import torch

from torch import nn

import torch.nn.functional as F

def relu():

    return nn.ReLU(inplace=True)

def conv(in_channels, out_channels, kernel_size=(3,3,3), stride=(1,1,1), padding = 1, nonlinearity = relu):

    conv_layer = nn.Conv3d(in_channels = in_channels, out_channels= out_channels, kernel_size = kernel_size, stride = stride, padding = padding, bias = False)

    nll_layer = nonlinearity()

    bn_layer = nn.BatchNorm3d(out_channels)

    layers = [conv_layer, bn_layer, nll_layer]

    return nn.Sequential(*layers)

def deconv(in_channels, out_channels, kernel_size=(3,3,3), stride=(1,1,1), padding = 1, nonlinearity = relu):

    conv_layer = nn.ConvTranspose3d(in_channels = in_channels, out_channels= out_channels, kernel_size = kernel_size, stride = stride, padding = padding, output_padding = 1, bias = False)

    nll_layer = nonlinearity()

    bn_layer = nn.BatchNorm3d(out_channels)

    layers = [conv_layer, bn_layer, nll_layer]

    return nn.Sequential(*layers)

def conv_blocks_2(in_channels, out_channels, strides=(1,1,1)):

    conv1 = conv(in_channels, out_channels, stride = strides)

    conv2 = conv(out_channels, out_channels, stride=(1,1,1))

    layers = [conv1, conv2]

    return nn.Sequential(*layers)

def conv_blocks_3(in_channels, out_channels, strides=(1,1,1)):

    conv1 = conv(in_channels, out_channels, stride = strides)

    conv2 = conv(out_channels, out_channels, stride=(1,1,1))

    conv3 = conv(out_channels, out_channels, stride=(1,1,1))

    layers = [conv1, conv2, conv3]

    return nn.Sequential(*layers)

def fullyconnect(in_features, out_features, out_channels, nonlinearity = relu):

    fc_layer = nn.Linear(in_features = in_features, out_features= out_features, bias = False)

    nll_layer = nonlinearity()

    bn_layer = nn.BatchNorm1d(out_channels)

    layers = [fc_layer, bn_layer, nll_layer]

    return nn.Sequential(*layers)

def conv_2D(in_channels, out_channels, kernel_size=3, stride=1, padding = 1, nonlinearity = relu):

    conv_layer = nn.Conv2d(in_channels = in_channels, out_channels= out_channels, kernel_size = kernel_size, stride = stride, padding = padding, bias = False)

    nll_layer = nonlinearity()

    bn_layer = nn.BatchNorm2d(out_channels)

    layers = [conv_layer, bn_layer, nll_layer]

    return nn.Sequential(*layers)

def conv_1D(in_channels, out_channels, kernel_size=3, stride=1, padding = 1, nonlinearity = relu):

    conv_layer = nn.Conv1d(in_channels = in_channels, out_channels= out_channels, kernel_size = kernel_size, stride = stride, padding = padding, bias = False)

    nll_layer = nonlinearity()

    bn_layer = nn.BatchNorm2d(out_channels)

    layers = [conv_layer, bn_layer, nll_layer]

    return nn.Sequential(*layers)

def deconv_2D(in_channels, out_channels, kernel_size=3, stride=1, padding = 1, nonlinearity = relu):

    conv_layer = nn.ConvTranspose2d(in_channels = in_channels, out_channels= out_channels, kernel_size = kernel_size, stride = stride, padding = padding, output_padding = 1, bias = False)

    nll_layer = nonlinearity()

    bn_layer = nn.BatchNorm2d(out_channels)

    layers = [conv_layer, bn_layer, nll_layer]

    return nn.Sequential(*layers)

def conv_blocks_2_2D(in_channels, out_channels, strides=1):

    conv1 = conv_2D(in_channels, out_channels, stride = strides)

    conv2 = conv_2D(out_channels, out_channels, stride=1)

    layers = [conv1, conv2]

    return nn.Sequential(*layers)

def conv_blocks_3_2D(in_channels, out_channels, strides=1):

    conv1 = conv_2D(in_channels, out_channels, stride = strides)

    conv2 = conv_2D(out_channels, out_channels, stride=1)

    conv3 = conv_2D(out_channels, out_channels, stride=1)

    layers = [conv1, conv2, conv3]

    return nn.Sequential(*layers)

