#include "NvCaffeParser.h"
#include "NvInfer.h"
#include "common.h"
#include "cuda_runtime_api.h"
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#ifdef _MSC_VER
#include <direct.h>
#else
#include <sys/stat.h>
#endif
#include <vector>

// stuff we know about the network and the input/output blobs
static const int INPUT_H = 28;
static const int INPUT_W = 28;
static const int OUTPUT_SIZE = 10;
static int gUseDLACore{-1};

const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";

using namespace nvinfer1;
using namespace nvcaffeparser1;
static Logger gLogger;

// Load weights from files shared with TensorRT samples.
// 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.");

    // 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 type, size;

        // Read name and type of blob
        std::string name;
        input >> name >> std::dec >> type >> size;
        wt.type = static_cast<DataType>(type);

        // Load blob
        if (wt.type == DataType::kFLOAT)
        {
            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;
        }
        else if (wt.type == DataType::kHALF)
        {
            uint16_t* val = reinterpret_cast<uint16_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;
}

// We have the data files located in a specific directory. This
// searches for that directory format from the current directory.
std::string locateFile(const std::string& input)
{
    std::vector<std::string> dirs{"data/samples/mnist/", "data/mnist/"};
    return locateFile(input, dirs);
}

// simple PGM (portable greyscale map) reader
void readPGMFile(const std::string& filename, uint8_t buffer[INPUT_H * INPUT_W])
{
    readPGMFile(locateFile(filename), buffer, INPUT_H, INPUT_W);
}

// Creat the engine using only the API and not any parser.
ICudaEngine* createMNISTEngine(unsigned int maxBatchSize, IBuilder* builder, DataType dt)
{
    INetworkDefinition* network = builder->createNetwork();

    // Create input tensor of shape { 1, 1, 28, 28 } with name INPUT_BLOB_NAME
    ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{1, INPUT_H, INPUT_W});
    assert(data);

    // Create scale layer with default power/shift and specified scale parameter.
    const float scaleParam = 0.0125f;
    const Weights power{DataType::kFLOAT, nullptr, 0};
    const Weights shift{DataType::kFLOAT, nullptr, 0};
    const Weights scale{DataType::kFLOAT, &scaleParam, 1};
    IScaleLayer* scale_1 = network->addScale(*data, ScaleMode::kUNIFORM, shift, scale, power);
    assert(scale_1);

    // Add convolution layer with 20 outputs and a 5x5 filter.
    std::map<std::string, Weights> weightMap = loadWeights(locateFile("mnistapi.wts"));
    IConvolutionLayer* conv1 = network->addConvolution(*scale_1->getOutput(0), 20, DimsHW{5, 5}, weightMap["conv1filter"], weightMap["conv1bias"]);
    assert(conv1);
    conv1->setStride(DimsHW{1, 1});

    // Add max pooling layer with stride of 2x2 and kernel size of 2x2.
    IPoolingLayer* pool1 = network->addPooling(*conv1->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
    assert(pool1);
    pool1->setStride(DimsHW{2, 2});

    // Add second convolution layer with 50 outputs and a 5x5 filter.
    IConvolutionLayer* conv2 = network->addConvolution(*pool1->getOutput(0), 50, DimsHW{5, 5}, weightMap["conv2filter"], weightMap["conv2bias"]);
    assert(conv2);
    conv2->setStride(DimsHW{1, 1});

    // Add second max pooling layer with stride of 2x2 and kernel size of 2x3>
    IPoolingLayer* pool2 = network->addPooling(*conv2->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
    assert(pool2);
    pool2->setStride(DimsHW{2, 2});

    // Add fully connected layer with 500 outputs.
    IFullyConnectedLayer* ip1 = network->addFullyConnected(*pool2->getOutput(0), 500, weightMap["ip1filter"], weightMap["ip1bias"]);
    assert(ip1);

    // Add activation layer using the ReLU algorithm.
    IActivationLayer* relu1 = network->addActivation(*ip1->getOutput(0), ActivationType::kRELU);
    assert(relu1);

    // Add second fully connected layer with 20 outputs.
    IFullyConnectedLayer* ip2 = network->addFullyConnected(*relu1->getOutput(0), OUTPUT_SIZE, weightMap["ip2filter"], weightMap["ip2bias"]);
    assert(ip2);

    // Add softmax layer to determine the probability.
    ISoftMaxLayer* prob = network->addSoftMax(*ip2->getOutput(0));
    assert(prob);
    prob->getOutput(0)->setName(OUTPUT_BLOB_NAME);
    network->markOutput(*prob->getOutput(0));

