
        # re-order the queue
  heapq.heapify(SCREAMING_SNAKE_CASE__						)
 return chosen_vertices


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       lowercase_	   =     {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
       print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")





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


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

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

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

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

                     lowercase_	   =     next_observation
       except KeyboardInterrupt:
              pass

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

