/* 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. */

#include "Layer.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/Vector.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"

namespace paddle {

class SequenceSliceLayer : public Layer {
public:
  explicit SequenceSliceLayer(const LayerConfig& config) : Layer(config) {}

  bool init(const LayerMap& layerMap,
            const ParameterMap& parameterMap) override;

  void forward(PassType passType) override;
  void backward(const UpdateCallback& callback = nullptr) override;

private:
  // TODO(caoying)
  // In PaddlePaddle, the currently available matrixes all a have real-typed
  // data field, but the selected indices information are actually int-typed
  // (with -1 as a special token). Storing indices information in real-typed
  // Matrix leads to converting real to int. This is very dangerous if a user
  // fills this matrix himself, invalid data may occur.
  // The selected indices should be stored in an int-typed matrix.

  MatrixPtr startIdsOnCpu_;
  MatrixPtr endIdsOnCpu_;

  std::vector<int> selectedRows_;
  IVectorPtr rowIndice_;
  std::vector<std::vector<int>> inputSeqInfoVec_;
  std::vector<int> outSubSeqStartPos_;
  std::vector<int> outSeqStartPos_;

  void checkInputs();
  void copySliceIdsToCpu();
  void calSelectedRows(const MatrixPtr starts, const MatrixPtr ends);
};

REGISTER_LAYER(seq_slice, SequenceSliceLayer);

bool SequenceSliceLayer::init(const LayerMap& layerMap,
                              const ParameterMap& parameterMap) {
  /* Initialize the basic parent class */
  Layer::init(layerMap, parameterMap);
  CHECK_GE(inputLayers_.size(), 2U);
  CHECK_LE(inputLayers_.size(), 3U);

  setNeedSequenceInfo(false);
  return true;
}

void SequenceSliceLayer::checkInputs() {
  const Argument& inputSeq = getInput(0);
  CHECK(inputSeq.hasSeq()) << "The first input of sequence slic layer "
                           << "must be a sequence.";
  // Check inputs
  const MatrixPtr indices1 = getInputValue(1);
  CHECK_EQ(indices1->getHeight(),
           inputSeq.hasSubseq() ? inputSeq.getNumSubSequences()
                                : inputSeq.getNumSequences())
      << "Height of the second input should be equal to number of sequence "
      << "in the first input.";
  if (inputLayers_.size() == 3) {
    const MatrixPtr indices2 = getInputValue(2);
    CHECK_EQ(indices2->getHeight(), indices1->getHeight())
        << "start indices and end indices should have the same height.";
    CHECK_EQ(indices2->getWidth(), indices1->getWidth())
        << "start indices and end indices should have the same Width.";
  }
}

void SequenceSliceLayer::copySliceIdsToCpu() {
  if (!useGpu_) {
    if (inputLayers_.size() == 2U) {
      if (config_.select_first()) {
        startIdsOnCpu_ = getInputValue(1);
        endIdsOnCpu_ = nullptr;
      } else {
        startIdsOnCpu_ = nullptr;
        endIdsOnCpu_ = getInputValue(1);
      }
    } else if (inputLayers_.size() == 3U) {
      startIdsOnCpu_ = getInputValue(1);
      endIdsOnCpu_ = getInputValue(2);
    }
    return;
  }

  const MatrixPtr indices1 = getInputValue(1);
  if (inputLayers_.size() == 2U) {
    if (config_.select_first()) {
      Matrix::resizeOrCreate(startIdsOnCpu_,
                             indices1->getHeight(),
                             indices1->getWidth(),
                             false /* trans */,
                             false /* useGpu */);
      startIdsOnCpu_->copyFrom(*indices1);
      endIdsOnCpu_ = nullptr;
    } else {
      Matrix::resizeOrCreate(endIdsOnCpu_,
                             indices1->getHeight(),
                             indices1->getWidth(),
                             false /* trans */,
                             false /* useGpu */);
      endIdsOnCpu_->copyFrom(*indices1);
      startIdsOnCpu_ = nullptr;
    }
  } else if (inputLayers_.size() == 3U) {
    Matrix::resizeOrCreate(startIdsOnCpu_,
                           indices1->getHeight(),
                           indices1->getWidth(),
                           false /* trans */,
                           false /* useGpu */);
    startIdsOnCpu_->copyFrom(*indices1);

