/**
 * Copyright (c) 2024 Huawei Technologies Co., Ltd.
 * This file is a part of the CANN Open Software.
 * Licensed under CANN Open Software License Agreement Version 1.0 (the "License").
 * Please refer to the License for details. You may not use this file except in compliance with the License.
 * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
 * INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
 * See LICENSE in the root of the software repository for the full text of the License.
 */
/*!
 * \file erf.h
 * \brief Defines a series of interface used to do elementwise math Erf calculation.
 * Formula: Error function or Gauss error function.
 * The Erf function does not have an elementary function expression, and there is calculating by
 * function approximation.
 * The approximate calculation formula is as follows:
 * Erf(x) = P(Clip(x)) / Q(Clip(x))
 * Clip(x) = Min(-3.92, Max(x, 3.92))
 * P(x) = (((((0.053443748819x^2+0.75517016694e1)x^2+0.10162808918e3)x^2+0.13938061484e4)x^2+0.50637915060e4)x^2
            +0.29639384698e
 * Q(x) = ((((x^2+0.31212858877e2)x^2+0.39856963806e3)x^2+0.30231248150e4)x^2+0.13243365831e5)x^2+0.26267224157e5
 */
#ifndef LIB_MATH_KERNEL_OPERATOR_ERF_INTF_H
#define LIB_MATH_KERNEL_OPERATOR_ERF_INTF_H
#if __CCE_AICORE__ == 220 || __CCE_AICORE__ == 200
#include "kernel_tensor.h"
#include "../../impl/math/erf/erf_common_impl.h"

namespace AscendC {
#pragma begin_pipe(V)
/*!
 * \ingroup Erf
 * \brief compute Erf elementwisely
 * \tparam T: half/float
 * \tparam isReuseSource: whether allows API to modify source data, usually for performance reason
 * \param [out] dstTensor: output LocalTensor
 * \param [in] srcTensor: input LocalTensor
 * \param [in] sharedTmpBuffer: extra temporary shared space used for intermediate values among calculation process,
 *             whose required space size should refer to corresponding tiling API, which is defined at erf_tiling.h.
 *             Generally, the more space you allocate, the better performance you will achieve, and the performance
 *             reaches peak when buffer size is maximum(calculated by tiling function). Moreover, it is not guaranteed
 *             that the shared space will be cleared after usage, the data could be anything.
 * \param [in] calCount: the number of elements to be processed.
 * \note src/dst Tensor must be 32B align, and it doesn't allow src/dst/sharedTmpBuffer tensor address overlap.
 */
template <typename T, bool isReuseSource = false>
__aicore__ inline void Erf(const LocalTensor<T> &dstTensor, const LocalTensor<T> &srcTensor,
    const LocalTensor<uint8_t> &sharedTmpBuffer, const uint32_t calCount)
{
    ErfImpl(dstTensor, srcTensor, sharedTmpBuffer, calCount);
}

/*!
 * \ingroup Erf
 * \brief compute Erf elementwisely for whole source tensor
 * \tparam T: half/float
 * \tparam isReuseSource: whether allows API to modify source data, usually for performance reason
 * \param [out] dstTensor: output LocalTensor
 * \param [in] srcTensor: input LocalTensor
 * \param [in] sharedTmpBuffer: extra temporary shared space used for intermediate values among calculation process,
 *             whose required space size should refer to corresponding tiling API, which is defined at erf_tiling.h.
 *             Generally, the more space you allocate, the better performance you will achieve, and the performance
 *             reaches peak when buffer size is maximum(calculated by tiling function). Moreover, it is not guaranteed
 *             that the shared space will be cleared after usage, the data could be anything.
 * \note src/dst Tensor must be 32B align, and it doesn't allow src/dst/sharedTmpBuffer tensor address overlap.
 */
template <typename T, bool isReuseSource = false>
__aicore__ inline void Erf(
    const LocalTensor<T> &dstTensor, const LocalTensor<T> &srcTensor, const LocalTensor<uint8_t> &sharedTmpBuffer)
{
    Erf<T, isReuseSource>(dstTensor, srcTensor, sharedTmpBuffer, srcTensor.GetSize());
}

/*!
 * \ingroup Erf
 * \brief compute Erf elementwisely
 * \tparam T: half/float
 * \tparam isReuseSource: whether allows API to modify source data, usually for performance reason
 * \param [out] dstTensor: output LocalTensor
 * \param [in] srcTensor: input LocalTensor
 * \param [in] calCount: the number of elements to be processed.
 * \note src/dst Tensor must be 32B align, and it doesn't allow src/dst/sharedTmpBuffer tensor address overlap.
 */
template <typename T, bool isReuseSource = false>
__aicore__ inline void Erf(const LocalTensor<T> &dstTensor, const LocalTensor<T> &srcTensor, const uint32_t calCount)
{
    ErfImpl(dstTensor, srcTensor, calCount);
}

/*!
 * \ingroup Erf
 * \brief compute Erf elementwisely for whole source tensor
 * \tparam T: half/float
 * \tparam isReuseSource: whether allows API to modify source data, usually for performance reason
 * \param [out] dstTensor: output LocalTensor
 * \param [in] srcTensor: input LocalTensor
 * \note src/dst Tensor must be 32B align, and it doesn't allow src/dst/sharedTmpBuffer tensor address overlap.
 */
template <typename T, bool isReuseSource = false>
__aicore__ inline void Erf(const LocalTensor<T> &dstTensor, const LocalTensor<T> &srcTensor)
{
    Erf<T, isReuseSource>(dstTensor, srcTensor, srcTensor.GetSize());
}

#pragma end_pipe
}  // namespace AscendC
#endif
#endif  // LIB_MATH_KERNEL_OPERATOR_ERF_INTF_H