

import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


__UpperCAmelCase    				=     'scheduler_config.json'





class A__    ( __lowercase ):



 """simple docstring"""







 _lowercase     :					Dict      =						1
 _lowercase     :					Optional[int]      =						2
 _lowercase     :					List[Any]      =						3
 _lowercase     :					List[str]      =						4
 _lowercase     :					str      =						5
 _lowercase     :					Dict      =						6
 _lowercase     :					Any      =						7
 _lowercase     :					Dict      =						8
 _lowercase     :					Union[str, Any]      =						9
 _lowercase     :					int      =						10
 _lowercase     :					Optional[Any]      =						11
 _lowercase     :					Tuple      =						12
 _lowercase     :					Optional[Any]      =						13
 _lowercase     :					int      =						14





@dataclass
class A__    ( __lowercase ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						42





class A__    :



 """simple docstring"""







 _lowercase     :					List[Any]      =						SCHEDULER_CONFIG_NAME
 _lowercase     :					List[Any]      =						[]
 _lowercase     :					List[str]      =						True

 @classmethod
 def    __magic_name__       (		cls :	int     ,						A_ :	Dict[str, Any] = None     ,						A_ :	Optional[str] = None     ,						A_ :	Tuple=False     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    cls.load_config(
          pretrained_model_name_or_path=A_     ,						subfolder=A_     ,						return_unused_kwargs=A_     ,						return_commit_hash=A_     ,						**A_     ,						)
      return cls.from_config(A_     ,						return_unused_kwargs=A_     ,						**A_       )

 def    __magic_name__       (		self :	Dict     ,						A_ :	Union[str, os.PathLike]     ,						A_ :	bool = False     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      self.save_config(save_directory=A_     ,						push_to_hub=A_     ,						**A_       )

 @property
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      return self._get_compatibles()


 @classmethod
 def    __magic_name__       (		cls :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    list(set([cls.__name__] + cls._compatibles       )       )
      _lowerCAmelCase   :					Union[str, Any]        =    importlib.import_module(__name__.split("."       )[0]       )
      _lowerCAmelCase   :					Any        =    [
          getattr(A_     ,						A_       ) for c in compatible_classes_str if hasattr(A_     ,						A_       )
      ]
      return compatible_classes





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()