    // Build engine
    builder->setMaxBatchSize(maxBatchSize);
    builder->setMaxWorkspaceSize(1 << 20);
    samplesCommon::enableDLA(builder, gUseDLACore);
    ICudaEngine* engine = builder->buildCudaEngine(*network);

    // Don't need the network any more
    network->destroy();

    // Release host memory
    for (auto& mem : weightMap)
    {
        free((void*) (mem.second.values));
    }

    return engine;
}

void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream)
{
    // Create builder
    IBuilder* builder = createInferBuilder(gLogger);

    // Create model to populate the network, then set the outputs and create an engine
    ICudaEngine* engine = createMNISTEngine(maxBatchSize, builder, DataType::kFLOAT);
    assert(engine != nullptr);

    // Serialize the engine
    (*modelStream) = engine->serialize();

    // Close everything down
    engine->destroy();
    builder->destroy();
}

void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
{
    const ICudaEngine& engine = context.getEngine();

    // Pointers to input and output device buffers to pass to engine.
    // Engine requires exactly IEngine::getNbBindings() number of buffers.
    assert(engine.getNbBindings() == 2);
    void* buffers[2];

    // In order to bind the buffers, we need to know the names of the input and output tensors.
    // Note that indices are guaranteed to be less than IEngine::getNbBindings()
    const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
    const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);

    // Create GPU buffers on device
    CHECK(cudaMalloc(&buffers[inputIndex], batchSize * INPUT_H * INPUT_W * sizeof(float)));
    CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));

    // Create stream
    cudaStream_t stream;
    CHECK(cudaStreamCreate(&stream));

    // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
    CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
    context.enqueue(batchSize, buffers, stream, nullptr);
    CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
    cudaStreamSynchronize(stream);

    // Release stream and buffers
    cudaStreamDestroy(stream);
    CHECK(cudaFree(buffers[inputIndex]));
    CHECK(cudaFree(buffers[outputIndex]));
}

int main(int argc, char** argv)
{
    gUseDLACore = samplesCommon::parseDLA(argc, argv);
    // create a model using the API directly and serialize it to a stream
    IHostMemory* modelStream{nullptr};
    APIToModel(1, &modelStream);
    assert(modelStream != nullptr);

    // Read random digit file
    srand(unsigned(time(nullptr)));
    uint8_t fileData[INPUT_H * INPUT_W];
    const int num = rand() % 10;
    readPGMFile(std::to_string(num) + ".pgm", fileData);

    // Print ASCII representation of digit image
    std::cout << "\nInput:\n"
              << std::endl;
    for (int i = 0; i < INPUT_H * INPUT_W; i++)
        std::cout << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % INPUT_W) ? "" : "\n");

    // Parse mean file
    ICaffeParser* parser = createCaffeParser();
    assert(parser != nullptr);
    IBinaryProtoBlob* meanBlob = parser->parseBinaryProto(locateFile("mnist_mean.binaryproto").c_str());
    parser->destroy();
    const float* meanData = reinterpret_cast<const float*>(meanBlob->getData());

    // Subtract mean from image
    float data[INPUT_H * INPUT_W];
    for (int i = 0; i < INPUT_H * INPUT_W; i++)
        data[i] = float(fileData[i]) - meanData[i];
    meanBlob->destroy();

    IRuntime* runtime = createInferRuntime(gLogger);
    assert(runtime != nullptr);
    if (gUseDLACore >= 0)
    {
        runtime->setDLACore(gUseDLACore);
    }
    ICudaEngine* engine = runtime->deserializeCudaEngine(modelStream->data(), modelStream->size(), nullptr);
    assert(engine != nullptr);
    modelStream->destroy();
    IExecutionContext* context = engine->createExecutionContext();
    assert(context != nullptr);

    // Run inference
    float prob[OUTPUT_SIZE];
    doInference(*context, data, prob, 1);

    // Destroy the engine
    context->destroy();
    engine->destroy();
    runtime->destroy();

    // Print histogram of the output distribution
    std::cout << "\nOutput:\n\n";
    float val{0.0f};
    int idx{0};
    for (unsigned int i = 0; i < 10; i++)
    {
        val = std::max(val, prob[i]);
        if (val == prob[i])
            idx = i;
        std::cout << i << ": " << std::string(int(std::floor(prob[i] * 10 + 0.5f)), '*') << "\n";
    }
    std::cout << std::endl;

    return (idx == num && val > 0.9f) ? EXIT_SUCCESS : EXIT_FAILURE;
}