/*
 * Copyright (c) 2018 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/NEON/kernels/NEElementwiseOperationKernel.h"

#include "arm_compute/core/CPP/Validate.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEAsymm.h"
#include "arm_compute/core/NEON/NEFixedPoint.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Validate.h"

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

namespace arm_compute
{
class Coordinates;

namespace
{
float32x4x4_t load_quantized(const uint8_t *input1_ptr, const int32x4_t &offset, const float32x4_t &scale)
{
    qasymm8x16_t        x = vld1q_u8(input1_ptr);
    const float32x4x4_t out =
    {
        {
            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(x))))), offset)), scale),
            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(x))))), offset)), scale),
            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(x))))), offset)), scale),
            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(x))))), offset)), scale),
        }
    };
    return out;
}

void store_quantized(uint8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset, const float32x4_t &invscale)
{
    int32x4x4_t out =
    {
        vcvtq_s32_f32(vmlaq_f32(offset, rf.val[0], invscale)),
        vcvtq_s32_f32(vmlaq_f32(offset, rf.val[1], invscale)),
        vcvtq_s32_f32(vmlaq_f32(offset, rf.val[2], invscale)),
        vcvtq_s32_f32(vmlaq_f32(offset, rf.val[3], invscale)),
    };

    const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[0]), vqmovn_s32(out.val[1])));
    const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[2]), vqmovn_s32(out.val[3])));
    vst1q_u8(output_ptr, vcombine_u8(pa, pb));
}

float32x4x4_t dup_quantized(qasymm8_t broadcast_value, int offset, float scale)
{
    const qasymm8x16_t broadcast_value_vec = vdupq_n_u8(broadcast_value);
    const int32x4_t    voffset             = vdupq_n_s32(offset);
    const float32x4_t  vscale              = vdupq_n_f32(scale);

    const float32x4x4_t broadcast_vector =
    {
        {
            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(broadcast_value_vec))))), voffset)), vscale),
            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(broadcast_value_vec))))), voffset)), vscale),
            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(broadcast_value_vec))))), voffset)), vscale),
            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(broadcast_value_vec))))), voffset)), vscale),
        }
    };
    return broadcast_vector;
}

template <ArithmeticOperation op, typename ScalarType>
inline ScalarType elementwise_op_scalar(const ScalarType &a, const ScalarType &b)
{
    auto res = ScalarType(0);

    switch(op)
    {
        case ArithmeticOperation::MAX:
            res = std::max(a, b);
            break;
        case ArithmeticOperation::MIN:
            res = std::min(a, b);
            break;
        case ArithmeticOperation::SQUARED_DIFF:
        {
            res = (a - b) * (a - b);
            break;
        }
        default:
            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
    }
    return res;
}

template <ArithmeticOperation op, typename VectorType>
inline VectorType elementwise_op(const VectorType &a, const VectorType &b)
{
    VectorType res = { 0, 0, 0, 0 };

    switch(op)
    {
        case ArithmeticOperation::MAX:
            res = wrapper::vmax(a, b);
            break;
        case ArithmeticOperation::MIN:
            res = wrapper::vmin(a, b);
            break;
        case ArithmeticOperation::SQUARED_DIFF:
        {
            const VectorType tmp = wrapper::vsub(a, b);
            res                  = wrapper::vmul(tmp, tmp);
            break;
        }

        default:
            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
    }

    return res;
}

template <ArithmeticOperation op, typename VectorType, typename ScalarType>
inline VectorType elementwise_op_broadcast(const VectorType &a, const ScalarType &broadcast_value)
{
    VectorType broadcast_vector = wrapper::vdup_n(broadcast_value, wrapper::traits::vector_128_tag());
    return elementwise_op<op>(a, broadcast_vector);
}

template <ArithmeticOperation op>
float32x4x4_t elementwise_op(const float32x4x4_t &a, const float32x4x4_t &b)
{
    float32x4x4_t out =
    {
        elementwise_op<op>(a.val[0], b.val[0]),
        elementwise_op<op>(a.val[1], b.val[1]),
        elementwise_op<op>(a.val[2], b.val[2]),
        elementwise_op<op>(a.val[3], b.val[3]),
    };
    return out;
}

template <ArithmeticOperation op, typename ScalarType>
void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
{
    // Create input windows
    Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
    Window input2_win = window.broadcast_if_dimension_le_one(in2->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));

    const int  window_step_x         = 16 / in1->info()->element_size();
    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 = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);

    if(is_broadcast_across_x)
    {
        // Select the broadcast input on the X axis
        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 ? in2 : in1;
        const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;

