



from typing import TYPE_CHECKING

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


UpperCamelCase_										=					{
    '''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
    '''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
    '''processing_whisper''': ['''WhisperProcessor'''],
    '''tokenization_whisper''': ['''WhisperTokenizer'''],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_										=					[
          '''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''WhisperForConditionalGeneration''',
          '''WhisperModel''',
          '''WhisperPreTrainedModel''',
          '''WhisperForAudioClassification''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_										=					[
          '''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFWhisperForConditionalGeneration''',
          '''TFWhisperModel''',
          '''TFWhisperPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_										=					[
          '''FlaxWhisperForConditionalGeneration''',
          '''FlaxWhisperModel''',
          '''FlaxWhisperPreTrainedModel''',
          '''FlaxWhisperForAudioClassification''',
      ]


if TYPE_CHECKING:
      from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
      from .feature_extraction_whisper import WhisperFeatureExtractor
      from .processing_whisper import WhisperProcessor
      from .tokenization_whisper import WhisperTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_whisper_fast import WhisperTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_whisper import (
                WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
                WhisperForAudioClassification,
                WhisperForConditionalGeneration,
                WhisperModel,
                WhisperPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_whisper import (
                TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFWhisperForConditionalGeneration,
                TFWhisperModel,
                TFWhisperPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_whisper import (
                FlaxWhisperForAudioClassification,
                FlaxWhisperForConditionalGeneration,
                FlaxWhisperModel,
                FlaxWhisperPreTrainedModel,
            )

else:
      import sys

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



from __future__ import annotations

from math import pi, sqrt


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):

						if inductance <= 0:
												raise ValueError('''Inductance cannot be 0 or negative'''       )

						elif capacitance <= 0:
												raise ValueError('''Capacitance cannot be 0 or negative'''       )

						else:
												return (
												    "Resonant frequency",
												    float(1 / (2 * pi * (sqrt(inductance * capacitance       )))       ),
												)


if __name__ == "__main__":
					import doctest

					doctest.testmod()