/*
 * Copyright (c) 2021-2023 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/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Types.h"
#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
#include "src/core/helpers/WindowHelpers.h"
#include "src/cpu/kernels/pool2d/neon/list.h"

namespace arm_compute
{
namespace cpu
{
namespace
{
void pooling2_f32_maxpool_indices(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
{
    const int window_start_x = window.x().start();
    const int window_end_x   = window.x().end();
    const int window_step_x  = 4;

    Window window_out = window;
    window_out.set(Window::DimX, Window::Dimension(0, 1, 1));

    Iterator in(src, window_src);
    Iterator out(dst0, window_out);
    Iterator indices(dst1, window_out);

    const int pool_pad_top  = pool_info.pad_stride_info.pad_top();
    const int pool_pad_left = pool_info.pad_stride_info.pad_left();

    int pool_stride_x = 0;
    int pool_stride_y = 0;
    std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();

    float32x4_t vres;
    float       res;

    const int pad_right      = src->info()->padding().right;
    const int pad_left       = src->info()->padding().left;
    const int pad_horizontal = pad_right + pad_left;
    const int in_stride_y    = static_cast<int>(src->info()->strides_in_bytes().y());
    const int in_stride_z    = static_cast<int>(src->info()->strides_in_bytes().z());

    execute_window_loop(window_out, [&](const Coordinates & id)
    {
        const int idx_width    = id.y() * pool_stride_x;
        const int idx_height   = id.z() * pool_stride_y;
        const int pool_limit_y = pool_pad_top - idx_height;
        const int pool_limit_x = pool_pad_left - idx_width;

        const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y);
        const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x);

        const int in_x0_offset = (pool_start_x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z());
        const int in_x1_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y - pool_pad_top) * static_cast<int>
                                 (src->info()->strides_in_bytes().z());
        const int in_x2_offset = (pool_start_x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int>
                                 (src->info()->strides_in_bytes().z());
        const int in_x3_offset = (pool_start_x + 1 - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (pool_start_y + 1 - pool_pad_top) * static_cast<int>
                                 (src->info()->strides_in_bytes().z());

        int x_off = window_start_x;
        for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x)
        {
            const auto in_x0_ptr = reinterpret_cast<const float *>(in.ptr() + in_x0_offset);
            const auto in_x1_ptr = reinterpret_cast<const float *>(in.ptr() + in_x1_offset);
            const auto in_x2_ptr = reinterpret_cast<const float *>(in.ptr() + in_x2_offset);
            const auto in_x3_ptr = reinterpret_cast<const float *>(in.ptr() + in_x3_offset);
            const auto v_x0      = vld1q_f32(in_x0_ptr + x_off);
            const auto v_x1      = vld1q_f32(in_x1_ptr + x_off);
            const auto v_x2      = vld1q_f32(in_x2_ptr + x_off);
            const auto v_x3      = vld1q_f32(in_x3_ptr + x_off);
            vres                 = vmaxq_f32(vmaxq_f32(v_x2, v_x3), vmaxq_f32(v_x0, v_x1));
            // Store result
            vst1q_f32(reinterpret_cast<float *>(out.ptr()) + x_off, vres);

            const uint32_t   offset_base  = offset_no_padding<float>(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NHWC);
            const uint32_t   offset_x0    = offset_base / sizeof(float) + x_off;
            const uint32_t   offset_x1    = offset_x0 + in_stride_y / sizeof(float) - pad_horizontal;
            const uint32_t   offset_x2    = offset_x0 + in_stride_z / sizeof(float) - pad_horizontal * src->info()->tensor_shape()[1];
            const uint32_t   offset_x3    = offset_x2 + in_stride_y / sizeof(float) - pad_horizontal;
            const uint32x4_t voffset_x0   = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3 };
            const uint32x4_t voffset_x1   = { offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3 };
            const uint32x4_t voffset_x2   = { offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3 };
            const uint32x4_t voffset_x3   = { offset_x3, offset_x3 + 1, offset_x3 + 2, offset_x3 + 3 };
            const uint32x4_t tmp_indices0 = vbslq_u32(vcgeq_f32(v_x0, v_x1), voffset_x0, voffset_x1);
            const uint32x4_t tmp_indices1 = vbslq_u32(vcgeq_f32(v_x2, v_x3), voffset_x2, voffset_x3);
            const uint32x4_t tmp_indices2 = vbslq_u32(vcgeq_f32(vmaxq_f32(v_x0, v_x1), vmaxq_f32(v_x2, v_x3)), tmp_indices0, tmp_indices1);

