
"""simple docstring"""







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate




@dataclass(frozen=_lowerCamelCase)
class 				A__ (			_lowerCamelCase):
		A_		: str            =			field(default='automatic-speech-recognition' ,  metadata={'include_in_asdict_even_if_is_default': True})
		A_		: ClassVar[Features]            =			Features({'audio': Audio()})
		A_		: ClassVar[Features]            =			Features({'transcription': Value('string')})
		A_		: str            =			"audio"
		A_		: str            =			"transcription"

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			if self.audio_column not in features:
				raise ValueError(f"Column {self.audio_column} is not present in features."				)
			if not isinstance(features[self.audio_column] ,	_SCREAMING_SNAKE_CASE				):
				raise ValueError(f"Column {self.audio_column} is not an Audio type."				)
			__lowerCAmelCase    :		List[Any]		    =   copy.deepcopy(self				)
			__lowerCAmelCase    :		Any		    =   self.input_schema.copy()
			__lowerCAmelCase    :		List[str]		    =   features[self.audio_column]
			__lowerCAmelCase    :		Tuple		    =   input_schema
			return task_template







		@property
		def 							__lowerCamelCase							(   self				):
			return {self.audio_column: "audio", self.transcription_column: "transcription"}

"""simple docstring"""







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


lowerCamelCase__           =	{
    """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__":
							lowerCamelCase__           =	"""hopper-medium-v2"""
							lowerCamelCase__           =	gym.make(env_name)

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

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

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

																					lowerCamelCase__           =	next_observation
							except KeyboardInterrupt:
														pass

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