

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								=			1_000
     __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					,     __UpperCAmelCase					,     __UpperCAmelCase					,     __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}')





def      snake_case_				()      ->     List[Any]:
   for n in range(1 ,       1_0_0_0_0_0_0					):
      yield n * (n + 1) // 2





def      snake_case_				(__A					:						Dict					)      ->     Tuple:
   __lowerCAmelCase  :      Optional[int]    =						1
   __lowerCAmelCase  :      Optional[int]    =						2
   while i * i <= n:
      __lowerCAmelCase  :      Optional[int]    =						0
      while n % i == 0:
         n //= i
         multiplicity += 1
      divisors_count *= multiplicity + 1
      i += 1
   if n > 1:
      divisors_count *= 2
   return divisors_count





def      snake_case_				()      ->     Dict:
   return next(i for i in triangle_number_generator() if count_divisors(__A					) > 5_0_0					)


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


