




'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__				     = logging.get_logger(__name__)







class a (     __UpperCAmelCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       str       	=		'''encoder-decoder'''
						UpperCamelCase_	:       List[Any]       	=		True






						def __init__( self :     Any				,							**lowerCamelCase__ :     int				)    ->						Optional[Any]:
													"""simple docstring"""


													super().__init__(**__SCREAMING_SNAKE_CASE				)
													assert (
													    "encoder" in kwargs and "decoder" in kwargs
													), "Config has to be initialized with encoder and decoder config"
													__lowercase      =    kwargs.pop('''encoder'''				)
													__lowercase      =    encoder_config.pop('''model_type'''				)
													__lowercase      =    kwargs.pop('''decoder'''				)
													__lowercase      =    decoder_config.pop('''model_type'''				)

													from ..auto.configuration_auto import AutoConfig

													__lowercase      =    AutoConfig.for_model(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE				)
													__lowercase      =    AutoConfig.for_model(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE				)
													__lowercase      =    True






						@classmethod
						def     UpperCAmelCase_      ( cls :     Tuple				,							lowerCamelCase__ :     PretrainedConfig				,							lowerCamelCase__ :     PretrainedConfig				,							**lowerCamelCase__ :     List[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''				)
													__lowercase      =    True
													__lowercase      =    True

													return cls(encoder=encoder_config.to_dict()				,							decoder=decoder_config.to_dict()				,							**__SCREAMING_SNAKE_CASE				)






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    copy.deepcopy(self.__dict__				)
													__lowercase      =    self.encoder.to_dict()
													__lowercase      =    self.decoder.to_dict()
													__lowercase      =    self.__class__.model_type
													return output












from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     float				)    ->						float:
													"""simple docstring"""


													return 0.0







def 		_A(       UpperCamelCase__							:			np.ndarray				,       UpperCamelCase__							:			int				)    -> tuple[int | float, int | float]:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

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

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

							plt.plot(UpperCamelCase__				)
							plt.show()







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

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

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






