""" Parts of the U-Net model """

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

from .graph_reasoning import GraphReasoning
from .acnet_builder import ACNetBuilder



class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, acnet_builder: ACNetBuilder, in_channels, out_channels):
        super().__init__()
        self.double_conv = nn.Sequential(
            acnet_builder.Conv2dBNReLU(in_channels=in_channels, out_channels=out_channels, kernel_size=3, padding=1),
            acnet_builder.Conv2dBNReLU(in_channels=out_channels, out_channels=out_channels, kernel_size=3, padding=1)
        )

    def forward(self, x):
        return self.double_conv(x)


class Conv_GloRe_Conv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, acnet_builder: ACNetBuilder, in_channels, out_channels):
        super().__init__()
        self.conv_GloRe = nn.Sequential(

            acnet_builder.Conv2dBNReLU(in_channels=in_channels, out_channels=out_channels, kernel_size=3, padding=1),
            GraphReasoning(in_channels=out_channels),
            acnet_builder.Conv2dBNReLU(in_channels=out_channels, out_channels=out_channels, kernel_size=3, padding=1)
        )

    def forward(self, x):
        return self.conv_GloRe(x)

class Down_GloRe(nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        builder = ACNetBuilder(base_config=None, deploy=False)
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            Conv_GloRe_Conv(builder, in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)

class Down(nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        builder = ACNetBuilder(base_config=None, deploy=False)
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(builder, in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)


class Up_GloRe(nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()
        builder = ACNetBuilder(base_config=None, deploy=False)

        # if bilinear, use the normal convolutions to reduce the number of channels
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)

        self.conv = Conv_GloRe_Conv(builder, in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        # input is CHW
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])
        # if you have padding issues, see
        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)


class Up(nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()
        builder = ACNetBuilder(base_config=None, deploy=False)
        # if bilinear, use the normal convolutions to reduce the number of channels
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)

        self.conv = DoubleConv(builder, in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        # input is CHW
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])
        # if you have padding issues, see
        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)


class OutConv(nn.Module):
    def __init__(self, acnet_builder: ACNetBuilder, in_channels, out_channels):
        super(OutConv, self).__init__()
        self.conv = acnet_builder.Conv2d(in_channels, out_channels, kernel_size=1)

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


if __name__ == '__main__':
    data = torch.autograd.Variable(torch.randn(4, 512, 64, 64))
    net = Conv_GloRe_Conv(in_channels=512, out_channels=1024)
    print(net(data).size())


# class DoubleConv(nn.Module):
#     """(convolution => [BN] => ReLU) * 2"""

#     def __init__(self, in_channels, out_channels):
#         super().__init__()
#         self.double_conv = nn.Sequential(
#             nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
#             nn.BatchNorm2d(out_channels),
#             nn.ReLU(inplace=True),
#             nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
#             nn.BatchNorm2d(out_channels),
#             nn.ReLU(inplace=True)
#         )

#     def forward(self, x):
#         return self.double_conv(x)


# class Conv_GloRe_Conv(nn.Module):
#     """(convolution => [BN] => ReLU) * 2"""

#     def __init__(self, in_channels, out_channels):
#         super().__init__()
#         self.conv_GloRe = nn.Sequential(

#             nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
#             nn.BatchNorm2d(out_channels),
#             nn.ReLU(inplace=True),

#             GraphReasoning(in_channels=out_channels),
#             # GloRe_Unit_2D(num_in=out_channels, num_mid=int(out_channels/4), normalize=False),
#             # ASPP(in_channels=out_channels, atrous_rates=[12, 24, 36]),

#             nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
#             nn.BatchNorm2d(out_channels),
#             nn.ReLU(inplace=True)
#         )

#     def forward(self, x):
#         return self.conv_GloRe(x)

# class Down_GloRe(nn.Module):
#     """Downscaling with maxpool then double conv"""

#     def __init__(self, in_channels, out_channels):
#         super().__init__()
#         self.maxpool_conv = nn.Sequential(
#             nn.MaxPool2d(2),
#             Conv_GloRe_Conv(in_channels, out_channels)
#         )

#     def forward(self, x):
#         return self.maxpool_conv(x)

# class Down(nn.Module):
#     """Downscaling with maxpool then double conv"""

#     def __init__(self, in_channels, out_channels):
#         super().__init__()
#         self.maxpool_conv = nn.Sequential(
#             nn.MaxPool2d(2),
#             DoubleConv(in_channels, out_channels)
#         )

#     def forward(self, x):
#         return self.maxpool_conv(x)


# class Up_GloRe(nn.Module):
#     """Upscaling then double conv"""

#     def __init__(self, in_channels, out_channels, bilinear=True):
#         super().__init__()

#         # if bilinear, use the normal convolutions to reduce the number of channels
#         if bilinear:
#             self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
#         else:
#             self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)

#         self.conv = Conv_GloRe_Conv(in_channels, out_channels)

#     def forward(self, x1, x2):
#         x1 = self.up(x1)
#         # input is CHW
#         diffY = x2.size()[2] - x1.size()[2]
#         diffX = x2.size()[3] - x1.size()[3]

#         x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
#                         diffY // 2, diffY - diffY // 2])
#         # if you have padding issues, see
#         # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
#         # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
#         x = torch.cat([x2, x1], dim=1)
#         return self.conv(x)


# class Up(nn.Module):
#     """Upscaling then double conv"""

#     def __init__(self, in_channels, out_channels, bilinear=True):
#         super().__init__()

#         # if bilinear, use the normal convolutions to reduce the number of channels
#         if bilinear:
#             self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
#         else:
#             self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)

#         self.conv = DoubleConv(in_channels, out_channels)

#     def forward(self, x1, x2):
#         x1 = self.up(x1)
#         # input is CHW
#         diffY = x2.size()[2] - x1.size()[2]
#         diffX = x2.size()[3] - x1.size()[3]

#         x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
#                         diffY // 2, diffY - diffY // 2])
#         # if you have padding issues, see
#         # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
#         # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
#         x = torch.cat([x2, x1], dim=1)
#         return self.conv(x)


# class OutConv(nn.Module):
#     def __init__(self, in_channels, out_channels):
#         super(OutConv, self).__init__()
#         self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

#     def forward(self, x):
#         return self.conv(x)


# if __name__ == '__main__':
#     data = torch.autograd.Variable(torch.randn(4, 512, 64, 64))
#     net = Conv_GloRe_Conv(in_channels=512, out_channels=1024)
#     print(net(data).size())
