import torch 
import torch.nn as nn 
from torch.quantization import fuse_modules 
from torch.nn import init
import torch.functional as F 
class DoubleConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv1d(in_ch, out_ch, 3, padding=1),
            nn.BatchNorm1d(out_ch),
            nn.ReLU(inplace=True),
            nn.Conv1d(out_ch, out_ch, 3, padding=1),
            nn.BatchNorm1d(out_ch),
            nn.ReLU(inplace=True)
        )

    def forward(self, input):
        return self.conv(input)

class Upsampling(nn.Module):
    def __init__(self, scale_factor) -> None:
        super().__init__()
        self.up = nn.UpsamplingBilinear2d(scale_factor=scale_factor) 
    def forward(self, x):
        x = x.unsqueeze(3) 
        x = self.up(x) 
        x = x.squeeze() 
        return x 

class UNetpp(nn.Module):
    def __init__(self, in_channel=3, out_channel=3):
        super().__init__()
        nb_filter = [32, 64, 128, 256, 512]

        self.pool = nn.MaxPool1d(2, 2)
        self.up = nn.Upsample(scale_factor=2, mode='nearest')

        self.conv0_0 = DoubleConv(in_channel, nb_filter[0])
        self.conv1_0 = DoubleConv(nb_filter[0], nb_filter[1])
        self.conv2_0 = DoubleConv(nb_filter[1], nb_filter[2])
        self.conv3_0 = DoubleConv(nb_filter[2], nb_filter[3])
        self.conv4_0 = DoubleConv(nb_filter[3], nb_filter[4])

        self.conv0_1 = DoubleConv(nb_filter[0]+nb_filter[1], nb_filter[0])
        self.conv1_1 = DoubleConv(nb_filter[1]+nb_filter[2], nb_filter[1])
        self.conv2_1 = DoubleConv(nb_filter[2]+nb_filter[3], nb_filter[2])
        self.conv3_1 = DoubleConv(nb_filter[3]+nb_filter[4], nb_filter[3])

        self.conv0_2 = DoubleConv(nb_filter[0]*2+nb_filter[1], nb_filter[0])
        self.conv1_2 = DoubleConv(nb_filter[1]*2+nb_filter[2], nb_filter[1])
        self.conv2_2 = DoubleConv(nb_filter[2]*2+nb_filter[3], nb_filter[2])

        self.conv0_3 = DoubleConv(nb_filter[0]*3+nb_filter[1], nb_filter[0])
        self.conv1_3 = DoubleConv(nb_filter[1]*3+nb_filter[2], nb_filter[1])

        self.conv0_4 = DoubleConv(nb_filter[0]*4+nb_filter[1], nb_filter[0])
        self.sigmoid = nn.Softmax(dim=1)
        self.final = nn.Conv1d(nb_filter[0], out_channel, kernel_size=1)


    def forward(self, input):
        x0_0 = self.conv0_0(input)
        x1_0 = self.conv1_0(self.pool(x0_0))
        x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1))

        x2_0 = self.conv2_0(self.pool(x1_0))
        x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1))
        x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1))

        x3_0 = self.conv3_0(self.pool(x2_0))
        x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1))
        x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1))
        x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1))

        x4_0 = self.conv4_0(self.pool(x3_0))
        x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))
        x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.up(x3_1)], 1))
        x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.up(x2_2)], 1))
        x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.up(x1_3)], 1))

        output = self.final(x0_4)
        output = self.sigmoid(output)
        return output
class Loss(nn.Module):
    def __init__(self) -> None:
        super().__init__() 
    def forward(self, x, d):
        loss = - (d * torch.log(x+1e-9)).sum()
        return loss 
if __name__ == "__main__":
    x = torch.randn([10, 3, 6144]) 
    model = NestedUNet() 
    x = model(x) 
    print(x.shape)