import torch
import torch.nn as nn
from .Attention import AxialAttention


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


class AxialBlock(nn.Module):
    expansion = 2

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None,
        kernel_size=56,
    ):
        super(AxialBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.0))
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv_down = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.hight_block = AxialAttention(
            width, width, groups=groups, kernel_size=kernel_size
        )
        self.width_block = AxialAttention(
            width,
            width,
            groups=groups,
            kernel_size=kernel_size,
            stride=stride,
            width=True,
        )
        self.conv_up = conv1x1(width, planes * self.expansion)
        self.bn2 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

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

        out = self.hight_block(out)
        out = self.width_block(out)
        out = self.relu(out)

        out = self.conv_up(out)
        out = self.bn2(out)

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

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

        return out


class AxialAttentionNet(nn.Module):
    def __init__(
        self,
        layers,
        num_classes=1000,
        zero_init_residual=True,
        groups=8,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        s=0.5,
    ):
        super(AxialAttentionNet, self).__init__()
        block = AxialBlock
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = int(64 * s)
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            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=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(
            block, int(128 * s), layers[0], kernel_size=56
        )
        self.layer2 = self._make_layer(
            block,
            int(256 * s),
            layers[1],
            stride=2,
            kernel_size=56,
            dilate=replace_stride_with_dilation[0],
        )
        self.layer3 = self._make_layer(
            block,
            int(512 * s),
            layers[2],
            stride=2,
            kernel_size=28,
            dilate=replace_stride_with_dilation[1],
        )
        self.layer4 = self._make_layer(
            block,
            int(1024 * s),
            layers[3],
            stride=2,
            kernel_size=14,
            dilate=replace_stride_with_dilation[2],
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(int(1024 * block.expansion * s), num_classes)

        for m in self.modules():
            if isinstance(m, (nn.Conv2d, nn.Conv1d)):
                if isinstance(m, nn.Conv1d):
                    pass
                else:
                    nn.init.kaiming_normal_(
                        m.weight, mode='fan_out', nonlinearity='relu'
                    )
            elif isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, AxialBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(
        self, block, planes, blocks, kernel_size=56, stride=1, dilate=False
    ):
        norm_layer = self._norm_layer
        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),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(
            block(
                self.inplanes,
                planes,
                stride,
                downsample,
                groups=self.groups,
                base_width=self.base_width,
                dilation=previous_dilation,
                norm_layer=norm_layer,
                kernel_size=kernel_size,
            )
        )
        self.inplanes = planes * block.expansion
        if stride != 1:
            kernel_size = kernel_size // 2

        for _ in range(1, blocks):
            layers.append(
                block(
                    self.inplanes,
                    planes,
                    groups=self.groups,
                    base_width=self.base_width,
                    dilation=self.dilation,
                    norm_layer=norm_layer,
                    kernel_size=kernel_size,
                )
            )

        return nn.Sequential(*layers)

    def _forward_impl(self, x, include_conv5=False, include_top=False):
        # See note [TorchScript super()]
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        if include_conv5:
            x = self.layer4(x)

        if include_top:
            x = self.avgpool(x)
            x = torch.flatten(x, 1)
            x = self.fc(x)

        return x

    def forward(self, x, include_conv5=False, include_top=False):
        return self._forward_impl(x, include_conv5, include_top)


if __name__ == "__main__":
    model = AxialAttentionNet([1, 2, 4, 1], s=0.5)
    # model = AxialAttentionNet([3, 4, 6, 3], s=0.25)
    # print(model)

    x = torch.randn([8, 3, 224, 224])
    print(model(x).shape)
    print(model(x, include_conv5=True).shape)
    print(model(x, include_conv5=True, include_top=True).shape)
