

from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=     {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     ['LxmertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

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









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()






