/*
 * Copyright (c) 2016-2020 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/NEPixelWiseMultiplicationKernel.h"

#include "arm_compute/core/CPP/Validate.h"
#include "arm_compute/core/NEON/NEAsymm.h"
#include "arm_compute/core/NEON/NESymm.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/TensorInfo.h"

#include <arm_neon.h>

#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
#include <arm_fp16.h> // needed for float16_t
#endif                /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

namespace arm_compute
{
namespace
{
const float       scale255_constant      = 1.f / 255.f;
const float32x4_t scale255_constant_f32q = vdupq_n_f32(scale255_constant);
const float32x4_t positive_round_f32q    = vdupq_n_f32(0.5f);

inline Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)
{
    ARM_COMPUTE_UNUSED(overflow_policy);
    ARM_COMPUTE_UNUSED(rounding_policy);

    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input1);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
                                                         DataType::S16, DataType::QSYMM16,
                                                         DataType::S32, DataType::F16, DataType::F32);
    if(is_data_type_quantized(input1->data_type()) || is_data_type_quantized(input2->data_type()))
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(overflow_policy == ConvertPolicy::WRAP, "ConvertPolicy cannot be WRAP if datatype is quantized");
    }

    if(output->total_size() > 0)
    {
        const TensorShape &out_shape = TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape());
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), "Wrong shape for output");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");

        ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::U8 && (input1->data_type() != DataType::U8 || input2->data_type() != DataType::U8),
                                        "Output can only be U8 if both inputs are U8");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::QASYMM8 && (input1->data_type() != DataType::QASYMM8 || input2->data_type() != DataType::QASYMM8),
                                        "Output can only be QASYMM8 if both inputs are QASYMM8");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::QASYMM8_SIGNED && (input1->data_type() != DataType::QASYMM8_SIGNED || input2->data_type() != DataType::QASYMM8_SIGNED),
                                        "Output can only be QASYMM8 if both inputs are QASYMM8");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::QSYMM16 && (input1->data_type() != DataType::QSYMM16 || input2->data_type() != DataType::QSYMM16),
                                        "Output can only be QSYMM16 if both inputs are QSYMM16");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::S32 && (input1->data_type() != DataType::QSYMM16 || input2->data_type() != DataType::QSYMM16),
                                        "Output can only be S32 if both inputs are QSYMM16");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::S32 && scale != 1.f, "Unsupported scale for QSYMM16 inputs and S32 output");
    }

    if(std::abs(scale - scale255_constant) < 0.00001f)
    {
        ARM_COMPUTE_RETURN_ERROR_ON(rounding_policy != RoundingPolicy::TO_NEAREST_UP && rounding_policy != RoundingPolicy::TO_NEAREST_EVEN);
    }
    else
    {
        ARM_COMPUTE_RETURN_ERROR_ON(rounding_policy != RoundingPolicy::TO_ZERO);

        int         exponent            = 0;
        const float normalized_mantissa = std::frexp(scale, &exponent);

        // Use int scaling if factor is equal to 1/2^n for 0 <= n <= 15
        // frexp returns 0.5 as mantissa which means that the exponent will be in the range of -1 <= e <= 14
        // Moreover, it will be negative as we deal with 1/2^n
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(!((normalized_mantissa == 0.5f) && (-14 <= exponent) && (exponent <= 1)), "Scale value not supported (Should be 1/(2^n) or 1/255");
    }

    return Status{};
}

/* Scales a given vector by 1/255.
 *
 * @note This does not work for all cases. e.g. for float of 0.49999999999999994 and large floats.
 *
 * @param in Input vector to scale.
 * @return   Scaled output rounded to nearest (round half up).
 */
inline int32x4_t scale255_S32_S32(int32x4_t in)
{
    // Scale
    const float32x4_t tmp = vmulq_f32(vcvtq_f32_s32(in), scale255_constant_f32q);
    // Round to nearest (round half up)
    // Add +0.5 for all values
    // Afterwards vcvt rounds toward zero
    return vcvtq_s32_f32(vaddq_f32(tmp, positive_round_f32q));
}

inline uint16x8_t scale255_U16_U16(uint16x8_t in)
{
    const int32x4_t tmp_s1 = scale255_S32_S32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(in))));
    const int32x4_t tmp_s2 = scale255_S32_S32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(in))));
    return vreinterpretq_u16_s16(vcombine_s16(vmovn_s32(tmp_s2), vmovn_s32(tmp_s1)));
}

template <typename T>
inline typename std::enable_if<std::is_same<T, int8_t>::value, int8x16_t>::type
vquantize(float32x4x4_t val, const UniformQuantizationInfo &info)
{
    return vquantize_signed(val, info);
}

template <typename T>
inline typename std::enable_if<std::is_same<T, uint8_t>::value, uint8x16_t>::type
vquantize(float32x4x4_t val, const UniformQuantizationInfo &info)
{
    return vquantize(val, info);
}

template <typename T>
inline typename std::enable_if<std::is_same<T, int8_t>::value, int8_t>::type
quantize(float val, const UniformQuantizationInfo &info)
{
    int32_t tmp = static_cast<int32_t>(val / info.scale) + info.offset;

