import torch
import torch.nn as nn
from utils import warp
from networks.blocks.ifrnet import (
    convrelu, resize,
    ResBlock,
)

def multi_flow_combine(comb_block, img0, img1, flow0, flow1, 
                       mask=None, img_res=None, mean=None):
        '''
            A parallel implementation of multiple flow field warping 
            comb_block: A nn.Seqential object.
            img shape: [b, c, h, w]
            flow shape: [b, 2*num_flows, h, w]
            mask (opt):
                If 'mask' is None, the function conduct a simple average.
            img_res (opt):
                If 'img_res' is None, the function adds zero instead. 
            mean (opt):
                If 'mean' is None, the function adds zero instead.       
        '''
        b, c, h, w = flow0.shape
        num_flows = c // 2
        flow0   =   flow0.reshape(b, num_flows, 2, h, w).reshape(-1, 2, h, w)
        flow1   =   flow1.reshape(b, num_flows, 2, h, w).reshape(-1, 2, h, w)
        
        mask    =    mask.reshape(b, num_flows, 1, h, w
                            ).reshape(-1, 1, h, w) if mask is not None else None
        img_res = img_res.reshape(b, num_flows, 3, h, w
                            ).reshape(-1, 3, h, w)  if img_res is not None else 0
        img0 = torch.stack([img0] * num_flows, 1).reshape(-1, 3, h, w)
        img1 = torch.stack([img1] * num_flows, 1).reshape(-1, 3, h, w)
        mean = torch.stack([mean] * num_flows, 1).reshape(-1, 1, 1, 1
                                                    ) if mean is not None else 0
        
        img0_warp = warp(img0, flow0)
        img1_warp = warp(img1, flow1)
        img_warps = mask * img0_warp + (1 - mask) * img1_warp + mean + img_res
        img_warps = img_warps.reshape(b, num_flows, 3, h, w)
        imgt_pred = img_warps.mean(1) + comb_block(img_warps.view(b, -1, h, w))
        return imgt_pred

class MultiFlowDecoder(nn.Module):
    def __init__(self, in_ch, skip_ch, num_flows=3):
        super(MultiFlowDecoder, self).__init__()
        self.num_flows = num_flows
        self.convblock = nn.Sequential(
            convrelu(in_ch*3+4, in_ch*3), 
            ResBlock(in_ch*3, skip_ch), 
            nn.ConvTranspose2d(in_ch*3, 8*num_flows, 4, 2, 1, bias=True)
        )
        
    def forward(self, ft_, f0, f1, flow0, flow1):
        n = self.num_flows
        f0_warp = warp(f0, flow0)
        f1_warp = warp(f1, flow1)
        out = self.convblock(torch.cat([ft_, f0_warp, f1_warp, flow0, flow1], 1))
        delta_flow0, delta_flow1, mask, img_res = torch.split(out, [2*n, 2*n, n, 3*n], 1)
        mask = torch.sigmoid(mask)
        
        flow0 = delta_flow0 + 2.0 * resize(flow0, scale_factor=2.0
                                           ).repeat(1, self.num_flows, 1, 1)
        flow1 = delta_flow1 + 2.0 * resize(flow1, scale_factor=2.0
                                           ).repeat(1, self.num_flows, 1, 1)
        
        return flow0, flow1, mask, img_res