"""VGG11/13/16/19 in Pytorch."""
import torch.nn as nn

cfg = {
    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG13':
    [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG16': [
        64,
        64,
        'M',
        128,
        128,
        'M',
        256,
        256,
        256,
        'M',
        512,
        512,
        512,
        'M',
        512,
        512,
        512,
        'M',
    ],
    'VGG19': [
        64,
        64,
        'M',
        128,
        128,
        'M',
        256,
        256,
        256,
        256,
        'M',
        512,
        512,
        512,
        512,
        'M',
        512,
        512,
        512,
        512,
        'M',
    ],
}


class VGG(nn.Module):

    def __init__(self, vgg_name, num_classes=10):
        super(VGG, self).__init__()
        self.features = self._make_layers(cfg[vgg_name.upper()])
        self.classifier = nn.Linear(512, num_classes)

    def forward(self, x):
        out = self.features(x)
        out = out.view(out.size(0), -1)
        out = self.classifier(out)
        return out

    def _make_layers(self, cfg):
        layers = []
        in_channels = 3
        for x in cfg:
            if x == 'M':
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            else:
                layers += [
                    nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
                    nn.BatchNorm2d(x),
                    nn.ReLU(inplace=True),
                ]
                in_channels = x
        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
        return nn.Sequential(*layers)


def vgg11(num_classes: int) -> VGG:
    return VGG('vgg11', num_classes=num_classes)


def vgg13(num_classes: int) -> VGG:
    return VGG('vgg13', num_classes=num_classes)


def vgg16(num_classes: int) -> VGG:
    return VGG('vgg16', num_classes=num_classes)


def vgg19(num_classes: int) -> VGG:
    return VGG('vgg19', num_classes=num_classes)