from torch import nn

# from torchvision.models.utils import load_state_dict_from_url
import torch

__all__ = ["MobileNetV2", "mobilenetv2"]


model_urls = {
    "mobilenetv2": "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
}


def _make_divisible(v, divisor, min_value=None):
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class ConvBNReLU(nn.Sequential):
    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
        padding = (kernel_size - 1) // 2
        super(ConvBNReLU, self).__init__(
            nn.Conv2d(
                in_planes,
                out_planes,
                kernel_size,
                stride,
                padding,
                groups=groups,
                bias=False,
            ),
            nn.BatchNorm2d(out_planes),
            nn.ReLU6(inplace=True),
        )


class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, expand_ratio):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]

        hidden_dim = int(round(inp * expand_ratio))
        self.use_res_connect = self.stride == 1 and inp == oup

        layers = []
        if expand_ratio != 1:
            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
        layers.extend(
            [
                ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            ]
        )
        self.conv = nn.Sequential(*layers)

    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)


class MobileNetV2(nn.Module):
    def __init__(
        self,
        num_classes=1000,
        width_mult=1.0,
        inverted_residual_setting=None,
        round_nearest=8,
    ):
        super(MobileNetV2, self).__init__()
        block = InvertedResidual
        input_channel = 32
        last_channel = 1280

        if inverted_residual_setting is None:
            inverted_residual_setting = [
                # t, c, n, s
                # 112, 112, 32 -> 112, 112, 16
                [1, 16, 1, 1],
                # 112, 112, 16 -> 56, 56, 24
                [6, 24, 2, 2],
                # 56, 56, 24 -> 28, 28, 32
                [6, 32, 3, 2],
                # 28, 28, 32 -> 14, 14, 64
                [6, 64, 4, 2],
                # 14, 14, 64 -> 14, 14, 96
                [6, 96, 3, 1],
                # 14, 14, 96 -> 7, 7, 160
                [6, 160, 3, 2],
                # 7, 7, 160 -> 7, 7, 320
                [6, 320, 1, 1],
            ]

        if (
            len(inverted_residual_setting) == 0
            or len(inverted_residual_setting[0]) != 4
        ):
            raise ValueError(
                "inverted_residual_setting should be non-empty "
                "or a 4-element list, got {}".format(inverted_residual_setting)
            )

        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
        self.last_channel = _make_divisible(
            last_channel * max(1.0, width_mult), round_nearest
        )

        # 224, 224, 3 -> 112, 112, 32
        features = [ConvBNReLU(3, input_channel, stride=2)]

        for t, c, n, s in inverted_residual_setting:
            output_channel = _make_divisible(c * width_mult, round_nearest)
            for i in range(n):
                stride = s if i == 0 else 1
                features.append(
                    block(input_channel, output_channel, stride, expand_ratio=t)
                )
                input_channel = output_channel

        # 7, 7, 320 -> 7,7,1280
        features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
        self.features = nn.Sequential(*features)

        self.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(self.last_channel, num_classes),
        )

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out")
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)

    def forward(self, x):
        x = self.features(x)
        # 1280
        x = x.mean([2, 3])
        x = self.classifier(x)
        return x

    def freeze_backbone(self):
        for param in self.features.parameters():
            param.requires_grad = False

    def Unfreeze_backbone(self):
        for param in self.features.parameters():
            param.requires_grad = True


def mobilenetv2(pretrained=False, progress=True, num_classes=1000):
    model = MobileNetV2()
    if pretrained:
        state_dict = torch.load(
            "F:/super-resolution/task/classification-pytorch/mobilenet_v2-b0353104.pth"
        )
        model.load_state_dict(state_dict)

    if num_classes != 1000:
        model.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(model.last_channel, num_classes),
        )
    return model