    T tmp_qua = static_cast<T>(tmp > SCHAR_MAX) ? SCHAR_MAX : ((tmp < SCHAR_MIN) ? SCHAR_MIN : tmp);
    return tmp_qua;
}

template <typename T>
inline typename std::enable_if<std::is_same<T, uint8_t>::value, uint8_t>::type
quantize(float val, const UniformQuantizationInfo &info)
{
    int32_t tmp = static_cast<int32_t>(val / info.scale) + info.offset;

    T tmp_qua = static_cast<T>((tmp > UCHAR_MAX) ? UCHAR_MAX : tmp);
    return tmp_qua;
}

template <typename T>
inline float dequantize(const T *input, const UniformQuantizationInfo &info)
{
    return static_cast<float>((*input) - info.offset) * info.scale;
}

template <typename T>
void mul_saturate_quantized_8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
{
    const UniformQuantizationInfo input1_qua_info = in1->info()->quantization_info().uniform();
    const UniformQuantizationInfo input2_qua_info = in2->info()->quantization_info().uniform();
    const UniformQuantizationInfo output_qua_info = out->info()->quantization_info().uniform();

    // Create input windows
    Window win        = window;
    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
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    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);

    const int  window_step_x  = 16 / sizeof(T);
    const auto window_start_x = static_cast<int>(window.x().start());
    const auto window_end_x   = static_cast<int>(window.x().end());

    const UniformQuantizationInfo tmp_qua_info = { output_qua_info.scale / scale, output_qua_info.offset };

    execute_window_loop(win, [&](const Coordinates &)
    {
        const auto input1_ptr = reinterpret_cast<const T *>(input1.ptr());
        const auto input2_ptr = reinterpret_cast<const T *>(input2.ptr());
        const auto output_ptr = reinterpret_cast<T *>(output.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 input1_q = wrapper::vloadq(input1_ptr + x);
            const auto input2_q = wrapper::vloadq(input2_ptr + x);

            // Dequantize inputs
            const float32x4x4_t in1_f32x4x4 = vdequantize(input1_q, input1_qua_info);
            const float32x4x4_t in2_f32x4x4 = vdequantize(input2_q, input2_qua_info);

            const float32x4x4_t out_f32x4x4 =
            {
                vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]),
                vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]),
                vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]),
                vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]),
            };

            // Quantize output
            const auto result = vquantize<T>(out_f32x4x4, tmp_qua_info);
            wrapper::vstore(output_ptr + x, result);
        }

        // Compute left-over elements
        for(; x < window_end_x; ++x)
        {
            // Dequantize inputs
            float tmp_in1 = dequantize(input1_ptr + x, input1_qua_info);
            float tmp_in2 = dequantize(input2_ptr + x, input2_qua_info);
            float tmp_f   = tmp_in1 * tmp_in2;

            // Quantize output
            const auto tmp_qua = quantize<T>(tmp_f, tmp_qua_info);
            *(output_ptr + x)  = tmp_qua;
        }
    },
    input1, input2, output);
}

void mul_saturate_QSYMM16_QSYMM16_QSYMM16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
{
    const UniformQuantizationInfo input1_qua_info = in1->info()->quantization_info().uniform();
    const UniformQuantizationInfo input2_qua_info = in2->info()->quantization_info().uniform();
    const UniformQuantizationInfo output_qua_info = out->info()->quantization_info().uniform();

    // Create input windows
    Window win        = window;
    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
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    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);

    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 UniformQuantizationInfo tmp_qua_info = { output_qua_info.scale / scale, output_qua_info.offset };

    execute_window_loop(win, [&](const Coordinates &)
    {
        const auto input1_ptr = reinterpret_cast<const qsymm16_t *>(input1.ptr());
        const auto input2_ptr = reinterpret_cast<const qsymm16_t *>(input2.ptr());
        const auto output_ptr = reinterpret_cast<qsymm16_t *>(output.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 qsymm16x8x2_t input1_q =
            {
                {
                    vld1q_s16(input1_ptr + x),
                    vld1q_s16(input1_ptr + x + 8),
                }
            };
            const qsymm16x8x2_t input2_q =
            {
                {
                    vld1q_s16(input2_ptr + x),
                    vld1q_s16(input2_ptr + x + 8),
                }
            };

