




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__						=		{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['ImageGPTFeatureExtractor']
							UpperCamelCase__						=		['ImageGPTImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ImageGPTForCausalImageModeling',
							    'ImageGPTForImageClassification',
							    'ImageGPTModel',
							    'ImageGPTPreTrainedModel',
							    'load_tf_weights_in_imagegpt',
							]


if TYPE_CHECKING:
							from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
														from .image_processing_imagegpt import ImageGPTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_imagegpt import (
														    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ImageGPTForCausalImageModeling,
														    ImageGPTForImageClassification,
														    ImageGPTModel,
														    ImageGPTPreTrainedModel,
														    load_tf_weights_in_imagegpt,
														)

else:
							import sys

							UpperCamelCase__						=		_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()




