import torch
import torch.nn as nn
import torch.nn.functional as F


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, is_last=False):
        super(BasicBlock, self).__init__()
        self.is_last = is_last
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        preact = out
        out = F.relu(out)
        if self.is_last:
            return out, preact
        else:
            return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1, is_last=False):
        super(Bottleneck, self).__init__()
        self.is_last = is_last
        self.conv1 = nn.Conv2d(in_planes, 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, self.expansion * planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion * planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        preact = out
        out = F.relu(out)
        if self.is_last:
            return out, preact
        else:
            return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, in_channel=3, zero_init_residual=False):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=3, stride=1, padding=1,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        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)

        # 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, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for i in range(num_blocks):
            stride = strides[i]
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x, layer=100):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avgpool(out)
        #now out dim: batch_size, 512, 1, 1
        out = torch.flatten(out, 1) #start_dim = 1
        return out

    # function to extact a specific feature
    def intermediate_forward(self, x, layer_index):
        out = F.relu(self.bn1(self.conv1(x)))
        # if layer_index == 1:
        #     out = self.layer1(out)
        # elif layer_index == 2:
        #     out = self.layer1(out)
        #     out = self.layer2(out)
        # elif layer_index == 3:
        #     out = self.layer1(out)
        #     out = self.layer2(out)
        #     out = self.layer3(out)
        # elif layer_index == 4:
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)              
        return out

    # function to extact the multiple features
    def feature_list(self, x):
        out_list = []
        out = F.relu(self.bn1(self.conv1(x)))
        # out_list.append(out)
        out = self.layer1(out)
        # out_list.append(out)
        out = self.layer2(out)
        # out_list.append(out)
        out = self.layer3(out)
        # out_list.append(out)
        out = self.layer4(out)
        out_list.append(out)
        # out = self.avgpool(out)
        # out = torch.flatten(out, 1)
        return out_list


def resnet18(**kwargs):
    return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)


def resnet34(**kwargs):
    return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)


def resnet50(**kwargs):
    return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)


def resnet101(**kwargs):
    return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)


model_dict = {
    'resnet18': [resnet18, 512],
    'resnet34': [resnet34, 512],
    'resnet50': [resnet50, 2048],
    'resnet101': [resnet101, 2048],
}


class LinearBatchNorm(nn.Module):
    """Implements BatchNorm1d by BatchNorm2d, for SyncBN purpose"""
    def __init__(self, dim, affine=True):
        super(LinearBatchNorm, self).__init__()
        self.dim = dim
        self.bn = nn.BatchNorm2d(dim, affine=affine)

    def forward(self, x):
        x = x.view(-1, self.dim, 1, 1)
        x = self.bn(x)
        x = x.view(-1, self.dim)
        return x


# class SupConResNet(nn.Module):
#     """backbone + projection head"""
#     def __init__(self, name='resnet50', head='mlp', feat_dim=128):
#         super(SupConResNet, self).__init__()
#         model_fun, dim_in = model_dict[name]
#         self.encoder = model_fun()
#         if head == 'linear':
#             self.head = nn.Linear(dim_in, feat_dim)
#         elif head == 'mlp':
#             self.head = nn.Sequential(
#                 nn.Linear(dim_in, dim_in),
#                 nn.ReLU(inplace=True),
#                 nn.Linear(dim_in, feat_dim)
#             )
#         else:
#             raise NotImplementedError(
#                 'head not supported: {}'.format(head))

#     def forward(self, x):
#         feat = self.encoder(x)
#         feat = F.normalize(self.head(feat), dim=1)
#         return feat


class SupCEResNet(nn.Module):
    """encoder + classifier"""
    def __init__(self, name='resnet18', normalize = False,  num_classes=10):
        super(SupCEResNet, self).__init__()
        model_fun, dim_in = model_dict[name]
        self.encoder = model_fun()
        self.fc = nn.Linear(dim_in, num_classes)
        self.normalize = normalize

    def forward(self, x):
        features = self.encoder(x)
        if self.normalize: 
            features =  F.normalize(features, dim=1)
        return self.fc(features)

class SupCEHeadResNet(nn.Module):
    """encoder + classifier"""
    def __init__(self, name='resnet50', head='mlp', feat_dim = 128, num_classes=10, multiplier = 1):
        super(SupCEHeadResNet, self).__init__()
        model_fun, dim_in = model_dict[name]
        self.encoder = model_fun()
        self.fc = nn.Linear(dim_in, num_classes)
        self.multiplier = multiplier
        #FOR ABLATION b, c
        # self.fc = nn.Linear(feat_dim, num_classes)
        #END

        if head == 'linear':
            self.head = nn.Linear(dim_in, feat_dim)
        elif head == 'mlp':
            self.head = nn.Sequential(
                nn.Linear(dim_in, dim_in),
                nn.ReLU(inplace=True),
                nn.Linear(dim_in, feat_dim)
            )
        #breakpoint() 
        #if name=='resnet18':
        #    self.head = nn.Linear(dim_in, feat_dim)

    def forward(self, x):
        features = self.encoder(x)
        #FOR ABLATION a
        # features = F.normalize(features, dim=1)
        #END
        #FOR ABLATION b
        # features = F.normalize(self.head(features), dim=1)
        #END
        #FOR ABLATION c
        # features = self.head(features)
        #END

        return self.fc(features)
    
    def intermediate_forward(self, x, layer_index):
        if layer_index == 0:
            return self.encoder.intermediate_forward(x, layer_index)
        elif layer_index == 1:
            feat = self.encoder(x)
            # feat = self.head(feat)
            feat = self.multiplier * F.normalize(self.head(feat), dim=1) 
            return feat

            





class LinearClassifier(nn.Module):
    """Linear classifier"""
    def __init__(self, name='resnet18',  normalize = False, num_classes=100):
        super(LinearClassifier, self).__init__()
        _, feat_dim = model_dict[name]
        self.fc = nn.Linear(feat_dim, num_classes)
        self.normalize = normalize

    def forward(self, features):
        if self.normalize: 
            features =  F.normalize(features, dim=1)
        return self.fc(features)
