






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


SCREAMING_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__":
  SCREAMING_SNAKE_CASE 							=						'hopper-medium-v2'
  SCREAMING_SNAKE_CASE 							=						gym.make(env_name)

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

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

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

      SCREAMING_SNAKE_CASE 							=						next_observation
  except KeyboardInterrupt:
    pass

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












import math

def      _lowerCamelCase	(						__A    :							int )      ->					int:

    if not isinstance(__A		,		__A ):
        _UpperCAmelCase				:      List[Any]			  =				f'''Input value of [number={number}] must be an integer'''
        raise TypeError(__A )

    if number < 1:
        _UpperCAmelCase				:      Optional[Any]			  =				f'''Input value of [number={number}] must be > 0'''
        raise ValueError(__A )
    elif number == 1:
        return 3
    elif number == 2:
        return 5
    else:
        _UpperCAmelCase				:      Any			  =				int(math.log(number // 3		,		2 ) ) + 2

        _UpperCAmelCase				:      Dict			  =				[3, 5]
        _UpperCAmelCase				:      Tuple			  =				2
        _UpperCAmelCase				:      Dict			  =				3
        for block in range(1		,		__A ):
            for _ in range(__A ):
                proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
                proth_index += 1
            increment *= 2

    return proth_list[number - 1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  for number in range(11):
    SCREAMING_SNAKE_CASE 							=						0
    try:
      SCREAMING_SNAKE_CASE 							=						proth(number)
    except ValueError:
      print(F'ValueError: there is no {number}th Proth number')
      continue

    print(F'The {number}th Proth number: {value}')




