


from typing import TYPE_CHECKING

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


__UpperCamelCase    :						Optional[int]     =							{'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

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

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


try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						Dict     =							[
        '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

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







def   _UpperCAmelCase       (     UpperCAmelCase      :			int = 4_000_000      ):
      """simple docstring"""




      __lowerCamelCase     :      Union[str, Any]       						=						[0, 1]
      __lowerCamelCase     :      Dict       						=						0
      while fib[i] <= n:
            fib.append(fib[i] + fib[i + 1]      )
            if fib[i + 2] > n:
                  break
            i += 1
      __lowerCamelCase     :      Any       						=						0
      for j in range(len(UpperCAmelCase      ) - 1      ):
            if fib[j] % 2 == 0:
                  total += fib[j]

      return total


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



