import torch
import torch.nn as nn
import torch.nn.functional as F
import math

def _make_divisible(v, divisor, min_value=None):
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


def hard_sigmoid(x, inplace: bool = False):
    if inplace:
        return x.add_(3.).clamp_(0., 6.).div_(6.)
    else:
        return F.relu6(x + 3.) / 6.


class SqueezeExcite(nn.Module):
    def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None,
                 act_layer=nn.PReLU, gate_fn=hard_sigmoid, divisor=4, **_):
        super(SqueezeExcite, self).__init__()
        self.gate_fn = gate_fn
        reduced_chs = _make_divisible((reduced_base_chs or in_chs) * se_ratio, divisor)
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
        self.act1 = act_layer()
        self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)

    def forward(self, x):
        x_se = self.avg_pool(x)
        x_se = self.conv_reduce(x_se)
        x_se = self.act1(x_se)
        x_se = self.conv_expand(x_se)
        x = x * self.gate_fn(x_se)
        return x    

    
class ConvBnAct(nn.Module):
    def __init__(self, in_chs, out_chs, kernel_size,
                 stride=1, act_layer=nn.PReLU):
        super(ConvBnAct, self).__init__()
        self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, kernel_size//2, bias=False)
        self.bn1 = nn.BatchNorm2d(out_chs)
        self.act1 = act_layer()

    def forward(self, x):
        x = self.conv(x)
        x = self.bn1(x)
        x = self.act1(x)
        return x
    
class ModifiedGDC(nn.Module):
    def __init__(self, image_size, in_chs, num_classes, dropout, emb=512): #embedding = 512 from original code
        super(ModifiedGDC, self).__init__()
        self.dropout = dropout

        self.conv_dw = nn.Conv2d(in_chs, in_chs, kernel_size=1,groups=in_chs, bias=False)
        self.bn1 = nn.BatchNorm2d(in_chs)
        self.conv = nn.Conv2d(in_chs, emb, kernel_size=1, bias=False)

        if image_size % 32 == 0:
            flattened_features = emb*((image_size//32)**2)
        else:
            flattened_features = emb*((image_size//32 + 1)**2)

        self.bn2 = nn.BatchNorm1d(flattened_features)
        self.linear = nn.Linear(flattened_features, num_classes) if num_classes else nn.Identity()

    def forward(self, x):
        x = self.conv_dw(x)
        x = self.bn1(x)
        x = self.conv(x)
        x = x.view(x.size(0), -1) #flatten
        if self.dropout > 0.:
            x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.bn2(x)
        x = self.linear(x)
        return x


class GhostModule(nn.Module):
    def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, prelu=True):
        super(GhostModule, self).__init__()
        self.oup = oup
        init_channels = math.ceil(oup / ratio)
        new_channels = init_channels*(ratio-1)

        self.primary_conv = nn.Sequential(
            nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False),
            nn.BatchNorm2d(init_channels),
            nn.PReLU() if prelu else nn.Sequential(),
        )

        self.cheap_operation = nn.Sequential(
            nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False),
            nn.BatchNorm2d(new_channels),
            nn.PReLU() if prelu else nn.Sequential(),
        )

    def forward(self, x):
        x1 = self.primary_conv(x)
        x2 = self.cheap_operation(x1)
        out = torch.cat([x1,x2], dim=1)
        return out[:,:self.oup,:,:]


class GhostBottleneck(nn.Module):
    """ Ghost bottleneck w/ optional SE"""

    def __init__(self, in_chs, mid_chs, out_chs, dw_kernel_size=3,
                 stride=1, act_layer=nn.PReLU, se_ratio=0.):
        super(GhostBottleneck, self).__init__()
        has_se = se_ratio is not None and se_ratio > 0.
        self.stride = stride

        # Point-wise expansion
        self.ghost1 = GhostModule(in_chs, mid_chs, prelu=True)

        # Depth-wise convolution
        if self.stride > 1:
            self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride=stride,
                             padding=(dw_kernel_size-1)//2,
                             groups=mid_chs, bias=False)
            self.bn_dw = nn.BatchNorm2d(mid_chs)

        # Squeeze-and-excitation
        if has_se:
            self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio)
        else:
            self.se = None

