import torch
import torch.nn as nn
from utils.flow_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: An 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
