/*
 * Copyright (c) 2019-2023 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 "src/cpu/kernels/boundingboxtransform/generic/neon/impl.h"

#include "src/cpu/CpuTypes.h"

namespace arm_compute
{
namespace cpu
{
void bounding_box_transform_qsymm16(const ITensor           *boxes,
                                    ITensor                 *pred_boxes,
                                    const ITensor           *deltas,
                                    BoundingBoxTransformInfo bbinfo,
                                    const Window            &window)

{
    const size_t num_classes  = deltas->info()->tensor_shape()[0] >> 2;
    const size_t deltas_width = deltas->info()->tensor_shape()[0];
    const int    img_h        = std::floor(bbinfo.img_height() / bbinfo.scale() + 0.5f);
    const int    img_w        = std::floor(bbinfo.img_width() / bbinfo.scale() + 0.5f);

    const auto scale_after  = (bbinfo.apply_scale() ? bbinfo.scale() : 1.f);
    const auto scale_before = bbinfo.scale();
    const auto offset       = (bbinfo.correct_transform_coords() ? 1.f : 0.f);

    auto pred_ptr =
        reinterpret_cast<uint16_t *>(pred_boxes->buffer() + pred_boxes->info()->offset_first_element_in_bytes());
    auto delta_ptr = reinterpret_cast<uint8_t *>(deltas->buffer() + deltas->info()->offset_first_element_in_bytes());

    const auto boxes_qinfo  = boxes->info()->quantization_info().uniform();
    const auto deltas_qinfo = deltas->info()->quantization_info().uniform();
    const auto pred_qinfo   = pred_boxes->info()->quantization_info().uniform();

    Iterator box_it(boxes, window);
    execute_window_loop(
        window,
        [&](const Coordinates &id)
        {
            const auto  ptr    = reinterpret_cast<uint16_t *>(box_it.ptr());
            const auto  b0     = dequantize_qasymm16(*ptr, boxes_qinfo);
            const auto  b1     = dequantize_qasymm16(*(ptr + 1), boxes_qinfo);
            const auto  b2     = dequantize_qasymm16(*(ptr + 2), boxes_qinfo);
            const auto  b3     = dequantize_qasymm16(*(ptr + 3), boxes_qinfo);
            const float width  = (b2 / scale_before) - (b0 / scale_before) + 1.f;
            const float height = (b3 / scale_before) - (b1 / scale_before) + 1.f;
            const float ctr_x  = (b0 / scale_before) + 0.5f * width;
            const float ctr_y  = (b1 / scale_before) + 0.5f * height;
            for (size_t j = 0; j < num_classes; ++j)
            {
                // Extract deltas
                const size_t delta_id = id.y() * deltas_width + 4u * j;
                const float  dx       = dequantize_qasymm8(delta_ptr[delta_id], deltas_qinfo) / bbinfo.weights()[0];
                const float  dy       = dequantize_qasymm8(delta_ptr[delta_id + 1], deltas_qinfo) / bbinfo.weights()[1];
                float        dw       = dequantize_qasymm8(delta_ptr[delta_id + 2], deltas_qinfo) / bbinfo.weights()[2];
                float        dh       = dequantize_qasymm8(delta_ptr[delta_id + 3], deltas_qinfo) / bbinfo.weights()[3];
                // Clip dw and dh
                dw = std::min(dw, bbinfo.bbox_xform_clip());
                dh = std::min(dh, bbinfo.bbox_xform_clip());
                // Determine the predictions
                const float pred_ctr_x = dx * width + ctr_x;
                const float pred_ctr_y = dy * height + ctr_y;
                const float pred_w     = std::exp(dw) * width;
                const float pred_h     = std::exp(dh) * height;
                // Store the prediction into the output tensor
                pred_ptr[delta_id] = quantize_qasymm16(
                    scale_after * utility::clamp<float>(pred_ctr_x - 0.5f * pred_w, 0.f, img_w - 1.f), pred_qinfo);
                pred_ptr[delta_id + 1] = quantize_qasymm16(
                    scale_after * utility::clamp<float>(pred_ctr_y - 0.5f * pred_h, 0.f, img_h - 1.f), pred_qinfo);
                pred_ptr[delta_id + 2] = quantize_qasymm16(
                    scale_after * utility::clamp<float>(pred_ctr_x + 0.5f * pred_w - offset, 0.f, img_w - 1.f),
                    pred_qinfo);
                pred_ptr[delta_id + 3] = quantize_qasymm16(
                    scale_after * utility::clamp<float>(pred_ctr_y + 0.5f * pred_h - offset, 0.f, img_h - 1.f),
                    pred_qinfo);
            }
        },
        box_it);
}
} // namespace cpu
} // namespace arm_compute