    const MatrixPtr indices2 = getInputValue(2);
    Matrix::resizeOrCreate(endIdsOnCpu_,
                           indices2->getHeight(),
                           indices2->getWidth(),
                           false /* trans */,
                           false /* useGpu */);
    endIdsOnCpu_->copyFrom(*indices2);
  }
}

void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts,
                                         const MatrixPtr ends) {
  outSeqStartPos_.resize(1, 0);
  outSubSeqStartPos_.resize(1, 0);
  selectedRows_.clear();

  size_t beamSize = starts ? starts->getWidth() : ends->getWidth();
  // iterate over sequence
  size_t rowIdx = 0;
  for (size_t i = 0; i < inputSeqInfoVec_.size(); ++i) {
    // iterate over sub-sequence in a sequence
    for (size_t j = 0; j < inputSeqInfoVec_[i].size() - 1; ++j) {
      // iterate over each index for slicing.
      for (size_t k = 0; k < beamSize; ++k) {
        if (starts) {
          if (starts->getElement(rowIdx, k) == -1.) break;
        } else if (ends->getElement(rowIdx, k) == -1.)
          break;

        int begPos = inputSeqInfoVec_[i][j];
        if (starts) begPos += starts->getElement(rowIdx, k);

        int endPos = inputSeqInfoVec_[i][j + 1] - 1;
        if (ends) endPos = inputSeqInfoVec_[i][j] + ends->getElement(rowIdx, k);

        int seqLen = endPos - begPos + 1;
        CHECK(seqLen);
        for (int m = begPos; m <= endPos; ++m) selectedRows_.push_back(m);
        inputSeqInfoVec_.size() > 1
            ? outSubSeqStartPos_.push_back(outSubSeqStartPos_.back() + seqLen)
            : outSeqStartPos_.push_back(outSeqStartPos_.back() + seqLen);
      }
      rowIdx++;
    }
    if (inputSeqInfoVec_.size() > 1)
      outSeqStartPos_.push_back(outSubSeqStartPos_.back());
  }

  if (useGpu_) {
    rowIndice_ = IVector::create(selectedRows_.size(), useGpu_);
    rowIndice_->copyFrom(selectedRows_.data(), selectedRows_.size());
  } else {
    rowIndice_ =
        IVector::create(selectedRows_.data(), selectedRows_.size(), useGpu_);
  }

  // create the sequence information for the output.
  ICpuGpuVector::resizeOrCreate(
      output_.sequenceStartPositions, outSeqStartPos_.size(), false);
  output_.sequenceStartPositions->copyFrom(
      outSeqStartPos_.data(), outSeqStartPos_.size(), false);

  if (inputSeqInfoVec_.size() > 1) {
    ICpuGpuVector::resizeOrCreate(
        output_.subSequenceStartPositions, outSubSeqStartPos_.size(), false);
    output_.subSequenceStartPositions->copyFrom(
        outSubSeqStartPos_.data(), outSubSeqStartPos_.size(), false);
  }
}

void SequenceSliceLayer::forward(PassType passType) {
  Layer::forward(passType);
  checkInputs();

  const Argument& inputSeq = getInput(0);
  inputSeqInfoVec_.clear();
  Argument::reorganizeSeqInfo(inputSeq.sequenceStartPositions,
                              inputSeq.subSequenceStartPositions,
                              inputSeqInfoVec_);
  copySliceIdsToCpu();

  // calculate the selected row indices in a batch,
  // and build the output sequence information.
  calSelectedRows(startIdsOnCpu_ ? startIdsOnCpu_ : nullptr,
                  endIdsOnCpu_ ? endIdsOnCpu_ : nullptr);

  resetOutput(selectedRows_.size(), getSize());

  getOutputValue()->selectRows(*getInputValue(0), *rowIndice_);
}

void SequenceSliceLayer::backward(const UpdateCallback& callback) {
  MatrixPtr inputSeqGrad = getInputGrad(0);
  MatrixPtr outputGrad = getOutputGrad();

  outputGrad->addToRows(*inputSeqGrad, *rowIndice_);
}

}  // namespace paddle