        # Point-wise linear projection
        self.ghost2 = GhostModule(mid_chs, out_chs, prelu=False)
        
        # shortcut
        if (in_chs == out_chs and self.stride == 1):
            self.shortcut = nn.Sequential()
        else:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride=stride,
                       padding=(dw_kernel_size-1)//2, groups=in_chs, bias=False),
                nn.BatchNorm2d(in_chs),
                nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(out_chs),
            )


    def forward(self, x):
        residual = x

        # 1st ghost bottleneck
        x = self.ghost1(x)

        # Depth-wise convolution
        if self.stride > 1:
            x = self.conv_dw(x)
            x = self.bn_dw(x)

        # Squeeze-and-excitation
        if self.se is not None:
            x = self.se(x)

        # 2nd ghost bottleneck
        x = self.ghost2(x)
        
        x += self.shortcut(residual)
        return x


class GhostFaceNetsV1(nn.Module):
    def __init__(self, cfgs=None, image_size=256, num_classes=1000, width=1.0, dropout=0.2,
                 add_pointwise_conv=False, bn_momentum=0.9, bn_epsilon=1e-5, init_kaiming=True):
        super(GhostFaceNetsV1, self).__init__()
        # setting of inverted residual blocks
        if cfgs==None:
            self.cfgs = [
                # k, t, c, SE, s 
                # stage1
                [[3,  16,  16, 0, 1]],
                # stage2
                [[3,  48,  24, 0, 2]],
                [[3,  72,  24, 0, 1]],
                # stage3
                [[5,  72,  40, 0.25, 2]],
                [[5, 120,  40, 0.25, 1]],
                # stage4
                [[3, 240,  80, 0, 2]],
                [[3, 200,  80, 0, 1],
                [3, 184,  80, 0, 1],
                [3, 184,  80, 0, 1],
                [3, 480, 112, 0.25, 1],
                [3, 672, 112, 0.25, 1]
                ],
                # stage5
                [[5, 672, 160, 0.25, 2]],
                [[5, 960, 160, 0, 1],
                [5, 960, 160, 0.25, 1],
                [5, 960, 160, 0, 1],
                [5, 960, 160, 0.25, 1]
                ]
            ]
        else:
            self.cfgs=cfgs

        self.dropout = dropout

        # building first layer
        output_channel = _make_divisible(16 * width, 4)
        self.conv_stem = nn.Conv2d(3, output_channel, 3, 2, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(output_channel)
        self.act1 = nn.PReLU()
        input_channel = output_channel

        # building inverted residual blocks
        stages = []
        block = GhostBottleneck
        for cfg in self.cfgs:
            layers = []
            for k, exp_size, c, se_ratio, s in cfg:
                output_channel = _make_divisible(c * width, 4)
                hidden_channel = _make_divisible(exp_size * width, 4)
                layers.append(block(input_channel, hidden_channel, output_channel, k, s,
                              se_ratio=se_ratio))
                input_channel = output_channel
            stages.append(nn.Sequential(*layers))

        output_channel = _make_divisible(exp_size * width, 4)
        stages.append(nn.Sequential(ConvBnAct(input_channel, output_channel, 1)))
        
        self.blocks = nn.Sequential(*stages)        

        # building last several layers
        pointwise_conv = []
        if add_pointwise_conv:
            pointwise_conv.append(nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=True))
            pointwise_conv.append(nn.BatchNorm2d(output_channel))
            pointwise_conv.append(nn.PReLU())
        else:
            pointwise_conv.append(nn.Sequential())

        self.pointwise_conv = nn.Sequential(*pointwise_conv)
        self.classifier = ModifiedGDC(image_size, output_channel, num_classes, dropout)

        # Initialize weights
        for m in self.modules():
            if init_kaiming:
                if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
                    fan_in, _ = nn.init._calculate_fan_in_and_fan_out(m.weight)
                    negative_slope = 0.25  # Default value for PReLU in PyTorch, change it if you use custom value
                    m.weight.data.normal_(0, math.sqrt(2. / (fan_in * (1 + negative_slope ** 2))))
            if isinstance(m, nn.BatchNorm2d):
                m.momentum, m.eps = bn_momentum, bn_epsilon

    def forward(self, x):
        x = self.conv_stem(x)
        x = self.bn1(x)
        x = self.act1(x)
        x = self.blocks(x)
        x = self.global_pool(x)
        x = self.conv_head(x)
        x = self.act2(x)
        x = x.view(x.size(0), -1)
        if self.dropout > 0.:
            x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.classifier(x)
        return x