// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#pragma once
#include <vector>
#include "lite/core/op_registry.h"
#include "lite/fluid/eigen.h"

namespace paddle {
namespace lite {
namespace kernels {
namespace x86 {

template <typename T,
          size_t D,
          int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenTensor = lite::fluid::EigenTensor<T, D, MajorType, IndexType>;
template <typename T,
          int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenScalar = lite::fluid::EigenScalar<T, MajorType, IndexType>;
template <typename T,
          int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenVector = lite::fluid::EigenVector<T, MajorType, IndexType>;

template <lite::TargetType Target,
          typename T,
          size_t D,
          size_t R_D,
          typename Functor>
// const lite::Context<Target>& context,
void ReduceFunctor(const lite::Tensor& input,
                   lite::Tensor* output,
                   const std::vector<int>& dims,
                   bool keep_dim) {
  auto te = strstr(typeid(Functor).name(), "SumFunctor");
  if (D == 3 && R_D == 1 && te != NULL) {
    const lite::DDim& input_dims = input.dims();
    const T* input_data = input.data<T>();
    T* output_data = output->mutable_data<T>();
    for (int i = 0; i < input_dims[0]; i++) {
      for (int k = 0; k < input_dims[2]; k++) {
        int out_d = i * input_dims[2] + k;
        T output_temp = 0;
        for (int j = 0; j < input_dims[1]; j++) {
          int input_d =
              i * input_dims[1] * input_dims[2] + j * input_dims[2] + k;
          output_temp = output_temp + input_data[input_d];
        }
        output_data[out_d] = output_temp;
      }
    }
  } else {
    auto x = EigenTensor<T, D>::From(input);

    auto reduce_dim = Eigen::array<int, R_D>();
    auto x_rank = static_cast<int>(x.dimensions().size());
    for (size_t i = 0; i < dims.size(); ++i) {
      if (dims[i] < 0) {
        reduce_dim[i] = x_rank + dims[i];
      } else {
        reduce_dim[i] = dims[i];
      }
    }

    Functor functor;
    if (D == 1) {
      auto out = EigenScalar<T>::From(output);
      functor(&x, &out, reduce_dim);
    } else {
      auto out = EigenTensor<T, (D - R_D)>::From(*output, output->dims());
      functor(&x, &out, reduce_dim);
    }
  }
}

}  // namespace x86
}  // namespace kernels
}  // namespace lite
}  // namespace paddle