import numpy as np
import torch
from torch import nn
from torch.nn import init
from torch.nn.parameter import Parameter


class ShuffleAttention(nn.Module):

    def __init__(self, channel=512, reduction=16, G=8):
        super().__init__()
        self.G = G
        self.channel = channel
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.gn = nn.GroupNorm(channel // (2 * G), channel // (2 * G))
        self.cweight = Parameter(torch.zeros(1, channel // (2 * G), 1, 1))
        self.cbias = Parameter(torch.ones(1, channel // (2 * G), 1, 1))
        self.sweight = Parameter(torch.zeros(1, channel // (2 * G), 1, 1))
        self.sbias = Parameter(torch.ones(1, channel // (2 * G), 1, 1))
        self.sigmoid = nn.Sigmoid()

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    @staticmethod
    def channel_shuffle(x, groups):
        b, c, h, w = x.shape
        x = x.reshape(b, groups, -1, h, w)
        x = x.permute(0, 2, 1, 3, 4)

        # flatten
        x = x.reshape(b, -1, h, w)

        return x

    def forward(self, x):
        b, c, h, w = x.size()
        # group into subfeatures
        x = x.view(b * self.G, -1, h, w)  # bs*G,c//G,h,w

        # channel_split
        x_0, x_1 = x.chunk(2, dim=1)  # bs*G,c//(2*G),h,w

        # channel attention
        x_channel = self.avg_pool(x_0)  # bs*G,c//(2*G),1,1
        x_channel = self.cweight * x_channel + self.cbias  # bs*G,c//(2*G),1,1
        x_channel = x_0 * self.sigmoid(x_channel)

        # spatial attention
        x_spatial = self.gn(x_1)  # bs*G,c//(2*G),h,w
        x_spatial = self.sweight * x_spatial + self.sbias  # bs*G,c//(2*G),h,w
        x_spatial = x_1 * self.sigmoid(x_spatial)  # bs*G,c//(2*G),h,w

        # concatenate along channel axis
        out = torch.cat([x_channel, x_spatial], dim=1)  # bs*G,c//G,h,w
        out = out.contiguous().view(b, -1, h, w)

        # channel shuffle
        out = self.channel_shuffle(out, 2)
        return out