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# Source code for torchvision.models.quantization.mobilenetv3

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

    import torch
    from torch import nn, Tensor
    from torch.ao.quantization import DeQuantStub, QuantStub

    from ...ops.misc import Conv2dNormActivation, SqueezeExcitation
    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 ..mobilenetv3 import (
        _mobilenet_v3_conf,
        InvertedResidual,
        InvertedResidualConfig,
        MobileNet_V3_Large_Weights,
        MobileNetV3,
    )
    from .utils import _fuse_modules, _replace_relu

    __all__ = [
        "QuantizableMobileNetV3",
        "MobileNet_V3_Large_QuantizedWeights",
        "mobilenet_v3_large",
    ]

    class QuantizableSqueezeExcitation(SqueezeExcitation):
        _version = 2

        def __init__(self, *args: Any, **kwargs: Any) -> None:
            kwargs["scale_activation"] = nn.Hardsigmoid
            super().__init__(*args, **kwargs)
            self.skip_mul = nn.quantized.FloatFunctional()

        def forward(self, input: Tensor) -> Tensor:
            return self.skip_mul.mul(self._scale(input), input)

        def fuse_model(self, is_qat: Optional[bool] = None) -> None:
            _fuse_modules(self, ["fc1", "activation"], is_qat, inplace=True)

        def _load_from_state_dict(
            self,
            state_dict,
            prefix,
            local_metadata,
            strict,
            missing_keys,
            unexpected_keys,
            error_msgs,
        ):
            version = local_metadata.get("version", None)

            if hasattr(self, "qconfig") and (version is None or version < 2):
                default_state_dict = {
                    "scale_activation.activation_post_process.scale": torch.tensor([1.0]),
                    "scale_activation.activation_post_process.activation_post_process.scale": torch.tensor([1.0]),
                    "scale_activation.activation_post_process.zero_point": torch.tensor([0], dtype=torch.int32),
                    "scale_activation.activation_post_process.activation_post_process.zero_point": torch.tensor(
                        [0], dtype=torch.int32
                    ),
                    "scale_activation.activation_post_process.fake_quant_enabled": torch.tensor([1]),
                    "scale_activation.activation_post_process.observer_enabled": torch.tensor([1]),
                }
                for k, v in default_state_dict.items():
                    full_key = prefix + k
                    if full_key not in state_dict:
                        state_dict[full_key] = v

            super()._load_from_state_dict(
                state_dict,
                prefix,
                local_metadata,
                strict,
                missing_keys,
                unexpected_keys,
                error_msgs,
            )

    class QuantizableInvertedResidual(InvertedResidual):
        # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            super().__init__(*args, se_layer=QuantizableSqueezeExcitation, **kwargs)  # type: ignore[misc]
            self.skip_add = nn.quantized.FloatFunctional()

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

    class QuantizableMobileNetV3(MobileNetV3):
        def __init__(self, *args: Any, **kwargs: Any) -> None:
            """
            MobileNet V3 main class

            Args:
               Inherits args from floating point MobileNetV3
            """
            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:
                    modules_to_fuse = ["0", "1"]
                    if len(m) == 3 and type(m[2]) is nn.ReLU:
                        modules_to_fuse.append("2")
                    _fuse_modules(m, modules_to_fuse, is_qat, inplace=True)
                elif type(m) is QuantizableSqueezeExcitation:
                    m.fuse_model(is_qat)

    def _mobilenet_v3_model(
        inverted_residual_setting: List[InvertedResidualConfig],
        last_channel: int,
        weights: Optional[WeightsEnum],
        progress: bool,
        quantize: bool,
        **kwargs: Any,
    ) -> QuantizableMobileNetV3:
        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 = QuantizableMobileNetV3(inverted_residual_setting, last_channel, block=QuantizableInvertedResidual, **kwargs)
        _replace_relu(model)

        if quantize:
            # Instead of quantizing the model and then loading the quantized weights we take a different approach.
            # We prepare the QAT model, load the QAT weights from training and then convert it.
            # This is done to avoid extremely low accuracies observed on the specific model. This is rather a workaround
            # for an unresolved bug on the eager quantization API detailed at: https://github.com/pytorch/vision/issues/5890
            model.fuse_model(is_qat=True)
            model.qconfig = torch.ao.quantization.get_default_qat_qconfig(backend)
            torch.ao.quantization.prepare_qat(model, inplace=True)

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

        if quantize:
            torch.ao.quantization.convert(model, inplace=True)
            model.eval()

        return model

    class MobileNet_V3_Large_QuantizedWeights(WeightsEnum):
        IMAGENET1K_QNNPACK_V1 = Weights(
            url="https://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth",
            transforms=partial(ImageClassification, crop_size=224),
            meta={
                "num_params": 5483032,
                "min_size": (1, 1),
                "categories": _IMAGENET_CATEGORIES,
                "backend": "qnnpack",
                "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv3",
                "unquantized": MobileNet_V3_Large_Weights.IMAGENET1K_V1,
                "_metrics": {
                    "ImageNet-1K": {
                        "acc@1": 73.004,
                        "acc@5": 90.858,
                    }
                },
                "_ops": 0.217,
                "_file_size": 21.554,
                "_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_v3_large")
    @handle_legacy_interface(
        weights=(
            "pretrained",
            lambda kwargs: MobileNet_V3_Large_QuantizedWeights.IMAGENET1K_QNNPACK_V1
            if kwargs.get("quantize", False)
            else MobileNet_V3_Large_Weights.IMAGENET1K_V1,
        )
    )
    def mobilenet_v3_large(
        *,
        weights: Optional[Union[MobileNet_V3_Large_QuantizedWeights, MobileNet_V3_Large_Weights]] = None,
        progress: bool = True,
        quantize: bool = False,
        **kwargs: Any,
    ) -> QuantizableMobileNetV3:
        """
        MobileNetV3 (Large) model from
        `Searching for MobileNetV3 <https://arxiv.org/abs/1905.02244>`_.

        .. 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_V3_Large_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The
                pretrained weights for the model. See
                :class:`~torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights` below for
                more details, and possible values. By default, no pre-trained
                weights are used.
            progress (bool): If True, displays a progress bar of the
                download to stderr. Default is True.
            quantize (bool): If True, return a quantized version of the model. Default is False.
            **kwargs: parameters passed to the ``torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/mobilenetv3.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights
            :members:
        .. autoclass:: torchvision.models.MobileNet_V3_Large_Weights
            :members:
            :noindex:
        """
        weights = (MobileNet_V3_Large_QuantizedWeights if quantize else MobileNet_V3_Large_Weights).verify(weights)

        inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs)
        return _mobilenet_v3_model(inverted_residual_setting, last_channel, weights, progress, quantize, **kwargs)

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