from typing import Any, List, Optional

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

from ..._internally_replaced_utils import load_state_dict_from_url
from ...ops.misc import Conv2dNormActivation, SqueezeExcitation
from ..mobilenetv3 import InvertedResidual, InvertedResidualConfig, MobileNetV3, model_urls, _mobilenet_v3_conf
from .utils import _fuse_modules, _replace_relu


__all__ = ["QuantizableMobileNetV3", "mobilenet_v3_large"]

quant_model_urls = {
    "mobilenet_v3_large_qnnpack": "https://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth",
}


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__(se_layer=QuantizableSqueezeExcitation, *args, **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 _load_weights(arch: str, model: QuantizableMobileNetV3, model_url: Optional[str], progress: bool) -> None:
    if model_url is None:
        raise ValueError(f"No checkpoint is available for {arch}")
    state_dict = load_state_dict_from_url(model_url, progress=progress)
    model.load_state_dict(state_dict)


def _mobilenet_v3_model(
    arch: str,
    inverted_residual_setting: List[InvertedResidualConfig],
    last_channel: int,
    pretrained: bool,
    progress: bool,
    quantize: bool,
    **kwargs: Any,
) -> QuantizableMobileNetV3:

    model = QuantizableMobileNetV3(inverted_residual_setting, last_channel, block=QuantizableInvertedResidual, **kwargs)
    _replace_relu(model)

    if quantize:
        backend = "qnnpack"

        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 pretrained:
            _load_weights(arch, model, quant_model_urls.get(arch + "_" + backend, None), progress)

        torch.ao.quantization.convert(model, inplace=True)
        model.eval()
    else:
        if pretrained:
            _load_weights(arch, model, model_urls.get(arch, None), progress)

    return model


def mobilenet_v3_large(
    pretrained: bool = False,
    progress: bool = True,
    quantize: bool = False,
    **kwargs: Any,
) -> QuantizableMobileNetV3:
    """
    Constructs a MobileNetV3 Large architecture from
    `"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_.

    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:
     pretrained (bool): If True, returns a model pre-trained on ImageNet.
     progress (bool): If True, displays a progress bar of the download to stderr
     quantize (bool): If True, returns a quantized model, else returns a float model
    """
    arch = "mobilenet_v3_large"
    inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs)
    return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, quantize, **kwargs)
