



"""simple docstring"""




from typing import TYPE_CHECKING

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


_UpperCamelCase:					Dict     				=	{
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:					Any     				=	['BartTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:					List[str]     				=	[
         'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BartForCausalLM',
         'BartForConditionalGeneration',
         'BartForQuestionAnswering',
         'BartForSequenceClassification',
         'BartModel',
         'BartPretrainedModel',
         'PretrainedBartModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:					Tuple     				=	[
         'TFBartForConditionalGeneration',
         'TFBartForSequenceClassification',
         'TFBartModel',
         'TFBartPretrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:					Optional[int]     				=	[
         'FlaxBartDecoderPreTrainedModel',
         'FlaxBartForCausalLM',
         'FlaxBartForConditionalGeneration',
         'FlaxBartForQuestionAnswering',
         'FlaxBartForSequenceClassification',
         'FlaxBartModel',
         'FlaxBartPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
     from .tokenization_bart import BartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_bart_fast import BartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_bart import (
              BART_PRETRAINED_MODEL_ARCHIVE_LIST,
              BartForCausalLM,
              BartForConditionalGeneration,
              BartForQuestionAnswering,
              BartForSequenceClassification,
              BartModel,
              BartPretrainedModel,
              PretrainedBartModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_bart import (
              TFBartForConditionalGeneration,
              TFBartForSequenceClassification,
              TFBartModel,
              TFBartPretrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_bart import (
              FlaxBartDecoderPreTrainedModel,
              FlaxBartForCausalLM,
              FlaxBartForConditionalGeneration,
              FlaxBartForQuestionAnswering,
              FlaxBartForSequenceClassification,
              FlaxBartModel,
              FlaxBartPreTrainedModel,
          )

else:
     import sys

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





"""simple docstring"""




from __future__ import annotations

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


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






      '''simple docstring'''


      lowercase	:   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:
            lowercase	:   str											= GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
            return {"force": force}
      elif mass_a == 0:
            lowercase	:   Optional[int]											= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_1": mass_a}
      elif mass_a == 0:
            lowercase	:   Optional[int]											= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_2": mass_a}
      elif distance == 0:
            lowercase	:   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()