            // Dequantize inputs
            const float32x4x4_t in1_f32x4x4 = vdequantize(input1_q, input1_qua_info);
            const float32x4x4_t in2_f32x4x4 = vdequantize(input2_q, input2_qua_info);

            const float32x4x4_t out_f32x4x4 =
            {
                vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]),
                vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]),
                vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]),
                vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]),
            };

            const qsymm16x8x2_t result = vquantize_qsymm16(out_f32x4x4, tmp_qua_info);
            vst1q_s16(output_ptr + x, result.val[0]);
            vst1q_s16(output_ptr + x + 8, result.val[1]);
        }

        // Compute left-over elements
        for(; x < window_end_x; ++x)
        {
            // Dequantize inputs
            float tmp_in1 = static_cast<float>(*(input1_ptr + x)) * input1_qua_info.scale;
            float tmp_in2 = static_cast<float>(*(input2_ptr + x)) * input2_qua_info.scale;
            float tmp_f   = tmp_in1 * tmp_in2;

            // Quantize output, lrintf() has same rounding mode as vcombine_s16
            int32_t   tmp     = lrintf(tmp_f / tmp_qua_info.scale);
            qsymm16_t tmp_qua = static_cast<qsymm16_t>(tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp);
            *(output_ptr + x) = tmp_qua;
        }
    },
    input1, input2, output);
}

void mul_QSYMM16_QSYMM16_S32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int scale)
{
    ARM_COMPUTE_UNUSED(scale);

    // Create input windows
    Window win        = window;
    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
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    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);

    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());

    execute_window_loop(win, [&](const Coordinates &)
    {
        const auto input1_ptr = reinterpret_cast<const qsymm16_t *>(input1.ptr());
        const auto input2_ptr = reinterpret_cast<const qsymm16_t *>(input2.ptr());
        const auto output_ptr = reinterpret_cast<int32_t *>(output.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 qsymm16x8x2_t input1_q =
            {
                {
                    vld1q_s16(input1_ptr + x),
                    vld1q_s16(input1_ptr + x + 8),
                }
            };
            const qsymm16x8x2_t input2_q =
            {
                {
                    vld1q_s16(input2_ptr + x),
                    vld1q_s16(input2_ptr + x + 8),
                }
            };

            const int32x4x4_t in1_s32 =
            {
                {
                    vmovl_s16(vget_low_s16(input1_q.val[0])),
                    vmovl_s16(vget_high_s16(input1_q.val[0])),
                    vmovl_s16(vget_low_s16(input1_q.val[1])),
                    vmovl_s16(vget_high_s16(input1_q.val[1])),
                }
            };
            const int32x4x4_t in2_s32 =
            {
                {
                    vmovl_s16(vget_low_s16(input2_q.val[0])),
                    vmovl_s16(vget_high_s16(input2_q.val[0])),
                    vmovl_s16(vget_low_s16(input2_q.val[1])),
                    vmovl_s16(vget_high_s16(input2_q.val[1])),
                }
            };

            const int32x4x4_t result =
            {
                {
                    vmulq_s32(in1_s32.val[0], in2_s32.val[0]),
                    vmulq_s32(in1_s32.val[1], in2_s32.val[1]),
                    vmulq_s32(in1_s32.val[2], in2_s32.val[2]),
                    vmulq_s32(in1_s32.val[3], in2_s32.val[3]),
                }
            };

            vst1q_s32(output_ptr + x, result.val[0]);
            vst1q_s32(output_ptr + x + 4, result.val[1]);
            vst1q_s32(output_ptr + x + 8, result.val[2]);
            vst1q_s32(output_ptr + x + 12, result.val[3]);
        }

        // Compute left-over elements
        for(; x < window_end_x; ++x)
        {
            int32_t tmp       = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));
            *(output_ptr + x) = tmp;
        }
    },
    input1, input2, output);
}

template <bool is_scale255, bool is_sat>
void mul_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
    // Create input windows
    Window win        = window;
    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
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    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);

    const int  window_step_x  = 16 / sizeof(uint8_t);
    const auto window_start_x = static_cast<int>(window.x().start());
    const auto window_end_x   = static_cast<int>(window.x().end());

    execute_window_loop(win, [&](const Coordinates &)
    {
        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());

