



from typing import TYPE_CHECKING

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


A   :					List[str]	=      {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

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

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

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					Optional[int]	=      [
        "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

    A   :					Union[str, Any]	=      _LazyModule(__name__, globals()["__file__"], _import_structure)






import os
from pathlib import Path

def    lowercase_	(	):

      """simple docstring"""




      from torch.utils.cpp_extension import load

      lowerCamelCase__	:       Any					=						Path(_A		).resolve().parent.parent.parent / "kernels" / "deformable_detr"
      lowerCamelCase__	:       Optional[int]					=						[
          root / filename
          for filename in [
              "vision.cpp",
              os.path.join("cpu"					,      "ms_deform_attn_cpu.cpp"		),
              os.path.join("cuda"					,      "ms_deform_attn_cuda.cu"		),
          ]
      ]

      load(
          "MultiScaleDeformableAttention"					,      _A					,      with_cuda=_A					,      extra_include_paths=[str(_A		)]					,      extra_cflags=["-DWITH_CUDA=1"]					,      extra_cuda_cflags=[
              "-DCUDA_HAS_FP16=1",
              "-D__CUDA_NO_HALF_OPERATORS__",
              "-D__CUDA_NO_HALF_CONVERSIONS__",
              "-D__CUDA_NO_HALF2_OPERATORS__",
          ]					,      )

      import MultiScaleDeformableAttention as MSDA

      return MSDA


