





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


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

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

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

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

      _snake_case           =			next_observation
  except KeyboardInterrupt:
    pass

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













from itertools import count


def _A       (   __magic_name__ = 50	):
   lowercase__			       =  [1] * min_block_length

   for n in count(__magic_name__	):
      fill_count_functions.append(1	)

      for block_length in range(__magic_name__  , n + 1	):
         for block_start in range(n - block_length	):
            fill_count_functions[n] += fill_count_functions[
                n - block_start - block_length - 1
            ]

         fill_count_functions[n] += 1

      if fill_count_functions[n] > 100_0000:
         break

   return n


if __name__ == "__main__":
  print(F"""{solution() = }""")






