// Copyright (c) 2022 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/phi/kernels/diagonal_grad_kernel.h"

#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/diagonal.h"

namespace phi {

template <typename T, typename Context>
void DiagonalGradKernel(const Context& dev_ctx,
                        const DenseTensor& x UNUSED,
                        const DenseTensor& out_grad,
                        int offset,
                        int axis1,
                        int axis2,
                        DenseTensor* in_grad) {
  const auto* dout = &out_grad;
  const T* dout_data = dout->data<T>();
  auto dout_dim = vectorize(dout->dims());

  auto* dx = in_grad;
  T* dx_data = dev_ctx.template Alloc<T>(dx);
  auto dx_dim = vectorize(dx->dims());
  auto dx_dim_size = dx_dim.size();

  const int64_t offset_ = offset;
  int64_t axis1_ =
      static_cast<int64_t>(axis1 < 0 ? dx_dim_size + axis1 : axis1);
  int64_t axis2_ =
      static_cast<int64_t>(axis2 < 0 ? dx_dim_size + axis2 : axis2);

  std::vector<int64_t> dout_stride = funcs::ComputeDimStride(dout_dim);
  std::vector<int64_t> dx_stride = funcs::ComputeDimStride(dx_dim);

  int64_t numel = dx->numel();

  for (int64_t idx = 0; idx < numel; idx++) {
    std::vector<int64_t> idx_dim(dx_dim_size);
    int64_t temp = 0;
    for (size_t i = 0; i < dx_dim_size; i++) {
      idx_dim[i] = (idx - temp) / dx_stride[i];
      temp = temp + idx_dim[i] * dx_stride[i];
    }

    int64_t axis1_dim = idx_dim[axis1_];
    int64_t axis2_dim = idx_dim[axis2_];

    idx_dim.erase(idx_dim.begin() + std::max(axis1_, axis2_));
    idx_dim.erase(idx_dim.begin() + std::min(axis1_, axis2_));

    bool flag = false;
    if (offset_ == 0 && axis1_dim == axis2_dim) {
      idx_dim.push_back(axis1_dim);
      flag = true;
    } else if (offset_ > 0 && (axis1_dim + offset_) == axis2_dim) {
      idx_dim.push_back(axis1_dim);
      flag = true;
    } else if (offset_ < 0 && (axis1_dim + offset_) == axis2_dim) {
      idx_dim.push_back(axis2_dim);
      flag = true;
    }
    if (flag) {
      int64_t idx_output = 0;
      for (size_t i = 0; i < idx_dim.size(); i++) {
        idx_output = idx_output + idx_dim[i] * dout_stride[i];
      }
      dx_data[idx] = dout_data[idx_output];
    } else {
      dx_data[idx] = static_cast<T>(0);
    }
  }
}
}  // namespace phi
PD_REGISTER_KERNEL(diagonal_grad,
                   CPU,
                   ALL_LAYOUT,
                   phi::DiagonalGradKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   bool,
                   phi::dtype::complex<float>,
                   phi::dtype::complex<double>) {}