            // Store indices
            vst1q_u32(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off, tmp_indices2);
        }

        // Left-overs loop
        for(; x_off < window_end_x; ++x_off)
        {
            const auto x0 = *(reinterpret_cast<const float *>(in.ptr() + in_x0_offset) + x_off);
            const auto x1 = *(reinterpret_cast<const float *>(in.ptr() + in_x1_offset) + x_off);
            const auto x2 = *(reinterpret_cast<const float *>(in.ptr() + in_x2_offset) + x_off);
            const auto x3 = *(reinterpret_cast<const float *>(in.ptr() + in_x3_offset) + x_off);
            res           = std::max(std::max(x2, x3), std::max(x0, x1));

            // Store result
            *(reinterpret_cast<float *>(out.ptr()) + x_off) = res;

            const uint32_t offset_base = offset_no_padding<float>(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NHWC);
            const uint32_t offset_x0   = offset_base / sizeof(float) + x_off;
            const uint32_t offset_x1   = offset_x0 + in_stride_y / sizeof(float) - pad_horizontal;
            const uint32_t offset_x2   = offset_x0 + in_stride_z / sizeof(float) - pad_horizontal * src->info()->tensor_shape()[1];
            const uint32_t offset_x3   = offset_x2 + in_stride_y / sizeof(float) - pad_horizontal;
            const uint32_t tmp_idx0    = (x0 >= x1) ? offset_x0 : offset_x1;
            const uint32_t tmp_idx1    = (x2 >= x3) ? offset_x2 : offset_x3;
            const uint32_t tmp_idx2    = (std::max(x0, x1) >= std::max(x2, x3)) ? tmp_idx0 : tmp_idx1;

            // Store indices
            *(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off) = tmp_idx2;
        }
    },
    in, out, indices);
}
} // namespace

void poolingMxN_fp32_neon_nhwc_kernel_indices(const ITensor *src, ITensor *dst0, ITensor *dst1, const PoolingLayerInfo &pool_info, const Window &window)
{
    const int window_start_x     = window.x().start();
    const int window_end_x       = window.x().end();
    constexpr int window_step_x  = 4;

    Window window_out = window;
    window_out.set(Window::DimX, Window::Dimension(0, 1, 1));

    Iterator out(dst0, window_out);
    Iterator indices(dst1, window_out);

    const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.width;
    const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().z() : pool_info.pool_size.height;

    const int pool_pad_top  = pool_info.pad_stride_info.pad_top();
    const int pool_pad_left = pool_info.pad_stride_info.pad_left();

    int pool_stride_x = 0;
    int pool_stride_y = 0;
    std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();

    const float min_value = get_initial_min<float>(pool_info.use_inf_as_limit);

    float32x4_t vres;
    uint32x4_t  vidx;

    constexpr int idx_width   = 1;
    constexpr int idx_height  = 2;
    constexpr int idx_batch   = 3;

    const int y_stride = static_cast<int>(src->info()->strides_in_bytes().y());
    const int z_stride = static_cast<int>(src->info()->strides_in_bytes().z());
    const int n_stride = static_cast<int>(src->info()->strides_in_bytes()[idx_batch]);

    const int input_dim_w = src->info()->dimension(idx_width);
    const int input_dim_h = src->info()->dimension(idx_height);

    const uint8_t *in_ptr_start = src->buffer() + src->info()->offset_first_element_in_bytes();

    execute_window_loop(window_out, [&](const Coordinates & id)
    {
        const int idx_width  = static_cast<int>(id.y()) * pool_stride_x - pool_pad_left;
        const int idx_height = static_cast<int>(id.z()) * pool_stride_y - pool_pad_top;

        const int pool_start_x = std::max(0, -idx_width);
        const int pool_start_y = std::max(0, -idx_height);

        const int pool_end_x = std::min(pool_size_x, input_dim_w - idx_width);
        const int pool_end_y = std::min(pool_size_y, input_dim_h - idx_height);

        const uint8_t *in_ptr_n = in_ptr_start + id[idx_batch] * n_stride;