        // 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 uint8x16_t ta1 = wrapper::vloadq(input1_ptr + x);
            const uint8x16_t ta2 = wrapper::vloadq(input2_ptr + x);

            uint16x8_t       tmp1_high = vmovl_u8(vget_high_u8(ta1));
            const uint16x8_t tmp2_high = vmovl_u8(vget_high_u8(ta2));
            uint16x8_t       tmp1_low  = vmovl_u8(vget_low_u8(ta1));
            const uint16x8_t tmp2_low  = vmovl_u8(vget_low_u8(ta2));

            tmp1_high = vmulq_u16(tmp1_high, tmp2_high);
            tmp1_low  = vmulq_u16(tmp1_low, tmp2_low);

            if(is_scale255)
            {
                tmp1_high = scale255_U16_U16(tmp1_high);
                tmp1_low  = scale255_U16_U16(tmp1_low);
            }
            else
            {
                const int16x8_t vn = vdupq_n_s16(-n);

                if(is_sat)
                {
                    tmp1_high = vqshlq_u16(tmp1_high, vn);
                    tmp1_low  = vqshlq_u16(tmp1_low, vn);
                }
                else
                {
                    tmp1_high = vshlq_u16(tmp1_high, vn);
                    tmp1_low  = vshlq_u16(tmp1_low, vn);
                }
            }
            if(is_sat)
            {
                vst1q_u8(output_ptr, vcombine_u8(vqmovn_u16(tmp1_low), vqmovn_u16(tmp1_high)));
            }
            else
            {
                vst1q_u8(output_ptr, vcombine_u8(vmovn_u16(tmp1_low), vmovn_u16(tmp1_high)));
            }
        }

        // Compute left-over elements
        for(; x < window_end_x; ++x)
        {
            uint16_t tmp = static_cast<uint16_t>(*(input1_ptr + x)) * static_cast<uint16_t>(*(input2_ptr + x));

            if(is_scale255)
            {
                float tmp_f = static_cast<float>(tmp) * scale255_constant;
                tmp         = static_cast<uint16_t>(tmp_f + 0.5f);
            }
            else
            {
                tmp >>= n;
            }
            if(is_sat && tmp > 255)
            {
                tmp = 255;
            }
            *(output_ptr + x) = static_cast<uint8_t>(tmp);
        }
    },
    input1, input2, output);
}

template <bool is_scale255, bool is_sat>
inline int16x8_t mul_S16_S16_S16_n_loop(const int16x8_t &input1, const int16x8_t &input2, int n)
{
    int32x4_t       tmp1_high = vmovl_s16(vget_high_s16(input1));
    const int32x4_t tmp2_high = vmovl_s16(vget_high_s16(input2));
    int32x4_t       tmp1_low  = vmovl_s16(vget_low_s16(input1));
    const int32x4_t tmp2_low  = vmovl_s16(vget_low_s16(input2));

    tmp1_high = vmulq_s32(tmp1_high, tmp2_high);
    tmp1_low  = vmulq_s32(tmp1_low, tmp2_low);

    if(is_scale255)
    {
        tmp1_high = scale255_S32_S32(tmp1_high);
        tmp1_low  = scale255_S32_S32(tmp1_low);
    }
    else
    {
        // Right shift amount
        const int32x4_t vn = vdupq_n_s32(-n);
        // Left shift amount
        const int32x4_t vnl = vdupq_n_s32(n);
        // Calculate conversion bit
        const uint32x4_t tmp1_high_u  = vreinterpretq_u32_s32(tmp1_high);
        const uint32x4_t tmp1_low_u   = vreinterpretq_u32_s32(tmp1_low);
        const uint32x4_t sign_high    = vshrq_n_u32(tmp1_high_u, 31);
        const uint32x4_t sign_low     = vshrq_n_u32(tmp1_low_u, 31);
        const int32x4_t  sign_high_s  = vreinterpretq_s32_u32(sign_high);
        const int32x4_t  sign_low_s   = vreinterpretq_s32_u32(sign_low);
        const int32x4_t  convert_high = vsubq_s32(vshlq_s32(sign_high_s, vnl), sign_high_s);
        const int32x4_t  convert_low  = vsubq_s32(vshlq_s32(sign_low_s, vnl), sign_low_s);
        if(is_sat)
        {
            tmp1_high = vqshlq_s32(vaddq_s32(tmp1_high, convert_high), vn);
            tmp1_low  = vqshlq_s32(vaddq_s32(tmp1_low, convert_low), vn);
        }
        else
        {
            tmp1_high = vshlq_s32(vaddq_s32(tmp1_high, convert_high), vn);
            tmp1_low  = vshlq_s32(vaddq_s32(tmp1_low, convert_low), vn);
        }
    }

    if(is_sat)
    {
        return vcombine_s16(vqmovn_s32(tmp1_low), vqmovn_s32(tmp1_high));
    }
    else
    {
        return vcombine_s16(vmovn_s32(tmp1_low), vmovn_s32(tmp1_high));
    }
}

