import math

import torch.nn as nn

from .registry import MODEL


# --- gaussian initialize ---
def init_layer(l):
    # Initialization using fan-in
    if isinstance(l, nn.Conv2d):
        n = l.kernel_size[0] * l.kernel_size[1] * l.out_channels
        l.weight.data.normal_(0, math.sqrt(2.0 / float(n)))
    elif isinstance(l, nn.BatchNorm2d):
        l.weight.data.fill_(1)
        l.bias.data.fill_(0)
    elif isinstance(l, nn.Linear):
        l.bias.data.fill_(0)


class Flatten(nn.Module):
    def __init__(self):
        super(Flatten, self).__init__()

    def forward(self, x):
        return x.view(x.size(0), -1)


class SimpleBlock(nn.Module):
    def __init__(self, in_channels, out_channels, half_res, leakyrelu=False):
        super(SimpleBlock, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels

        self.block = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2 if half_res else 1, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True) if not leakyrelu else nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
        )
        self.relu = nn.ReLU(inplace=True) if not leakyrelu else nn.LeakyReLU(0.2, inplace=True)
        if in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 1, 2 if half_res else 1, bias=False),
                nn.BatchNorm2d(out_channels)
            )
        else:
            self.shortcut = nn.Identity()

    def forward(self, x):
        o = self.block(x)
        return self.relu(o + self.shortcut(x))


class ResNet(nn.Module):
    def __init__(self, block, layers, dims, num_classes=None, classifier_type="linear", flatten=True, leakyrelu=False):
        super().__init__()
        assert len(layers) == 4, 'Can have only four stages'
        self.inplanes = 64

        self.start = nn.Sequential(
            nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(self.inplanes),
            nn.ReLU(inplace=True) if not leakyrelu else nn.LeakyReLU(0.2, inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )

        trunk = []
        in_channels = self.inplanes
        for i in range(4):
            for j in range(layers[i]):
                half_res = i >= 1 and j == 0
                trunk.append(block(in_channels, dims[i], half_res, leakyrelu))
                in_channels = dims[i]
        if flatten:
            trunk.append(nn.AvgPool2d(7))
            trunk.append(Flatten())
        if num_classes is not None:
            if classifier_type == "linear":
                trunk.append(nn.Linear(in_channels, num_classes))
            elif classifier_type == "distlinear":
                pass
            else:
                raise ValueError(f"invalid classifier_type:{classifier_type}")
        self.trunk = nn.Sequential(*trunk)
        self.apply(init_layer)

    def forward(self, x):
        return self.trunk(self.start(x))


@MODEL.register_module()
def resnet10(num_classes=None, classifier_type="linear", flatten=True, leakyrelu=False):
    return ResNet(SimpleBlock, [1, 1, 1, 1], [64, 128, 256, 512], num_classes, classifier_type, flatten, leakyrelu)


@MODEL.register_module()
def resnet18(num_classes=None, classifier_type="linear", flatten=True, leakyrelu=False):
    return ResNet(SimpleBlock, [2, 2, 2, 2], [64, 128, 256, 512], num_classes, classifier_type, flatten, leakyrelu)


@MODEL.register_module()
def resnet34(num_classes=None, classifier_type="linear", flatten=True, leakyrelu=False):
    return ResNet(SimpleBlock, [3, 4, 6, 3], [64, 128, 256, 512], num_classes, classifier_type, flatten, leakyrelu)