import torch
from torch import nn

__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1, bias=False):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes,
                     out_planes,
                     kernel_size=3,
                     stride=stride,
                     padding=dilation,
                     groups=groups,
                     bias=bias,
                     dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1, bias=False):
    """1x1 convolution"""
    return nn.Conv2d(in_planes,
                     out_planes,
                     kernel_size=1,
                     stride=stride,
                     bias=bias)


class IBasicBlock(nn.Module):
    expansion = 1
    def __init__(self, inplanes, planes, stride=1, downsample=None,
                 groups=1, base_width=64, dilation=1):
        super(IBasicBlock, self).__init__()
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
        self.conv1 = conv3x3(inplanes, planes)
        self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
        self.prelu = nn.PReLU(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x
        out = self.bn1(x)
        out = self.conv1(out)
        out = self.bn2(out)
        out = self.prelu(out)
        out = self.conv2(out)
        out = self.bn3(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        return out


class IResNet(nn.Module):
    fc_scale = 7 * 7
    def __init__(self,
                 block, layers, dropout=0, num_features=512, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False,
                 resolution=112):
        super(IResNet, self).__init__()
        self.fp16 = fp16
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
        self.prelu = nn.PReLU(self.inplanes)
        self.layer1 = self._make_layer(block, 64, layers[0], stride=(2 if resolution%112==0 else 1))
        self.layer2 = self._make_layer(block,
                                       128,
                                       layers[1],
                                       stride=(2 if resolution%56==0 else 1), #2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block,
                                       256,
                                       layers[2],
                                       stride=(2 if resolution%28==0 else 1),
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block,
                                       512,
                                       layers[3],
                                       stride=(2 if resolution%14==0 else 1),
                                       dilate=replace_stride_with_dilation[2])
        self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
        self.dropout = nn.Dropout(p=dropout, inplace=True)
        self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
        nn.init.constant_(self.features.weight, 1.0)
        self.features.weight.requires_grad = False

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.normal_(m.weight, 0, 0.1)
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, IBasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                  self.base_width, previous_dilation))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(
                block(self.inplanes,
                      planes,
                      groups=self.groups,
                      base_width=self.base_width,
                      dilation=self.dilation))

        return nn.Sequential(*layers)

    def forward(self, x):
        with torch.cuda.amp.autocast(self.fp16):
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.prelu(x)
            for i in range(len(self.layer1)):
                x = self.layer1[i](x)
                if i == 0:
                    feat1 = x
            for i in range(len(self.layer2)):
                x = self.layer2[i](x)
                if i == 0:
                    feat2 = x
            for i in range(len(self.layer3)):
                x = self.layer3[i](x)
                if i == 0:
                    feat3 = x
            for i in range(len(self.layer4)):
                x = self.layer4[i](x)
                if i == 0:
                    feat4 = x
            x = self.bn2(x)
            x = torch.flatten(x, 1)
            x = self.dropout(x)
        x = self.fc(x.float() if self.fp16 else x)
        x = self.features(x)
        feats = [feat1, feat2, feat3, feat4]
        return x, feats   #reture output and feature maps


def _iresnet(arch, block, layers, progress, resolution,  **kwargs):
    model = IResNet(block, layers, resolution=resolution, **kwargs)
    return model


def iresnet18(progress=True, resolution=112, **kwargs):
    return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2],
                    progress, resolution, **kwargs)


def iresnet34(progress=True, resolution=112, **kwargs):
    return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3],
                    progress, resolution, **kwargs)


def iresnet50(progress=True, resolution=112, **kwargs):
    return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3],
                    progress, resolution, **kwargs)


def iresnet100(progress=True, resolution=112, **kwargs):
    return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3],
                    progress, resolution, **kwargs)


def iresnet200(progress=True, resolution=112, **kwargs):
    return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6],
                    progress, resolution, **kwargs)
