from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			float   ) -> float:




    '''simple docstring'''




    return 0.0






def       __lowerCAmelCase      (						A_   : np.ndarray						,	A_   : int					)    ->  tuple[int | float, int | float]:
  __UpperCAmelCase						    =       min([-20, np.min(fft_results[1 : samplerate // 2 - 1]					)]					)
  __UpperCAmelCase						    =       max([20, np.max(fft_results[1 : samplerate // 2 - 1]					)]					)
  return lowest, highest




def       __lowerCAmelCase      (						A_   : FilterType						,	A_   : int					)    ->  None:
  __UpperCAmelCase						    =       5_12
  __UpperCAmelCase						    =       [1] + [0] * (size - 1)
  __UpperCAmelCase						    =       [filter_type.process(A_					) for item in inputs]

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

  # 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
  __UpperCAmelCase						    =       get_bounds(A_						,	A_					)
  plt.ylim(max([-80, bounds[0]]					)						,	min([80, bounds[1]]					)					)
  plt.ylabel("Gain (dB)"					)

  plt.plot(A_					)
  plt.show()





def       __lowerCAmelCase      (						A_   : FilterType						,	A_   : int					)    ->  None:
  __UpperCAmelCase						    =       5_12
  __UpperCAmelCase						    =       [1] + [0] * (size - 1)
  __UpperCAmelCase						    =       [filter_type.process(A_					) for item in inputs]

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

  # 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(A_						,	-2 * pi					)					)
  plt.show()




import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union


@dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	Optional[Union[str, Path]]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	Optional[Dict]    		= None
  lowerCAmelCase__		:	Optional[str]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= True
  lowerCAmelCase__		:	Optional[int]    		= None
  lowerCAmelCase__		:	int    		= 1
  lowerCAmelCase__		:	Optional[Union[str, bool]]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	Optional[Dict]    		= None
  lowerCAmelCase__		:	Optional[str]    		= None






  def 	_UpperCAmelCase				(				self:			Any   ) -> "DownloadConfig":




    '''simple docstring'''




    return self.__class__(**{k: copy.deepcopy(__lowerCAmelCase   ) for k, v in self.__dict__.items()}   )



