
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A     :	Any    = {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'XLMForMultipleChoice',
					    'XLMForQuestionAnswering',
					    'XLMForQuestionAnsweringSimple',
					    'XLMForSequenceClassification',
					    'XLMForTokenClassification',
					    'XLMModel',
					    'XLMPreTrainedModel',
					    'XLMWithLMHeadModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Any    = [
					    'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFXLMForMultipleChoice',
					    'TFXLMForQuestionAnsweringSimple',
					    'TFXLMForSequenceClassification',
					    'TFXLMForTokenClassification',
					    'TFXLMMainLayer',
					    'TFXLMModel',
					    'TFXLMPreTrainedModel',
					    'TFXLMWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

					A     :	Optional[int]    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    (1 - _cos) / 2
				__a										=    1 - _cos

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    (1 + _cos) / 2
				__a										=    -1 - _cos

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    _sin / 2
				__a										=    0
				__a										=    -ba

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    1 - alpha
				__a										=    -2 * _cos
				__a										=    1 + alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([ba, ba, ba]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)

				__a										=    1 + alpha * big_a
				__a										=    -2 * _cos
				__a										=    1 - alpha * big_a
				__a										=    1 + alpha / big_a
				__a										=    -2 * _cos
				__a										=    1 - alpha / big_a

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)
				__a										=    (big_a + 1) - (big_a - 1) * _cos
				__a										=    (big_a + 1) + (big_a - 1) * _cos
				__a										=    (big_a - 1) - (big_a + 1) * _cos
				__a										=    (big_a - 1) + (big_a + 1) * _cos
				__a										=    2 * sqrt(a__  ) * alpha

				__a										=    big_a * (pmc + aaa)
				__a										=    2 * big_a * mpc
				__a										=    big_a * (pmc - aaa)
				__a										=    ppmc + aaa
				__a										=    -2 * pmpc
				__a										=    ppmc - aaa

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt






def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)
				__a										=    (big_a + 1) - (big_a - 1) * _cos
				__a										=    (big_a + 1) + (big_a - 1) * _cos
				__a										=    (big_a - 1) - (big_a + 1) * _cos
				__a										=    (big_a - 1) + (big_a + 1) * _cos
				__a										=    2 * sqrt(a__  ) * alpha

				__a										=    big_a * (ppmc + aaa)
				__a										=    -2 * big_a * pmpc
				__a										=    big_a * (ppmc - aaa)
				__a										=    pmc + aaa
				__a										=    2 * mpc
				__a										=    pmc - aaa

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt