/*
 * Copyright (c) 2022-2023 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.
 */
#ifndef ACL_SRC_CPU_KERNELS_SELECT_GENERIC_NEON_IMPL_H
#define ACL_SRC_CPU_KERNELS_SELECT_GENERIC_NEON_IMPL_H

#include "arm_compute/core/TensorInfo.h"

#include "src/core/NEON/NEAsymm.h"
#include "src/cpu/kernels/select/generic/neon/impl.h"

#include <arm_neon.h>
#include <map>
#include <string>

namespace arm_compute
{
namespace cpu
{
template <typename ScalarType, typename VectorType>
void select_op(const ITensor *cond,
               const ITensor *in1,
               const ITensor *in2,
               ITensor       *out,
               const Window  &window,
               const int      window_step_x,
               const int      window_start_x,
               const int      window_end_x,
               const int      limit,
               VectorType (*condition_conversion)(const uint8_t *))
{
    Window win = window;
    win.set(Window::DimX, Window::Dimension(0, 1, 1));

    Iterator condition(cond, win);
    Iterator input1(in1, win);
    Iterator input2(in2, win);
    Iterator output(out, win);

    execute_window_loop(
        win,
        [&](const Coordinates &)
        {
            auto       output_ptr    = reinterpret_cast<ScalarType *>(output.ptr());
            const auto condition_ptr = reinterpret_cast<const uint8_t *>(condition.ptr());
            const auto input1_ptr    = reinterpret_cast<const ScalarType *>(input1.ptr());
            const auto input2_ptr    = reinterpret_cast<const ScalarType *>(input2.ptr());

            int x = window_start_x;
            for (; x <= limit; x += window_step_x)
            {
                const auto c = (*condition_conversion)(condition_ptr + x);
                const auto a = wrapper::vloadq(input1_ptr + x);
                const auto b = wrapper::vloadq(input2_ptr + x);
                wrapper::vstore(output_ptr + x, wrapper::vbsl(c, a, b));
            }
            for (; x < window_end_x; ++x)
            {
                const auto c      = *(condition_ptr + x);
                const auto a      = *(input1_ptr + x);
                const auto b      = *(input2_ptr + x);
                *(output_ptr + x) = static_cast<bool>(c) ? a : b;
            }
        },
        condition, input1, input2, output);
}

template <typename ScalarType, typename VectorType>
void select_op_8(const ITensor *cond, const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
{
    const auto window_step_x  = 16 / sizeof(ScalarType);
    const auto window_start_x = static_cast<int>(window.x().start());
    const auto window_end_x   = static_cast<int>(window.x().end());

    select_op<ScalarType, VectorType>(
        cond, in1, in2, out, window, window_step_x, window_start_x, window_end_x, window_end_x - window_step_x,
        [](const uint8_t *condition_ptr) -> VectorType
        {
            static const auto zero =
                wrapper::vdup_n(static_cast<uint8_t>(0), arm_compute::wrapper::traits::vector_128_tag());
            return wrapper::vcgt(wrapper::vloadq(condition_ptr), zero);
        });
}

template <typename ScalarType, typename VectorType>
void select_op_16(const ITensor *cond, const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
{
    const auto window_step_x  = 16 / sizeof(ScalarType);
    const auto window_start_x = static_cast<int>(window.x().start());
    const auto window_end_x   = static_cast<int>(window.x().end());

    select_op<ScalarType, VectorType>(
        cond, in1, in2, out, window, window_step_x, window_start_x, window_end_x, window_end_x - window_step_x,
        [](const uint8_t *condition_ptr) -> VectorType
        {
            static const auto zero =
                wrapper::vdup_n(static_cast<uint16_t>(0), arm_compute::wrapper::traits::vector_128_tag());
            return wrapper::vcgt(wrapper::vmovl(wrapper::vload(condition_ptr)), zero);
        });
}

template <typename ScalarType, typename VectorType>
void select_op_32(const ITensor *cond, const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
{
    const auto window_step_x  = 16 / sizeof(ScalarType);
    const auto window_start_x = static_cast<int>(window.x().start());
    const auto window_end_x   = static_cast<int>(window.x().end());

    select_op<ScalarType, VectorType>(
        cond, in1, in2, out, window, window_step_x, window_start_x, window_end_x, window_end_x - window_step_x,
        [](const uint8_t *condition_ptr) -> VectorType
        {
            static const auto zero =
                wrapper::vdup_n(static_cast<uint32_t>(0), arm_compute::wrapper::traits::vector_128_tag());
            return wrapper::vcgt(wrapper::vmovl(wrapper::vgetlow(wrapper::vmovl(wrapper::vload(condition_ptr)))), zero);
        });
}

template <typename ScalarType>
void select_op_not_same_rank(
    const ITensor *cond, const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
{
    ARM_COMPUTE_UNUSED(window);

    auto       output_ptr    = reinterpret_cast<ScalarType *>(out->buffer());
    const auto condition_ptr = reinterpret_cast<const uint8_t *>(cond->buffer());
    const auto input1_ptr    = reinterpret_cast<const ScalarType *>(in1->buffer());
    const auto input2_ptr    = reinterpret_cast<const ScalarType *>(in2->buffer());

    const int outer_size = cond->info()->total_size() / cond->info()->element_size();
    const int inner_size = (in1->info()->total_size() / in1->info()->element_size()) / outer_size;
    int       offset     = 0;
    const int step       = 16 / in1->info()->element_size();

    for (int i = 0; i < outer_size; ++i)
    {
        int        x         = offset;
        const auto input_ptr = static_cast<bool>(*(condition_ptr + i)) ? input1_ptr : input2_ptr;
        for (; x <= offset + inner_size - step; x += step)
        {
            wrapper::vstore(output_ptr + x, wrapper::vloadq(input_ptr + x));
        }
        if (x <= offset + inner_size - (step / 2))
        {
            wrapper::vstore(output_ptr + x, wrapper::vload(input_ptr + x));
            x += step / 2;
        }
        for (; x < offset + inner_size; ++x)
        {
            *(output_ptr + x) = *(input_ptr + x);
        }
        offset += inner_size;
    }
}
} // namespace cpu
} // namespace arm_compute
#endif // ACL_SRC_CPU_KERNELS_SELECT_GENERIC_NEON_IMPL_H