





"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

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






@dataclass(frozen=A  )
class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      str							=  field(default='''audio-classification'''    ,	metadata={'''include_in_asdict_even_if_is_default''': True}  )
       SCREAMING_SNAKE_CASE_   :      ClassVar[Features]							=  Features({'''audio''': Audio()}  )
       SCREAMING_SNAKE_CASE_   :      ClassVar[Features]							=  Features({'''labels''': ClassLabel}  )
       SCREAMING_SNAKE_CASE_   :      str							=  "audio"
       SCREAMING_SNAKE_CASE_   :      str							=  "labels"




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      int:





             """simple docstring"""






             if self.label_column not in features:
                   raise ValueError(f'''Column {self.label_column} is not present in features.'''							)
             if not isinstance(features[self.label_column]		,SCREAMING_SNAKE_CASE__							):
                   raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''							)
             __SCREAMING_SNAKE_CASE   :str						=  copy.deepcopy(self							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  self.label_schema.copy()
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  features[self.label_column]
             __SCREAMING_SNAKE_CASE   :int						=  label_schema
             return task_template




       @property
       def     _UpperCamelCase     (						self							)			->      Dict[str, str]:





             """simple docstring"""






             return {
                 self.audio_column: "audio",
                 self.label_column: "labels",
             }






"""simple docstring"""
from collections.abc import Callable

import numpy as np



def      __lowerCamelCase   (							a_     :		Callable ,   a_     :		float ,   a_     :		float ,   a_     :		float ,   a_     :		float							)			->      np.ndarray:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  int(np.ceil((x_end - xa) / step_size							)							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.zeros((n + 1,)							)
      __SCREAMING_SNAKE_CASE   :int						=  ya
      __SCREAMING_SNAKE_CASE   :str						=  xa

      for k in range(a_							):
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  y[k] + step_size * ode_func(a_ ,   y[k]							)
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()