// 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 <utility>
#include <vector>
#include "boost/optional.hpp"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/concat_and_split.h"

namespace paddle {
namespace operators {

namespace detail {
template <typename Container>
inline framework::LoD ConcatLoD(const Container &xs,
                                std::vector<framework::Tensor> *xs_in_order) {
  std::vector<size_t> result;
  result.resize(xs[0].get().lod()[0].size());

  for (size_t i = 1; i < result.size(); ++i) {
    size_t sum = 0;
    for (size_t j = 0; j < xs.size(); ++j) {
      auto &x_lod = xs[j].get().lod()[0];
      const framework::Tensor &tensor = xs[j].get();
      if (x_lod[i - 1] < x_lod[i]) {
        xs_in_order->emplace_back(tensor.Slice(x_lod[i - 1], x_lod[i]));
      }
      sum += x_lod[i];
    }
    result[i] = sum;
  }
  framework::LoD lod;
  lod.emplace_back(result);
  return lod;
}

template <typename T, typename... ARGS>
inline std::vector<std::reference_wrapper<T>> GetDataVectorSafely(
    const std::vector<T *> &vec, ARGS &&... args) {
  std::vector<std::reference_wrapper<T>> result;
  result.reserve(vec.size());
  for (auto *ptr : vec) {
    PADDLE_ENFORCE_NOT_NULL(ptr, platform::errors::InvalidArgument(
                                     "The input variable X contains nullptr."));
    result.emplace_back(*ptr);
  }
  return result;
}
}  // namespace detail

template <typename DeviceContext, typename T>
class SeqConcatKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &context) const override {
    auto xs = detail::GetDataVectorSafely(
        context.MultiInput<framework::LoDTensor>("X"));
    auto &out = *context.Output<framework::LoDTensor>("Out");

    size_t lod_size = 0;
    for (auto &x : xs) {
      if (lod_size == 0) {
        PADDLE_ENFORCE_EQ(x.get().lod().empty(), false,
                          "Input(X) Tensor of SequenceConcatOp does not "
                          "contain LoD information.");
        lod_size = x.get().lod()[0].size();
      } else {
        PADDLE_ENFORCE_EQ(
            lod_size, x.get().lod()[0].size(),
            "The number of sequence must be same between each input");
      }
    }
    PADDLE_ENFORCE_NE(lod_size, 0, "Each input must have sequence information");

    std::vector<framework::Tensor> x_in_order;
    out.set_lod(detail::ConcatLoD(xs, &x_in_order));
    out.mutable_data<T>(context.GetPlace());
    math::ConcatFunctor<DeviceContext, T> functor;
    functor(context.template device_context<DeviceContext>(), x_in_order, 0,
            &out);
  }
};

template <typename DeviceContext, typename T>
class SeqConcatGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &context) const override {
    auto xs = context.MultiInput<framework::LoDTensor>("X");
    auto dxs =
        context.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
    PADDLE_ENFORCE_EQ(xs.size(), dxs.size());
    for (size_t i = 0; i < dxs.size(); ++i) {
      if (dxs[i] != nullptr) {
        dxs[i]->set_lod(xs[i]->lod());
        dxs[i]->mutable_data<T>(context.GetPlace());
      }
    }

    std::vector<framework::Tensor> sliced_x;
    std::vector<boost::optional<framework::Tensor>> sliced_dx;

    for (size_t i = 1; i < xs[0]->lod()[0].size(); ++i) {
      for (size_t j = 0; j < xs.size(); ++j) {
        const framework::LoDTensor *x = xs[j];
        framework::DDim x_dims = x->dims();

        framework::LoDTensor *dx = dxs[j];
        auto &x_lod = x->lod()[0];
        if (x_lod[i - 1] == x_lod[i]) continue;

        auto prev_lod = x_lod[i - 1];
        auto next_lod = x_lod[i];

        x_dims[0] = next_lod - prev_lod;

        sliced_x.emplace_back();
        sliced_x.back().Resize(x_dims);

        if (dx) {
          sliced_dx.emplace_back(dx->Slice(prev_lod, next_lod));
        } else {
          sliced_dx.emplace_back(boost::none);
        }
      }
    }

    std::vector<const framework::Tensor *> sliced_x_ptr;
    sliced_x_ptr.reserve(sliced_x.size());
    for (auto &x : sliced_x) {
      sliced_x_ptr.emplace_back(&x);
    }

    std::vector<framework::Tensor *> sliced_dx_ptr;
    sliced_dx_ptr.reserve(sliced_dx.size());
    for (auto &dx : sliced_dx) {
      if (dx) {
        sliced_dx_ptr.emplace_back(&dx.get());
      }
    }

    math::SplitFunctor<DeviceContext, T> functor;
    functor(context.template device_context<DeviceContext>(),
            GET_DATA_SAFELY(
                context.Input<framework::Tensor>(framework::GradVarName("Out")),
                "Input", framework::GradVarName("Out"), "SeqConcatGrad"),
            sliced_x_ptr, 0, &sliced_dx_ptr);
  }
};

}  // namespace operators
}  // namespace paddle
