

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

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class 							A__   (	A__  ):





  def 						A  (		self				:     List[Any]							, _a				:     float			)     ->   float:





       '''simple docstring'''






       return 0.0


def 	_lowerCAmelCase (						_UpperCamelCase						:					np.ndarray							,			_UpperCamelCase						:					int    )				->      tuple[int | float, int | float]:
     """simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					FilterType							,			_UpperCamelCase						:					int    )				->      None:
     """simple docstring"""




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

     _SCREAMING_SNAKE_CASE							   =[0] * (samplerate - size)  # zero-padding
     outputs += filler
     _SCREAMING_SNAKE_CASE							   =np.abs(np.fft.fft(UpperCAmelCase_    )    )
     _SCREAMING_SNAKE_CASE							   =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
     _SCREAMING_SNAKE_CASE							   =get_bounds(UpperCAmelCase_							,			UpperCAmelCase_    )
     plt.ylim(max([-80, bounds[0]]    )							,			min([80, bounds[1]]    )    )
     plt.ylabel('Gain (dB)'    )

     plt.plot(UpperCAmelCase_    )
     plt.show()







def 	_lowerCAmelCase (						_UpperCamelCase						:					FilterType							,			_UpperCamelCase						:					int    )				->      None:
     """simple docstring"""




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

     _SCREAMING_SNAKE_CASE							   =[0] * (samplerate - size)  # zero-padding
     outputs += filler
     _SCREAMING_SNAKE_CASE							   =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()



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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase			:				Tuple 			=					{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				List[Any] 			=					["FNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["FNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FNetForMaskedLM",
         "FNetForMultipleChoice",
         "FNetForNextSentencePrediction",
         "FNetForPreTraining",
         "FNetForQuestionAnswering",
         "FNetForSequenceClassification",
         "FNetForTokenClassification",
         "FNetLayer",
         "FNetModel",
         "FNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
