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


SCREAMING_SNAKE_CASE							:Tuple							 =				{
    '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							:List[str]							 =				'hopper-medium-v2'
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				gym.make(env_name)

					SCREAMING_SNAKE_CASE							:List[Any]							 =				ValueGuidedRLPipeline.from_pretrained(
					    'bglick13/hopper-medium-v2-value-function-hor32',
					    env=env,
					)

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

															# execute action in environment
															SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE							:Tuple							 =				env.step(denorm_actions)
															SCREAMING_SNAKE_CASE							:Optional[Any]							 =				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							:List[str]							 =				next_observation
					except KeyboardInterrupt:
										pass

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






"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					list    ):
  """simple docstring"""

  if len(snake_case__    ) <= 1:
    return [tuple(snake_case__    )]

  _snake_case							:		List[Any]								=  []

  def generate(snake_case__							:					int		,   snake_case__							:					list    ):
    if k == 1:
      res.append(tuple(arr[:]    )    )
      return

    generate(k - 1		,   snake_case__    )

    for i in range(k - 1    ):
      if k % 2 == 0:  # k is even
        _snake_case		,					_snake_case							:		Optional[Any]								=  arr[k - 1], arr[i]
      else:  # k is odd
        _snake_case		,					_snake_case							:		List[str]								=  arr[k - 1], arr[0]
      generate(k - 1		,   snake_case__    )

  generate(len(snake_case__    )		,   snake_case__    )
  return res


if __name__ == "__main__":
     A_						=				input('''Enter numbers separated by a comma:\n''').strip()
     A_						=				[int(item) for item in user_input.split(''',''')]
     print(heaps(arr))


