#include "group_points.h"
#include "utils.h"

void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample,
                                 const float *points, const int *idx,
                                 float *out);

void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints,
                                      int nsample, const float *grad_out,
                                      const int *idx, float *grad_points);

at::Tensor group_points(at::Tensor points, at::Tensor idx) {
  CHECK_CONTIGUOUS(points);
  CHECK_CONTIGUOUS(idx);
  CHECK_IS_FLOAT(points);
  CHECK_IS_INT(idx);

  if (points.type().is_cuda()) {
    CHECK_CUDA(idx);
  }

  at::Tensor output =
      torch::zeros({points.size(0), points.size(1), idx.size(1), idx.size(2)},
                   at::device(points.device()).dtype(at::ScalarType::Float));

  if (points.type().is_cuda()) {
    group_points_kernel_wrapper(points.size(0), points.size(1), points.size(2),
                                idx.size(1), idx.size(2), points.data<float>(),
                                idx.data<int>(), output.data<float>());
  } else {
    AT_CHECK(false, "CPU not supported");
  }

  return output;
}

at::Tensor group_points_grad(at::Tensor grad_out, at::Tensor idx, const int n) {
  CHECK_CONTIGUOUS(grad_out);
  CHECK_CONTIGUOUS(idx);
  CHECK_IS_FLOAT(grad_out);
  CHECK_IS_INT(idx);

  if (grad_out.type().is_cuda()) {
    CHECK_CUDA(idx);
  }

  at::Tensor output =
      torch::zeros({grad_out.size(0), grad_out.size(1), n},
                   at::device(grad_out.device()).dtype(at::ScalarType::Float));

  if (grad_out.type().is_cuda()) {
    group_points_grad_kernel_wrapper(
        grad_out.size(0), grad_out.size(1), n, idx.size(1), idx.size(2),
        grad_out.data<float>(), idx.data<int>(), output.data<float>());
  } else {
    AT_CHECK(false, "CPU not supported");
  }

  return output;
}