        // 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 output(out, win);

        execute_window_loop(win, [&](const Coordinates & id)
        {
            auto             output_ptr              = reinterpret_cast<ScalarType *>(output.ptr());
            const auto       non_broadcast_input_ptr = reinterpret_cast<const ScalarType *>(non_broadcast_input.ptr());
            const ScalarType broadcast_value         = *reinterpret_cast<const ScalarType *>(broadcast_input.ptr());

            int x = window_start_x;
            for(; x <= (window_end_x - window_step_x); x += window_step_x)
            {
                const auto a = wrapper::vloadq((non_broadcast_input_ptr + x));
                wrapper::vstore(output_ptr + x, elementwise_op_broadcast<op>(a, broadcast_value));
            }
            for(; x < window_end_x; ++x)
            {
                const auto a      = *(non_broadcast_input_ptr + x);
                *(output_ptr + x) = elementwise_op_scalar<op>(a, broadcast_value);
            }
        },
        broadcast_input, non_broadcast_input, output);
    }
    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(in1, input1_win);
        Iterator input2(in2, input2_win);
        Iterator output(out, win);

        execute_window_loop(win, [&](const Coordinates & id)
        {
            auto       output_ptr = reinterpret_cast<ScalarType *>(output.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 <= (window_end_x - window_step_x); x += window_step_x)
            {
                const auto a = wrapper::vloadq(input1_ptr + x);
                const auto b = wrapper::vloadq(input2_ptr + x);
                wrapper::vstore(output_ptr + x, elementwise_op<op>(a, b));
            }
            for(; x < window_end_x; ++x)
            {
                const auto a      = *(input1_ptr + x);
                const auto b      = *(input2_ptr + x);
                *(output_ptr + x) = elementwise_op_scalar<op>(a, b);
            }

        },
        input1, input2, output);
    }
}

template <ArithmeticOperation op>
void elementwise_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
{
    // Create input windows
    Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
    Window input2_win = window.broadcast_if_dimension_le_one(in2->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));

    const int  window_step_x         = 16;
    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 = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);

    const float output_scale  = out->info()->quantization_info().scale;
    const int   output_offset = out->info()->quantization_info().offset;

    // Output quantization info (add 0.5 to round toward the nearest integer - 0.5 rounds away from zero)
    const float32x4_t voffseto   = vdupq_n_f32(output_offset + 0.5f);
    const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_scale);

    if(is_broadcast_across_x)
    {
        // Select the broadcast input on the X axis
        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 ? in2 : in1;
        const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;

        const QuantizationInfo broadcast_qinfo     = broadcast_tensor->info()->quantization_info();
        const QuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info();

        const int32x4_t   voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset);
        const float32x4_t vscale_non_broadcast  = vdupq_n_f32(non_broadcast_qinfo.scale);

        // 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 output(out, win);

        execute_window_loop(win, [&](const Coordinates & id)
        {
            const auto non_broadcast_input_ptr = reinterpret_cast<const uint8_t *>(non_broadcast_input.ptr());
            const auto output_ptr              = reinterpret_cast<uint8_t *>(output.ptr());

            const uint8_t       broadcast_value  = *reinterpret_cast<const uint8_t *>(broadcast_input.ptr());
            const float32x4x4_t broadcast_vector = dup_quantized(broadcast_value, broadcast_qinfo.offset, broadcast_qinfo.scale);

            int x = window_start_x;
            for(; x <= (window_end_x - window_step_x); x += window_step_x)
            {
                const float32x4x4_t af = load_quantized(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast);
                const float32x4x4_t rf = elementwise_op<op>(af, broadcast_vector);
                store_quantized(output_ptr + x, rf, voffseto, invvscaleo);
            }
            for(; x < window_end_x; ++x)
            {
                const float afs   = static_cast<int32_t>(*(non_broadcast_input_ptr + x) - non_broadcast_qinfo.offset) * non_broadcast_qinfo.scale;
                const float bfs   = static_cast<int32_t>(broadcast_value - broadcast_qinfo.offset) * broadcast_qinfo.scale;
                *(output_ptr + x) = out->info()->quantization_info().quantize(elementwise_op_scalar<op>(afs, bfs), RoundingPolicy::TO_NEAREST_UP);
            }
        },
        broadcast_input, non_broadcast_input, output);
    }
    else
    {
        // Input1 quantization info
        const int32x4_t   voffset1 = vdupq_n_s32(in1->info()->quantization_info().offset);
        const float32x4_t vscale1  = vdupq_n_f32(in1->info()->quantization_info().scale);

        // Input2 quantization info
        const int32x4_t   voffset2 = vdupq_n_s32(in2->info()->quantization_info().offset);
        const float32x4_t vscale2  = vdupq_n_f32(in2->info()->quantization_info().scale);

