/*
MIT License

Copyright (c) 2019 - 2025 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.
*/

#include <hip/hip_runtime.h>
#include "rpp_hip_host_decls.hpp"

#define saturate_8u(value) ((value) > 255 ? 255 : ((value) < 0 ? 0 : (value)))

__device__ float gaussian(int x, int y, float std_dev)
{
    float res, pi = 3.14;
    res = 1 / (2 * pi * std_dev * std_dev);
    float exp1, exp2;
    exp1 = -(x * x) / (2 * std_dev * std_dev);
    exp2 = -(y * y) / (2 * std_dev * std_dev);
    exp1 = exp1 + exp2;
    exp1 = exp(exp1);
    res *= exp1;
    return res;
}

extern "C" __global__ void non_linear_blend_batch(unsigned char *input1,
                                                  unsigned char *input2,
                                                  unsigned char *output,
                                                  float *std_dev,
                                                  unsigned int *xroi_begin,
                                                  unsigned int *xroi_end,
                                                  unsigned int *yroi_begin,
                                                  unsigned int *yroi_end,
                                                  unsigned int *height,
                                                  unsigned int *width,
                                                  unsigned int *max_width,
                                                  unsigned long long *batch_index,
                                                  const unsigned int channel,
                                                  unsigned int *src_inc,
                                                  unsigned int *dst_inc, // use width * height for pln and 1 for pkd
                                                  const int in_plnpkdind,
                                                  const int out_plnpkdind) // use 1 pln 3 for pkd
{
    int id_x = hipBlockIdx_x * hipBlockDim_x + hipThreadIdx_x;
    int id_y = hipBlockIdx_y * hipBlockDim_y + hipThreadIdx_y;
    int id_z = hipBlockIdx_z * hipBlockDim_z + hipThreadIdx_z;

    unsigned long src_pix_idx = batch_index[id_z] + (id_x + id_y * max_width[id_z]) * in_plnpkdind;
    unsigned long dst_pix_idx = batch_index[id_z] + (id_x + id_y * max_width[id_z]) * out_plnpkdind;

    if ((id_y >= yroi_begin[id_z]) && (id_y <= yroi_end[id_z]) && (id_x >= xroi_begin[id_z]) && (id_x <= xroi_end[id_z]))
    {
        int x = (id_x - (width[id_z] >> 1));
        int y = (id_y - (height[id_z] >> 1));
        float gaussianvalue = gaussian(x, y, std_dev[id_z]) / gaussian(0.0, 0.0, std_dev[id_z]);

        for (int indextmp = 0; indextmp < channel; indextmp++)
        {
            output[dst_pix_idx] = gaussianvalue * input1[src_pix_idx] + (1 - gaussianvalue) * input2[src_pix_idx];
            src_pix_idx += src_inc[id_z];
            dst_pix_idx += dst_inc[id_z];
        }
    }
    else
    {
        for (int indextmp = 0; indextmp < channel; indextmp++)
        {
            output[dst_pix_idx] = 0;
            dst_pix_idx += dst_inc[id_z];
        }
    }
}

// extern "C" __global__ void non_linear_blend_batch_fp16(
//     half *input1, half *input2, half *output,
//     float *std_dev, int *xroi_begin, int *xroi_end,
//     int *yroi_begin, int *yroi_end,
//     unsigned int *height, unsigned int *width,
//     unsigned int *max_width, unsigned long *batch_index,
//     const unsigned int channel, unsigned int *src_inc,
//     unsigned int *dst_inc, // use width * height for pln and 1 for pkd
//     const int in_plnpkdind, const int out_plnpkdind // use 1 pln 3 for pkd
// ) {
//   int id_x = hipBlockIdx_x * hipBlockDim_x + hipThreadIdx_x;
//     int id_y = hipBlockIdx_y * hipBlockDim_y + hipThreadIdx_y;
//     int id_z = hipBlockIdx_z * hipBlockDim_z + hipThreadIdx_z;
//   int indextmp = 0;
//   unsigned long src_pix_idx =
//       batch_index[id_z] + (id_x + id_y * max_width[id_z]) * in_plnpkdind;
//   unsigned long dst_pix_idx =
//       batch_index[id_z] + (id_x + id_y * max_width[id_z]) * out_plnpkdind;

