#include <cuda_runtime_api.h>
#include <torch/script.h>
// clang-format off
// TODO: make spconv_utils.h order agnostic
#include "../spconv_utils.h"
// clang-format on
#include <utils/spconv/spconv/indice.h>
#include <utils/spconv/spconv/reordering.h>

#include "pytorch_cuda_helper.hpp"

torch::Tensor FusedIndiceConvBatchnormCUDAKernelLauncher(
    torch::Tensor features, torch::Tensor filters, torch::Tensor bias,
    torch::Tensor indicePairs, torch::Tensor indiceNum, int64_t numActOut,
    int64_t _inverse, int64_t _subM) {
  at::cuda::CUDAGuard device_guard(features.device());
  bool subM = _subM != 0;
  bool inverse = _inverse != 0;
  auto device = features.device().type();
  auto ndim = filters.dim() - 2;
  auto kernelVolume = indicePairs.size(0);
  auto numInPlanes = features.size(1);
  auto numOutPlanes = filters.size(ndim + 1);
  auto indicePairNumCpu = indiceNum.to({torch::kCPU});
  auto indicePairMaxSizeIter =
      std::max_element(indicePairNumCpu.data_ptr<int>(),
                       indicePairNumCpu.data_ptr<int>() + kernelVolume);
  int indicePairMaxOffset =
      indicePairMaxSizeIter - indicePairNumCpu.data_ptr<int>();
  int indicePairMaxSize = *indicePairMaxSizeIter;

  auto options =
      torch::TensorOptions().dtype(features.dtype()).device(features.device());

  torch::Tensor output =
      torch::zeros({numActOut, numOutPlanes}, options).copy_(bias);
  torch::Tensor inputBuffer =
      torch::zeros({indicePairMaxSize, numInPlanes}, options);
  torch::Tensor outputBuffer =
      torch::zeros({indicePairMaxSize, numOutPlanes}, options);
  filters = filters.view({-1, numInPlanes, numOutPlanes});
  if (subM) {  // the center index of subm conv don't need gather and scatter
               // add.
    torch::mm_out(output, features, filters[indicePairMaxOffset]);
  }
  double totalGatherTime = 0;
  double totalGEMMTime = 0;
  double totalSAddTime = 0;
  for (int i = 0; i < kernelVolume; ++i) {
    auto nHot = indicePairNumCpu.data_ptr<int>()[i];
    if (nHot <= 0 || (subM && i == indicePairMaxOffset)) {
      continue;
    }

    AT_DISPATCH_FLOATING_TYPES_AND_HALF(
        features.scalar_type(), "FusedIndiceConvBatchnormKernel", [&] {
          auto outputBufferBlob = torch::from_blob(
              outputBuffer.data_ptr<scalar_t>(), {nHot, numOutPlanes}, options);
          auto inputBufferBlob = torch::from_blob(
              inputBuffer.data_ptr<scalar_t>(), {nHot, numInPlanes}, options);

          if (device == torch::kCPU) {
            functor::SparseGatherFunctor<tv::CPU, scalar_t, int> gatherFtor;
            gatherFtor(tv::CPU(), tv::torch2tv<scalar_t>(inputBuffer),
                       tv::torch2tv<const scalar_t>(features),
                       tv::torch2tv<const int>(indicePairs).subview(i, inverse),
                       nHot);
          } else {
            functor::SparseGatherFunctor<tv::TorchGPU, scalar_t, int>
                gatherFtor;
            gatherFtor(tv::TorchGPU(), tv::torch2tv<scalar_t>(inputBuffer),
                       tv::torch2tv<const scalar_t>(features),
                       tv::torch2tv<const int>(indicePairs).subview(i, inverse),
                       nHot);
            TV_CHECK_CUDA_ERR();
            /* slower than SparseGatherFunctor, may due to int->long conversion
            auto indicePairLong = indicePairs[i][inverse].to(torch::kInt64);
            auto indicePairBlob =
            torch::from_blob(indicePairLong.data_ptr<long>(), {nHot},
            indicePairOptions); torch::index_select_out(inputBufferBlob,
            features, 0, indicePairBlob);*/
          }
          torch::mm_out(outputBufferBlob, inputBufferBlob, filters[i]);

          if (device == torch::kCPU) {
            functor::SparseScatterAddFunctor<tv::CPU, scalar_t, int>
                scatterFtor;
            scatterFtor(
                tv::CPU(), tv::torch2tv<scalar_t>(output),
                tv::torch2tv<const scalar_t>(outputBuffer),
                tv::torch2tv<const int>(indicePairs).subview(i, !inverse), nHot,
                true);
          } else {
            functor::SparseScatterAddFunctor<tv::TorchGPU, scalar_t, int>
                scatterFtor;
            scatterFtor(
                tv::TorchGPU(), tv::torch2tv<scalar_t>(output),
                tv::torch2tv<const scalar_t>(outputBuffer),
                tv::torch2tv<const int>(indicePairs).subview(i, !inverse), nHot,
                true);
            TV_CHECK_CUDA_ERR();
          }
        });
  }

  return output;
}
