#ifndef UNET_COMMON_H_
#define UNET_COMMON_H_

#include <fstream>
#include <map>
#include <sstream>
#include <vector>
#include <opencv2/opencv.hpp>
#include <dirent.h>
#include "NvInfer.h"


#define CHECK(status) \
    do\
    {\
        auto ret = (status);\
        if (ret != 0)\
        {\
            std::cerr << "Cuda failure: " << ret << std::endl;\
            abort();\
        }\
    } while (0)

using namespace nvinfer1;





// TensorRT weight files have a simple space delimited format:
// [type] [size] <data x size in hex>
// std::map<std::string, Weights> loadWeights(const std::string file) {
//     std::cout << "Loading weights: " << file << std::endl;
//     std::map<std::string, Weights> weightMap;

//     // Open weights file
//     std::ifstream input(file);
//     assert(input.is_open() && "Unable to load weight file. please check if the .wts file path is right!!!!!!");

//     // Read number of weight blobs
//     int32_t count;
//     input >> count;
//     assert(count > 0 && "Invalid weight map file.");

//     while (count--)
//     {
//         Weights wt{DataType::kFLOAT, nullptr, 0};
//         uint32_t size;

//         // Read name and type of blob
//         std::string name;
//         input >> name >> std::dec >> size;
//         wt.type = DataType::kFLOAT;

//         // Load blob
//         uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
//         for (uint32_t x = 0, y = size; x < y; ++x)
//         {
//             input >> std::hex >> val[x];
//         }
//         wt.values = val;
        
//         wt.count = size;
//         weightMap[name] = wt;
//     }

//     return weightMap;
// }

// IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
//     float *gamma = (float*)weightMap[lname + ".weight"].values;
//     float *beta = (float*)weightMap[lname + ".bias"].values;
//     float *mean = (float*)weightMap[lname + ".running_mean"].values;
//     float *var = (float*)weightMap[lname + ".running_var"].values;
//     int len = weightMap[lname + ".running_var"].count;

//     float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
//     for (int i = 0; i < len; i++) {
//         scval[i] = gamma[i] / sqrt(var[i] + eps);
//     }
//     Weights scale{DataType::kFLOAT, scval, len};
    
//     float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
//     for (int i = 0; i < len; i++) {
//         shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
//     }
//     Weights shift{DataType::kFLOAT, shval, len};

//     float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
//     for (int i = 0; i < len; i++) {
//         pval[i] = 1.0;
//     }
//     Weights power{DataType::kFLOAT, pval, len};

//     weightMap[lname + ".scale"] = scale;
//     weightMap[lname + ".shift"] = shift;
//     weightMap[lname + ".power"] = power;
//     IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
//     assert(scale_1);
//     return scale_1;
// }


// ILayer* convBlock(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname) {
//     Weights emptywts{DataType::kFLOAT, nullptr, 0};
//     int p = ksize / 2;
//     IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[lname + ".conv.weight"], emptywts);
//     assert(conv1);
//     conv1->setStrideNd(DimsHW{s, s});
//     conv1->setPaddingNd(DimsHW{p, p});
//     conv1->setNbGroups(g);
//     IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".bn", 1e-3);

//     // hard_swish = x * hard_sigmoid
//     auto hsig = network->addActivation(*bn1->getOutput(0), ActivationType::kHARD_SIGMOID);
//     assert(hsig);
//     hsig->setAlpha(1.0 / 6.0);
//     hsig->setBeta(0.5);
//     auto ew = network->addElementWise(*bn1->getOutput(0), *hsig->getOutput(0), ElementWiseOperation::kPROD);
//     assert(ew);
//     return ew;
// }



// int read_files_in_dir(const char *p_dir_name, std::vector<std::string> &file_names) {
//     DIR *p_dir = opendir(p_dir_name);
//     if (p_dir == nullptr) {
//         return -1;
//     }

//     struct dirent* p_file = nullptr;
//     while ((p_file = readdir(p_dir)) != nullptr) {
//         if (strcmp(p_file->d_name, ".") != 0 &&
//                 strcmp(p_file->d_name, "..") != 0) {
//             //std::string cur_file_name(p_dir_name);
//             //cur_file_name += "/";
//             //cur_file_name += p_file->d_name;
//             std::string cur_file_name(p_file->d_name);
//             file_names.push_back(cur_file_name);
//         }
//     }

//     closedir(p_dir);
//     return 0;
// }

#endif