





'''simple docstring'''


from __future__ import annotations

# Define the Gravitational Constant G and the function
A_				     =							6.6_7_4_3e-1_1  # unit of G : m^3 * kg^-1 * s^-2




def       A							(      _UpperCAmelCase   :				float						,_UpperCAmelCase   :				float						,_UpperCAmelCase   :				float						,_UpperCAmelCase   :				float					)  ->				dict[str, float]:


  '''simple docstring'''




  __lowerCAmelCase		:    Dict			     =  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:
    __lowerCAmelCase		:    Dict			     =  GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
    return {"force": force}
  elif mass_a == 0:
    __lowerCAmelCase		:    int			     =  (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_1": mass_a}
  elif mass_a == 0:
    __lowerCAmelCase		:    Any			     =  (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_2": mass_a}
  elif distance == 0:
    __lowerCAmelCase		:    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()












'''simple docstring'''


from typing import TYPE_CHECKING

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


A_				     =							{
    "configuration_sam": [
        "SAM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SamConfig",
        "SamMaskDecoderConfig",
        "SamPromptEncoderConfig",
        "SamVisionConfig",
    ],
    "processing_sam": ["SamProcessor"],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_				     =							[
        "SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SamModel",
        "SamPreTrainedModel",
    ]
try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_				     =							[
        "TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFSamModel",
        "TFSamPreTrainedModel",
    ]
try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_				     =							["SamImageProcessor"]


if TYPE_CHECKING:
    from .configuration_sam import (
        SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SamConfig,
        SamMaskDecoderConfig,
        SamPromptEncoderConfig,
        SamVisionConfig,
    )
    from .processing_sam import SamProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_sam import SamImageProcessor

else:
    import sys

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





