'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:     List[Any]  ={
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:     List[str]  =[
        'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ResNetForImageClassification',
        'ResNetModel',
        'ResNetPreTrainedModel',
        'ResNetBackbone',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:     Dict  =[
        'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFResNetForImageClassification',
        'TFResNetModel',
        'TFResNetPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:     Any  =[
        'FlaxResNetForImageClassification',
        'FlaxResNetModel',
        'FlaxResNetPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_resnet import (
            RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            ResNetBackbone,
            ResNetForImageClassification,
            ResNetModel,
            ResNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_resnet import (
            TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFResNetForImageClassification,
            TFResNetModel,
            TFResNetPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
    import sys

    SCREAMING_SNAKE_CASE_:     List[str]  =_LazyModule(__name__, globals()['__file__'], _import_structure)





'''simple docstring'''





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : int			)    ->      int:


     '''simple docstring'''




     return x if y == 0 else greatest_common_divisor(snake_case_					,    x % y			)





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : int			)    ->      int:


     '''simple docstring'''




     return (x * y) // greatest_common_divisor(snake_case_					,    snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : int = 20			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   1
     for i in range(1					,    n + 1			):
          UpperCAmelCase_           =   lcm(snake_case_					,    snake_case_			)
     return g


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




