// 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/where_kernel.h"

#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {

template <typename T, typename Context>
void WhereKernel(const Context& ctx,
                 const DenseTensor& condition,
                 const DenseTensor& x,
                 const DenseTensor& y,
                 DenseTensor* out) {
  const bool* cond_data = condition.data<bool>();
  const T* x_data = x.data<T>();
  const T* y_data = y.data<T>();
  T* out_data = ctx.template Alloc<T>(out);

  auto cond_dims = phi::vectorize<int>(condition.dims());
  auto x_dims = phi::vectorize<int>(x.dims());

  // use [1] to replace [], because xpu not support []
  if (cond_dims.size() == 0) {
    cond_dims = std::vector<int>({1});
  }
  if (x_dims.size() == 0) {
    x_dims = std::vector<int>({1});
  }

  int ret = xpu::select(
      ctx.x_context(), cond_data, x_data, y_data, out_data, cond_dims, x_dims);

  PADDLE_ENFORCE_XDNN_SUCCESS(ret, "select");
}

}  // namespace phi

PD_REGISTER_KERNEL(
    where, XPU, ALL_LAYOUT, phi::WhereKernel, float, int, int64_t) {}