# Flatten layer

class Flatten(nn.Module):

    def forward(self, input):

        return input.view(input.size(0), -1)

def generate_grid(x, offset):

    x_shape = x.size()

    grid_d, grid_w, grid_h = torch.meshgrid([torch.linspace(-1, 1, x_shape[2]), torch.linspace(-1, 1, x_shape[3]), torch.linspace(-1, 1, x_shape[4])])  # (h, w, h)

    grid_d = grid_d.cuda().float()

    grid_w = grid_w.cuda().float()

    grid_h = grid_h.cuda().float()

    grid_d = nn.Parameter(grid_d, requires_grad=False)

    grid_w = nn.Parameter(grid_w, requires_grad=False)

    grid_h = nn.Parameter(grid_h, requires_grad=False)

    offset_h, offset_w, offset_d = torch.split(offset, 1, 1)

    offset_d = offset_d.contiguous().view(-1, int(x_shape[2]), int(x_shape[3]), int(x_shape[4]))  # (b*c, d, w, h)

    offset_w = offset_w.contiguous().view(-1, int(x_shape[2]), int(x_shape[3]), int(x_shape[4]))  # (b*c, d, w, h)

    offset_h = offset_h.contiguous().view(-1, int(x_shape[2]), int(x_shape[3]), int(x_shape[4]))  # (b*c, d, w, h)

    offset_d = grid_d + offset_d

    offset_w = grid_w + offset_w

    offset_h = grid_h + offset_h

    offsets = torch.stack((offset_h, offset_w, offset_d), 4) # should have the same order as offset

    return offsets

def transform(seg_source, loc, mode='bilinear'):

    grid = generate_grid(seg_source, loc)

    # seg_source: NCDHW

    # grid: NDHW3

    out = F.grid_sample(seg_source, grid, mode=mode, align_corners=True)

    return out

class MotionMesh_25d(nn.Module):

    """Deformable registration network with input from image space """

    def __init__(self, n_ch=64, mesh_dim=10000):

        super(MotionMesh_25d, self).__init__()

        self.conv_blocks_2D = [conv_blocks_2_2D(n_ch, 64), conv_blocks_2_2D(64, 128, 2), conv_blocks_3_2D(128, 256, 2),

                            conv_blocks_3_2D(256, 512, 2), conv_blocks_3_2D(512, 512, 2)]

        self.conv_list_2D = []

        for in_filters in [128, 256, 512, 1024, 1024]:

            self.conv_list_2D += [conv_2D(in_filters, 64)]

        self.conv_blocks_2D = nn.Sequential(*self.conv_blocks_2D)

        self.conv_list_2D = nn.Sequential(*self.conv_list_2D)

        self.conv6 = conv_2D(64 * 15, 64, 1, 1, 0)

        self.conv7 = conv_2D(64, 64, 1, 1, 0)

        self.conv3d9 = conv(1, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1))

        self.conv3d10 = conv(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2))

        self.conv3d10_1 = conv(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1))

        self.conv3d11 = conv(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2))

        self.conv3d11_1 = conv(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1))

        self.conv3d12 = conv(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2))

        self.conv3d12_1 = conv(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1))

        self.conv3d13 = deconv(256, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2))

        self.conv3d13_1 = conv(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1))

        self.conv3d14 = deconv(128, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2))

        self.conv3d14_1 = conv(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1))

        self.conv3d15 = deconv(64, 32, kernel_size=(3, 3, 3), stride=(2, 2, 2))

        self.conv3d15_1 = conv(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1))

        self.conv3d16 = nn.Conv3d(32, 3, 1, stride=(1, 1, 1))

    def forward(self, x_sa, x_saed, x_2ch, x_2ched, x_4ch, x_4ched):

        # x: source image; x_pred: target image;

        net = {}

        net['conv0_sa'] = x_sa

        net['conv0_sa_ed'] = x_saed

        net['conv0_2ch'] = x_2ch

        net['conv0_2ch_ed'] = x_2ched

        net['conv0_4ch'] = x_4ch

        net['conv0_4ch_ed'] = x_4ched

        # 5 refers to 5 output or 5 blocks

        for i in range(5):

            net['conv%d_sa' % (i + 1)] = self.conv_blocks_2D[i](net['conv%d_sa' % i])

            net['conv%d_sa_ed' % (i + 1)] = self.conv_blocks_2D[i](net['conv%d_sa_ed' % i])

            net['conv%d_2ch' % (i + 1)] = self.conv_blocks_2D[i](net['conv%d_2ch' % i])

            net['conv%d_2ch_ed' % (i + 1)] = self.conv_blocks_2D[i](net['conv%d_2ch_ed' % i])

            net['conv%d_4ch' % (i + 1)] = self.conv_blocks_2D[i](net['conv%d_4ch' % i])

            net['conv%d_4ch_ed' % (i + 1)] = self.conv_blocks_2D[i](net['conv%d_4ch_ed' % i])

