/* 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. */

#include "paddle/fluid/operators/math/sequence_padding.h"

namespace paddle {
namespace operators {
namespace math {

enum CopyType { kSeqToPad, kPadToSeq };

template <typename T>
void CopyValidData(framework::Tensor* dst_tensor,
                   const framework::Tensor* src_tensor,
                   const framework::Vector<size_t>& seq_offsets,
                   int pad_seq_len, int step_width, bool norm_by_len,
                   CopyType type, PadLayout layout) {
  int seq_num = seq_offsets.size() - 1;
  const T* src_data = src_tensor->data<T>();
  T* dst_data = dst_tensor->data<T>();

  int seq_cpy_gap = step_width;
  int pad_cpy_gap =
      layout == kBatchLengthWidth ? step_width : seq_num * step_width;
  for (int seq_idx = 0; seq_idx < seq_num; ++seq_idx) {
    int valid_seq_len = seq_offsets[seq_idx + 1] - seq_offsets[seq_idx];
    PADDLE_ENFORCE_GE(
        pad_seq_len, valid_seq_len,
        "The padded sequence length can not be less than its original length.");
    int seq_data_offset = seq_offsets[seq_idx] * step_width;
    int pad_data_offset = layout == kBatchLengthWidth
                              ? seq_idx * pad_seq_len * step_width
                              : seq_idx * step_width;
    float scale = 1.0f / static_cast<float>(valid_seq_len);

    for (int step_idx = 0; step_idx < valid_seq_len; ++step_idx) {
      const T* src =
          src_data + (type == kSeqToPad ? seq_data_offset : pad_data_offset);
      T* dst =
          dst_data + (type == kSeqToPad ? pad_data_offset : seq_data_offset);
      memcpy(dst, src, step_width * sizeof(T));
      if (norm_by_len) {
        for (int i = 0; i < step_width; ++i) {
          *(dst + i) *= scale;
        }
      }
      seq_data_offset += seq_cpy_gap;
      pad_data_offset += pad_cpy_gap;
    }
  }
}

template <typename T>
class PaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
 public:
  void operator()(const platform::CPUDeviceContext& context,
                  const framework::LoDTensor& seq_tensor,
                  framework::LoDTensor* pad_tensor,
                  std::vector<T> pad_value = {0}, int pad_seq_len = -1,
                  int lod_level = 0, bool norm_by_times = false,
                  const PadLayout layout = kBatchLengthWidth) {
    auto seq_offsets = framework::ToAbsOffset(seq_tensor.lod())[lod_level];
    auto seq_tensor_dims = seq_tensor.dims();
    auto pad_tensor_dims = pad_tensor->dims();
    if (pad_seq_len == -1) {
      pad_seq_len = MaximumSequenceLength(seq_offsets);
    }
    int step_width = seq_tensor.numel() / seq_tensor_dims[0];

    CheckDims(seq_tensor_dims, pad_tensor_dims, seq_offsets, pad_seq_len,
              step_width, layout);
    PADDLE_ENFORCE(pad_value.size() == 1 ||
                       static_cast<int>(pad_value.size()) == step_width,
                   "The size of 'pad_value' can only be 1 or be equal to the "
                   "'step_width'.");

    if (pad_value.size() == 1) {
      pad_value = std::vector<T>(step_width, pad_value[0]);
    }

    // fill padding value
    T* pad_data = pad_tensor->data<T>();
    for (int i = 0; i < pad_tensor->numel() / step_width; ++i) {
      memcpy(pad_data, pad_value.data(), step_width * sizeof(T));
    }

    CopyValidData<T>(pad_tensor, &seq_tensor, seq_offsets, pad_seq_len,
                     step_width, norm_by_times, kSeqToPad, layout);
  }
};

template <typename T>
class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
 public:
  void operator()(const platform::CPUDeviceContext& context,
                  const framework::LoDTensor& pad_tensor,
                  framework::LoDTensor* seq_tensor, int pad_seq_len = -1,
                  int lod_level = 0, bool norm_by_times = false,
                  const PadLayout& layout = kBatchLengthWidth) {
    auto seq_offsets = framework::ToAbsOffset(seq_tensor->lod())[lod_level];
    auto seq_tensor_dims = seq_tensor->dims();
    auto pad_tensor_dims = pad_tensor.dims();
    if (pad_seq_len == -1) {
      pad_seq_len = MaximumSequenceLength(seq_offsets);
    }
    int step_width = seq_tensor->numel() / seq_tensor_dims[0];

    CheckDims(seq_tensor_dims, pad_tensor_dims, seq_offsets, pad_seq_len,
              step_width, layout);

    CopyValidData<T>(seq_tensor, &pad_tensor, seq_offsets, pad_seq_len,
                     step_width, norm_by_times, kPadToSeq, layout);
  }
};

template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, int>;
template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, int64_t>;
template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, float>;
template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, double>;

template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, int>;
template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, int64_t>;
template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, float>;
template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, double>;

}  // namespace math
}  // namespace operators
}  // namespace paddle