// 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.

#ifndef _USE_MATH_DEFINES
#define _USE_MATH_DEFINES  // use M_2_SQRTPI on Windows
#endif

#include "paddle/phi/kernels/erfinv_kernel.h"

#include <limits>

#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"

namespace phi {

template <typename T, typename Context>
void ErfinvKernel(const Context& dev_ctx,
                  const DenseTensor& x,
                  DenseTensor* out) {
  dev_ctx.template Alloc<T>(out);
  if (out && out->numel() == 0) {
    return;
  }
  auto eigen_in = EigenVector<T>::Flatten(x);
  auto eigen_out = EigenVector<T>::Flatten(*out);
  auto& place = *dev_ctx.eigen_device();
  constexpr T half = static_cast<T>(0.5);
  constexpr T half_sqrt = static_cast<T>(M_SQRT1_2);
  constexpr T one = static_cast<T>(1);
  const T nan_val = std::numeric_limits<T>::quiet_NaN();
  // erfinv is only defined on [-1, 1]; align with PyTorch/scipy by returning
  // NaN for |x| > 1 (boundary +/-1 still yields +/-inf through ndtri).
  eigen_out.device(place) =
      (eigen_in.abs() > eigen_in.constant(one))
          .select(eigen_in.constant(nan_val),
                  (eigen_in * half + half).ndtri() * half_sqrt);
}

}  // namespace phi

PD_REGISTER_KERNEL(erfinv, CPU, ALL_LAYOUT, phi::ErfinvKernel, float, double) {}