



'''simple docstring'''






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


A_				:					Any							=      {
    """configuration_ernie_m""": ["""ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieMConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_				:					List[str]							=      ["""ErnieMTokenizer"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_				:					Optional[int]							=      [
       """ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ErnieMForMultipleChoice""",
       """ErnieMForQuestionAnswering""",
       """ErnieMForSequenceClassification""",
       """ErnieMForTokenClassification""",
       """ErnieMModel""",
       """ErnieMPreTrainedModel""",
       """ErnieMForInformationExtraction""",
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

   A_				:					Dict							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''



from __future__ import annotations

# Define the Gravitational Constant G and the function
_lowercase       :						List[str]						=6.6743E-11  # unit of G : m^3 * kg^-1 * s^-2


def 							__UpperCAmelCase				(				UpperCamelCase__				:float   ,				UpperCamelCase__				:float   ,				UpperCamelCase__				:float   ,				UpperCamelCase__				:float			)		->						dict[str, float]:
				snake_case__      :   str      =    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:
								snake_case__      :   Dict      =    GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
								return {"force": force}
				elif mass_a == 0:
								snake_case__      :   List[str]      =    (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
								return {"mass_1": mass_a}
				elif mass_a == 0:
								snake_case__      :   List[Any]      =    (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
								return {"mass_2": mass_a}
				elif distance == 0:
								snake_case__      :   str      =    (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()




