





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase     :     List[Any]							=	{"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase     :     Optional[Any]							=	["""BeitFeatureExtractor"""]
   UpperCAmelCase     :     Optional[int]							=	["""BeitImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase     :     int							=	[
       """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BeitForImageClassification""",
       """BeitForMaskedImageModeling""",
       """BeitForSemanticSegmentation""",
       """BeitModel""",
       """BeitPreTrainedModel""",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase     :     List[str]							=	[
       """FlaxBeitForImageClassification""",
       """FlaxBeitForMaskedImageModeling""",
       """FlaxBeitModel""",
       """FlaxBeitPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_beit import BeitFeatureExtractor
      from .image_processing_beit import BeitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_beit import (
          BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
          BeitPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_beit import (
          FlaxBeitForImageClassification,
          FlaxBeitForMaskedImageModeling,
          FlaxBeitModel,
          FlaxBeitPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase     :     str							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













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
UpperCAmelCase     :     Union[str, Any]							=	1.054571817E-34  # unit of ℏ : J * s

UpperCAmelCase     :     Union[str, Any]							=	3E8  # unit of c : m * s^-1
def 					_A	(		SCREAMING_SNAKE_CASE					:       float	,	SCREAMING_SNAKE_CASE					:       float	,	SCREAMING_SNAKE_CASE					:       float						):





  """simple docstring"""



  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:
    a__     : Tuple  =(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
        240 * (distance) ** 4
    )
    return {"force": force}
  elif area == 0:
    a__     : Any  =(240 * force * (distance) ** 4) / (
        REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
    )
    return {"area": area}
  elif distance == 0:
    a__     : List[str]  =(
        (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * 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()






