import torch

from torch import nn

import torch.nn.functional as F

"""

Differences with V-Net

Adding nn.Tanh in the end of the conv. to make the outputs in [-1, 1].

"""

class ConvBlock(nn.Module):

    def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):

        super(ConvBlock, self).__init__()

        ops = []

        for i in range(n_stages):

            if i==0:

                input_channel = n_filters_in

            else:

                input_channel = n_filters_out

            ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))

            if normalization == 'batchnorm':

                ops.append(nn.BatchNorm3d(n_filters_out))

            elif normalization == 'groupnorm':

                ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))

            elif normalization == 'instancenorm':

                ops.append(nn.InstanceNorm3d(n_filters_out))

            elif normalization != 'none':

                assert False

            ops.append(nn.ReLU(inplace=True))

        self.conv = nn.Sequential(*ops)

    def forward(self, x):

        x = self.conv(x)

        return x

class ResidualConvBlock(nn.Module):

    def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):

        super(ResidualConvBlock, self).__init__()

        ops = []

        for i in range(n_stages):

            if i == 0:

                input_channel = n_filters_in

            else:

                input_channel = n_filters_out

            ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))

            if normalization == 'batchnorm':

                ops.append(nn.BatchNorm3d(n_filters_out))

            elif normalization == 'groupnorm':

                ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))

            elif normalization == 'instancenorm':

                ops.append(nn.InstanceNorm3d(n_filters_out))

            elif normalization != 'none':

                assert False

            if i != n_stages-1:

                ops.append(nn.ReLU(inplace=True))

        self.conv = nn.Sequential(*ops)

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):

        x = (self.conv(x) + x)

        x = self.relu(x)

        return x

class DownsamplingConvBlock(nn.Module):

    def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):

        super(DownsamplingConvBlock, self).__init__()

        ops = []

        if normalization != 'none':

            ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))

            if normalization == 'batchnorm':

                ops.append(nn.BatchNorm3d(n_filters_out))

            elif normalization == 'groupnorm':

                ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))

            elif normalization == 'instancenorm':

                ops.append(nn.InstanceNorm3d(n_filters_out))

            else:

                assert False

        else:

            ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))

        ops.append(nn.ReLU(inplace=True))

        self.conv = nn.Sequential(*ops)

    def forward(self, x):

        x = self.conv(x)

        return x

class UpsamplingDeconvBlock(nn.Module):

    def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):

        super(UpsamplingDeconvBlock, self).__init__()

        ops = []

        if normalization != 'none':

            ops.append(nn.ConvTranspose3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))

            if normalization == 'batchnorm':

                ops.append(nn.BatchNorm3d(n_filters_out))

            elif normalization == 'groupnorm':

                ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))

            elif normalization == 'instancenorm':

                ops.append(nn.InstanceNorm3d(n_filters_out))

            else:

                assert False

        else:

            ops.append(nn.ConvTranspose3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))

        ops.append(nn.ReLU(inplace=True))

        self.conv = nn.Sequential(*ops)

    def forward(self, x):

        x = self.conv(x)

        return x

class Upsampling(nn.Module):

    def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):

        super(Upsampling, self).__init__()

        ops = []

        ops.append(nn.Upsample(scale_factor=stride, mode='trilinear',align_corners=False))

        ops.append(nn.Conv3d(n_filters_in, n_filters_out, kernel_size=3, padding=1))

        if normalization == 'batchnorm':

            ops.append(nn.BatchNorm3d(n_filters_out))

        elif normalization == 'groupnorm':

            ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))

        elif normalization == 'instancenorm':

            ops.append(nn.InstanceNorm3d(n_filters_out))

        elif normalization != 'none':

            assert False

        ops.append(nn.ReLU(inplace=True))

        self.conv = nn.Sequential(*ops)

    def forward(self, x):

        x = self.conv(x)

        return x

class VNet(nn.Module):

    def __init__(self, n_channels=3, n_classes=2, n_filters=16, normalization='none', has_dropout=False, has_residual=False):

        super(VNet, self).__init__()

        self.has_dropout = has_dropout

        convBlock = ConvBlock if not has_residual else ResidualConvBlock

        self.block_one = convBlock(1, n_channels, n_filters, normalization=normalization)

        self.block_one_dw = DownsamplingConvBlock(n_filters, 2 * n_filters, normalization=normalization)

        self.block_two = convBlock(2, n_filters * 2, n_filters * 2, normalization=normalization)