template <bool is_scale255, bool is_sat>
inline int16x8x2_t mul_S16_S16_S16_n_k(const int16x8x2_t &input1, const int16x8x2_t &input2, int n)
{
    const int16x8x2_t result =
    {
        {
            // First 8 elements
            mul_S16_S16_S16_n_loop<is_scale255, is_sat>(input1.val[0], input2.val[0], n),
            // Second 8 elements
            mul_S16_S16_S16_n_loop<is_scale255, is_sat>(input1.val[1], input2.val[1], n)
        }
    };

    return result;
}

template <bool is_scale255, bool is_sat>
void mul_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
    // Create input windows
    Window win        = window;
    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
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    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);

    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());

    execute_window_loop(win, [&](const Coordinates &)
    {
        const auto input1_ptr = reinterpret_cast<const int16_t *>(input1.ptr());
        const auto input2_ptr = reinterpret_cast<const int16_t *>(input2.ptr());
        const auto output_ptr = reinterpret_cast<int16_t *>(output.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 int16x8x2_t ta1 =
            {
                {
                    vld1q_s16(input1_ptr + x),
                    vld1q_s16(input1_ptr + x + 8),
                }
            };
            const int16x8x2_t ta2 =
            {
                {
                    vld1q_s16(input2_ptr + x),
                    vld1q_s16(input2_ptr + x + 8),
                }
            };
            const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n);

            vst1q_s16(output_ptr + x, result.val[0]);
            vst1q_s16(output_ptr + x + 8, result.val[1]);
        }

        // Compute left-over elements
        for(; x < window_end_x; ++x)
        {
            int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));

            if(is_scale255)
            {
                float tmp_f = static_cast<float>(tmp) * scale255_constant;

                tmp = static_cast<int32_t>(tmp_f + 0.5f);
            }
            else
            {
                if(tmp >= 0)
                {
                    tmp >>= n;
                }
                else
                {
                    uint32_t mask = (1u << n) - 1;
                    tmp           = (tmp + static_cast<int32_t>(mask)) >> n;
                }
            }
            if(is_sat)
            {
                tmp = (tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp);
            }
            *(output_ptr + x) = static_cast<int16_t>(tmp);
        }
    },
    input1, input2, output);
}

void mul_F32_F32_F32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
{
    // 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));

    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 = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);

    Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()));
    Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape()));
    Iterator output(out, window);

    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 ? 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 &)
        {
            const auto non_broadcast_input_ptr = reinterpret_cast<const float *>(non_broadcast_input.ptr());
            const auto output_ptr              = reinterpret_cast<float *>(output.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, 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 &)
        {
            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 *>(output.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, output);
    }
}

void c_mul_F32_F32_F32_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr)
{
    const auto input1 = static_cast<const float *__restrict>(input1_ptr);
    const auto input2 = static_cast<const float *__restrict>(input2_ptr);
    const auto output = static_cast<float *__restrict>(output_ptr);

    const float32x4_t a = wrapper::vloadq(input1);
    float32x4_t       b = wrapper::vloadq(input2);

    using ExactTagType = typename wrapper::traits::neon_vector<float, 2>::tag_type;

    const float32x4_t mask  = { -1.0f, 1.0f, -1.0f, 1.0f };
    const float32x2_t tmp00 = wrapper::vdup_n(wrapper::vgetlane(a, 0), ExactTagType{});
    const float32x2_t tmp01 = wrapper::vdup_n(wrapper::vgetlane(a, 1), ExactTagType{});
    const float32x2_t tmp10 = wrapper::vdup_n(wrapper::vgetlane(a, 2), ExactTagType{});
    const float32x2_t tmp11 = wrapper::vdup_n(wrapper::vgetlane(a, 3), ExactTagType{});

    const float32x4_t tmp0 = wrapper::vcombine(tmp00, tmp10);
    const float32x4_t tmp1 = wrapper::vcombine(tmp01, tmp11);

    float32x4_t res = wrapper::vmul(tmp0, b);

    b = wrapper::vrev64(b);
    b = wrapper::vmul(b, mask);

    res = wrapper::vmla(res, tmp1, b);
    wrapper::vstore(output, res);
}

#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
void mul_F16_F16_F16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
{
    // Create input windows
    Window win        = window;
    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
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    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);

