// 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.

#pragma once

#include "paddle/phi/kernels/impl/lu_kernel_impl.h"

namespace phi {

template <typename T>
struct LuUnpackEyeFunctor {
  LuUnpackEyeFunctor(int64_t num_columns, T* output)
      : num_columns_(num_columns), output_(output) {}

  HOSTDEVICE void operator()(size_t idx) const {
    output_[idx * num_columns_ + idx % num_columns_] = static_cast<T>(1);
  }

  int64_t num_columns_;
  T* output_;
};

template <typename T, typename Context>
void LUUnpackKernel(const Context& dev_ctx,
                    const DenseTensor& x,
                    const DenseTensor& pivots,
                    bool unpack_ludata,
                    bool unpack_pivots,
                    DenseTensor* pmat,
                    DenseTensor* l,
                    DenseTensor* u) {
  auto xdims = x.dims();
  int xrank = xdims.size();
  int64_t m = xdims[xrank - 2];
  int64_t n = xdims[xrank - 1];
  int64_t k = std::min(m, n);

  if (unpack_ludata) {
    dev_ctx.template Alloc<T>(l);
    dev_ctx.template Alloc<T>(u);

    if (x.numel() != 0) {
      DenseTensor L, U;
      LU_Unpack<Context, T>(dev_ctx, &x, &L, &U);

      if (m >= n) {
        Copy(dev_ctx, L, dev_ctx.GetPlace(), false, l);
        Tensor_narrow<Context, T>(dev_ctx, &U, u, 0, k, 0, k);
      } else {
        Copy(dev_ctx, U, dev_ctx.GetPlace(), false, u);
        Tensor_narrow<Context, T>(dev_ctx, &L, l, 0, k, 0, k);
      }
    }
  }

  if (unpack_pivots) {
    dev_ctx.template Alloc<T>(pmat);
    if (x.numel() == 0 || pivots.numel() == 0) {
      // columns is the last dim.
      auto pmat_dims = pmat->dims();
      int64_t columns = pmat_dims[pmat_dims.size() - 1];
      if (columns == 0) return;

      T* pmat_data = pmat->data<T>();
      funcs::SetConstant<Context, T> set_zero;
      set_zero(dev_ctx, pmat, static_cast<T>(0));
      int64_t rows = pmat->numel() / columns;
      funcs::ForRange<Context> for_range(dev_ctx, rows);
      LuUnpackEyeFunctor<T> functor(columns, pmat_data);
      for_range(functor);
      return;
    }

    PADDLE_ENFORCE_EQ(
        pivots.dtype(),
        DataType::INT32,
        common::errors::InvalidArgument(
            "The pivots of lu_unpack must be of type int32, but received [%s].",
            pivots.dtype()));

    Unpack_Pivot<Context, T>(dev_ctx, pivots, pmat, m, k);
  }
}

}  // namespace phi