//   if ((id_y >= yroi_begin[id_z]) && (id_y <= yroi_end[id_z]) &&
//       (id_x >= xroi_begin[id_z]) && (id_x <= xroi_end[id_z])) {
//     int x = (id_x - (width[id_z] >> 1));
//     int y = (id_y - (height[id_z] >> 1));
//     float gaussianvalue =
//         gaussian(x, y, std_dev[id_z]) / gaussian(0.0, 0.0, std_dev[id_z]);
//     for (indextmp = 0; indextmp < channel; indextmp++) {
//       output[dst_pix_idx] = (half) gaussianvalue * input1[src_pix_idx] +
//                             (1 - gaussianvalue) * input2[src_pix_idx];
//       src_pix_idx += src_inc[id_z];
//       dst_pix_idx += dst_inc[id_z];
//     }
//   } else {
//     for (indextmp = 0; indextmp < channel; indextmp++) {
//       output[dst_pix_idx] = 0;
//       dst_pix_idx += dst_inc[id_z];
//     }
//   }
// }

extern "C" __global__ void non_linear_blend_batch_fp32(float *input1,
                                                       float *input2,
                                                       float *output,
                                                       float *std_dev,
                                                       unsigned int *xroi_begin,
                                                       unsigned int *xroi_end,
                                                       unsigned int *yroi_begin,
                                                       unsigned int *yroi_end,
                                                       unsigned int *height,
                                                       unsigned int *width,
                                                       unsigned int *max_width,
                                                       unsigned long long *batch_index,
                                                       const unsigned int channel,
                                                       unsigned int *src_inc,
                                                       unsigned int *dst_inc, // use width * height for pln and 1 for pkd
                                                       const int in_plnpkdind,
                                                       const int out_plnpkdind) // use 1 pln 3 for pkd
{
    int id_x = hipBlockIdx_x * hipBlockDim_x + hipThreadIdx_x;
    int id_y = hipBlockIdx_y * hipBlockDim_y + hipThreadIdx_y;
    int id_z = hipBlockIdx_z * hipBlockDim_z + hipThreadIdx_z;

    unsigned long src_pix_idx = batch_index[id_z] + (id_x + id_y * max_width[id_z]) * in_plnpkdind;
    unsigned long dst_pix_idx = batch_index[id_z] + (id_x + id_y * max_width[id_z]) * out_plnpkdind;

    if ((id_y >= yroi_begin[id_z]) && (id_y <= yroi_end[id_z]) && (id_x >= xroi_begin[id_z]) && (id_x <= xroi_end[id_z]))
    {
        int x = (id_x - (width[id_z] >> 1));
        int y = (id_y - (height[id_z] >> 1));
        float gaussianvalue = gaussian(x, y, std_dev[id_z]) / gaussian(0.0, 0.0, std_dev[id_z]);
        for (int indextmp = 0; indextmp < channel; indextmp++)
        {
            output[dst_pix_idx] = gaussianvalue * input1[src_pix_idx] + (1 - gaussianvalue) * input2[src_pix_idx];
            src_pix_idx += src_inc[id_z];
            dst_pix_idx += dst_inc[id_z];
        }
    }
    else
    {
        for (int indextmp = 0; indextmp < channel; indextmp++)
        {
            output[dst_pix_idx] = 0;
            dst_pix_idx += dst_inc[id_z];
        }
    }
}

extern "C" __global__ void non_linear_blend_batch_int8(signed char *input1,
                                                       signed char *input2,
                                                       signed char *output,
                                                       float *std_dev,
                                                       unsigned int *xroi_begin,
                                                       unsigned int *xroi_end,
                                                       unsigned int *yroi_begin,
                                                       unsigned int *yroi_end,
                                                       unsigned int *height,
                                                       unsigned int *width,
                                                       unsigned int *max_width,
                                                       unsigned long long *batch_index,
                                                       const unsigned int channel,
                                                       unsigned int *src_inc,
                                                       unsigned int *dst_inc, // use width * height for pln and 1 for pkd
                                                       const int in_plnpkdind,
                                                       const int out_plnpkdind) // use 1 pln 3 for pkd
{
    int id_x = hipBlockIdx_x * hipBlockDim_x + hipThreadIdx_x;
    int id_y = hipBlockIdx_y * hipBlockDim_y + hipThreadIdx_y;
    int id_z = hipBlockIdx_z * hipBlockDim_z + hipThreadIdx_z;

    unsigned long src_pix_idx = batch_index[id_z] + (id_x + id_y * max_width[id_z]) * in_plnpkdind;
    unsigned long dst_pix_idx = batch_index[id_z] + (id_x + id_y * max_width[id_z]) * out_plnpkdind;