    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());

    execute_window_loop(win, [&](const Coordinates &)
    {
        const auto input1_ptr = reinterpret_cast<const float16_t *>(input1.ptr());
        const auto input2_ptr = reinterpret_cast<const float16_t *>(input2.ptr());
        const auto output_ptr = reinterpret_cast<float16_t *>(output.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 float16x8x2_t ta1 =
            {
                {
                    vld1q_f16(input1_ptr + x),
                    vld1q_f16(input1_ptr + x + 8),
                }
            };
            const float16x8x2_t ta2 =
            {
                {
                    vld1q_f16(input2_ptr + x),
                    vld1q_f16(input2_ptr + x + 8),
                }
            };
            const float16x8_t   scale_vec = vdupq_n_f16(scale);
            const float16x8x2_t result =
            {
                {
                    vmulq_f16(vmulq_f16(ta1.val[0], ta2.val[0]), scale_vec),
                    vmulq_f16(vmulq_f16(ta1.val[1], ta2.val[1]), scale_vec),
                }
            };
            vst1q_f16(output_ptr + x, result.val[0]);
            vst1q_f16(output_ptr + x + 8, result.val[1]);
        }

        // 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, output);
}
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

template <bool is_scale255, bool is_sat>
void mul_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
    // Create input windows
    Window win        = window;
    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
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    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);

    const int  window_step_x  = 16 / sizeof(uint8_t);
    const auto window_start_x = static_cast<int>(window.x().start());
    const auto window_end_x   = static_cast<int>(window.x().end());

    execute_window_loop(win, [&](const Coordinates &)
    {
        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<int16_t *>(output.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 uint8x16_t bv = wrapper::vloadq(input2_ptr + x);
            const uint8x16_t av = wrapper::vloadq(input1_ptr + x);

            uint16x8_t tmp_low  = vmovl_u8(vget_low_u8(av));
            uint16x8_t tmp_high = vmovl_u8(vget_high_u8(av));
            tmp_low             = vmulq_u16(tmp_low, vmovl_u8(vget_low_u8(bv)));
            tmp_high            = vmulq_u16(tmp_high, vmovl_u8(vget_high_u8(bv)));

            if(is_scale255)
            {
                tmp_low  = scale255_U16_U16(tmp_low);
                tmp_high = scale255_U16_U16(tmp_high);
            }
            else
            {
                const int16x8_t vn = vdupq_n_s16(-n);

                if(is_sat)
                {
                    tmp_low  = vqshlq_u16(tmp_low, vn);
                    tmp_high = vqshlq_u16(tmp_high, vn);
                }
                else
                {
                    tmp_low  = vshlq_u16(tmp_low, vn);
                    tmp_high = vshlq_u16(tmp_high, vn);
                }
            }

            if(is_sat)
            {
                static const uint16x8_t max = vdupq_n_u16(SHRT_MAX);

                tmp_low  = vminq_u16(tmp_low, max);
                tmp_high = vminq_u16(tmp_high, max);
            }

            vst1q_s16(output_ptr + x, vreinterpretq_s16_u16(tmp_low));
            vst1q_s16(output_ptr + x + 8, vreinterpretq_s16_u16(tmp_high));
        }

        // Compute left-over elements
        for(; x < window_end_x; ++x)
        {
            int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));

            if(is_scale255)
            {
                float tmp_f = static_cast<float>(tmp) * scale255_constant;
                tmp         = static_cast<int32_t>(tmp_f + 0.5f);
            }
            else
            {
                tmp >>= n;
            }

            if(is_sat)
            {
                tmp = (tmp > SHRT_MAX) ? SHRT_MAX : tmp;
            }

            *(output_ptr + x) = static_cast<int16_t>(tmp);
        }
    },
    input1, input2, output);
}

template <bool is_scale255, bool is_sat>
void mul_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
    // Create input windows
    Window win        = window;
    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
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    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);

    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());

    execute_window_loop(win, [&](const Coordinates &)
    {
        const auto input1_ptr = reinterpret_cast<const int16_t *>(input1.ptr());
        const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
        const auto output_ptr = reinterpret_cast<int16_t *>(output.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 int16x8x2_t ta1 =
            {
                {
                    vld1q_s16(input1_ptr + x),
                    vld1q_s16(input1_ptr + x + 8),
                }
            };
            const uint8x8x2_t ta2u =
            {
                {
                    vld1_u8(input2_ptr + x),
                    vld1_u8(input2_ptr + x + 8),
                }
            };
            const int16x8x2_t ta2 =
            {
                {
                    vreinterpretq_s16_u16(vmovl_u8(ta2u.val[0])),
                    vreinterpretq_s16_u16(vmovl_u8(ta2u.val[1]))
                }
            };

            const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n);

            vst1q_s16(output_ptr + x, result.val[0]);
            vst1q_s16(output_ptr + x + 8, result.val[1]);
        }

