


'''simple docstring'''



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


A__						:   Optional[Any]				=    {
    """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__						:   Optional[Any]				=    """hopper-medium-v2"""
      A__						:   str				=    gym.make(env_name)

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

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

                  # execute action in environment
                  A__		,   A__		,   A__		,   A__						:   Dict				=    env.step(denorm_actions)
                  A__						:   Optional[int]				=    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__						:   List[Any]				=    next_observation
      except KeyboardInterrupt:
            pass

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










'''simple docstring'''



A__						:   dict[tuple[int, int, int], int]				=    {}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int				)	->						int:

     # if we are absent twice, or late 3 consecutive days,
     # no further prize strings are possible
     if late == 3 or absent == 2:
          return 0

     # if we have no days left, and have not failed any other rules,
     # we have a prize string
     if days == 0:
          return 1

     # No easy solution, so now we need to do the recursive calculation

     # First, check if the combination is already in the cache, and
     # if yes, return the stored value from there since we already
     # know the number of possible prize strings from this point on
     __lowerCamelCase  :      List[Any]														=						(days, absent, late)
     if key in cache:
          return cache[key]

     # now we calculate the three possible ways that can unfold from
     # this point on, depending on our attendance today

     # 1) if we are late (but not absent), the "absent" counter stays as
     # it is, but the "late" counter increases by one
     __lowerCamelCase  :      Tuple														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       late + 1				)

     # 2) if we are absent, the "absent" counter increases by 1, and the
     # "late" counter resets to 0
     __lowerCamelCase  :      int														=						_calculate(days - 1 ,       absent + 1 ,       0				)

     # 3) if we are on time, this resets the "late" counter and keeps the
     # absent counter
     __lowerCamelCase  :      List[Any]														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       0				)

     __lowerCamelCase  :      Optional[int]														=						state_late + state_absent + state_ontime

     __lowerCamelCase  :      Union[str, Any]														=						prizestrings
     return prizestrings



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 30				)	->						int:
     return _calculate(UpperCAmelCase_ ,       absent=0 ,       late=0				)


if __name__ == "__main__":
      print(solution())