            net['concat%d_sa' % (i + 1)] = torch.cat((net['conv%d_sa' % (i + 1)], net['conv%d_sa_ed' % (i + 1)]), 1)

            net['concat%d_2ch' % (i + 1)] = torch.cat((net['conv%d_2ch' % (i + 1)], net['conv%d_2ch_ed' % (i + 1)]), 1)

            net['concat%d_4ch' % (i + 1)] = torch.cat((net['conv%d_4ch' % (i + 1)], net['conv%d_4ch_ed' % (i + 1)]), 1)

            net['out%d_sa' % (i + 1)] = self.conv_list_2D[i](net['concat%d_sa' % (i + 1)])

            net['out%d_2ch' % (i + 1)] = self.conv_list_2D[i](net['concat%d_2ch' % (i + 1)])

            net['out%d_4ch' % (i + 1)] = self.conv_list_2D[i](net['concat%d_4ch' % (i + 1)])

            if i > 0:

                # upsample

                # only upsample HW dimension

                # net['out%d_sa_up' % (i + 1)] = F.interpolate(net['out%d_sa' % (i + 1)],

                #                                              scale_factor=(1, 2 ** i, 2 ** i), mode='trilinear',

                #                                              align_corners=True)

                # upsample DHW dimension

                net['out%d_sa_up' % (i + 1)] = F.interpolate(net['out%d_sa' % (i + 1)], scale_factor=2 ** i, mode='bilinear', align_corners=True)

                net['out%d_2ch_up' % (i + 1)] = F.interpolate(net['out%d_2ch' % (i + 1)], scale_factor=2 ** i, mode='bilinear', align_corners=True)

                net['out%d_4ch_up' % (i + 1)] = F.interpolate(net['out%d_4ch' % (i + 1)], scale_factor=2 ** i, mode='bilinear', align_corners=True)

                # output: net['out1_sa'], net['out2_sa_up'], net['out3_sa_up'], net['out4_sa_up'], net['out5_sa_up'] are used for multiscale fusion

        net['concat_sa'] = torch.cat(

                (net['out1_sa'], net['out2_sa_up'], net['out3_sa_up'], net['out4_sa_up'], net['out5_sa_up']), 1)

        net['concat_2ch'] = torch.cat(

                (net['out1_2ch'], net['out2_2ch_up'], net['out3_2ch_up'], net['out4_2ch_up'], net['out5_2ch_up']), 1)

        net['concat_4ch'] = torch.cat(

                (net['out1_4ch'], net['out2_4ch_up'], net['out3_4ch_up'], net['out4_4ch_up'], net['out5_4ch_up']), 1)

        net['concat'] = torch.cat((net['concat_sa'], net['concat_2ch'], net['concat_4ch']), 1)

        net['comb_1'] = self.conv6(net['concat'])

        net['comb_2'] = self.conv7(net['comb_1'])

        net['conv3d0f'] = net['comb_2'].unsqueeze(1)

        net['conv3d_1'] = self.conv3d9(net['conv3d0f'])

        net['conv3d_2'] = self.conv3d10_1(self.conv3d10(net['conv3d_1']))

        net['conv3d_3'] = self.conv3d11_1(self.conv3d11(net['conv3d_2']))

        net['conv3d_4'] = self.conv3d12_1(self.conv3d12(net['conv3d_3']))

        net['conv3d_5'] = self.conv3d13_1(self.conv3d13(net['conv3d_4']))

        net['conv3d_6'] = self.conv3d14_1(self.conv3d14(net['conv3d_5']))

        net['conv3d_7'] = self.conv3d15_1(self.conv3d15(net['conv3d_6']))

        net['out'] = torch.tanh(self.conv3d16(net['conv3d_7']))

        return net

class Mesh_2d(nn.Module):

    """Deformable registration network with input from image space """

    def __init__(self, n_ch=1):

        super(Mesh_2d, self).__init__()

        self.conv1 = conv_2D(n_ch, 32)

        self.conv2 = conv_2D(32, 64)

    def forward(self, x_2ch, x_2ched, x_4ch, x_4ched):

        # x: source image; x_pred: target image;

        net = {}

        net['conv1_2ch'] = self.conv1(x_2ch)

        net['conv1_4ch'] = self.conv1(x_4ch)

        net['conv1s_2ch'] = self.conv1(x_2ched)

        net['conv1s_4ch'] = self.conv1(x_4ched)

        net['conv2_2ch'] = self.conv2(net['conv1_2ch'])

        net['conv2_4ch'] = self.conv2(net['conv1_4ch'])

        net['conv2s_2ch'] = self.conv2(net['conv1s_2ch'])

        net['conv2s_4ch'] = self.conv2(net['conv1s_4ch'])

        return net