

from __future__ import annotations

# Define the Gravitational Constant G and the function
UpperCAmelCase_       :				Any		   =      6.6743e-11  # unit of G : m^3 * kg^-1 * s^-2
def        __SCREAMING_SNAKE_CASE    (      a__						:      float ,a__						:      float ,a__						:      float ,a__						:      float					)				->							Union[str, Any]:
	__A   : List[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:
		__A   : Dict								= GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
		return {"force": force}
	elif mass_a == 0:
		__A   : int								= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_1": mass_a}
	elif mass_a == 0:
		__A   : Dict								= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_2": mass_a}
	elif distance == 0:
		__A   : Optional[int]								= (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()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_       :				Optional[Any]		   =      {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_       :				List[str]		   =      [
			    '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ProphetNetDecoder''',
			    '''ProphetNetEncoder''',
			    '''ProphetNetForCausalLM''',
			    '''ProphetNetForConditionalGeneration''',
			    '''ProphetNetModel''',
			    '''ProphetNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
			from .tokenization_prophetnet import ProphetNetTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_prophetnet import (
						    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ProphetNetDecoder,
						    ProphetNetEncoder,
						    ProphetNetForCausalLM,
						    ProphetNetForConditionalGeneration,
						    ProphetNetModel,
						    ProphetNetPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_       :				Any		   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





