import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn import Parameter

__all__ = ['ResNet_s', 'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110', 'resnet1202']


def _weights_init(m):
    classname = m.__class__.__name__
    if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
        init.kaiming_normal_(m.weight)


class NormedLinear(nn.Module):

    def __init__(self, in_features, out_features):
        super(NormedLinear, self).__init__()
        self.weight = Parameter(torch.Tensor(in_features, out_features))
        self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)

    def forward(self, x):
        out = F.normalize(x, dim=1).mm(F.normalize(self.weight, dim=0))
        return out


class LambdaLayer(nn.Module):

    def __init__(self, lambd):
        super(LambdaLayer, self).__init__()
        self.lambd = lambd

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


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, option='A'):
        super(BasicBlock, self).__init__()
        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 != planes:
            if option == 'A':
                """
                For CIFAR10 ResNet paper uses option A.
                """
                self.shortcut = LambdaLayer(lambda x:
                                            F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes//4, planes//4), "constant", 0))
            elif option == 'B':
                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)
        out = F.relu(out)
        return out


class ResNet_s(nn.Module):

    def __init__(self, block, num_blocks, num_classes=10, use_norm=False, return_features=False):
        super(ResNet_s, self).__init__()
        factor = 1 
        self.in_planes = 16 * factor

        self.conv1 = nn.Conv2d(3, 16 * factor, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16 * factor)
        self.layer1 = self._make_layer(block, 16 * factor, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 32 * factor, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 64 * factor, num_blocks[2], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        if use_norm:
            self.fc = NormedLinear(64 * factor, num_classes)
        else:
            self.fc = nn.Linear(64 * factor, num_classes)
        self.apply(_weights_init)
        self.return_encoding = return_features
        

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

        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.avgpool(out)
        encoding = out.view(out.size(0), -1)
        out = self.fc(encoding)
        if self.return_encoding:
            return out, encoding
        else:
            return out


def resnet20():
    return ResNet_s(BasicBlock, [3, 3, 3])


def resnet32(num_classes=100, use_norm=False, return_features=False):
    return ResNet_s(BasicBlock, [5, 5, 5], num_classes=num_classes, use_norm=use_norm, return_features=return_features)


def resnet44():
    return ResNet_s(BasicBlock, [7, 7, 7])


def resnet56():
    return ResNet_s(BasicBlock, [9, 9, 9])


def resnet110():
    return ResNet_s(BasicBlock, [18, 18, 18])


def resnet1202():
    return ResNet_s(BasicBlock, [200, 200, 200])


def test(net):
    import numpy as np
    total_params = 0

    for x in filter(lambda p: p.requires_grad, net.parameters()):
        total_params += np.prod(x.data.numpy().shape)
    print("Total number of params", total_params)
    print("Total layers", len(list(filter(lambda p: p.requires_grad and len(p.data.size()) > 1, net.parameters()))))


if __name__ == "__main__":
    for net_name in __all__:
        if net_name.startswith('resnet'):
            print(net_name)
            test(globals()[net_name]())
            print()
