/* Copyright (c) 2016 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 <algorithm>
#include <vector>

#include "paddle/fluid/platform/device_context.h"
#include "paddle/phi/core/mixed_vector.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"

namespace phi {
namespace funcs {

template <typename T,
          int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenMatrix = phi::EigenMatrix<T, MajorType, IndexType>;

template <typename DeviceContext, typename T>
class CopyMatrixRowsFunctor {
 public:
  // If is_src_index is true,
  // copy the indexed rows of input src to the output dst.
  // If is_src_index is false,
  // copy the input src to the indexed rows of output dst.
  // The indexed rows are based on the input index.
  void operator()(const DeviceContext& context,
                  const phi::DenseTensor& src,
                  phi::Vector<size_t> index_lod,
                  phi::DenseTensor* dst,
                  bool is_src_index);
};

template <typename DeviceContext, typename T>
class LoDTensor2BatchFunctor {
  // Calculate the length of each sequence and
  // sort sequence index by the length.
  // example:  sequences = {s0, s1, s2}
  //           s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
  //           seq_info[3] = {(4, 5, 1), (0, 4, 0), (9, 3, 2)}
  //
  struct SeqInfo {
    SeqInfo(size_t start, size_t length, size_t seq_idx)
        : start(start), length(length), seq_idx(seq_idx) {}
    size_t start;
    size_t length;
    size_t seq_idx;
  };

 public:
  void operator()(const DeviceContext& context,
                  const phi::DenseTensor& lod_tensor,
                  phi::DenseTensor* batch,
                  bool is_cal_batch_lod,
                  bool is_reverse = false) const {
    if (!is_cal_batch_lod) {
      auto lods = batch->lod();
      PADDLE_ENFORCE_GT(
          lods.size(),
          2UL,
          phi::errors::InvalidArgument(
              "The LoD of LoDTensor should inlcude at least 2-level "
              "sequence information, but got the LoD level is %lu. Please "
              "check the input value.",
              lods.size()));
      PADDLE_ENFORCE_EQ(
          lods[1].size(),
          static_cast<size_t>(lod_tensor.dims()[0]),
          phi::errors::InvalidArgument(
              "The LoD information should be consistent with the dims, but got "
              "%lu != %lu. Please check the input value.",
              lods[1].size(),
              static_cast<size_t>(lod_tensor.dims()[0])));
      CopyMatrixRowsFunctor<DeviceContext, T> to_batch;
      to_batch(context, lod_tensor, lods[1], batch, true);
      return;
    }

    auto lods = lod_tensor.lod();
    PADDLE_ENFORCE_EQ(lods.size(),
                      1UL,
                      phi::errors::InvalidArgument(
                          "Only support one level sequence now, but got the "
                          "LoD level is %lu. Please check the input value.",
                          lods.size()));

    const auto& lod = lods[0];

    std::vector<SeqInfo> seq_info;
    for (size_t seq_id = 0; seq_id < lod.size() - 1; ++seq_id) {
      size_t length = lod[seq_id + 1] - lod[seq_id];
      seq_info.emplace_back(lod[seq_id], length, seq_id);
    }

    std::sort(seq_info.begin(), seq_info.end(), [](SeqInfo a, SeqInfo b) {
      return a.length > b.length;
    });

    // Calculate the start position of each batch.
    // example:  sequences = {s0, s1, s2}
    //           s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
    //           max_seqlen = 5,
    //           batchIndex = {b0, b1, b2, b3, b4}
    //           b0: 1 0 2, b1: 1 0 2, b2: 1 0 2, b3: 1 0, b4: 1
    //           batch_start_positions[6] = {0, 3, 6, 9, 11, 12}
    //              batch_start_positions[0] = len(b0)
    //              batch_start_positions[1] = len(b0) + len(b1)
    //              batch_start_positions[2] = len(b0) + len(b1) + len(b2)
    //              ...
    //           seq2batch_idx[12] = {4, 0, 9,
    //                                5, 1, 10,
    //                                6, 2, 11,
    //                                7, 3,
    //                                8}
    //           seq_order = {1, 0, 2}, the sort order.
    //               where 1 is the second sequence,
    //                     0 is the first sequence,
    //                     2 is the third sequence.
    // The max_seqlen represents batch size after rearranging the
    // input LodTensor. It is also the maximum length of input sequence.

    phi::LoD batch_lods;
    batch_lods.emplace_back(std::vector<size_t>{0});
    batch_lods.emplace_back(std::vector<size_t>{0});
    batch_lods.emplace_back(std::vector<size_t>{0});

    // batch_lods[0] is the start positions for batch LoDTensor
    size_t max_seqlen = seq_info[0].length;
    batch_lods[0].resize(max_seqlen + 1);
    // batch_lods[1] is the raw index in the input LoDTensor
    batch_lods[1].resize(static_cast<size_t>(lod_tensor.dims()[0]));
    // batch_lods[2] is the sort order for the input LoDTensor.
    batch_lods[2].resize(seq_info.size());

    size_t* batch_starts = batch_lods[0].data();
    size_t* seq2batch_idx = batch_lods[1].data();
    batch_starts[0] = 0;
    for (size_t n = 0; n < max_seqlen; n++) {
      size_t batch_id = batch_starts[n];
      for (size_t i = 0; i < seq_info.size(); ++i) {
        size_t seq_len = seq_info[i].length;
        size_t start = seq_info[i].start;
        if (n < seq_len) {
          seq2batch_idx[batch_id] =
              is_reverse ? start + seq_len - 1 - n : start + n;
          batch_id++;
        } else {
          break;
        }
      }
      batch_starts[n + 1] = batch_id;
    }
    size_t* seq_order = batch_lods[2].data();
    for (size_t i = 0; i < seq_info.size(); ++i) {
      seq_order[i] = seq_info[i].seq_idx;
    }
    batch->set_lod(batch_lods);

    CopyMatrixRowsFunctor<DeviceContext, T> to_batch;
    to_batch(context, lod_tensor, batch_lods[1], batch, true);
  }
};

template <typename DeviceContext, typename T>
class Batch2LoDTensorFunctor {
 public:
  void operator()(const DeviceContext& context,
                  const phi::DenseTensor& batch,
                  phi::DenseTensor* lod_tensor) const {
    auto in_lod = batch.lod();
    PADDLE_ENFORCE_GT(
        in_lod.size(),
        2UL,
        phi::errors::InvalidArgument(
            "The LoD of LoDTensor should inlcude at least 2-level "
            "sequence information, but got the LoD level is %lu. Please check "
            "the input value.",
            in_lod.size()));
    PADDLE_ENFORCE_EQ(
        in_lod[1].size(),
        static_cast<size_t>(lod_tensor->dims()[0]),
        phi::errors::InvalidArgument(
            "The LoD information should be consistent with the dims, but got "
            "%lu != %lu. Please check the input value.",
            in_lod[1].size(),
            static_cast<size_t>(lod_tensor->dims()[0])));
    CopyMatrixRowsFunctor<DeviceContext, T> to_seq;
    to_seq(context, batch, in_lod[1], lod_tensor, false);
  }
};

}  // namespace funcs
}  // namespace phi