    if ((id_y >= yroi_begin[id_z]) && (id_y <= yroi_end[id_z]) && (id_x >= xroi_begin[id_z]) && (id_x <= xroi_end[id_z]))
    {
        int x = (id_x - (width[id_z] >> 1));
        int y = (id_y - (height[id_z] >> 1));
        float gaussianvalue = gaussian(x, y, std_dev[id_z]) / gaussian(0.0, 0.0, std_dev[id_z]);
        for (int indextmp = 0; indextmp < channel; indextmp++)
        {
            output[dst_pix_idx] = gaussianvalue * input1[src_pix_idx] + (1 - gaussianvalue) * input2[src_pix_idx];
            src_pix_idx += src_inc[id_z];
            dst_pix_idx += dst_inc[id_z];
        }
    }
    else
    {
        for (int indextmp = 0; indextmp < channel; indextmp++)
        {
            output[dst_pix_idx] = 0;
            dst_pix_idx += dst_inc[id_z];
        }
    }
}

RppStatus hip_exec_non_linear_blend_batch(Rpp8u *srcPtr1, Rpp8u *srcPtr2, Rpp8u *dstPtr, rpp::Handle& handle, RPPTensorFunctionMetaData &tensor_info, Rpp32s in_plnpkdind, Rpp32s out_plnpkdind, Rpp32u max_height, Rpp32u max_width)
{
    int localThreads_x = 32;
    int localThreads_y = 32;
    int localThreads_z = 1;
    int globalThreads_x = (max_width + 31) & ~31;
    int globalThreads_y = (max_height + 31) & ~31;
    int globalThreads_z = handle.GetBatchSize();
    InitHandle *handle_obj = handle.GetInitHandle();

    hipLaunchKernelGGL(non_linear_blend_batch,
                       dim3(ceil((float)globalThreads_x/localThreads_x), ceil((float)globalThreads_y/localThreads_y), ceil((float)globalThreads_z/localThreads_z)),
                       dim3(localThreads_x, localThreads_y, localThreads_z),
                       0,
                       handle.GetStream(),
                       srcPtr1,
                       srcPtr2,
                       dstPtr,
                       handle_obj->mem.mgpu.floatArr[0].floatmem,
                       handle_obj->mem.mgpu.roiPoints.x,
                       handle_obj->mem.mgpu.roiPoints.roiWidth,
                       handle_obj->mem.mgpu.roiPoints.y,
                       handle_obj->mem.mgpu.roiPoints.roiHeight,
                       handle_obj->mem.mgpu.srcSize.height,
                       handle_obj->mem.mgpu.srcSize.width,
                       handle_obj->mem.mgpu.maxSrcSize.width,
                       handle_obj->mem.mgpu.srcBatchIndex,
                       tensor_info._in_channels,
                       handle_obj->mem.mgpu.inc,
                       handle_obj->mem.mgpu.dstInc,
                       in_plnpkdind,
                       out_plnpkdind);

    return RPP_SUCCESS;
}

RppStatus hip_exec_non_linear_blend_batch_fp16(Rpp16f *srcPtr1, Rpp16f *srcPtr2, Rpp16f *dstPtr, rpp::Handle& handle, RPPTensorFunctionMetaData &tensor_info, Rpp32s in_plnpkdind, Rpp32s out_plnpkdind, Rpp32u max_height, Rpp32u max_width)
{
//     int localThreads_x = 32;
//     int localThreads_y = 32;
//     int localThreads_z = 1;
//     int globalThreads_x = (max_width + 31) & ~31;
//     int globalThreads_y = (max_height + 31) & ~31;
//     int globalThreads_z = handle.GetBatchSize();

//     hipLaunchKernelGGL(non_linear_blend_batch_fp16,
//                        dim3(ceil((float)globalThreads_x/localThreads_x), ceil((float)globalThreads_y/localThreads_y), ceil((float)globalThreads_z/localThreads_z)),
//                        dim3(localThreads_x, localThreads_y, localThreads_z),
//                        0,
//                        handle.GetStream(),
//                        srcPtr1,
//                        srcPtr2,
//                        dstPtr,
//                        handle_obj->mem.mgpu.floatArr[0].floatmem,
//                        handle_obj->mem.mgpu.roiPoints.x,
//                        handle_obj->mem.mgpu.roiPoints.roiWidth,
//                        handle_obj->mem.mgpu.roiPoints.y,
//                        handle_obj->mem.mgpu.roiPoints.roiHeight,
//                        handle_obj->mem.mgpu.srcSize.height,
//                        handle_obj->mem.mgpu.srcSize.width,
//                        handle_obj->mem.mgpu.maxSrcSize.width,
//                        handle_obj->mem.mgpu.srcBatchIndex,
//                        tensor_info._in_channels,
//                        handle_obj->mem.mgpu.inc,
//                        handle_obj->mem.mgpu.dstInc,
//                        in_plnpkdind,
//                        out_plnpkdind);

