





"""simple docstring"""




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


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

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

     env.seed(0)
     _a   :	str 					=     env.reset()
     _a   :	Optional[Any] 					=     0
     _a   :	Optional[int] 					=     0
     _a   :	Optional[Any] 					=     1_000
     _a   :	Dict 					=     [obs.copy()]
     try:
          for t in tqdm.tqdm(range(T)):
               # call the policy
               _a   :	Optional[int] 					=     pipeline(obs, planning_horizon=32)

               # execute action in environment
               _a   :	Union[str, Any] 					=     env.step(denorm_actions)
               _a   :	Dict 					=     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   :	Optional[Any] 					=     next_observation
     except KeyboardInterrupt:
          pass

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





"""simple docstring"""




_a   :	Optional[Any] 					=     '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a   :	List[str] 					=     [{'type': 'code', 'content': INSTALL_CONTENT}]
_a   :	str 					=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




