


'''simple docstring'''







from typing import TYPE_CHECKING

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


a__    :   Tuple								=			{
    'auto_factory': ['get_values'],
    'configuration_auto': ['ALL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CONFIG_MAPPING', 'MODEL_NAMES_MAPPING', 'AutoConfig'],
    'feature_extraction_auto': ['FEATURE_EXTRACTOR_MAPPING', 'AutoFeatureExtractor'],
    'image_processing_auto': ['IMAGE_PROCESSOR_MAPPING', 'AutoImageProcessor'],
    'processing_auto': ['PROCESSOR_MAPPING', 'AutoProcessor'],
    'tokenization_auto': ['TOKENIZER_MAPPING', 'AutoTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    :   Dict								=			[
      'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
      'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING',
      'MODEL_FOR_AUDIO_XVECTOR_MAPPING',
      'MODEL_FOR_BACKBONE_MAPPING',
      'MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING',
      'MODEL_FOR_CAUSAL_LM_MAPPING',
      'MODEL_FOR_CTC_MAPPING',
      'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
      'MODEL_FOR_DEPTH_ESTIMATION_MAPPING',
      'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
      'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING',
      'MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING',
      'MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
      'MODEL_FOR_MASKED_LM_MAPPING',
      'MODEL_FOR_MASK_GENERATION_MAPPING',
      'MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
      'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
      'MODEL_FOR_OBJECT_DETECTION_MAPPING',
      'MODEL_FOR_PRETRAINING_MAPPING',
      'MODEL_FOR_QUESTION_ANSWERING_MAPPING',
      'MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
      'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
      'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
      'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
      'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
      'MODEL_FOR_TEXT_ENCODING_MAPPING',
      'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
      'MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING',
      'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING',
      'MODEL_FOR_VISION_2_SEQ_MAPPING',
      'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING',
      'MODEL_MAPPING',
      'MODEL_WITH_LM_HEAD_MAPPING',
      'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
      'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING',
      'AutoModel',
      'AutoBackbone',
      'AutoModelForAudioClassification',
      'AutoModelForAudioFrameClassification',
      'AutoModelForAudioXVector',
      'AutoModelForCausalLM',
      'AutoModelForCTC',
      'AutoModelForDepthEstimation',
      'AutoModelForImageClassification',
      'AutoModelForImageSegmentation',
      'AutoModelForInstanceSegmentation',
      'AutoModelForMaskGeneration',
      'AutoModelForTextEncoding',
      'AutoModelForMaskedImageModeling',
      'AutoModelForMaskedLM',
      'AutoModelForMultipleChoice',
      'AutoModelForNextSentencePrediction',
      'AutoModelForObjectDetection',
      'AutoModelForPreTraining',
      'AutoModelForQuestionAnswering',
      'AutoModelForSemanticSegmentation',
      'AutoModelForSeq2SeqLM',
      'AutoModelForSequenceClassification',
      'AutoModelForSpeechSeq2Seq',
      'AutoModelForTableQuestionAnswering',
      'AutoModelForTokenClassification',
      'AutoModelForUniversalSegmentation',
      'AutoModelForVideoClassification',
      'AutoModelForVision2Seq',
      'AutoModelForVisualQuestionAnswering',
      'AutoModelForDocumentQuestionAnswering',
      'AutoModelWithLMHead',
      'AutoModelForZeroShotImageClassification',
      'AutoModelForZeroShotObjectDetection',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    :   Optional[int]								=			[
      'TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
      'TF_MODEL_FOR_CAUSAL_LM_MAPPING',
      'TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
      'TF_MODEL_FOR_MASK_GENERATION_MAPPING',
      'TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
      'TF_MODEL_FOR_MASKED_LM_MAPPING',
      'TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
      'TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
      'TF_MODEL_FOR_PRETRAINING_MAPPING',
      'TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
      'TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
      'TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
      'TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
      'TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
      'TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
      'TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
      'TF_MODEL_FOR_TEXT_ENCODING_MAPPING',
      'TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
      'TF_MODEL_FOR_VISION_2_SEQ_MAPPING',
      'TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
      'TF_MODEL_MAPPING',
      'TF_MODEL_WITH_LM_HEAD_MAPPING',
      'TFAutoModel',
      'TFAutoModelForAudioClassification',
      'TFAutoModelForCausalLM',
      'TFAutoModelForImageClassification',
      'TFAutoModelForMaskedImageModeling',
      'TFAutoModelForMaskedLM',
      'TFAutoModelForMaskGeneration',
      'TFAutoModelForMultipleChoice',
      'TFAutoModelForNextSentencePrediction',
      'TFAutoModelForPreTraining',
      'TFAutoModelForDocumentQuestionAnswering',
      'TFAutoModelForQuestionAnswering',
      'TFAutoModelForSemanticSegmentation',
      'TFAutoModelForSeq2SeqLM',
      'TFAutoModelForSequenceClassification',
      'TFAutoModelForSpeechSeq2Seq',
      'TFAutoModelForTableQuestionAnswering',
      'TFAutoModelForTextEncoding',
      'TFAutoModelForTokenClassification',
      'TFAutoModelForVision2Seq',
      'TFAutoModelForZeroShotImageClassification',
      'TFAutoModelWithLMHead',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    :   List[Any]								=			[
      'FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
      'FLAX_MODEL_FOR_CAUSAL_LM_MAPPING',
      'FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
      'FLAX_MODEL_FOR_MASKED_LM_MAPPING',
      'FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
      'FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
      'FLAX_MODEL_FOR_PRETRAINING_MAPPING',
      'FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
      'FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
      'FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
      'FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
      'FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
      'FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING',
      'FLAX_MODEL_MAPPING',
      'FlaxAutoModel',
      'FlaxAutoModelForCausalLM',
      'FlaxAutoModelForImageClassification',
      'FlaxAutoModelForMaskedLM',
      'FlaxAutoModelForMultipleChoice',
      'FlaxAutoModelForNextSentencePrediction',
      'FlaxAutoModelForPreTraining',
      'FlaxAutoModelForQuestionAnswering',
      'FlaxAutoModelForSeq2SeqLM',
      'FlaxAutoModelForSequenceClassification',
      'FlaxAutoModelForSpeechSeq2Seq',
      'FlaxAutoModelForTokenClassification',
      'FlaxAutoModelForVision2Seq',
  ]


