





from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
_A					:				List[str]				=			1.0_5457_1817E-34  # unit of ℏ : J * s

_A					:				str				=			3E8  # unit of c : m * s^-1


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						dict[str, float]:

    if (force, area, distance).count(0	) != 1:
        raise ValueError('''One and only one argument must be 0'''	)
    if force < 0:
        raise ValueError('''Magnitude of force can not be negative'''	)
    if distance < 0:
        raise ValueError('''Distance can not be negative'''	)
    if area < 0:
        raise ValueError('''Area can not be negative'''	)
    if force == 0:
        SCREAMING_SNAKE_CASE__											=			(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
            2_4_0 * (distance) ** 4
        )
        return {"force": force}
    elif area == 0:
        SCREAMING_SNAKE_CASE__											=			(2_4_0 * force * (distance) ** 4) / (
            REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
        )
        return {"area": area}
    elif distance == 0:
        SCREAMING_SNAKE_CASE__											=			(
            (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
        ) ** (1 / 4)
        return {"distance": distance}
    raise ValueError('''One and only one argument must be 0'''	)


# Run doctest
if __name__ == "__main__":
  import doctest

  doctest.testmod()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:				List[str]				=			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Dict				=			[
      """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """WavLMForAudioFrameClassification""",
      """WavLMForCTC""",
      """WavLMForSequenceClassification""",
      """WavLMForXVector""",
      """WavLMModel""",
      """WavLMPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
