





from typing import TYPE_CHECKING

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


snake_case_				=			{'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_				=			['BeitFeatureExtractor']
    snake_case_				=			['BeitImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_				=			[
        'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BeitForImageClassification',
        'BeitForMaskedImageModeling',
        'BeitForSemanticSegmentation',
        'BeitModel',
        'BeitPreTrainedModel',
    ]


try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_				=			[
        'FlaxBeitForImageClassification',
        'FlaxBeitForMaskedImageModeling',
        'FlaxBeitModel',
        'FlaxBeitPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_beit import BeitFeatureExtractor
        from .image_processing_beit import BeitImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_beit import (
            BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BeitForImageClassification,
            BeitForMaskedImageModeling,
            BeitForSemanticSegmentation,
            BeitModel,
            BeitPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_beit import (
            FlaxBeitForImageClassification,
            FlaxBeitForMaskedImageModeling,
            FlaxBeitModel,
            FlaxBeitPreTrainedModel,
        )


else:
    import sys

    snake_case_				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








def lowerCamelCase__    (							)      ->				int:
   return [
       a * b * (1000 - a - b)
       for a in range(1       ,      999       )
       for b in range(snake_case_       ,      999       )
       if (a * a + b * b == (1000 - a - b) ** 2)
   ][0]


if __name__ == "__main__":
    print(F'{solution() = }')

