
from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class        lowerCAmelCase__      (   __lowercase	):

      def  A_				(			self       ,							a				)					->      float:


        '''simple docstring'''

        return 0.0




def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				tuple[int | float, int | float]:

  """simple docstring"""






  _UpperCamelCase						    =	min([-2_0, np.min(fft_results[1 : samplerate // 2 - 1] )] )
  _UpperCamelCase						    =	max([2_0, np.max(fft_results[1 : samplerate // 2 - 1] )] )
  return lowest, highest


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				None:

  """simple docstring"""






  _UpperCamelCase						    =	5_1_2
  _UpperCamelCase						    =	[1] + [0] * (size - 1)
  _UpperCamelCase						    =	[filter_type.process(lowerCAmelCase ) for item in inputs]

  _UpperCamelCase						    =	[0] * (samplerate - size)  # zero-padding
  outputs += filler
  _UpperCamelCase						    =	np.abs(np.fft.fft(lowerCAmelCase ) )
  _UpperCamelCase						    =	2_0 * np.logaa(lowerCAmelCase )

  # Frequencies on log scale from 24 to nyquist frequency
  plt.xlim(2_4						,  samplerate / 2 - 1 )
  plt.xlabel("""Frequency (Hz)""" )
  plt.xscale("""log""" )

  # Display within reasonable bounds
  _UpperCamelCase						    =	get_bounds(lowerCAmelCase						,  lowerCAmelCase )
  plt.ylim(max([-8_0, bounds[0]] )						,  min([8_0, bounds[1]] ) )
  plt.ylabel("""Gain (dB)""" )

  plt.plot(lowerCAmelCase )
  plt.show()







def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				None:

  """simple docstring"""






  _UpperCamelCase						    =	5_1_2
  _UpperCamelCase						    =	[1] + [0] * (size - 1)
  _UpperCamelCase						    =	[filter_type.process(lowerCAmelCase ) for item in inputs]

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

  # Frequencies on log scale from 24 to nyquist frequency
  plt.xlim(2_4						,  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(lowerCAmelCase						,  -2 * pi ) )
  plt.show()





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							*a       ,							**a				)					->      None:


								'''simple docstring'''

								warnings.warn(
								    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
								    """ use BeitImageProcessor instead."""       ,							a       ,							)
								super().__init__(*a       ,							**a				)



