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# Source code for torchvision.models.quantization.mobilenetv2

    from functools import partial
    from typing import Any, Optional, Union

    from torch import nn, Tensor
    from torch.ao.quantization import DeQuantStub, QuantStub
    from torchvision.models.mobilenetv2 import InvertedResidual, MobileNet_V2_Weights, MobileNetV2

    from ...ops.misc import Conv2dNormActivation
    from ...transforms._presets import ImageClassification
    from .._api import register_model, Weights, WeightsEnum
    from .._meta import _IMAGENET_CATEGORIES
    from .._utils import _ovewrite_named_param, handle_legacy_interface
    from .utils import _fuse_modules, _replace_relu, quantize_model

    __all__ = [
        "QuantizableMobileNetV2",
        "MobileNet_V2_QuantizedWeights",
        "mobilenet_v2",
    ]

    class QuantizableInvertedResidual(InvertedResidual):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, **kwargs)
            self.skip_add = nn.quantized.FloatFunctional()

        def forward(self, x: Tensor) -> Tensor:
            if self.use_res_connect:
                return self.skip_add.add(x, self.conv(x))
            else:
                return self.conv(x)

        def fuse_model(self, is_qat: Optional[bool] = None) -> None:
            for idx in range(len(self.conv)):
                if type(self.conv[idx]) is nn.Conv2d:
                    _fuse_modules(self.conv, [str(idx), str(idx + 1)], is_qat, inplace=True)

    class QuantizableMobileNetV2(MobileNetV2):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            """
            MobileNet V2 main class

            Args:
               Inherits args from floating point MobileNetV2
            """
            super().__init__(*args, **kwargs)
            self.quant = QuantStub()
            self.dequant = DeQuantStub()

        def forward(self, x: Tensor) -> Tensor:
            x = self.quant(x)
            x = self._forward_impl(x)
            x = self.dequant(x)
            return x

        def fuse_model(self, is_qat: Optional[bool] = None) -> None:
            for m in self.modules():
                if type(m) is Conv2dNormActivation:
                    _fuse_modules(m, ["0", "1", "2"], is_qat, inplace=True)
                if type(m) is QuantizableInvertedResidual:
                    m.fuse_model(is_qat)

    class MobileNet_V2_QuantizedWeights(WeightsEnum):
        IMAGENET1K_QNNPACK_V1 = Weights(
            url="https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth",
            transforms=partial(ImageClassification, crop_size=224),
            meta={
                "num_params": 3504872,
                "min_size": (1, 1),
                "categories": _IMAGENET_CATEGORIES,
                "backend": "qnnpack",
                "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2",
                "unquantized": MobileNet_V2_Weights.IMAGENET1K_V1,
                "_metrics": {
                    "ImageNet-1K": {
                        "acc@1": 71.658,
                        "acc@5": 90.150,
                    }
                },
                "_ops": 0.301,
                "_file_size": 3.423,
                "_docs": """
                    These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized
                    weights listed below.
                """,
            },
        )
        DEFAULT = IMAGENET1K_QNNPACK_V1

    @register_model(name="quantized_mobilenet_v2")
    @handle_legacy_interface(
        weights=(
            "pretrained",
            lambda kwargs: MobileNet_V2_QuantizedWeights.IMAGENET1K_QNNPACK_V1
            if kwargs.get("quantize", False)
            else MobileNet_V2_Weights.IMAGENET1K_V1,
        )
    )
    def mobilenet_v2(
        *,
        weights: Optional[Union[MobileNet_V2_QuantizedWeights, MobileNet_V2_Weights]] = None,
        progress: bool = True,
        quantize: bool = False,
        **kwargs: Any,
    ) -> QuantizableMobileNetV2:
        """
        Constructs a MobileNetV2 architecture from
        `MobileNetV2: Inverted Residuals and Linear Bottlenecks
        <https://arxiv.org/abs/1801.04381>`_.

        .. note::
            Note that ``quantize = True`` returns a quantized model with 8 bit
            weights. Quantized models only support inference and run on CPUs.
            GPU inference is not yet supported.

        Args:
            weights (:class:`~torchvision.models.quantization.MobileNet_V2_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V2_Weights`, optional): The
                pretrained weights for the model. See
                :class:`~torchvision.models.quantization.MobileNet_V2_QuantizedWeights` below for
                more details, and possible values. By default, no pre-trained
                weights are used.
            progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True.
            quantize (bool, optional): If True, returns a quantized version of the model. Default is False.
            **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableMobileNetV2``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/mobilenetv2.py>`_
                for more details about this class.
        .. autoclass:: torchvision.models.quantization.MobileNet_V2_QuantizedWeights
            :members:
        .. autoclass:: torchvision.models.MobileNet_V2_Weights
            :members:
            :noindex:
        """
        weights = (MobileNet_V2_QuantizedWeights if quantize else MobileNet_V2_Weights).verify(weights)

        if weights is not None:
            _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
            if "backend" in weights.meta:
                _ovewrite_named_param(kwargs, "backend", weights.meta["backend"])
        backend = kwargs.pop("backend", "qnnpack")

        model = QuantizableMobileNetV2(block=QuantizableInvertedResidual, **kwargs)
        _replace_relu(model)
        if quantize:
            quantize_model(model, backend)

        if weights is not None:
            model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))

        return model

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