


"""simple docstring"""




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


__magic_name__						: Any    =				{
    """n_samples""": 6_4,
    """horizon""": 3_2,
    """num_inference_steps""": 2_0,
    """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__":
					__magic_name__						: Any    =				"""hopper-medium-v2"""
					__magic_name__						: Optional[Any]    =				gym.make(env_name)

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

					env.seed(0)
					__magic_name__						: List[Any]    =				env.reset()
					__magic_name__						: Optional[Any]    =				0
					__magic_name__						: Optional[int]    =				0
					__magic_name__						: List[str]    =				1_0_0_0
					__magic_name__						: str    =				[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															__magic_name__						: str    =				pipeline(obs, planning_horizon=3_2)

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

															__magic_name__						: Tuple    =				next_observation
					except KeyboardInterrupt:
										pass

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






from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


lowerCamelCase__     :		Any        = datasets.utils.logging.get_logger(__name__)



class    _snake_case (						folder_based_builder.FolderBasedBuilderConfig				):
 __lowerCAmelCase	:  bool	   =      None
 __lowerCAmelCase	:  bool	   =      None



class    _snake_case (						folder_based_builder.FolderBasedBuilder				):
 __lowerCAmelCase	:  Optional[Any]	   =      datasets.Audio()
 __lowerCAmelCase	:  Union[str, Any]	   =      'audio'
 __lowerCAmelCase	:  str	   =      AudioFolderConfig
 __lowerCAmelCase	:  List[str]  # definition at the bottom of the script
 __lowerCAmelCase	:  Optional[int]	   =      AudioClassification(audio_column='audio'	,    label_column='label'				)

lowerCamelCase__     :		int        = [
    """.aiff""",
    """.au""",
    """.avr""",
    """.caf""",
    """.flac""",
    """.htk""",
    """.svx""",
    """.mat4""",
    """.mat5""",
    """.mpc2k""",
    """.ogg""",
    """.paf""",
    """.pvf""",
    """.raw""",
    """.rf64""",
    """.sd2""",
    """.sds""",
    """.ircam""",
    """.voc""",
    """.w64""",
    """.wav""",
    """.nist""",
    """.wavex""",
    """.wve""",
    """.xi""",
    """.mp3""",
    """.opus""",
]
lowerCamelCase__     :		int        = AUDIO_EXTENSIONS






