def scaling_fixed_get_vmae_optical_flow_crop_batched_smoothed(generator,
                                                              mask_generator,
                                                              img1,
                                                              img2,
                                                              neg_back_flow=True,
                                                              num_scales=1,
                                                              min_scale=400,
                                                              N_mask_samples=100,
                                                              mask_ratio=0.8,
                                                              smoothing_factor=1):
    B = img1.shape[0]
    assert len(img1.shape) == 4
    assert num_scales >= 1

    # For scaling
    h1 = img2.shape[-2]
    w1 = img2.shape[-1]
    assert min_scale < h1 and min_scale >= 360  # Below 360p, the flows look terrible

    if neg_back_flow is False:
        print('WARNING: Not calculating negative backward flow')

    alpha = (min_scale / img1.shape[-2]) ** (1 / (num_scales - 1)) if num_scales > 1 else 1

    frame_size = 224 // generator.patch_size[-1]

    all_fwd_flows_e2d = []

    s_hs = []
    s_ws = []

    for aidx in range(num_scales):
        print(aidx)

        # print('aidx: ', aidx)

        img1_scaled = F.interpolate(img1.clone(), [int((alpha ** aidx) * h1), int((alpha ** aidx) * w1)],
                                    mode='bicubic', align_corners=True)
        img2_scaled = F.interpolate(img2.clone(), [int((alpha ** aidx) * h1), int((alpha ** aidx) * w1)],
                                    mode='bicubic', align_corners=True)

        h2 = img2_scaled.shape[-2]
        w2 = img2_scaled.shape[-1]

        s_h = h1 / h2
        s_w = w1 / w2

        s_hs.append(s_h)
        s_ws.append(s_w)

        # Because technically the compute_optical_flow function returns neg back flow
        if neg_back_flow is True:
            video = torch.cat([img2_scaled.unsqueeze(1), img1_scaled.unsqueeze(1)], 1)
        else:
            video = torch.cat([img1_scaled.unsqueeze(1), img2_scaled.unsqueeze(1)], 1)

        # Should work, even if the incoming video is already 224x224
        crops1, c_pos1 = get_minimal_224_crops_new_batched(video, 1)

        num_crops = len(crops1)

        crop_flows_enc = []
        crop_flows_enc2dec = []
        N_samples = N_mask_samples

        crop = torch.cat(crops1, 0).cuda()

        optical_flows_enc2dec = torch.zeros(B * num_crops, 2, frame_size, frame_size).cuda()
        mask_counts = torch.zeros(frame_size, frame_size).cuda()

        i = 0
        while i < N_samples or (mask_counts == 0).any().item():
            if i % 100 == 0:
                pass  # print(i)
            mask_generator.mask_ratio = mask_ratio

            # This would be that every sample has the same mask. For now that's okay I think
            mask = mask_generator()[None]
            mask_2f = ~mask[0, frame_size * frame_size:]
            mask_counts += mask_2f.reshape(frame_size, frame_size)

            with torch.cuda.amp.autocast(enabled=True):

                processed_x = generator._preprocess(crop)

                encoder_out = generator.predictor.encoder(processed_x.to(torch.float16), mask.repeat(B * num_crops, 1))
                encoder_to_decoder = generator.predictor.encoder_to_decoder(encoder_out)

                optical_flow_e2d = []
                # one per batch element for now
                for b in range(B * num_crops):
                    batch_flow = compute_optical_flow(encoder_to_decoder[b].unsqueeze(0), mask, frame_size)
                    optical_flow_e2d.append(average_crops(batch_flow, smoothing_factor).unsqueeze(0))

                optical_flow_e2d = torch.cat(optical_flow_e2d, 0)
                optical_flows_enc2dec += optical_flow_e2d
            i += 1

        optical_flows_enc2dec = optical_flows_enc2dec / mask_counts

        # split the crops back up
        crop_flows_enc2dec = optical_flows_enc2dec.split(B, 0)

        T1 = [F.interpolate(_, [int(224), int(224)], mode='bicubic', align_corners=True).unsqueeze(1).cpu() for _ in
              crop_flows_enc2dec]

        optical_flows_enc2dec_joined = reconstruct_video_new_2_batched(T1, c_pos1, (
        B, 1, 2, video.shape[-2], video.shape[-1])).squeeze(1)

        all_fwd_flows_e2d.append(optical_flows_enc2dec_joined)

    all_fwd_flows_e2d_new = []

    for ridx, r in enumerate(all_fwd_flows_e2d):
        # print('ridx', ridx)
        # print('sh', s_hs[ridx])
        # print('sw', s_ws[ridx])
        # print('scale_fac y', scale_ys[ridx])
        # print('scale_fac x', scale_xs[ridx])

        _sh = s_hs[ridx]
        _sw = s_ws[ridx]
        _sfy = generator.patch_size[-1]
        _sfx = generator.patch_size[-1]

        # plt.figure(figsize=(20, 20))

        # plt.subplot(1,3,1)
        # plt.imshow(f2rgb(-r).cpu().numpy()[0].transpose(1,2,0))

        # plt.subplot(1,3,2)
        new_r = F.interpolate(r, [int(all_fwd_flows_e2d[0].shape[-2]), int(all_fwd_flows_e2d[0].shape[-1])],
                              mode='bicubic', align_corners=True)
        # plt.imshow(f2rgb(-new_r).cpu().numpy()[0].transpose(1,2,0))

        scaled_new_r = torch.zeros_like(new_r)
        scaled_new_r[:, 0, :, :] = new_r[:, 0, :, :] * _sfx * _sw
        scaled_new_r[:, 1, :, :] = new_r[:, 1, :, :] * _sfy * _sh

        # plt.subplot(1,3,3)
        # plt.imshow(f2rgb(-scaled_new_r).cpu().numpy()[0].transpose(1,2,0))

        # plt.show()

        all_fwd_flows_e2d_new.append(scaled_new_r.unsqueeze(-1))
    return_flow = torch.cat(all_fwd_flows_e2d_new, -1).mean(-1)

    if neg_back_flow is True:
        return_flow = -return_flow
        all_fwd_flows_e2d_new = [-_ for _ in all_fwd_flows_e2d_new]

    return return_flow, all_fwd_flows_e2d_new