/*
MIT License

Copyright (c) 2019 - 2024 Advanced Micro Devices, Inc.

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.
*/

/*To be used by the library alone*/
#ifndef RPP_CL_COMMON_H
#define RPP_CL_COMMON_H

#include <stdlib.h>
#include <stdio.h>
#include <string>
#include <iostream>
#include <math.h>
#include <algorithm>
#include <rppdefs.h>
#include <half/half.hpp>
using half_float::half;
typedef half Rpp16f;

#define __CL_ENABLE_EXCEPTIONS
#define CL_USE_DEPRECATED_OPENCL_1_2_APIS

enum class RPPTensorDataType
{
    U8 = 0,
    FP32,
    FP16,
    I8,
};

struct RPPTensorFunctionMetaData
{
    RPPTensorDataType _in_type = RPPTensorDataType::U8;
    RPPTensorDataType _out_type = RPPTensorDataType::U8;
    RppiChnFormat _in_format = RppiChnFormat::RPPI_CHN_PACKED;
    RppiChnFormat _out_format = RppiChnFormat::RPPI_CHN_PLANAR;
    Rpp32u _in_channels = 3;

    RPPTensorFunctionMetaData(RppiChnFormat in_chn_format, RPPTensorDataType in_tensor_type,
                              RPPTensorDataType out_tensor_type, Rpp32u in_channels,
                              bool out_format_change) : _in_format(in_chn_format), _in_type(in_tensor_type),
                                                        _out_type(out_tensor_type), _in_channels(in_channels)
    {
        if (out_format_change)
        {
            if (_in_format == RPPI_CHN_PLANAR)
                _out_format = RppiChnFormat::RPPI_CHN_PACKED;
            else
                _out_format = RppiChnFormat::RPPI_CHN_PLANAR;
        }
        else
            _out_format = _in_format;
    }
};

inline int getplnpkdind(RppiChnFormat &format)
{
    return format == RPPI_CHN_PLANAR ? 1 : 3;
}

inline void generate_gaussian_kernel_gpu(Rpp32f stdDev, Rpp32f *kernel, Rpp32u kernelSize)
{
    Rpp32f s, sum = 0.0, multiplier;
    int bound = ((kernelSize - 1) / 2);
    Rpp32u c = 0;
    s = 1 / (2 * stdDev * stdDev);
    multiplier = (1 / M_PI) * (s);
    for (int i = -bound; i <= bound; i++)
    {
        for (int j = -bound; j <= bound; j++)
        {
            kernel[c] = multiplier * exp((-1) * (s) * (i * i + j * j));
            sum += kernel[c];
            c += 1;
        }
    }
    for (int i = 0; i < (kernelSize * kernelSize); i++)
    {
        kernel[i] /= sum;
    }
}
#endif //RPP_CL_COMMON_H
