// ----------------------------------------------------------------------------
// -                        Open3D: www.open3d.org                            -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2023 www.open3d.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
//
//    Based on PVCNN Library (MIT License):
//    https://github.com/mit-han-lab/pvcnn
//
// Copyright (c) 2018 Zhijian Liu, Haotian Tang, Yujun Lin
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.
// ----------------------------------------------------------------------------

#include <math.h>
#include <stdio.h>
#include <stdlib.h>

#include "ATen/cuda/CUDAContext.h"
#include "open3d/ml/contrib/TrilinearDevoxelize.cuh"
#include "open3d/ml/contrib/cuda_utils.h"
#include "open3d/ml/pytorch/pvcnn/TrilinearDevoxelizeKernel.h"

using namespace open3d::ml::contrib;

void TrilinearDevoxelize(int b,
                         int c,
                         int n,
                         int r,
                         int r2,
                         int r3,
                         bool training,
                         const float *coords,
                         const float *feat,
                         int *inds,
                         float *wgts,
                         float *outs) {
    cudaError_t err;

    auto stream = at::cuda::getCurrentCUDAStream();

    TrilinearDevoxelizeKernel<<<b, OptNumThreads(n), 0, stream>>>(
            b, c, n, r, r2, r3, training, coords, feat, inds, wgts, outs);

    err = cudaGetLastError();
    if (cudaSuccess != err) {
        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
        exit(-1);
    }
}

void TrilinearDevoxelizeGrad(int b,
                             int c,
                             int n,
                             int r3,
                             const int *inds,
                             const float *wgts,
                             const float *grad_y,
                             float *grad_x) {
    cudaError_t err;

    auto stream = at::cuda::getCurrentCUDAStream();

    TrilinearDevoxelizeGradKernel<<<b, OptNumThreads(n), 0, stream>>>(
            b, c, n, r3, inds, wgts, grad_y, grad_x);

    err = cudaGetLastError();
    if (cudaSuccess != err) {
        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
        exit(-1);
    }
}