






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


A_						:						List[str]						      =					{
    '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_						:						int						      =					'hopper-medium-v2'
   A_						:						List[Any]						      =					gym.make(env_name)

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

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

         # execute action in environment
         A_      ,					A_      ,					A_      ,					A_						:						List[str]						      =					env.step(denorm_actions)
         A_						:						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())

         A_						:						Union[str, Any]						      =					next_observation
   except KeyboardInterrupt:
      pass

   print(F'''Total reward: {total_reward}''')
def _lowerCamelCase  (		snake_case  ):
							assert (
							    isinstance(snake_case     , snake_case  ) and number_of_steps > 0
							), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
							if number_of_steps == 1:
														return 1
							_lowerCAmelCase			,		_lowerCAmelCase							=							1, 1
							for _ in range(number_of_steps - 1  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							current + previous, current
							return current


if __name__ == "__main__":
				import doctest

				doctest.testmod()