        const int in_ptr_y_offset = (z_stride * idx_height) + (pool_start_y * z_stride);
        const int in_ptr_x_offset = (y_stride * idx_width) + (pool_start_x * y_stride);

        int x_off = window_start_x;

        for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x)
        {
            vres              = vdupq_n_f32(min_value);
            vidx              = vdupq_n_u32(0U);
            const uint8_t *in_ptr_y = in_ptr_n + in_ptr_y_offset + in_ptr_x_offset;
            uint32_t    curr_kernel_index = pool_size_x * pool_start_y;
            for(int y = pool_start_y; y < pool_end_y; ++y)
            {
                const uint8_t *in_ptr_x = in_ptr_y + (x_off * sizeof(float));
                curr_kernel_index += pool_start_x;
                for(int x = pool_start_x; x < pool_end_x; ++x)
                {
                    const float32x4_t data              = vld1q_f32(reinterpret_cast<const float *>(in_ptr_x));
                    const uint32x4_t  vidx_curr         = vdupq_n_u32(curr_kernel_index);
                    const uint32x4_t idxMask = vcgtq_f32(data, vres);
                    vidx                     = vbslq_u32(idxMask, vidx_curr, vidx);
                    vres                     = vmaxq_f32(vres, data);
                    in_ptr_x += y_stride;
                    curr_kernel_index++;
                }
                curr_kernel_index += (pool_size_x - pool_end_x);
                in_ptr_y += z_stride;
            }
            // Store result
            vst1q_f32(reinterpret_cast<float *>(out.ptr()) + x_off, vres);
            vst1q_u32(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off, vidx);
        }

        // Left-overs loop
        for(; x_off < window_end_x; ++x_off)
        {
            float    res      = min_value;
            uint32_t idx      = 0U;
            const uint8_t *in_ptr_y = in_ptr_n + in_ptr_y_offset + in_ptr_x_offset;
            for(int y = pool_start_y; y < pool_end_y; ++y)
            {
                const uint8_t *in_ptr_x = in_ptr_y + (x_off * sizeof(float));
                for(int x = pool_start_x; x < pool_end_x; ++x)
                {
                    const float data = *(reinterpret_cast<const float *>(in_ptr_x));
                    if(data > res)
                    {
                        idx = pool_size_x * y + x;
                        res = data;
                    }
                    in_ptr_x += y_stride;
                }
                in_ptr_y += z_stride;
            }

            // Store result
            *(reinterpret_cast<float *>(out.ptr()) + x_off)        = res;
            *(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off) = idx;
        }
    },
    out, indices);
}

void poolingMxN_fp32_neon_nhwc(const ITensor *src, ITensor *dst0, ITensor *dst1, PoolingLayerInfo &pool_info, const Window &window_src, const Window &window)
{
    if((pool_info.pool_type == PoolingType::MAX) && pool_info.use_kernel_indices && (dst1 != nullptr))
    {
        poolingMxN_fp32_neon_nhwc_kernel_indices(src, dst0, dst1, pool_info, window);
    }
    else if(pool_info.pool_size == Size2D(2, 2) && pool_info.pool_type == PoolingType::MAX && !pool_info.pad_stride_info.has_padding() && (dst1 != nullptr))
    {
        pooling2_f32_maxpool_indices(src, dst0, dst1, pool_info, window_src, window);
    }
    else
    {
        const int window_start_x = window.x().start();
        const int window_end_x   = window.x().end();
        const int window_step_x  = 4;

        Window window_out = window;
        window_out.set(Window::DimX, Window::Dimension(0, 1, 1));

        Iterator in(src, window_src);
        Iterator out(dst0, window_out);

        const int pool_size_x     = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.width;
        const int pool_size_y     = pool_info.is_global_pooling ? src->info()->tensor_shape().z() : pool_info.pool_size.height;
        const int pool_pad_right  = pool_info.pad_stride_info.pad_right();
        const int pool_pad_top    = pool_info.pad_stride_info.pad_top();
        const int pool_pad_left   = pool_info.pad_stride_info.pad_left();
        const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom();
        int       pool_stride_x   = 0;
        int       pool_stride_y   = 0;
        std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
        const int   upper_bound_w = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_right);
        const int   upper_bound_h = src->info()->dimension(2) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
        const float min_value     = get_initial_min<float>(pool_info.use_inf_as_limit);
        float32x4_t vres;