if TYPE_CHECKING:
  from .auto_factory import get_values
  from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
  from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
  from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
  from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
  from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_auto import (
        MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
        MODEL_FOR_AUDIO_XVECTOR_MAPPING,
        MODEL_FOR_BACKBONE_MAPPING,
        MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
        MODEL_FOR_CAUSAL_LM_MAPPING,
        MODEL_FOR_CTC_MAPPING,
        MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
        MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
        MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
        MODEL_FOR_MASK_GENERATION_MAPPING,
        MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
        MODEL_FOR_MASKED_LM_MAPPING,
        MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        MODEL_FOR_OBJECT_DETECTION_MAPPING,
        MODEL_FOR_PRETRAINING_MAPPING,
        MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_TEXT_ENCODING_MAPPING,
        MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
        MODEL_FOR_VISION_2_SEQ_MAPPING,
        MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
        MODEL_MAPPING,
        MODEL_WITH_LM_HEAD_MAPPING,
        AutoBackbone,
        AutoModel,
        AutoModelForAudioClassification,
        AutoModelForAudioFrameClassification,
        AutoModelForAudioXVector,
        AutoModelForCausalLM,
        AutoModelForCTC,
        AutoModelForDepthEstimation,
        AutoModelForDocumentQuestionAnswering,
        AutoModelForImageClassification,
        AutoModelForImageSegmentation,
        AutoModelForInstanceSegmentation,
        AutoModelForMaskedImageModeling,
        AutoModelForMaskedLM,
        AutoModelForMaskGeneration,
        AutoModelForMultipleChoice,
        AutoModelForNextSentencePrediction,
        AutoModelForObjectDetection,
        AutoModelForPreTraining,
        AutoModelForQuestionAnswering,
        AutoModelForSemanticSegmentation,
        AutoModelForSeqaSeqLM,
        AutoModelForSequenceClassification,
        AutoModelForSpeechSeqaSeq,
        AutoModelForTableQuestionAnswering,
        AutoModelForTextEncoding,
        AutoModelForTokenClassification,
        AutoModelForUniversalSegmentation,
        AutoModelForVideoClassification,
        AutoModelForVisionaSeq,
        AutoModelForVisualQuestionAnswering,
        AutoModelForZeroShotImageClassification,
        AutoModelForZeroShotObjectDetection,
        AutoModelWithLMHead,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_auto import (
        TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASK_GENERATION_MAPPING,
        TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
        TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_MAPPING,
        TF_MODEL_WITH_LM_HEAD_MAPPING,
        TFAutoModel,
        TFAutoModelForAudioClassification,
        TFAutoModelForCausalLM,
        TFAutoModelForDocumentQuestionAnswering,
        TFAutoModelForImageClassification,
        TFAutoModelForMaskedImageModeling,
        TFAutoModelForMaskedLM,
        TFAutoModelForMaskGeneration,
        TFAutoModelForMultipleChoice,
        TFAutoModelForNextSentencePrediction,
        TFAutoModelForPreTraining,
        TFAutoModelForQuestionAnswering,
        TFAutoModelForSemanticSegmentation,
        TFAutoModelForSeqaSeqLM,
        TFAutoModelForSequenceClassification,
        TFAutoModelForSpeechSeqaSeq,
        TFAutoModelForTableQuestionAnswering,
        TFAutoModelForTextEncoding,
        TFAutoModelForTokenClassification,
        TFAutoModelForVisionaSeq,
        TFAutoModelForZeroShotImageClassification,
        TFAutoModelWithLMHead,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_auto import (
        FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
        FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_MASKED_LM_MAPPING,
        FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        FLAX_MODEL_FOR_PRETRAINING_MAPPING,
        FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
        FLAX_MODEL_MAPPING,
        FlaxAutoModel,
        FlaxAutoModelForCausalLM,
        FlaxAutoModelForImageClassification,
        FlaxAutoModelForMaskedLM,
        FlaxAutoModelForMultipleChoice,
        FlaxAutoModelForNextSentencePrediction,
        FlaxAutoModelForPreTraining,
        FlaxAutoModelForQuestionAnswering,
        FlaxAutoModelForSeqaSeqLM,
        FlaxAutoModelForSequenceClassification,
        FlaxAutoModelForSpeechSeqaSeq,
        FlaxAutoModelForTokenClassification,
        FlaxAutoModelForVisionaSeq,
    )

else:
  import sys

  a__    :   Optional[int]								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









'''simple docstring'''







from __future__ import annotations

# Define the Gravitational Constant G and the function
a__    :   Optional[int]								=			6.67_43E-11  # unit of G : m^3 * kg^-1 * s^-2







def _UpperCamelCase			(	__A ,     __A ,     __A ,     __A       )				->						dict[str, float]:


      '''simple docstring'''



      UpperCamelCase__      =							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__      =							GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
            return {"force": force}
      elif mass_a == 0:
            UpperCamelCase__      =							(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_1": mass_a}
      elif mass_a == 0:
            UpperCamelCase__      =							(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_2": mass_a}
      elif distance == 0:
            UpperCamelCase__      =							(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()





