/*===---- cmath - CUDA wrapper for <cmath> ---------------------------------===
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 * THE SOFTWARE.
 *
 *===-----------------------------------------------------------------------===
 */

#ifndef __CLANG_CUDA_WRAPPERS_CMATH
#define __CLANG_CUDA_WRAPPERS_CMATH

#include_next <cmath>

#if defined(_LIBCPP_STD_VER)

// libc++ will need long double variants of these functions, but CUDA does not
// provide them. We'll provide their declarations, which should allow the
// headers to parse, but would not allow accidental use of them on a GPU.

__attribute__((device)) long double logb(long double);
__attribute__((device)) long double scalbn(long double, int);

namespace std {

// For __constexpr_fmin/fmax we only need device-side overloads before c++14
// where they are not constexpr.
#if _LIBCPP_STD_VER < 14

__attribute__((device))
inline _LIBCPP_HIDE_FROM_ABI _LIBCPP_CONSTEXPR_SINCE_CXX14 float __constexpr_fmax(float __x, float __y) _NOEXCEPT {
  return __builtin_fmaxf(__x, __y);
}

__attribute__((device))
inline _LIBCPP_HIDE_FROM_ABI _LIBCPP_CONSTEXPR_SINCE_CXX14 double __constexpr_fmax(double __x, double __y) _NOEXCEPT {
  return __builtin_fmax(__x, __y);
}

__attribute__((device))
inline _LIBCPP_HIDE_FROM_ABI _LIBCPP_CONSTEXPR_SINCE_CXX14 long double
__constexpr_fmax(long double __x, long double __y) _NOEXCEPT {
  return __builtin_fmaxl(__x, __y);
}

template <class _Tp, class _Up, __enable_if_t<is_arithmetic<_Tp>::value && is_arithmetic<_Up>::value, int> = 0>
__attribute__((device))
_LIBCPP_HIDE_FROM_ABI _LIBCPP_CONSTEXPR_SINCE_CXX14 typename __promote<_Tp, _Up>::type
__constexpr_fmax(_Tp __x, _Up __y) _NOEXCEPT {
  using __result_type = typename __promote<_Tp, _Up>::type;
  return std::__constexpr_fmax(static_cast<__result_type>(__x), static_cast<__result_type>(__y));
}
#endif // _LIBCPP_STD_VER < 14

// For logb/scalbn templates we must always provide device overloads because
// libc++ implementation uses __builtin_XXX which gets translated into a libcall
// which we can't handle on GPU. We need to forward those to CUDA-provided
// implementations.

template <class _Tp>
__attribute__((device))
_LIBCPP_HIDE_FROM_ABI _LIBCPP_CONSTEXPR_SINCE_CXX14 _Tp __constexpr_logb(_Tp __x) {
  return ::logb(__x);
}

template <class _Tp>
__attribute__((device))
_LIBCPP_HIDE_FROM_ABI _LIBCPP_CONSTEXPR_SINCE_CXX20 _Tp __constexpr_scalbn(_Tp __x, int __exp) {
  return ::scalbn(__x, __exp);
}

} // namespace std//

#endif // _LIBCPP_STD_VER

#endif // include guard