'''simple docstring'''
from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return 0.0







def  _UpperCamelCase (     _a	:	np.ndarray				,     _a	:	int			):




    """simple docstring"""



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







def  _UpperCamelCase (     _a	:	FilterType				,     _a	:	int			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  5_1_2
    __UpperCamelCase	:	Union[str, Any]		    =  [1] + [0] * (size - 1)
    __UpperCamelCase	:	List[str]		    =  [filter_type.process(_a			) for item in inputs]

    __UpperCamelCase	:	Any		    =  [0] * (samplerate - size)  # zero-padding
    outputs += filler
    __UpperCamelCase	:	Any		    =  np.abs(np.fft.fft(_a			)			)
    __UpperCamelCase	:	Union[str, Any]		    =  2_0 * np.logaa(_a			)

    # 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	:	int		    =  get_bounds(_a				,     _a			)
    plt.ylim(max([-8_0, bounds[0]]			)				,     min([8_0, bounds[1]]			)			)
    plt.ylabel('Gain (dB)'			)

    plt.plot(_a			)
    plt.show()







def  _UpperCamelCase (     _a	:	FilterType				,     _a	:	int			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  5_1_2
    __UpperCamelCase	:	int		    =  [1] + [0] * (size - 1)
    __UpperCamelCase	:	List[str]		    =  [filter_type.process(_a			) for item in inputs]

    __UpperCamelCase	:	str		    =  [0] * (samplerate - size)  # zero-padding
    outputs += filler
    __UpperCamelCase	:	Optional[int]		    =  np.angle(np.fft.fft(_a			)			)

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




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a=  {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ViTMSNModel''',
      '''ViTMSNForImageClassification''',
      '''ViTMSNPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



