'''
For MEMO implementations of ImageNet-ResNet
Reference:
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
'''
import logging
import torch
import torch.nn as nn
try:
    from torchvision.models.utils import load_state_dict_from_url
except:
    from torch.hub import load_state_dict_from_url

__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
           'wide_resnet50_2', 'wide_resnet101_2']


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}


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
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None, nconv=2):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        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")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.nconv = nconv
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
        if self.nconv == 2:
            self.conv2 = conv3x3(planes, planes)
            self.bn2 = norm_layer(planes)
        

    def forward(self, x):
        identity = x

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

        if self.nconv == 2:
            out = self.conv2(out)
            out = self.bn2(out)

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

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

        return out

class BasicBlock1Conv(nn.Module):
    expansion = 1
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock1Conv, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        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")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        
        self.downsample = downsample
        self.stride = stride

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


class Bottleneck(nn.Module):
    expansion = 4
    __constants__ = ['downsample']

    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
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = 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.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:
            identity = self.downsample(x)

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

        return out


class GeneralizedResNet_imagenet(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):
        super(GeneralizedResNet_imagenet, self).__init__()
        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,  # stride=2 -> stride=1 for cifar
                               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)  # Removed in _forward_impl for cifar
        logging.debug("Making layer 1")
        self.layer1 = self._make_layer(block, 64, layers[0])
        logging.debug("Making layer 2")
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        
        logging.debug("Making layer 3")
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                        dilate=replace_stride_with_dilation[1])
        self.out_dim = 512 * block.expansion

        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, 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 = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )
        layers = []
        logging.debug("(inplanes: {}, planes: {}) for first layer".format(self.inplanes, planes))
        
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        logging.debug("self.inplanes changed to {}".format(self.inplanes))
        for _ in range(1, blocks):
            logging.debug("(inplanes: {}, planes: {}) for next layer -->".format(self.inplanes, planes))
            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)  
        logging.debug("GB:conv1 --> {}".format(x.shape))
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        logging.debug("GB:maxpool --> {}".format(x.shape))
        x_1 = self.layer1(x)  
        logging.debug("GB:x_1 --> {}".format(x_1.shape))
        x_2 = self.layer2(x_1)  
        logging.debug("GB:x_2 --> {}".format(x_2.shape))
       
        x_3 = self.layer3(x_2)  
        logging.debug("GB:x_3 --> {}".format(x_3.shape))
        return x_3
        

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

class SpecializedResNet_imagenet(nn.Module):
    def __init__(self, block, layers, nconv, num_classes=1000, zero_init_residual=False,
                    groups=1, width_per_group=64, replace_stride_with_dilation=None,
        norm_layer=None):
        super(SpecializedResNet_imagenet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        
        self.feature_dim = 512 * block.expansion
        self.inplanes = 256 * block.expansion
        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
        
        logging.debug("Making layer 4")
        self.layer4 = self._make_layer(block, 512, layers[3], nconv=nconv, stride=2,
                                        dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.out_dim = 512 * block.expansion

        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)

    def _make_layer(self, block, planes, blocks, nconv, 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 = []
        logging.debug("(inplanes: {}, planes: {}) for first layer".format(self.inplanes, planes))
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer, nconv[0]))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            logging.debug("(inplanes: {}, planes: {}) for next layer".format(self.inplanes, planes))
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer, nconv=nconv[1]))

        return nn.Sequential(*layers)
    
    def forward(self, x):
        logging.debug("SB:x --> {}".format(x.shape))        
        x_4 = self.layer4(x)  # [bs, 512, 4, 4]
        logging.debug("SB:x_4 --> {}".format(x_4.shape))
        pooled = self.avgpool(x_4)  # [bs, 512, 1, 1]
        logging.debug("SB:pooled --> {}".format(pooled.shape))
        features = torch.flatten(pooled, 1)  # [bs, 512]
        logging.debug("SB:features --> {}".format(features.shape))
        return features

def get_resnet10_imagenet():
    basenet = GeneralizedResNet_imagenet(BasicBlock,[1, 1, 1, 1])
    adaptivenet = SpecializedResNet_imagenet(BasicBlock, [1, 1, 1, 1])
    return basenet,adaptivenet


def get_resnet18_imagenet(args):    
    logging.debug('Curr Task : {}'.format(args['curr']))

    nconv = args['layers'][args['curr']] if args['curr'] > -1 else [2, 2]
    logging.debug('Curr Task {}: nconv {}'.format(args['curr'], nconv))
    basenet = GeneralizedResNet_imagenet(BasicBlock,[2, 2, 2, 2])
    adaptivenet = SpecializedResNet_imagenet(BasicBlock, layers=[2,2,2,len(nconv)], nconv=nconv)
    return basenet,adaptivenet

def get_resnet26_imagenet():
    basenet = GeneralizedResNet_imagenet(Bottleneck,[2, 2, 2, 2])
    adaptivenet = SpecializedResNet_imagenet(Bottleneck, [2, 2, 2, 2])
    return basenet,adaptivenet

def get_resnet34_imagenet():
    basenet = GeneralizedResNet_imagenet(BasicBlock,[3, 4, 6, 3])
    adaptivenet = SpecializedResNet_imagenet(BasicBlock, [3, 4, 6, 3])
    return basenet,adaptivenet
    
def get_resnet50_imagenet():
    basenet = GeneralizedResNet_imagenet(Bottleneck,[3, 4, 6, 3])
    adaptivenet = SpecializedResNet_imagenet(Bottleneck, [3, 4, 6, 3])
    return basenet,adaptivenet


if __name__ == '__main__':
    model2imagenet = 3*224*224

    a, b = get_resnet10_imagenet()
    _base = sum(p.numel() for p in a.parameters())
    _adap = sum(p.numel() for p in b.parameters())
    print(f"resnet10 #params:{_base+_adap}")

    a, b = get_resnet18_imagenet()
    _base = sum(p.numel() for p in a.parameters())
    _adap = sum(p.numel() for p in b.parameters())
    print(f"resnet18 #params:{_base+_adap}")
    
    a, b = get_resnet26_imagenet()
    _base = sum(p.numel() for p in a.parameters())
    _adap = sum(p.numel() for p in b.parameters())
    print(f"resnet26 #params:{_base+_adap}")

    a, b = get_resnet34_imagenet()
    _base = sum(p.numel() for p in a.parameters())
    _adap = sum(p.numel() for p in b.parameters())
    print(f"resnet34 #params:{_base+_adap}")

    a, b = get_resnet50_imagenet()
    _base = sum(p.numel() for p in a.parameters())
    _adap = sum(p.numel() for p in b.parameters())
    print(f"resnet50 #params:{_base+_adap}")