import torch
import torch.nn as nn
from spikingjelly.clock_driven import layer
from spikingjelly.cext import neuron as cext_neuron

__all__ = ['SpikingResNet', 'spiking_resnet18', 'spiking_resnet34', 'spiking_resnet50', 'spiking_resnet101',
           'spiking_resnet152']



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


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 BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('MemAddBasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in MemAddBasicBlock")


        self.conv1 = layer.SeqToANNContainer(
            conv3x3(inplanes, planes, stride),
            norm_layer(planes)
        )
        self.sn1 = cext_neuron.MultiStepIFNode(detach_reset=True)

        self.conv2 = layer.SeqToANNContainer(
            conv3x3(planes, planes),
            norm_layer(planes)
        )
        self.sn2 = cext_neuron.MultiStepIFNode(detach_reset=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.sn1(self.conv1(x))

        out = self.conv2(out)

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

        out += identity

        return self.sn2(out)


class Bottleneck(nn.Module):

    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups

        self.conv1 = layer.SeqToANNContainer(
            conv1x1(inplanes, width),
            norm_layer(width)
        )
        self.sn1 = cext_neuron.MultiStepIFNode(detach_reset=True)


        self.conv2 = layer.SeqToANNContainer(
            conv3x3(width, width, stride, groups, dilation),
            norm_layer(width)
        )
        self.sn2 = cext_neuron.MultiStepIFNode(detach_reset=True)

        self.conv3 = layer.SeqToANNContainer(
            conv1x1(width, planes * self.expansion),
            norm_layer(planes * self.expansion)
        )
        self.sn3 = cext_neuron.MultiStepIFNode(detach_reset=True)

        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.sn1(self.conv1(x))

        out = self.sn2(self.conv2(out))

        out = self.conv3(out)

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

        out += identity

        return self.sn3(out)


def zero_init_blocks(net: nn.Module):
    for m in net.modules():
        if isinstance(m, Bottleneck):
            nn.init.constant_(m.conv3.module[1].weight, 0)
        elif isinstance(m, BasicBlock):
            nn.init.constant_(m.conv2.module[1].weight, 0)

class SpikingResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None, T=4):
        super(SpikingResNet, self).__init__()
        self.T = T
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        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.sn1 = cext_neuron.MultiStepIFNode(detach_reset=True)
        self.maxpool = layer.SeqToANNContainer(nn.MaxPool2d(kernel_size=3, stride=2, padding=1))

        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = layer.SeqToANNContainer(nn.AdaptiveAvgPool2d((1, 1)))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

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

        if zero_init_residual:
            zero_init_blocks(self)

    def _make_layer(self, block, planes, blocks, 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 = layer.SeqToANNContainer(
                    conv1x1(self.inplanes, planes * block.expansion, stride),
                    norm_layer(planes * block.expansion),
                )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        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,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x.unsqueeze_(0)
        x = x.repeat(self.T, 1, 1, 1, 1)
        x = self.sn1(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 2)
        return self.fc(x.mean(dim=0))

    def forward(self, x):
        return self._forward_impl(x)


def _spiking_resnet(block, layers, **kwargs):
    model = SpikingResNet(block, layers, **kwargs)
    return model


def spiking_resnet18(**kwargs):

    return _spiking_resnet(BasicBlock, [2, 2, 2, 2], **kwargs)


def spiking_resnet34(**kwargs):

    return _spiking_resnet(BasicBlock, [3, 4, 6, 3], **kwargs)


def spiking_resnet50(**kwargs):

    return _spiking_resnet(Bottleneck, [3, 4, 6, 3], **kwargs)


def spiking_resnet101(**kwargs):
    return _spiking_resnet(Bottleneck, [3, 4, 23, 3], **kwargs)


def spiking_resnet152(**kwargs):

    return _spiking_resnet(Bottleneck, [3, 8, 36, 3], **kwargs)


if __name__ == "__main__":
    with torch.no_grad():
        device = 'cuda:0'
        # test create net
        # net = spiking_resnet18()
        # print(net)

        # test zero init
        net = BasicBlock(inplanes=64, planes=64)
        net.to(device)
        zero_init_blocks(net)
        x = torch.rand([2, 1, 64, 16, 16], device=device)
        x = (x >= 0.5).float()
        y = net(x)
        assert (y - x).abs().max().item() == 0

        net = Bottleneck(inplanes=64, planes=16)
        net.to(device)
        zero_init_blocks(net)
        x = torch.rand([2, 1, 64, 16, 16], device=device)
        x = (x >= 0.5).float()
        y = net(x)
        assert (y - x).abs().max().item() == 0