        // 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));

        const QuantizationInfo input1_qinfo = in1->info()->quantization_info();
        const QuantizationInfo input2_qinfo = in2->info()->quantization_info();

        Iterator input1(in1, input1_win);
        Iterator input2(in2, input2_win);
        Iterator output(out, win);

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

            int x = window_start_x;
            for(; x <= (window_end_x - window_step_x); x += window_step_x)
            {
                // Get inputs and compute output
                const float32x4x4_t af = load_quantized(input1_ptr + x, voffset1, vscale1);
                const float32x4x4_t bf = load_quantized(input2_ptr + x, voffset2, vscale2);
                const float32x4x4_t rf = elementwise_op<op>(af, bf);
                store_quantized(output_ptr + x, rf, voffseto, invvscaleo);
            }
            for(; x < window_end_x; ++x)
            {
                const float afs   = static_cast<int32_t>((*(input1_ptr + x)) - input1_qinfo.offset) * input1_qinfo.scale;
                const float bfs   = static_cast<int32_t>((*(input2_ptr + x)) - input2_qinfo.offset) * input2_qinfo.scale;
                *(output_ptr + x) = out->info()->quantization_info().quantize(elementwise_op_scalar<op>(afs, bfs), RoundingPolicy::TO_NEAREST_UP);
            }
        },
        input1, input2, output);
    }
}

Status validate_arguments_arithmetic(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
{
    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input1);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::S32, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::S32, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);

    const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());

    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");

    // Validate in case of configured output
    if(output.total_size() > 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
                                        "Wrong shape for output");
    }

    return Status{};
}
} // namespace

NEElementwiseOperationKernel::NEElementwiseOperationKernel()
    : _op(), _func(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr)
{
}
template <ArithmeticOperation op>
void NEElementwiseOperationKernel::configure_common(const ITensor *input1, const ITensor *input2, ITensor *output)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info()));

    // Configure kernel window
    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1->info(), *input2->info());
    const TensorShape &out_shape    = broadcast_pair.first;
    const ValidRegion &valid_region = broadcast_pair.second;

    // Auto initialize output if not initialized
    auto_init_if_empty(*output->info(), out_shape, 1, input1->info()->data_type());

    Window win = calculate_max_window(valid_region);

    static std::map<std::string, ElementwiseFunction *> map_function =
    {
        { "op_F32_F32_F32", &elementwise_op<op, float> },
        { "op_S16_S16_S16", &elementwise_op<op, int16_t> },
        { "op_S32_S32_S32", &elementwise_op<op, int32_t> },
        { "op_QASYMM8_QASYMM8_QASYMM8", &elementwise_op_quantized<op> }
    };
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
    map_function["op_F16_F16_F16"] = &elementwise_op<op, float16_t>;
#endif /* ARM_COMPUTE_AARCH64_V8_2 */
    _input1 = input1;
    _input2 = input2;
    _output = output;

    std::string function_to_call("op_");
    function_to_call += string_from_data_type(input1->info()->data_type()) + "_";
    function_to_call += string_from_data_type(input2->info()->data_type()) + "_";
    function_to_call += string_from_data_type(output->info()->data_type());
    auto it = map_function.find(function_to_call);

    if(it != map_function.end())
    {
        _func = it->second;
    }

    INEKernel::configure(win);
}

void NEElementwiseOperationKernel::run(const Window &window, const ThreadInfo &info)
{
    ARM_COMPUTE_UNUSED(info);
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
    ARM_COMPUTE_ERROR_ON(_func == nullptr);

    (*_func)(_input1, _input2, _output, window);
}

/** Arithmetic operators (min, max, squared_diff) */

void NEArithmeticOperationKernel::configure(ArithmeticOperation op, const ITensor *input1, const ITensor *input2, ITensor *output)
{
    _op = op;
    switch(op)
    {
        case ArithmeticOperation::MAX:
            configure_common<ArithmeticOperation::MAX>(input1, input2, output);
            break;
        case ArithmeticOperation::MIN:
            configure_common<ArithmeticOperation::MIN>(input1, input2, output);
            break;
        case ArithmeticOperation::SQUARED_DIFF:
            configure_common<ArithmeticOperation::SQUARED_DIFF>(input1, input2, output);
            break;
        default:
            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
    }
}

Status NEArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
{
    ARM_COMPUTE_UNUSED(op);
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);

    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_arithmetic(*input1, *input2, *output));
    return Status{};
}

Status NEArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
{
    return validate_arguments_arithmetic(input1, input2, output);
}
} // namespace arm_compute