    return RPP_SUCCESS;
}

RppStatus hip_exec_non_linear_blend_batch_fp32(Rpp32f *srcPtr1, Rpp32f *srcPtr2, Rpp32f *dstPtr, rpp::Handle& handle, RPPTensorFunctionMetaData &tensor_info, Rpp32s in_plnpkdind, Rpp32s out_plnpkdind, Rpp32u max_height, Rpp32u max_width)
{
    int localThreads_x = 32;
    int localThreads_y = 32;
    int localThreads_z = 1;
    int globalThreads_x = (max_width + 31) & ~31;
    int globalThreads_y = (max_height + 31) & ~31;
    int globalThreads_z = handle.GetBatchSize();
    InitHandle *handle_obj = handle.GetInitHandle();

    hipLaunchKernelGGL(non_linear_blend_batch_fp32,
                       dim3(ceil((float)globalThreads_x/localThreads_x), ceil((float)globalThreads_y/localThreads_y), ceil((float)globalThreads_z/localThreads_z)),
                       dim3(localThreads_x, localThreads_y, localThreads_z),
                       0,
                       handle.GetStream(),
                       srcPtr1,
                       srcPtr2,
                       dstPtr,
                       handle_obj->mem.mgpu.floatArr[0].floatmem,
                       handle_obj->mem.mgpu.roiPoints.x,
                       handle_obj->mem.mgpu.roiPoints.roiWidth,
                       handle_obj->mem.mgpu.roiPoints.y,
                       handle_obj->mem.mgpu.roiPoints.roiHeight,
                       handle_obj->mem.mgpu.srcSize.height,
                       handle_obj->mem.mgpu.srcSize.width,
                       handle_obj->mem.mgpu.maxSrcSize.width,
                       handle_obj->mem.mgpu.srcBatchIndex,
                       tensor_info._in_channels,
                       handle_obj->mem.mgpu.inc,
                       handle_obj->mem.mgpu.dstInc,
                       in_plnpkdind,
                       out_plnpkdind);

    return RPP_SUCCESS;
}

RppStatus hip_exec_non_linear_blend_batch_int8(Rpp8s *srcPtr1, Rpp8s *srcPtr2, Rpp8s *dstPtr, rpp::Handle& handle, RPPTensorFunctionMetaData &tensor_info, Rpp32s in_plnpkdind, Rpp32s out_plnpkdind, Rpp32u max_height, Rpp32u max_width)
{
    int localThreads_x = 32;
    int localThreads_y = 32;
    int localThreads_z = 1;
    int globalThreads_x = (max_width + 31) & ~31;
    int globalThreads_y = (max_height + 31) & ~31;
    int globalThreads_z = handle.GetBatchSize();
    InitHandle *handle_obj = handle.GetInitHandle();

    hipLaunchKernelGGL(non_linear_blend_batch_int8,
                       dim3(ceil((float)globalThreads_x/localThreads_x), ceil((float)globalThreads_y/localThreads_y), ceil((float)globalThreads_z/localThreads_z)),
                       dim3(localThreads_x, localThreads_y, localThreads_z),
                       0,
                       handle.GetStream(),
                       srcPtr1,
                       srcPtr2,
                       dstPtr,
                       handle_obj->mem.mgpu.floatArr[0].floatmem,
                       handle_obj->mem.mgpu.roiPoints.x,
                       handle_obj->mem.mgpu.roiPoints.roiWidth,
                       handle_obj->mem.mgpu.roiPoints.y,
                       handle_obj->mem.mgpu.roiPoints.roiHeight,
                       handle_obj->mem.mgpu.srcSize.height,
                       handle_obj->mem.mgpu.srcSize.width,
                       handle_obj->mem.mgpu.maxSrcSize.width,
                       handle_obj->mem.mgpu.srcBatchIndex,
                       tensor_info._in_channels,
                       handle_obj->mem.mgpu.inc,
                       handle_obj->mem.mgpu.dstInc,
                       in_plnpkdind,
                       out_plnpkdind);
    return RPP_SUCCESS;
}
