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

def _make_divisible(v, divisor, min_value=None):
    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


class swish(nn.Module):
    def forward(self, x):
        return x * torch.sigmoid(x)


class h_swish(nn.Module):
    def __init__(self, inplace=False):
        super(h_swish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0


class h_sigmoid(nn.Module):
    def __init__(self, inplace=True, h_max=1):
        super(h_sigmoid, self).__init__()
        self.relu = nn.ReLU6(inplace=inplace)
        self.h_max = h_max

    def forward(self, x):
        return self.relu(x + 3) * self.h_max / 6


class DYReLU(nn.Module):
    def __init__(self, inp, oup, reduction=4, lambda_a=1.0, K2=True, use_bias=True, use_spatial=False,
                 init_a=[1.0, 0.0], init_b=[0.0, 0.0]):
        super(DYReLU, self).__init__()
        self.oup = oup
        self.lambda_a = lambda_a * 2
        self.K2 = K2
        self.avg_pool = nn.AdaptiveAvgPool2d(1)

        self.use_bias = use_bias
        if K2:
            self.exp = 4 if use_bias else 2
        else:
            self.exp = 2 if use_bias else 1
        self.init_a = init_a
        self.init_b = init_b

        # determine squeeze
        if reduction == 4:
            squeeze = inp // reduction
        else:
            squeeze = _make_divisible(inp // reduction, 4)
        # print('reduction: {}, squeeze: {}/{}'.format(reduction, inp, squeeze))
        # print('init_a: {}, init_b: {}'.format(self.init_a, self.init_b))

        self.fc = nn.Sequential(
            nn.Linear(inp, squeeze),
            nn.ReLU(inplace=True),
            nn.Linear(squeeze, oup * self.exp),
            h_sigmoid()
        )
        if use_spatial:
            self.spa = nn.Sequential(
                nn.Conv2d(inp, 1, kernel_size=1),
                nn.BatchNorm2d(1),
            )
        else:
            self.spa = None

    def forward(self, x):
        if isinstance(x, list):
            x_in = x[0]
            x_out = x[1]
        else:
            x_in = x
            x_out = x
        b, c, h, w = x_in.size()
        y = self.avg_pool(x_in).view(b, c)
        y = self.fc(y).view(b, self.oup * self.exp, 1, 1)
        if self.exp == 4:
            a1, b1, a2, b2 = torch.split(y, self.oup, dim=1)
            a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0]  # 1.0
            a2 = (a2 - 0.5) * self.lambda_a + self.init_a[1]

            b1 = b1 - 0.5 + self.init_b[0]
            b2 = b2 - 0.5 + self.init_b[1]
            out = torch.max(x_out * a1 + b1, x_out * a2 + b2)
        elif self.exp == 2:
            if self.use_bias:  # bias but not PL
                a1, b1 = torch.split(y, self.oup, dim=1)
                a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0]  # 1.0
                b1 = b1 - 0.5 + self.init_b[0]
                out = x_out * a1 + b1

            else:
                a1, a2 = torch.split(y, self.oup, dim=1)
                a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0]  # 1.0
                a2 = (a2 - 0.5) * self.lambda_a + self.init_a[1]
                out = torch.max(x_out * a1, x_out * a2)

        elif self.exp == 1:
            a1 = y
            a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0]  # 1.0
            out = x_out * a1

        if self.spa:
            ys = self.spa(x_in).view(b, -1)
            ys = F.softmax(ys, dim=1).view(b, 1, h, w) * h * w
            ys = F.hardtanh(ys, 0, 3, inplace=True)/3
            out = out * ys

        return out