        // Compute left-over elements
        for(; x < window_end_x; ++x)
        {
            int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));

            if(is_scale255)
            {
                float tmp_f = static_cast<float>(tmp) * scale255_constant;

                tmp = static_cast<int32_t>(tmp_f + 0.5f);
            }
            else
            {
                if(tmp >= 0)
                {
                    tmp >>= n;
                }
                else
                {
                    uint32_t mask = (1u << n) - 1;
                    tmp           = (tmp + static_cast<int32_t>(mask)) >> n;
                }
            }
            if(is_sat)
            {
                tmp = (tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp);
            }
            *(output_ptr + x) = static_cast<int16_t>(tmp);
        }
    },
    input1, input2, output);
}

template <bool is_scale255, bool is_sat>
void mul_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
    // Simply swap the two input buffers
    mul_S16_U8_S16<is_scale255, is_sat>(in2, in1, out, window, n);
}
} // namespace

NEPixelWiseMultiplicationKernel::NEPixelWiseMultiplicationKernel()
    : _func_float(nullptr), _func_int(nullptr), _func_quantized(nullptr), _scale{ 0 }, _scale_exponent{ 0 }
{
}

void NEPixelWiseMultiplicationKernel::configure(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)
{
    ARM_COMPUTE_UNUSED(rounding_policy);
    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);

    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1, input2, output, scale, overflow_policy, rounding_policy));

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

    // Auto initialize output if not initialized
    set_shape_if_empty(*output, out_shape);

    _scale          = scale;
    _scale_exponent = 0;
    _func_quantized = nullptr;
    _func_int       = nullptr;
    _func_float     = nullptr;

    bool is_scale_255 = false;
    // Check and validate scaling factor
    if(std::abs(scale - scale255_constant) < 0.00001f)
    {
        is_scale_255 = true;
    }
    else
    {
        int exponent = 0;

        std::frexp(scale, &exponent);

        // Store the positive exponent. We know that we compute 1/2^n
        // Additionally we need to subtract 1 to compensate that frexp used a mantissa of 0.5
        _scale_exponent = std::abs(exponent - 1);
    }

    const DataType dt_input1 = input1->data_type();
    const DataType dt_input2 = input2->data_type();
    const DataType dt_output = output->data_type();
    const bool     is_sat    = (overflow_policy == ConvertPolicy::SATURATE);

    switch(dt_input1)
    {
        case DataType::QASYMM8:
            if(dt_input2 == DataType::QASYMM8 && dt_output == DataType::QASYMM8)
            {
                _func_quantized = &mul_saturate_quantized_8<uint8_t>;
            }
            break;
        case DataType::QASYMM8_SIGNED:
            if(dt_input2 == DataType::QASYMM8_SIGNED)
            {
                _func_quantized = &mul_saturate_quantized_8<int8_t>;
                ;
            }
            break;
        case DataType::QSYMM16:
            if(dt_input2 == DataType::QSYMM16 && dt_output == DataType::QSYMM16)
            {
                _func_quantized = &mul_saturate_QSYMM16_QSYMM16_QSYMM16;
            }
            else if(dt_input2 == DataType::QSYMM16 && dt_output == DataType::S32)
            {
                _func_int = &mul_QSYMM16_QSYMM16_S32;
            }
            break;
        case DataType::S16:
            if(DataType::U8 == dt_input2 && DataType::S16 == dt_output)
            {
                if(is_scale_255)
                {
                    _func_int = is_sat ? &mul_S16_U8_S16<true, true> : &mul_S16_U8_S16<true, false>;
                }
                else
                {
                    _func_int = is_sat ? &mul_S16_U8_S16<false, true> : &mul_S16_U8_S16<false, false>;
                }
            }
            if(DataType::S16 == dt_input2 && DataType::S16 == dt_output)
            {
                if(is_scale_255)
                {
                    _func_int = is_sat ? &mul_S16_S16_S16<true, true> : &mul_S16_S16_S16<true, false>;
                }
                else
                {
                    _func_int = is_sat ? &mul_S16_S16_S16<false, true> : &mul_S16_S16_S16<false, false>;
                }
            }
            break;
        case DataType::U8:
            if(DataType::U8 == dt_input2 && DataType::U8 == dt_output)
            {
                if(is_scale_255)
                {
                    _func_int = is_sat ? &mul_U8_U8_U8<true, true> : &mul_U8_U8_U8<true, false>;
                }
                else
                {
                    _func_int = is_sat ? &mul_U8_U8_U8<false, true> : &mul_U8_U8_U8<false, false>;
                }
            }
            else if(DataType::U8 == dt_input2 && DataType::S16 == dt_output)
            {
                if(is_scale_255)
                {
                    _func_int = is_sat ? &mul_U8_U8_S16<true, true> : &mul_U8_U8_S16<true, false>;
                }
                else
                {
                    _func_int = is_sat ? &mul_U8_U8_S16<false, true> : &mul_U8_U8_S16<false, false>;
                }
            }
            else if(DataType::S16 == dt_input2 && DataType::S16 == dt_output)
            {
                if(is_scale_255)
                {
                    _func_int = is_sat ? &mul_U8_S16_S16<true, true> : &mul_U8_S16_S16<true, false>;
                }
                else
                {
                    _func_int = is_sat ? &mul_U8_S16_S16<false, true> : &mul_U8_S16_S16<false, false>;
                }
            }
            break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
        case DataType::F16:
            _func_float = &mul_F16_F16_F16;
            break;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
        case DataType::F32:
            _func_float = &mul_F32_F32_F32;
            break;
        default:
            ARM_COMPUTE_ERROR("You called with the wrong img formats");
    }