        self.block_two_dw = DownsamplingConvBlock(n_filters * 2, n_filters * 4, normalization=normalization)

        self.block_three = convBlock(3, n_filters * 4, n_filters * 4, normalization=normalization)

        self.block_three_dw = DownsamplingConvBlock(n_filters * 4, n_filters * 8, normalization=normalization)

        self.block_four = convBlock(3, n_filters * 8, n_filters * 8, normalization=normalization)

        self.block_four_dw = DownsamplingConvBlock(n_filters * 8, n_filters * 16, normalization=normalization)

        self.block_five = convBlock(3, n_filters * 16, n_filters * 16, normalization=normalization)

        self.block_five_up = UpsamplingDeconvBlock(n_filters * 16, n_filters * 8, normalization=normalization)

        self.block_six = convBlock(3, n_filters * 8, n_filters * 8, normalization=normalization)

        self.block_six_up = UpsamplingDeconvBlock(n_filters * 8, n_filters * 4, normalization=normalization)

        self.block_seven = convBlock(3, n_filters * 4, n_filters * 4, normalization=normalization)

        self.block_seven_up = UpsamplingDeconvBlock(n_filters * 4, n_filters * 2, normalization=normalization)

        self.block_eight = convBlock(2, n_filters * 2, n_filters * 2, normalization=normalization)

        self.block_eight_up = UpsamplingDeconvBlock(n_filters * 2, n_filters, normalization=normalization)

        self.block_nine = convBlock(1, n_filters, n_filters, normalization=normalization)

        self.out_conv = nn.Conv3d(n_filters, n_classes, 1, padding=0)

        self.out_conv2 = nn.Conv3d(n_filters, n_classes, 1, padding=0)

        self.tanh = nn.Tanh()

        self.dropout = nn.Dropout3d(p=0.5, inplace=False)

        # self.__init_weight()

    def encoder(self, input):

        x1 = self.block_one(input)

        x1_dw = self.block_one_dw(x1)

        x2 = self.block_two(x1_dw)

        x2_dw = self.block_two_dw(x2)

        x3 = self.block_three(x2_dw)

        x3_dw = self.block_three_dw(x3)

        x4 = self.block_four(x3_dw)

        x4_dw = self.block_four_dw(x4)

        x5 = self.block_five(x4_dw)

        # x5 = F.dropout3d(x5, p=0.5, training=True)

        if self.has_dropout:

            x5 = self.dropout(x5)

        res = [x1, x2, x3, x4, x5]

        return res

    def decoder(self, features):

        x1 = features[0]

        x2 = features[1]

        x3 = features[2]

        x4 = features[3]

        x5 = features[4]

        x5_up = self.block_five_up(x5)

        x5_up = x5_up + x4

        x6 = self.block_six(x5_up)

        x6_up = self.block_six_up(x6)

        x6_up = x6_up + x3

        x7 = self.block_seven(x6_up)

        x7_up = self.block_seven_up(x7)

        x7_up = x7_up + x2

        x8 = self.block_eight(x7_up)

        x8_up = self.block_eight_up(x8)

        x8_up = x8_up + x1

        x9 = self.block_nine(x8_up)

        # x9 = F.dropout3d(x9, p=0.5, training=True)

        if self.has_dropout:

            x9 = self.dropout(x9)

        out = self.out_conv(x9)

        out_tanh = self.tanh(out)

        out_seg = self.out_conv2(x9)

        return out_tanh, out_seg

    def forward(self, input, turnoff_drop=False):

        if turnoff_drop:

            has_dropout = self.has_dropout

            self.has_dropout = False

        features = self.encoder(input)

        out_tanh, out_seg = self.decoder(features)

        if turnoff_drop:

            self.has_dropout = has_dropout

        return out_tanh,out_seg

    # def __init_weight(self):

    #     for m in self.modules():

    #         if isinstance(m, nn.Conv3d):

    #             torch.nn.init.kaiming_normal_(m.weight)

    #         elif isinstance(m, nn.BatchNorm3d):

    #             m.weight.data.fill_(1)

# if __name__ == '__main__':

#     # compute FLOPS & PARAMETERS

#     # from thop import profile

#     # from thop import clever_format

#     # model = VNet(n_channels=1, n_classes=2)

#     # input = torch.randn(4, 1, 112, 112, 80)

#     # flops, params = profile(model, inputs=(input,))

#     # macs, params = clever_format([flops, params], "%.3f")

#     # print(macs, params)