




'''simple docstring'''





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

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







@dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       str       	=		field(default='image-classification'       ,						metadata={'include_in_asdict_even_if_is_default': True}  )
						UpperCamelCase_	:       ClassVar[Features]       	=		Features({'image': Image()}  )
						UpperCamelCase_	:       ClassVar[Features]       	=		Features({'labels': ClassLabel}  )
						UpperCamelCase_	:       str       	=		"image"
						UpperCamelCase_	:       str       	=		"labels"






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     int				)    ->						List[Any]:
													"""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]				,							lowerCamelCase__				):
																				raise ValueError(f'Column {self.label_column} is not a ClassLabel.'				)
													__lowercase      =    copy.deepcopy(self				)
													__lowercase      =    self.label_schema.copy()
													__lowercase      =    features[self.label_column]
													__lowercase      =    label_schema
													return task_template






						@property
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Dict[str, str]:
													"""simple docstring"""


													return {
													    self.image_column: "image",
													    self.label_column: "labels",
													}












from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     float				)    ->						float:
													"""simple docstring"""


													return 0.0







def 		_A(       UpperCamelCase__							:			np.ndarray				,       UpperCamelCase__							:			int				)    -> tuple[int | float, int | float]:
							'''simple docstring'''


							__lowercase      =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
							__lowercase      =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
							return lowest, highest







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

							__lowercase      =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__lowercase      =    np.abs(np.fft.fft(UpperCamelCase__				)				)
							__lowercase      =    20 * np.logaa(UpperCamelCase__				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				,       samplerate / 2 - 1				)
							plt.xlabel('''Frequency (Hz)'''				)
							plt.xscale('''log'''				)

							# Display within reasonable bounds
							__lowercase      =    get_bounds(UpperCamelCase__				,       UpperCamelCase__				)
							plt.ylim(max([-80, bounds[0]]				)				,       min([80, bounds[1]]				)				)
							plt.ylabel('''Gain (dB)'''				)

							plt.plot(UpperCamelCase__				)
							plt.show()







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

							__lowercase      =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__lowercase      =    np.angle(np.fft.fft(UpperCamelCase__				)				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				,       samplerate / 2 - 1				)
							plt.xlabel('''Frequency (Hz)'''				)
							plt.xscale('''log'''				)

							plt.ylim(-2 * pi				,       2 * pi				)
							plt.ylabel('''Phase shift (Radians)'''				)
							plt.plot(np.unwrap(UpperCamelCase__				,       -2 * pi				)				)
							plt.show()






