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

#Ref: https://github.com/princeton-vl/RAFT/blob/master/core/update.py
class FlowHead(nn.Module):
    def __init__(self, input_dim=128, hidden_dim=256):
        super(FlowHead, self).__init__()
        self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
        self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        return self.conv2(self.relu(self.conv1(x)))


class SepConvGRU(nn.Module):
    def __init__(self, hidden_dim=128, input_dim=192+128):
        super(SepConvGRU, self).__init__()
        self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
        self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
        self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))

        self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
        self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
        self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))

    def forward(self, h, x):
        # horizontal
        hx = torch.cat([h, x], dim=1)
        z = torch.sigmoid(self.convz1(hx))
        r = torch.sigmoid(self.convr1(hx))
        q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1)))        
        h = (1-z) * h + z * q

        # vertical
        hx = torch.cat([h, x], dim=1)
        z = torch.sigmoid(self.convz2(hx))
        r = torch.sigmoid(self.convr2(hx))
        q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1)))       
        h = (1-z) * h + z * q

        return h


class BasicMotionEncoder(nn.Module):
    def __init__(self, cor_planes):
        super(BasicMotionEncoder, self).__init__()
        
        self.convc1 = nn.Conv2d(cor_planes, 256, 1, padding=0)
        self.convc2 = nn.Conv2d(256, 192, 3, padding=1)
        self.convf1 = nn.Conv2d(2, 128, 7, padding=3)
        self.convf2 = nn.Conv2d(128, 64, 3, padding=1)
        self.conv = nn.Conv2d(64+192, 128-2, 3, padding=1)

    def forward(self, flow, corr):
        cor = F.relu(self.convc1(corr))
        cor = F.relu(self.convc2(cor))
        flo = F.relu(self.convf1(flow))
        flo = F.relu(self.convf2(flo))

        cor_flo = torch.cat([cor, flo], dim=1)
        out = F.relu(self.conv(cor_flo))
        return torch.cat([out, flow], dim=1)


class BasicUpdateBlock(nn.Module):
    def __init__(self, hidden_dim, cor_planes, mask_size=8):
        super(BasicUpdateBlock, self).__init__()

        self.encoder = BasicMotionEncoder(cor_planes)
        self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128+hidden_dim)
        self.flow_head = FlowHead(hidden_dim, hidden_dim=256)

        self.mask = nn.Sequential(
            nn.Conv2d(128, 256, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, mask_size**2 *9, 1, padding=0))

    def forward(self, net, inp, corr, flow, upsample=True):
        # print(inp.shape, corr.shape, flow.shape)
        motion_features = self.encoder(flow, corr)
        # print(motion_features.shape, inp.shape)
        inp = torch.cat((inp, motion_features), dim=1)

        net = self.gru(net, inp)
        delta_flow = self.flow_head(net)

        # scale mask to balence gradients
        mask = .25 * self.mask(net)
        return net, mask, delta_flow