// Copyright (c) 2024 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 "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"

namespace phi {

template <typename T>
void CvmComputeKernel(const bool use_cvm,
                      const int64_t item_width,
                      const T** X,
                      T** Y) {
  const auto cvm_offset = use_cvm ? 0 : 2;

  std::memcpy(*Y, *X + cvm_offset, (item_width - cvm_offset) * sizeof(T));

  if (use_cvm) {
    (*Y)[0] = log((*Y)[0] + 1);
    (*Y)[1] = log((*Y)[1] + 1) - (*Y)[0];
  }

  (*X) += item_width;
  (*Y) += item_width - cvm_offset;
}

template <typename T>
void CvmGradComputeKernel(const bool use_cvm,
                          const int64_t item_width,
                          const T& CVM,
                          const T** DY,
                          T** DX) {
  const auto cvm_offset = use_cvm ? 0 : 2;

  std::memcpy(*DX + cvm_offset, *DY, (item_width - cvm_offset) * sizeof(T));

  (*DX)[0] = (&CVM)[0];
  (*DX)[1] = (&CVM)[1];

  (*DX) += item_width;
  (*DY) += item_width - cvm_offset;
}

template <typename T, typename Context>
void CVMOpKernel(const Context& dev_ctx,
                 const DenseTensor& x_in,
                 const DenseTensor& cvm,
                 bool use_cvm,
                 DenseTensor* out) {
  const auto* x = &x_in;
  const T* x_data = x->data<T>();

  auto batch_size = x->dims()[0];
  auto item_size = x->numel() / batch_size;

  auto* y = out;
  T* y_data = dev_ctx.template Alloc<T>(y);

  // for Input X do not have Lod Information.
  if (x->NumLevels() == 0) {
    if (use_cvm) {
      for (int i = 0; i < batch_size; i++) {
        int64_t cursor = i * item_size;
        y_data[cursor] = log(x_data[cursor] + 1);
        y_data[cursor + 1] = log(x_data[cursor + 1] + 1) - y_data[cursor];
        for (int j = 2; j < item_size; j++) {
          y_data[cursor + j] = x_data[cursor + j];
        }
      }
    } else {
      for (int i = 0; i < batch_size; i++) {
        CvmComputeKernel(use_cvm, item_size, &x_data, &y_data);
      }
    }
  } else {
    auto lod = x->lod()[0];
    for (size_t i = 0; i < lod.size() - 1; ++i) {
      for (size_t j = 0; j < lod[i + 1] - lod[i]; ++j) {
        CvmComputeKernel(use_cvm, item_size, &x_data, &y_data);
      }
    }
  }
}

template <typename T, typename Context>
void CVMGradOpKernel(const Context& dev_ctx,
                     const DenseTensor& x_in,
                     const DenseTensor& cvm_in,
                     const DenseTensor& out_grad,
                     bool use_cvm,
                     DenseTensor* x_grad) {
  auto* dx = x_grad;
  T* dx_data = dev_ctx.template Alloc<T>(dx);

  const DenseTensor* cvm = &cvm_in;
  const T* cvm_data = cvm->data<T>();

  const auto* dOut = &out_grad;
  const T* dout_data = dOut->data<T>();

  auto offset = 2;
  auto batch_size = dx->dims()[0];
  auto item_size = dx->numel() / batch_size;

  // for Input X do not have Lod Information.
  if (dx->NumLevels() == 0) {
    for (int x = 0; x < batch_size; ++x) {
      CvmGradComputeKernel(use_cvm, item_size, *cvm_data, &dout_data, &dx_data);
      cvm_data += offset;
    }
  } else {
    auto lod = dx->lod()[0];
    int seq_num = static_cast<int>(lod.size()) - 1;
    for (int i = 0; i < seq_num; ++i) {
      for (size_t j = 0; j < lod[i + 1] - lod[i]; ++j) {
        CvmGradComputeKernel(
            use_cvm, item_size, *cvm_data, &dout_data, &dx_data);
      }
      cvm_data += offset;
    }
  }
}
}  // namespace phi