"""
Adapted from https://github.com/cavalleria/cavaface.pytorch/blob/master/backbone/mobilefacenet.py
Original author cavalleria
"""
import torch
import torch.nn as nn
from torch.nn import BatchNorm1d
from torch.nn import BatchNorm2d
from torch.nn import Conv2d
from torch.nn import Linear
from torch.nn import Module
from torch.nn import PReLU
from torch.nn import Sequential


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


class ConvBlock(Module):
    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
        super(ConvBlock, self).__init__()
        self.layers = nn.Sequential(
            Conv2d(in_c, out_c, kernel, groups=groups, stride=stride, padding=padding, bias=False),
            BatchNorm2d(num_features=out_c),
            PReLU(num_parameters=out_c),
        )

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


class LinearBlock(Module):
    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
        super(LinearBlock, self).__init__()
        self.layers = nn.Sequential(
            Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False), BatchNorm2d(num_features=out_c)
        )

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


class DepthWise(Module):
    def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):
        super(DepthWise, self).__init__()
        self.residual = residual
        self.layers = nn.Sequential(
            ConvBlock(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)),
            ConvBlock(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride),
            LinearBlock(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1)),
        )

    def forward(self, x):
        short_cut = None
        if self.residual:
            short_cut = x
        x = self.layers(x)
        if self.residual:
            output = short_cut + x
        else:
            output = x
        return output


class Residual(Module):
    def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)):
        super(Residual, self).__init__()
        modules = []
        for _ in range(num_block):
            modules.append(DepthWise(c, c, True, kernel, stride, padding, groups))
        self.layers = Sequential(*modules)

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


class GDC(Module):
    def __init__(self, embedding_size):
        super(GDC, self).__init__()
        self.layers = nn.Sequential(
            LinearBlock(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)),
            Flatten(),
            Linear(512, embedding_size, bias=False),
            BatchNorm1d(embedding_size),
        )

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


class MobileFaceNet(Module):
    def __init__(self, fp16=False, num_features=512, blocks=(1, 4, 6, 2), scale=2):
        super(MobileFaceNet, self).__init__()
        self.scale = scale
        self.fp16 = fp16
        self.layers = nn.ModuleList()
        self.layers.append(ConvBlock(3, 64 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1)))
        if blocks[0] == 1:
            self.layers.append(
                ConvBlock(64 * self.scale, 64 * self.scale, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64)
            )
        else:
            self.layers.append(
                Residual(
                    64 * self.scale, num_block=blocks[0], groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)
                ),
            )

        self.layers.extend(
            [
                DepthWise(64 * self.scale, 64 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128),
                Residual(
                    64 * self.scale, num_block=blocks[1], groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)
                ),
                DepthWise(64 * self.scale, 128 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256),
                Residual(
                    128 * self.scale, num_block=blocks[2], groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)
                ),
                DepthWise(128 * self.scale, 128 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512),
                Residual(
                    128 * self.scale, num_block=blocks[3], groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)
                ),
            ]
        )

        self.conv_sep = ConvBlock(128 * self.scale, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0))
        self.features = GDC(num_features)
        self._initialize_weights()

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
                if m.bias is not None:
                    m.bias.data.zero_()

    def forward(self, x):
        with torch.cuda.amp.autocast(self.fp16):
            for func in self.layers:
                x = func(x)
        x = self.conv_sep(x.float() if self.fp16 else x)
        x = self.features(x)
        return x


def get_mbf(fp16, num_features, blocks=(1, 4, 6, 2), scale=2):
    return MobileFaceNet(fp16, num_features, blocks, scale=scale)


def get_mbf_large(fp16, num_features, blocks=(2, 8, 12, 4), scale=4):
    return MobileFaceNet(fp16, num_features, blocks, scale=scale)