




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int				,					a_	:							float = 1 / sqrt(2			)			):
  __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 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int				,					a_	:							float = 1 / sqrt(2			)			):
  __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 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int				,					a_	:							float = 1 / sqrt(2			)			):
  __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 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int				,					a_	:							float = 1 / sqrt(2			)			):
  __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 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int				,					a_	:							float				,					a_	:							float = 1 / sqrt(2			)				,					):
  __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 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int				,					a_	:							float				,					a_	:							float = 1 / sqrt(2			)				,					):
  __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 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int				,					a_	:							float				,					a_	:							float = 1 / sqrt(2			)				,					):
  __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









'''simple docstring'''


from typing import TYPE_CHECKING

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


UpperCAmelCase_ 		=      {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      ["RoFormerTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoFormerForCausalLM",
      "RoFormerForMaskedLM",
      "RoFormerForMultipleChoice",
      "RoFormerForQuestionAnswering",
      "RoFormerForSequenceClassification",
      "RoFormerForTokenClassification",
      "RoFormerLayer",
      "RoFormerModel",
      "RoFormerPreTrainedModel",
      "load_tf_weights_in_roformer",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRoFormerForCausalLM",
      "TFRoFormerForMaskedLM",
      "TFRoFormerForMultipleChoice",
      "TFRoFormerForQuestionAnswering",
      "TFRoFormerForSequenceClassification",
      "TFRoFormerForTokenClassification",
      "TFRoFormerLayer",
      "TFRoFormerModel",
      "TFRoFormerPreTrainedModel",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FlaxRoFormerForMaskedLM",
      "FlaxRoFormerForMultipleChoice",
      "FlaxRoFormerForQuestionAnswering",
      "FlaxRoFormerForSequenceClassification",
      "FlaxRoFormerForTokenClassification",
      "FlaxRoFormerModel",
      "FlaxRoFormerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_ 		=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



