





"""simple docstring"""


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


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

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

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

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

            lowerCamelCase__				  =   next_observation
    except KeyboardInterrupt:
        pass

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







"""simple docstring"""


def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       set()
      # edges = list of graph's edges
      _UpperCamelCase      :	Union[str, Any]						=       get_edges(lowercase_			)

      # While there are still elements in edges list, take an arbitrary edge
      # (from_node, to_node) and add his extremity to chosen_vertices and then
      # remove all arcs adjacent to the from_node and to_node
      while edges:
            _UpperCamelCase,  _UpperCamelCase      :	str						=       edges.pop()
            chosen_vertices.add(lowercase_			)
            chosen_vertices.add(lowercase_			)
            for edge in edges.copy():
                  if from_node in edge or to_node in edge:
                        edges.discard(lowercase_			)
      return chosen_vertices




def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       set()
      for from_node, to_nodes in graph.items():
            for to_node in to_nodes:
                  edges.add((from_node, to_node)			)
      return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
