#include "precomp.h"

using namespace std;
using namespace paraformer;

ModelImp::ModelImp(const char* path,int nNumThread, bool quantize)
:env_(ORT_LOGGING_LEVEL_ERROR, "paraformer"),sessionOptions{}{
    string model_path;
    string cmvn_path;
    string config_path;

    if(quantize)
    {
        model_path = pathAppend(path, "model_quant.onnx");
    }else{
        model_path = pathAppend(path, "model.onnx");
    }
    cmvn_path = pathAppend(path, "am.mvn");
    config_path = pathAppend(path, "config.yaml");

    // knf options
    fbank_opts.frame_opts.dither = 0;
    fbank_opts.mel_opts.num_bins = 80;
    fbank_opts.frame_opts.samp_freq = model_sample_rate;
    fbank_opts.frame_opts.window_type = "hamming";
    fbank_opts.frame_opts.frame_shift_ms = 10;
    fbank_opts.frame_opts.frame_length_ms = 25;
    fbank_opts.energy_floor = 0;
    fbank_opts.mel_opts.debug_mel = false;
    //fbank_ = std::make_unique<knf::OnlineFbank>(fbank_opts);

    //sessionOptions.SetInterOpNumThreads(1);
    sessionOptions.SetIntraOpNumThreads(nNumThread);
    sessionOptions.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED);

#ifdef _WIN32
    wstring wstrPath = strToWstr(model_path);
    m_session = std::make_unique<Ort::Session>(env_, model_path.c_str(), sessionOptions);
#else
    m_session = std::make_unique<Ort::Session>(env_, model_path.c_str(), sessionOptions);
#endif

    string strName;
    getInputName(m_session.get(), strName);
    m_strInputNames.push_back(strName.c_str());
    getInputName(m_session.get(), strName,1);
    m_strInputNames.push_back(strName);
    
    getOutputName(m_session.get(), strName);
    m_strOutputNames.push_back(strName);
    getOutputName(m_session.get(), strName,1);
    m_strOutputNames.push_back(strName);

    for (auto& item : m_strInputNames)
        m_szInputNames.push_back(item.c_str());
    for (auto& item : m_strOutputNames)
        m_szOutputNames.push_back(item.c_str());
    vocab = new Vocab(config_path.c_str());
    load_cmvn(cmvn_path.c_str());
}

ModelImp::~ModelImp()
{
    if(vocab)
        delete vocab;
}

void ModelImp::reset()
{
}

vector<float> ModelImp::FbankKaldi(float sample_rate, const float* waves, int len) {
    knf::OnlineFbank fbank_(fbank_opts);
    fbank_.AcceptWaveform(sample_rate, waves, len);
    //fbank_->InputFinished();
    int32_t frames = fbank_.NumFramesReady();
    int32_t feature_dim = fbank_opts.mel_opts.num_bins;
    vector<float> features(frames * feature_dim);
    float *p = features.data();

    for (int32_t i = 0; i != frames; ++i) {
        const float *f = fbank_.GetFrame(i);
        std::copy(f, f + feature_dim, p);
        p += feature_dim;
    }

    return features;
}

void ModelImp::load_cmvn(const char *filename)
{
    ifstream cmvn_stream(filename);
    string line;

    while (getline(cmvn_stream, line)) {
        istringstream iss(line);
        vector<string> line_item{istream_iterator<string>{iss}, istream_iterator<string>{}};
        if (line_item[0] == "<AddShift>") {
            getline(cmvn_stream, line);
            istringstream means_lines_stream(line);
            vector<string> means_lines{istream_iterator<string>{means_lines_stream}, istream_iterator<string>{}};
            if (means_lines[0] == "<LearnRateCoef>") {
                for (int j = 3; j < means_lines.size() - 1; j++) {
                    means_list.push_back(stof(means_lines[j]));
                }
                continue;
            }
        }
        else if (line_item[0] == "<Rescale>") {
            getline(cmvn_stream, line);
            istringstream vars_lines_stream(line);
            vector<string> vars_lines{istream_iterator<string>{vars_lines_stream}, istream_iterator<string>{}};
            if (vars_lines[0] == "<LearnRateCoef>") {
                for (int j = 3; j < vars_lines.size() - 1; j++) {
                    vars_list.push_back(stof(vars_lines[j])*scale);
                }
                continue;
            }
        }
    }
}

string ModelImp::greedy_search(float * in, int nLen )
{
    vector<int> hyps;
    int Tmax = nLen;
    for (int i = 0; i < Tmax; i++) {
        int max_idx;
        float max_val;
        findmax(in + i * 8404, 8404, max_val, max_idx);
        hyps.push_back(max_idx);
    }

    return vocab->vector2stringV2(hyps);
}

vector<float> ModelImp::ApplyLFR(const std::vector<float> &in) 
{
    int32_t in_feat_dim = fbank_opts.mel_opts.num_bins;
    int32_t in_num_frames = in.size() / in_feat_dim;
    int32_t out_num_frames =
        (in_num_frames - lfr_window_size) / lfr_window_shift + 1;
    int32_t out_feat_dim = in_feat_dim * lfr_window_size;

    std::vector<float> out(out_num_frames * out_feat_dim);

    const float *p_in = in.data();
    float *p_out = out.data();

    for (int32_t i = 0; i != out_num_frames; ++i) {
      std::copy(p_in, p_in + out_feat_dim, p_out);

      p_out += out_feat_dim;
      p_in += lfr_window_shift * in_feat_dim;
    }

    return out;
  }

  void ModelImp::ApplyCMVN(std::vector<float> *v)
  {
    int32_t dim = means_list.size();
    int32_t num_frames = v->size() / dim;

    float *p = v->data();

    for (int32_t i = 0; i != num_frames; ++i) {
      for (int32_t k = 0; k != dim; ++k) {
        p[k] = (p[k] + means_list[k]) * vars_list[k];
      }

      p += dim;
    }
  }

//   void ParaformerOnnxAsrModel::ForwardFunc(
//     const std::vector<std::vector<float>>& chunk_feats,
//     std::vector<std::vector<float>>* out_prob) {
//   Ort::MemoryInfo memory_info =
//       Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
//   // 1. Prepare onnx required data, splice cached_feature_ and chunk_feats
//   // chunk
// //  int num_frames = cached_feature_.size() + chunk_feats.size();
//   int num_frames = chunk_feats.size();
//   const int feature_dim = chunk_feats[0].size();

