




from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def        a__				(       self      :						int		,							A_      :						float						)     ->			float:

       """simple docstring"""






       return 0.0

def     _SCREAMING_SNAKE_CASE    (							lowercase			: np.ndarray			,							lowercase			: int  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: FilterType			,							lowercase			: int  ):


      '''simple docstring'''



      lowerCamelCase_	  =					5_12
      lowerCamelCase_	  =					[1] + [0] * (size - 1)
      lowerCamelCase_	  =					[filter_type.process(lowercase  ) for item in inputs]

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

      # 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
      lowerCamelCase_	  =					get_bounds(lowercase			,							lowercase  )
      plt.ylim(max([-80, bounds[0]]  )			,							min([80, bounds[1]]  )  )
      plt.ylabel('Gain (dB)'  )

      plt.plot(lowercase  )
      plt.show()






def     _SCREAMING_SNAKE_CASE    (							lowercase			: FilterType			,							lowercase			: int  ):


      '''simple docstring'''



      lowerCamelCase_	  =					5_12
      lowerCamelCase_	  =					[1] + [0] * (size - 1)
      lowerCamelCase_	  =					[filter_type.process(lowercase  ) for item in inputs]

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

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









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

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







@dataclass(frozen=UpperCamelCase     )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		field(default='''language-modeling'''							,   metadata={'''include_in_asdict_even_if_is_default''': True}     )

 UpperCamelCase		     =		Features({'''text''': Value('''string'''     )}     )
 UpperCamelCase		     =		Features({}     )
 UpperCamelCase		     =		"text"

 @property
 def        a__				(       self      :						List[Any]						)     ->			Dict[str, str]:

       """simple docstring"""






       return {self.text_column: "text"}



