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

# VariationalHidDropout from https://github.com/locuslab/trellisnet/tree/master/TrellisNet
class VariationalHidDropout(nn.Module):
    def __init__(self, dropout=0.0):
        """
        Hidden-to-hidden (VD-based) dropout that applies the same mask at every time step and every layer of TrellisNet
        :param dropout: The dropout rate (0 means no dropout is applied)
        """
        super(VariationalHidDropout, self).__init__()
        self.dropout = dropout
        self.mask = None

    def reset_mask(self, x):
        dropout = self.dropout

        # Dimension (N, C, L)
        n, c, h, w = x.shape
        m = x.data.new(n, c, 1, 1).bernoulli_(1 - dropout)
        with torch.no_grad():
            mask = m / (1 - dropout)
            self.mask = mask
        return mask

    def forward(self, x):
        if not self.training or self.dropout == 0:
            return x
        assert self.mask is not None, "You need to reset mask before using VariationalHidDropout"
        return self.mask * x


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 ConvGRU(nn.Module):
    def __init__(self, hidden_dim=128, input_dim=192+128):
        super(ConvGRU, self).__init__()
        self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
        self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
        self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)

    def forward(self, h, x):
        hx = torch.cat([h, x], dim=1)

        z = torch.sigmoid(self.convz(hx))
        r = torch.sigmoid(self.convr(hx))
        q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)))

        h = (1-z) * h + z * q
        return h


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 SmallMotionEncoder(nn.Module):
    def __init__(self, args):
        super(SmallMotionEncoder, self).__init__()
        cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
        self.convc1 = nn.Conv2d(cor_planes, 96, 1, padding=0)
        self.convf1 = nn.Conv2d(2, 64, 7, padding=3)
        self.convf2 = nn.Conv2d(64, 32, 3, padding=1)
        self.conv = nn.Conv2d(128, 80, 3, padding=1)

    def forward(self, flow, corr):
        cor = F.relu(self.convc1(corr))
        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 BasicMotionEncoder(nn.Module):
    def __init__(self, args):
        super(BasicMotionEncoder, self).__init__()
        cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
        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 SmallUpdateBlock(nn.Module):
    def __init__(self, args, hidden_dim=96):
        super(SmallUpdateBlock, self).__init__()
        self.encoder = SmallMotionEncoder(args)
        self.gru = ConvGRU(hidden_dim=hidden_dim, input_dim=82+64)
        self.flow_head = FlowHead(hidden_dim, hidden_dim=128)

    def forward(self, net, inp, corr, flow):
        motion_features = self.encoder(flow, corr)
        inp = torch.cat([inp, motion_features], dim=1)
        net = self.gru(net, inp)
        delta_flow = self.flow_head(net)

        return net, delta_flow

class BasicUpdateBlock(nn.Module):
    def __init__(self, args, hidden_dim=128, input_dim=128):
        super(BasicUpdateBlock, self).__init__()
        self.encoder = BasicMotionEncoder(args)
        self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128+hidden_dim)
        self.flow_head = FlowHead(hidden_dim, hidden_dim=256)

        self.drop_inp = VariationalHidDropout(dropout=args.dropout)
        self.drop_net = VariationalHidDropout(dropout=args.dropout)

    def reset_mask(self, net, inp):
        self.drop_inp.reset_mask(inp)
        self.drop_net.reset_mask(net)

    def forward(self, net, inp, corr, flow):
        motion_features = self.encoder(flow, corr)
        inp = torch.cat([inp, motion_features], dim=1)

        if self.training:
            net = self.drop_net(net)
            inp = self.drop_inp(inp)

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

        return net, delta_flow