code: 
  
  
  
  from __future__ import annotations
  
  # Define the Gravitational Constant G and the function
  a__						:			List[str]										=			6.6743e-11  # unit of G : m^3 * kg^-1 * s^-2
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  mass_a * mass_a
  
   if (force, mass_a, mass_a, distance).count(0       ) != 1:
    raise ValueError("""One and only one argument must be 0"""       )
   if force < 0:
    raise ValueError("""Gravitational force can not be negative"""       )
   if distance < 0:
    raise ValueError("""Distance can not be negative"""       )
   if mass_a < 0 or mass_a < 0:
    raise ValueError("""Mass can not be negative"""       )
   if force == 0:
    __UpperCamelCase				      =  GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
    return {"force": force}
   elif mass_a == 0:
    __UpperCamelCase				      =  (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_1": mass_a}
   elif mass_a == 0:
    __UpperCamelCase				      =  (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_2": mass_a}
   elif distance == 0:
    __UpperCamelCase				      =  (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
    return {"distance": distance}
   raise ValueError("""One and only one argument must be 0"""       )
  
  
  # Run doctest
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  a__						:			Optional[int]										=			{
      'configuration_bark': [
          'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'BarkCoarseConfig',
          'BarkConfig',
          'BarkFineConfig',
          'BarkSemanticConfig',
      ],
      'processing_bark': ['BarkProcessor'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a__						:			Any										=			[
       'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BarkFineModel',
       'BarkSemanticModel',
       'BarkCoarseModel',
       'BarkModel',
       'BarkPreTrainedModel',
       'BarkCausalModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_bark import (
       BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BarkCoarseConfig,
       BarkConfig,
       BarkFineConfig,
       BarkSemanticConfig,
   )
   from .processing_bark import BarkProcessor
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_bark import (
        BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
        BarkCausalModel,
        BarkCoarseModel,
        BarkFineModel,
        BarkModel,
        BarkPreTrainedModel,
        BarkSemanticModel,
    )
  
  else:
   import sys
  
   a__						:			Tuple										=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  