        execute_window_loop(window_out, [&](const Coordinates & id)
        {
            const int idx_width    = id.y() * pool_stride_x;
            const int idx_height   = id.z() * pool_stride_y;
            const int pool_limit_y = pool_pad_top - idx_height;
            const int pool_limit_x = pool_pad_left - idx_width;

            const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y);
            const int pool_end_y   = std::min(pool_size_y, window_src.z().end() + pool_limit_y);
            const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x);
            const int pool_end_x   = std::min(pool_size_x, window_src.y().end() + pool_limit_x);

            int x_off = window_start_x;
            for(; x_off <= (window_end_x - window_step_x); x_off += window_step_x)
            {
                if(pool_info.pool_type != PoolingType::MAX)
                {
                    // Calculate scale
                    const float scale = calculate_avg_scale_pool2d(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x,
                                                                   pool_stride_y);
                    const float32x4_t scale_v = vdupq_n_f32(scale);

                    // Perform pooling
                    vres = vdupq_n_f32(0.0f);

                    for(int y = pool_start_y; y < pool_end_y; ++y)
                    {
                        for(int x = pool_start_x; x < pool_end_x; ++x)
                        {
                            const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
                                                                                               (src->info()->strides_in_bytes().z())) + x_off);

                            // Get power of 2 in case of l2 pooling and accumulate
                            if(pool_info.pool_type == PoolingType::L2)
                            {
                                vres = vmlaq_f32(vres, data, data);
                            }
                            else
                            {
                                vres = vaddq_f32(vres, data);
                            }
                        }
                    }
                    // Divide by scale
                    vres = vmulq_f32(vres, scale_v);
                }
                else
                {
                    vres = vdupq_n_f32(min_value);
                    for(int y = pool_start_y; y < pool_end_y; ++y)
                    {
                        for(int x = pool_start_x; x < pool_end_x; ++x)
                        {
                            const float32x4_t data = vld1q_f32(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
                                                                                               (src->info()->strides_in_bytes().z())) + x_off);
                            vres                   = vmaxq_f32(vres, data);
                        }
                    }
                }

                // Calculate square-root in case of l2 pooling
                if(pool_info.pool_type == PoolingType::L2)
                {
                    float32x4_t l2_res = { static_cast<float>(sqrt(vgetq_lane_f32(vres, 0))),
                                           static_cast<float>(sqrt(vgetq_lane_f32(vres, 1))),
                                           static_cast<float>(sqrt(vgetq_lane_f32(vres, 2))),
                                           static_cast<float>(sqrt(vgetq_lane_f32(vres, 3)))
                                         };
                    vres = l2_res;
                }

                // Store result
                vst1q_f32(reinterpret_cast<float *>(out.ptr()) + x_off, vres);
            }

            // Left-overs loop
            for(; x_off < window_end_x; ++x_off)
            {
                float res = 0.0f;

                if(pool_info.pool_type != PoolingType::MAX)
                {
                    // Calculate scale
                    const float scale = calculate_avg_scale_pool2d(pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x,
                                                                   pool_stride_y);

                    for(int y = pool_start_y; y < pool_end_y; ++y)
                    {
                        for(int x = pool_start_x; x < pool_end_x; ++x)
                        {
                            const float data = *(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
                                                                                 (src->info()->strides_in_bytes().z())) + x_off);

                            // Get power of 2 in case of l2 pooling and accumulate
                            if(pool_info.pool_type == PoolingType::L2)
                            {
                                res += data * data;
                            }
                            else
                            {
                                res += data;
                            }
                        }
                    }

                    // Divide by scale
                    res *= scale;
                }
                else
                {
                    res = min_value;
                    for(int y = pool_start_y; y < pool_end_y; ++y)
                    {
                        for(int x = pool_start_x; x < pool_end_x; ++x)
                        {
                            const float data = *(reinterpret_cast<const float *>(in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) + (y - pool_pad_top) * static_cast<int>
                                                                                 (src->info()->strides_in_bytes().z())) + x_off);
                            res              = std::max(res, data);
                        }
                    }
                }

                // Calculate square-root in case of l2 pooling
                if(pool_info.pool_type == PoolingType::L2)
                {
                    res = std::sqrt(res);
                }

                // Store result
                *(reinterpret_cast<float *>(out.ptr()) + x_off) = res;
            }
        },
        in, out);
    }
}
} // namespace cpu
} // namespace arm_compute