/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

   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 "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"

namespace paddle {
namespace operators {

template <typename Place, typename T>
class LoDResetKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const {
    auto* out = ctx.Output<framework::LoDTensor>("Out");
    auto* in = ctx.Input<framework::LoDTensor>("X");
    auto* lod_t = ctx.Input<framework::Tensor>("TargetLoD");

    std::vector<int> level0;
    if (lod_t) {
      auto* lod = lod_t->data<int>();
      if (platform::is_gpu_place(ctx.GetPlace())) {
        framework::Tensor lod_cpu;
        lod_cpu.CopyFrom(*lod_t, platform::CPUPlace(), ctx.device_context());
        lod = lod_cpu.data<int>();
      }
      level0 = std::vector<int>(lod, lod + lod_t->numel());
    } else {
      level0 = ctx.Attr<std::vector<int>>("target_lod");
    }

    PADDLE_ENFORCE(level0.size() > 1UL,
                   "The size of target LoD should be greater than 1.");
    PADDLE_ENFORCE(level0[0] == 0,
                   "Target LoD should be a vector starting from 0.");
    PADDLE_ENFORCE(level0.back() == in->dims()[0],
                   "Target LoD should be a vector end with the "
                   "first dimension of Input(X).");
    for (size_t i = 0; i < level0.size() - 1; ++i) {
      PADDLE_ENFORCE(level0[i + 1] > level0[i],
                     "Target LoD should be an ascending vector.");
    }

    out->ShareDataWith(*in);
    // cast level0 to size_t
    std::vector<size_t> ulevel0(level0.size(), 0);
    std::transform(level0.begin(), level0.end(), ulevel0.begin(),
                   [](int a) { return static_cast<size_t>(a); });
    framework::LoD target_lod;
    target_lod.push_back(ulevel0);
    out->set_lod(target_lod);
  }
};

template <typename Place, typename T>
class LoDResetGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const {
    auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
    auto* d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));

    d_x->ShareDataWith(*d_out);
  }
};
}  // namespace operators
}  // namespace paddle
