from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout2d, Dropout, AvgPool2d, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module, Parameter
import torch.nn.functional as F
import torch
import torch.nn as nn
from collections import namedtuple
import math
import pdb

##################################  Original Arcface Model #############################################################

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

##################################  MobileFaceNet #############################################################
    
class Conv_block(Module):
    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
        super(Conv_block, self).__init__()
        self.conv = Conv2d(in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False)
        self.bn = BatchNorm2d(out_c)
        self.relu = ReLU(out_c)
    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x

class Linear_block(Module):
    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
        super(Linear_block, self).__init__()
        self.conv = Conv2d(in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False)
        self.bn = BatchNorm2d(out_c)
    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        return x

class Depth_Wise(Module):
     def __init__(self, in_c, out_c, residual = False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):
        super(Depth_Wise, self).__init__()
        self.conv = Conv_block(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
        self.conv_dw = Conv_block(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride)
        self.project = Linear_block(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
        self.residual = residual
     def forward(self, x):
        if self.residual:
            short_cut = x
        x = self.conv(x)
        x = self.conv_dw(x)
        x = self.project(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(Depth_Wise(c, c, residual=True, kernel=kernel, padding=padding, stride=stride, groups=groups))
        self.model = Sequential(*modules)
    def forward(self, x):
        return self.model(x)

class GDC(Module):
    def __init__(self, embedding_size):
        super(GDC, self).__init__()
        self.conv_6_dw = Linear_block(512, 512, groups=512, kernel=(4,4), stride=(1, 1), padding=(0, 0))
        self.linear = Linear(512, embedding_size, bias=True)
        self.bn = BatchNorm1d(embedding_size)

    def forward(self, x):
        x = self.conv_6_dw(x)
        x = torch.flatten(x, 1)
        x = self.linear(x)
        x = self.bn(x)
        return x

class MobileFaceNet(Module):
    def __init__(self, input_size, embedding_size = 512):
        super(MobileFaceNet, self).__init__()
        self.conv1 = Conv_block(1, 32, kernel=(3, 3), stride=(2, 2), padding=(1, 1))
        self.conv2_dw = Conv_block(32, 32, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=32)
        self.conv_23 = Depth_Wise(32, 32, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=64)
        self.conv_3 = Residual(32, num_block=3, groups=64, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
        self.conv_34 = Depth_Wise(32, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128)
        self.conv_4 = Residual(64, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
        self.conv_45 = Depth_Wise(64, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256)
        self.conv_5 = Residual(64, num_block=2, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
        self.conv_6_sep = Conv_block(64, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0))
        self.output_layer = GDC(embedding_size)
        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):
        out = self.conv1(x)
        out = self.conv2_dw(out)
        out = self.conv_23(out)
        out = self.conv_3(out)
        out = self.conv_34(out)
        out = self.conv_4(out)
        out = self.conv_45(out)
        out = self.conv_5(out)
        conv_features = self.conv_6_sep(out)
        out = self.output_layer(conv_features)
        return out