
from typing import TYPE_CHECKING

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


lowercase__							:							Union[str, Any]									=			{
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase__							:							Any									=			[
        "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ResNetForImageClassification",
        "ResNetModel",
        "ResNetPreTrainedModel",
        "ResNetBackbone",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase__							:							int									=			[
        "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFResNetForImageClassification",
        "TFResNetModel",
        "TFResNetPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase__							:							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

    lowercase__							:							List[str]									=			_LazyModule(__name__, globals()["__file__"], _import_structure)






def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)




def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




