

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCAmelCase    				=     logging.get_logger(__name__)

__UpperCAmelCase    				=     {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}





class A__    ( A  ,		A ):



 """simple docstring"""







 _lowercase     :					Any      =						'''bit'''
 _lowercase     :					Optional[int]      =						['''preactivation''', '''bottleneck''']
 _lowercase     :					Optional[Any]      =						['''SAME''', '''VALID''']

 def __init__(		self :	List[Any]     ,						A_ :	Union[str, Any]=3     ,						A_ :	List[str]=6_4     ,						A_ :	Union[str, Any]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]     ,						A_ :	str=[3, 4, 6, 3]     ,						A_ :	Optional[int]="preactivation"     ,						A_ :	Union[str, Any]="relu"     ,						A_ :	str=None     ,						A_ :	str=3_2     ,						A_ :	List[str]=0.0     ,						A_ :	Optional[int]=False     ,						A_ :	List[Any]=3_2     ,						A_ :	Optional[int]=1     ,						A_ :	str=None     ,						A_ :	Optional[int]=None     ,						**A_ :	Any     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      if layer_type not in self.layer_types:
           raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
      if global_padding is not None:
           if global_padding.upper() in self.supported_padding:
                _lowerCAmelCase   :					Optional[Any]        =    global_padding.upper()
           else:
                raise ValueError(F'''Padding strategy {global_padding} not supported'''       )
      _lowerCAmelCase   :					Optional[Any]        =    num_channels
      _lowerCAmelCase   :					Any        =    embedding_size
      _lowerCAmelCase   :					Union[str, Any]        =    hidden_sizes
      _lowerCAmelCase   :					Any        =    depths
      _lowerCAmelCase   :					Dict        =    layer_type
      _lowerCAmelCase   :					int        =    hidden_act
      _lowerCAmelCase   :					Optional[int]        =    global_padding
      _lowerCAmelCase   :					Dict        =    num_groups
      _lowerCAmelCase   :					Dict        =    drop_path_rate
      _lowerCAmelCase   :					Optional[int]        =    embedding_dynamic_padding
      _lowerCAmelCase   :					List[str]        =    output_stride
      _lowerCAmelCase   :					Any        =    width_factor

      _lowerCAmelCase   :					Dict        =    ["stem"] + [F'''stage{idx}''' for idx in range(1     ,						len(A_       ) + 1       )]
      _lowerCAmelCase   :					Dict        =    get_aligned_output_features_output_indices(
          out_features=A_     ,						out_indices=A_     ,						stage_names=self.stage_names       )





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	List[Any]     ,						A_ :	float       ):






      '''simple docstring'''




      return 0.0







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      tuple[int | float, int | float]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     _lowerCAmelCase   :					Optional[Any]        =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Optional[Any]        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Dict        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Dict        =    np.abs(np.fft.fft(SCREAMING_SNAKE_CASE				)				)
     _lowerCAmelCase   :					str        =    20 * np.logaa(SCREAMING_SNAKE_CASE				)

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

     plt.plot(SCREAMING_SNAKE_CASE				)
     plt.show()







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Dict        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Tuple        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Tuple        =    np.angle(np.fft.fft(SCREAMING_SNAKE_CASE				)				)

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






