/*
 * Copyright (c) 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.
 */

#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/TensorInfo.h"

#include "src/core/CPP/Validate.h"
#include "src/core/NEON/wrapper/wrapper.h"
#include "src/cpu/CpuTypes.h"

namespace arm_compute
{
namespace cpu
{
void mul_F32_F32_F32(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale)
{
    // Create input windows
    Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
    Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape());

    // Clear X Dimension on execution window as we handle manually
    Window win = window;
    win.set(Window::DimX, Window::Dimension(0, 1, 1));

    constexpr int window_step_x         = 16 / sizeof(float);
    const auto    window_start_x        = static_cast<int>(window.x().start());
    const auto    window_end_x          = static_cast<int>(window.x().end());
    const bool    is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x();

    using ExactTagType = typename wrapper::traits::neon_vector<float, window_step_x>::tag_type;

    if (is_broadcast_across_x)
    {
        const bool     is_broadcast_input_2 = input2_win.x().step() == 0;
        Window         broadcast_win        = is_broadcast_input_2 ? input2_win : input1_win;
        Window         non_broadcast_win    = !is_broadcast_input_2 ? input2_win : input1_win;
        const ITensor *broadcast_tensor     = is_broadcast_input_2 ? src2 : src1;
        const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1;

        // Clear X Dimension on execution window as we handle manually
        non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));

        Iterator broadcast_input(broadcast_tensor, broadcast_win);
        Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
        Iterator dst(out, win);

        execute_window_loop(
            win,
            [&](const Coordinates &)
            {
                const auto non_broadcast_input_ptr = reinterpret_cast<const float *>(non_broadcast_input.ptr());
                const auto output_ptr              = reinterpret_cast<float *>(dst.ptr());

                const float broadcast_value     = *reinterpret_cast<const float *>(broadcast_input.ptr());
                const auto  broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{});
                const auto  scale_vec           = wrapper::vdup_n(scale, ExactTagType{});

                // Compute window_step_x elements per iteration
                int x = window_start_x;
                for (; x <= (window_end_x - window_step_x); x += window_step_x)
                {
                    const auto non_broadcast_v = wrapper::vloadq(non_broadcast_input_ptr + x);
                    auto       res = wrapper::vmul(wrapper::vmul(broadcast_value_vec, non_broadcast_v), scale_vec);
                    wrapper::vstore(output_ptr + x, res);
                }

                // Compute left-over elements
                for (; x < window_end_x; ++x)
                {
                    const auto non_broadcast_v = *(non_broadcast_input_ptr + x);
                    *(output_ptr + x)          = broadcast_value * non_broadcast_v * scale;
                }
            },
            broadcast_input, non_broadcast_input, dst);
    }
    else
    {
        // Clear X Dimension on execution window as we handle manually
        input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
        input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));

        Iterator input1(src1, input1_win);
        Iterator input2(src2, input2_win);
        Iterator dst(out, win);

        execute_window_loop(
            win,
            [&](const Coordinates &)
            {
                const auto input1_ptr = reinterpret_cast<const float *>(input1.ptr());
                const auto input2_ptr = reinterpret_cast<const float *>(input2.ptr());
                const auto output_ptr = reinterpret_cast<float *>(dst.ptr());

                // Compute window_step_x elements per iteration
                int x = window_start_x;
                for (; x <= (window_end_x - window_step_x); x += window_step_x)
                {
                    const auto ta1       = wrapper::vloadq(input1_ptr + x);
                    const auto ta2       = wrapper::vloadq(input2_ptr + x);
                    const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{});
                    const auto res       = wrapper::vmul(wrapper::vmul(ta1, ta2), scale_vec);
                    wrapper::vstore(output_ptr + x, res);
                }

                // Compute left-over elements
                for (; x < window_end_x; ++x)
                {
                    const auto ta1    = *(input1_ptr + x);
                    const auto ta2    = *(input2_ptr + x);
                    *(output_ptr + x) = ta1 * ta2 * scale;
                }
            },
            input1, input2, dst);
    }
}
} // namespace cpu
} // namespace arm_compute