/*
 * Copyright (c) 2021 Arm Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * 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.
 */
#if defined(ENABLE_SVE)
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/Traits.h"
#include "arm_compute/core/utils/misc/Traits.h"
#include "src/core/NEON/SVEMath.h"
#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
#include <arm_sve.h>

namespace arm_compute
{
namespace cpu
{
template <typename ScalarType>
void sve_logits_1d_max(const ITensor *in, ITensor *out, const Window &window)
{
    const auto all_true_pg    = wrapper::svptrue<ScalarType>();
    const auto window_start_x = static_cast<int>(window.x().start());
    const auto window_end_x   = static_cast<int>(window.x().end());

    Window win{ window };
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    Iterator input(in, win);
    Iterator output(out, win);

    execute_window_loop(win, [&](const Coordinates &)
    {
        // Get pointers
        const auto in_ptr  = reinterpret_cast<const ScalarType *>(input.ptr());
        const auto out_ptr = reinterpret_cast<ScalarType *>(output.ptr());

        // Init max value
        auto vec_max = wrapper::svdup_n(support::cpp11::lowest<ScalarType>());

        int      x  = window_start_x;
        svbool_t pg = wrapper::svwhilelt<ScalarType>(x, window_end_x);
        do
        {
            const auto current_value = svld1(pg, in_ptr + x);
            vec_max                  = svmax_m(pg, vec_max, current_value);

            x += wrapper::svcnt<ScalarType>();
            pg = wrapper::svwhilelt<ScalarType>(x, window_end_x);
        }
        while(svptest_any(all_true_pg, pg));

        auto max_val = svmaxv(all_true_pg, vec_max);

        *out_ptr = max_val;
    },
    input, output);
}

template <typename ScalarType>
void sve_softmax_logits_1d_float(const ITensor *in, const ITensor *max, void *const tmp,
                                 ITensor *out, const float beta, bool is_log, const Window &window)
{
    const int start_x     = in->info()->valid_region().anchor.x();
    const int input_width = in->info()->valid_region().shape.x();

    Iterator in_it(in, window);
    Iterator max_it(max, window);
    Iterator out_it(out, window);

    const auto all_true_pg = wrapper::svptrue<ScalarType>();

    execute_window_loop(window, [&](const Coordinates &)
    {
        /* Get pointers */
        const auto in_ptr  = reinterpret_cast<const ScalarType *>(in_it.ptr()) + start_x;
        const auto out_ptr = reinterpret_cast<ScalarType *>(out_it.ptr()) + start_x;
        const auto tmp_ptr = reinterpret_cast<ScalarType *>(tmp);

        ScalarType sum{ 0 };

        /* Compute exponentials and sum */
        {
            /* Get max value */
            const auto max_val = *reinterpret_cast<const ScalarType *>(max_it.ptr());
            const auto vec_max = wrapper::svdup_n(max_val);

            /* Init sum to zero */
            auto vec_sum = wrapper::svdup_n(static_cast<ScalarType>(0));

            /* Loop over row and compute exponentials and sum */
            int      x  = 0;
            svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width);
            do
            {
                auto vec_elements = svld1(pg, in_ptr + x);
                vec_elements      = svsub_z(pg, vec_elements, vec_max);
                if(is_log)
                {
                    vec_elements = svmul_z(pg, vec_elements, wrapper::svdup_n(static_cast<ScalarType>(beta)));
                    vec_sum      = svadd_m(pg, vec_sum, wrapper::svexp_z(pg, vec_elements));
                }
                else
                {
                    vec_elements = wrapper::svexp_z(pg, svmul_z(pg, vec_elements, wrapper::svdup_n(static_cast<ScalarType>(beta))));
                    vec_sum      = svadd_m(pg, vec_sum, vec_elements);
                }
                svst1(pg, tmp_ptr + x, vec_elements);

                x += wrapper::svcnt<ScalarType>();
                pg = wrapper::svwhilelt<ScalarType>(x, input_width);
            }
            while(svptest_any(all_true_pg, pg));

            /* Reduce sum */
            sum = svaddv(all_true_pg, vec_sum);

            if(is_log)
            {
                sum = static_cast<ScalarType>(std::log(sum));
            }
            else
            {
                sum = ScalarType(1) / sum;
            }
        }

        /* Normalize exponentials */
        {
            /* Loop over row and compute softmax */
            int      x  = 0;
            svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width);
            do
            {
                auto vec_in           = svld1(pg, tmp_ptr + x);
                auto normalized_value = wrapper::svdup_n(static_cast<ScalarType>(0));
                if(is_log)
                {
                    normalized_value = svsub_z(pg, vec_in, wrapper::svdup_n(static_cast<ScalarType>(sum)));
                }
                else
                {
                    normalized_value = svmul_z(pg, vec_in, wrapper::svdup_n(static_cast<ScalarType>(sum)));
                }
                svst1(pg, out_ptr + x, normalized_value);

                x += wrapper::svcnt<ScalarType>();
                pg = wrapper::svwhilelt<ScalarType>(x, input_width);
            }
            while(svptest_any(all_true_pg, pg));
        }
    },
    in_it, max_it, out_it);
}

template void sve_logits_1d_max<float>(const ITensor *in, ITensor *out, const Window &window);
template void sve_logits_1d_max<float16_t>(const ITensor *in, ITensor *out, const Window &window);
template void sve_logits_1d_max<qasymm8_t>(const ITensor *in, ITensor *out, const Window &window);
template void sve_logits_1d_max<qasymm8_signed_t>(const ITensor *in, ITensor *out, const Window &window);

template void sve_softmax_logits_1d_float<float>(const ITensor *in, const ITensor *max, void *const tmp,
                                                 ITensor *out, const float beta, bool is_log, const Window &window);
template void sve_softmax_logits_1d_float<float16_t>(const ITensor *in, const ITensor *max, void *const tmp,
                                                     ITensor *out, const float beta, bool is_log, const Window &window);
} // namespace cpu
} // namespace arm_compute
#endif /* defined(ENABLE_SVE) */