





'''simple docstring'''







from __future__ import annotations

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





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












'''simple docstring'''






from typing import TYPE_CHECKING

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


__UpperCamelCase      :   List[str]								=     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   List[Any]								=     [
	    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTJForCausalLM''',
	    '''GPTJForQuestionAnswering''',
	    '''GPTJForSequenceClassification''',
	    '''GPTJModel''',
	    '''GPTJPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Optional[int]								=     [
	    '''TFGPTJForCausalLM''',
	    '''TFGPTJForQuestionAnswering''',
	    '''TFGPTJForSequenceClassification''',
	    '''TFGPTJModel''',
	    '''TFGPTJPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Union[str, Any]								=     [
	    '''FlaxGPTJForCausalLM''',
	    '''FlaxGPTJModel''',
	    '''FlaxGPTJPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	__UpperCamelCase      :   List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