//   //  2. Generate 2 input nodes tensor
//   // speech node { batch,frame number,feature dim }
//   const int64_t paraformer_feats_shape[3] = {1, num_frames, feature_dim};
//   std::vector<float> paraformer_feats;
//   for (const auto & chunk_feat : chunk_feats) {
//     paraformer_feats.insert(paraformer_feats.end(), chunk_feat.begin(), chunk_feat.end());
//   }
//   Ort::Value paraformer_feats_ort = Ort::Value::CreateTensor<float>(
//           memory_info, paraformer_feats.data(), paraformer_feats.size(), paraformer_feats_shape, 3);
//   // speech_lengths node {speech length,}
//   const int64_t paraformer_length_shape[1] = {1};
//   std::vector<int32_t> paraformer_length;
//   paraformer_length.emplace_back(num_frames);
//   Ort::Value paraformer_length_ort = Ort::Value::CreateTensor<int32_t>(
//           memory_info, paraformer_length.data(), paraformer_length.size(), paraformer_length_shape, 1);

//   // 3. Put nodes into onnx input vector
//   std::vector<Ort::Value> paraformer_inputs;
//   paraformer_inputs.emplace_back(std::move(paraformer_feats_ort));
//   paraformer_inputs.emplace_back(std::move(paraformer_length_ort));

//   // 4. Onnx infer
//   std::vector<Ort::Value> paraformer_ort_outputs;
//   try{
//     VLOG(3) << "Start infer";
//     paraformer_ort_outputs = paraformer_session_->Run(
//             Ort::RunOptions{nullptr}, paraformer_in_names_.data(), paraformer_inputs.data(),
//             paraformer_inputs.size(), paraformer_out_names_.data(), paraformer_out_names_.size());
//   }catch (std::exception const& e) {
//     //  Catch "Non-zero status code returned error",usually because there is no asr result.
//     // Need funasr to resolve.
//     LOG(ERROR) << e.what();
//     return;
//   }

//   // 5. Change infer result to output shapes
//   float* logp_data = paraformer_ort_outputs[0].GetTensorMutableData<float>();
//   auto type_info = paraformer_ort_outputs[0].GetTensorTypeAndShapeInfo();

//   int num_outputs = type_info.GetShape()[1];
//   int output_dim = type_info.GetShape()[2];
//   out_prob->resize(num_outputs);
//   for (int i = 0; i < num_outputs; i++) {
//     (*out_prob)[i].resize(output_dim);
//     memcpy((*out_prob)[i].data(), logp_data + i * output_dim,
//            sizeof(float) * output_dim);
//   }
// }

string ModelImp::forward(float* din, int len, int flag)
{

    int32_t in_feat_dim = fbank_opts.mel_opts.num_bins;
    std::vector<float> wav_feats = FbankKaldi(model_sample_rate, din, len);
    wav_feats = ApplyLFR(wav_feats);
    ApplyCMVN(&wav_feats);

    int32_t feat_dim = lfr_window_size*in_feat_dim;
    int32_t num_frames = wav_feats.size() / feat_dim;

#ifdef _WIN_X86
        Ort::MemoryInfo m_memoryInfo = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
#else
        Ort::MemoryInfo m_memoryInfo = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
#endif

    const int64_t input_shape_[3] = {1, num_frames, feat_dim};
    Ort::Value onnx_feats = Ort::Value::CreateTensor<float>(m_memoryInfo,
        wav_feats.data(),
        wav_feats.size(),
        input_shape_,
        3);

    const int64_t paraformer_length_shape[1] = {1};
    std::vector<int32_t> paraformer_length;
    paraformer_length.emplace_back(num_frames);
    Ort::Value onnx_feats_len = Ort::Value::CreateTensor<int32_t>(
          m_memoryInfo, paraformer_length.data(), paraformer_length.size(), paraformer_length_shape, 1);
    
    std::vector<Ort::Value> input_onnx;
    input_onnx.emplace_back(std::move(onnx_feats));
    input_onnx.emplace_back(std::move(onnx_feats_len));

    string result;
    try {
        auto outputTensor = m_session->Run(Ort::RunOptions{nullptr}, m_szInputNames.data(), input_onnx.data(), input_onnx.size(), m_szOutputNames.data(), m_szOutputNames.size());
        std::vector<int64_t> outputShape = outputTensor[0].GetTensorTypeAndShapeInfo().GetShape();

        int64_t outputCount = std::accumulate(outputShape.begin(), outputShape.end(), 1, std::multiplies<int64_t>());
        float* floatData = outputTensor[0].GetTensorMutableData<float>();
        auto encoder_out_lens = outputTensor[1].GetTensorMutableData<int64_t>();
        result = greedy_search(floatData, *encoder_out_lens);
    }
    catch (...)
    {
        result = "";
    }

    return result;
}

string ModelImp::forward_chunk(float* din, int len, int flag)
{

    printf("Not Imp!!!!!!\n");
    return "Hello";
}

string ModelImp::rescoring()
{
    printf("Not Imp!!!!!!\n");
    return "Hello";
}