import torch
import torch.nn as nn
from styleformer.utils.registry import ARCH_REGISTRY


def conv3x3(inplanes, outplanes, stride=1):
    """A simple wrapper for 3x3 convolution with padding.

    Args:
        inplanes (int): Channel number of inputs.
        outplanes (int): Channel number of outputs.
        stride (int): Stride in convolution. Default: 1.
    """
    return nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False)


class BasicBlock(nn.Module):
    """Basic residual block used in the ResNetArcFace architecture.

    Args:
        inplanes (int): Channel number of inputs.
        planes (int): Channel number of outputs.
        stride (int): Stride in convolution. Default: 1.
        downsample (nn.Module): The downsample module. Default: None.
    """
    expansion = 1  # output channel expansion ratio

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class IRBlock(nn.Module):
    """Improved residual block (IR Block) used in the ResNetArcFace architecture.

    Args:
        inplanes (int): Channel number of inputs.
        planes (int): Channel number of outputs.
        stride (int): Stride in convolution. Default: 1.
        downsample (nn.Module): The downsample module. Default: None.
        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
    """
    expansion = 1  # output channel expansion ratio

    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
        super(IRBlock, self).__init__()
        self.bn0 = nn.BatchNorm2d(inplanes)
        self.conv1 = conv3x3(inplanes, inplanes)
        self.bn1 = nn.BatchNorm2d(inplanes)
        self.prelu = nn.PReLU()
        self.conv2 = conv3x3(inplanes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
        self.use_se = use_se
        if self.use_se:
            self.se = SEBlock(planes)

    def forward(self, x):
        residual = x
        out = self.bn0(x)
        out = self.conv1(out)
        out = self.bn1(out)
        out = self.prelu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        if self.use_se:
            out = self.se(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.prelu(out)

        return out


class Bottleneck(nn.Module):
    """Bottleneck block used in the ResNetArcFace architecture.

    Args:
        inplanes (int): Channel number of inputs.
        planes (int): Channel number of outputs.
        stride (int): Stride in convolution. Default: 1.
        downsample (nn.Module): The downsample module. Default: None.
    """
    expansion = 4  # output channel expansion ratio

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class SEBlock(nn.Module):
    """The squeeze-and-excitation block (SEBlock) used in the IRBlock.

    Args:
        channel (int): Channel number of inputs.
        reduction (int): Channel reduction ration. Default: 16.
    """

    def __init__(self, channel, reduction=16):
        super(SEBlock, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)  # pool to 1x1 without spatial information
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction), nn.PReLU(), nn.Linear(channel // reduction, channel),
            nn.Sigmoid())

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y


@ARCH_REGISTRY.register()
class ResNetArcFace(nn.Module):
    """ArcFace with ResNet architectures.

    Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition.

    Args:
        block (str): Block used in the ArcFace architecture.
        layers (tuple(int)): Block numbers in each layer.
        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
    """

    def __init__(self, in_channels, block, layers, use_se=True, embedding=256, load_path=None):
        if block == 'IRBlock':
            block = IRBlock
        self.inplanes = 32
        self.use_se = use_se
        super(ResNetArcFace, self).__init__()

        self.conv1 = nn.Conv2d(in_channels, self.inplanes, kernel_size=3, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.inplanes)
        self.prelu = nn.PReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.bn4 = nn.BatchNorm2d(512)
        self.dropout = nn.Dropout()
        self.fc5 = nn.Linear(512 * 8 * 8, embedding)
        self.bn5 = nn.BatchNorm1d(embedding)

        if load_path:
            self.load_state_dict(torch.load(load_path)['params'], strict=True)
        else:
            # initialization
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    nn.init.xavier_normal_(m.weight)
                elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
                    nn.init.constant_(m.weight, 1)
                    nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.Linear):
                    nn.init.xavier_normal_(m.weight)
                    nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, num_blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
        self.inplanes = planes
        for _ in range(1, num_blocks):
            layers.append(block(self.inplanes, planes, use_se=self.use_se))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.prelu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.bn4(x)
        x = self.dropout(x)
        x = x.view(x.size(0), -1)
        x = self.fc5(x)
        x = self.bn5(x)

        return x

if __name__ == '__main__':
    import torch
    device = torch.device('cpu')
    x = torch.Tensor(1,3,256,256).to(device)

    model = ResNetArcFace(IRBlock, [2,2,2,2])

    print(model)
    model_params = sum(map(lambda x: x.numel(), model.parameters()))
    print(f'Network with parameters: {model_params:d} ')    
    model.to(device)
    model.eval()

    with torch.no_grad():
        output = model(x)