    // Configure kernel window
    Coordinates coord;
    coord.set_num_dimensions(output->num_dimensions());
    output->set_valid_region(valid_region);
    Window win = calculate_max_window(valid_region, Steps());

    INEKernel::configure(win);
}

Status NEPixelWiseMultiplicationKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy,
                                                 RoundingPolicy rounding_policy)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output, scale, overflow_policy, rounding_policy));

    return Status{};
}

void NEPixelWiseMultiplicationKernel::run_op(const InputTensorMap &inputs, const OutputTensorMap &outputs, 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);

    auto input1 = inputs.at(TensorType::ACL_SRC_0);
    auto input2 = inputs.at(TensorType::ACL_SRC_1);
    auto output = outputs.at(TensorType::ACL_DST);

    if(_func_quantized != nullptr)
    {
        (*_func_quantized)(input1, input2, output, window, _scale);
    }
    else if(_func_int != nullptr)
    {
        (*_func_int)(input1, input2, output, window, _scale_exponent);
    }
    else
    {
        ARM_COMPUTE_ERROR_ON(_func_float == nullptr);
        (*_func_float)(input1, input2, output, window, _scale);
    }
}
namespace
{
constexpr unsigned int num_elems_processed_per_iteration_complex = 2;

Status validate_arguments_complex(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
{
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 2, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 2, DataType::F32);

    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_DATA_TYPE_CHANNEL_NOT_IN(output, 2, DataType::F32);
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), "Wrong shape for output");
    }

    return Status{};
}

std::pair<Status, Window> validate_and_configure_window_complex(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output)
{
    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2);
    const TensorShape &out_shape    = broadcast_pair.first;
    const ValidRegion &valid_region = broadcast_pair.second;

    // Auto initialize output if not initialized
    const TensorInfo out_info(out_shape, input1->num_channels(), input1->data_type());
    auto_init_if_empty(*output, out_info);

    Window win        = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration_complex));
    Window win_input1 = win.broadcast_if_dimension_le_one(*input1);
    Window win_input2 = win.broadcast_if_dimension_le_one(*input2);

    AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration_complex);
    AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration_complex);
    AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_complex);

    bool window_changed = update_window_and_padding(win_input1, input1_access)
                          || update_window_and_padding(win_input2, input2_access)
                          || update_window_and_padding(win, output_access);

    output_access.set_valid_region(win, valid_region);

    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
    return std::make_pair(err, win);
}
} // namespace

void NEComplexPixelWiseMultiplicationKernel::configure(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_complex(input1, input2, output));

    // Configure kernel window
    auto win_config = validate_and_configure_window_complex(input1, input2, output);
    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);

    // Create kernel
    INEKernel::configure(win_config.second);
}

Status NEComplexPixelWiseMultiplicationKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_complex(input1, input2, output));
    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_complex(input1->clone().get(), input2->clone().get(), output->clone().get()).first);

    return Status{};
}

void NEComplexPixelWiseMultiplicationKernel::run_op(const InputTensorMap &inputs, const OutputTensorMap &outputs, 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);

    auto input1 = inputs.at(TensorType::ACL_SRC_0);
    auto input2 = inputs.at(TensorType::ACL_SRC_1);
    auto output = outputs.at(TensorType::ACL_DST);

    Iterator input1_it(input1, window.broadcast_if_dimension_le_one(input1->info()->tensor_shape()));
    Iterator input2_it(input2, window.broadcast_if_dimension_le_one(input2->info()->tensor_shape()));
    Iterator output_it(output, window);

    execute_window_loop(window, [&](const Coordinates &)
    {
        c_mul_F32_F32_F32_n(input1_it.ptr(), input2_it.ptr(), output_it.ptr());
    },
    input1_it, input2_it, output_it);
}
} // namespace arm_compute