// sherpa-onnx/csrc/offline-stream.cc
//
// Copyright (c)  2023  Xiaomi Corporation

#include "sherpa-onnx/csrc/offline-stream.h"

#include <assert.h>

#include <algorithm>
#include <cmath>
#include <iomanip>

#include "kaldi-native-fbank/csrc/online-feature.h"
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/offline-recognizer.h"
#include "sherpa-onnx/csrc/resample.h"

namespace sherpa_onnx {

/* Compute mean and inverse stddev over rows.
 *
 * @param p  A pointer to a 2-d array of shape (num_rows, num_cols)
 * @param num_rows Number of rows
 * @param num_cols Number of columns
 * @param mean On return, it contains p.mean(axis=0)
 * @param inv_stddev On return, it contains 1/p.std(axis=0)
 */
static void ComputeMeanAndInvStd(const float *p, int32_t num_rows,
                                 int32_t num_cols, std::vector<float> *mean,
                                 std::vector<float> *inv_stddev) {
  std::vector<float> sum(num_cols);
  std::vector<float> sum_sq(num_cols);

  for (int32_t i = 0; i != num_rows; ++i) {
    for (int32_t c = 0; c != num_cols; ++c) {
      auto t = p[c];
      sum[c] += t;
      sum_sq[c] += t * t;
    }
    p += num_cols;
  }

  mean->resize(num_cols);
  inv_stddev->resize(num_cols);

  for (int32_t i = 0; i != num_cols; ++i) {
    auto t = sum[i] / num_rows;
    (*mean)[i] = t;

    float stddev = std::sqrt(sum_sq[i] / num_rows - t * t);
    (*inv_stddev)[i] = 1.0f / (stddev + 1e-5f);
  }
}

void OfflineFeatureExtractorConfig::Register(ParseOptions *po) {
  po->Register("sample-rate", &sampling_rate,
               "Sampling rate of the input waveform. "
               "Note: You can have a different "
               "sample rate for the input waveform. We will do resampling "
               "inside the feature extractor");

  po->Register("feat-dim", &feature_dim,
               "Feature dimension. Must match the one expected by the model.");
}

std::string OfflineFeatureExtractorConfig::ToString() const {
  std::ostringstream os;

  os << "OfflineFeatureExtractorConfig(";
  os << "sampling_rate=" << sampling_rate << ", ";
  os << "feature_dim=" << feature_dim << ")";

  return os.str();
}

class OfflineStream::Impl {
 public:
  explicit Impl(const OfflineFeatureExtractorConfig &config,
                ContextGraphPtr context_graph)
      : config_(config), context_graph_(context_graph) {
    opts_.frame_opts.dither = 0;
    opts_.frame_opts.snip_edges = false;
    opts_.frame_opts.samp_freq = config.sampling_rate;
    opts_.mel_opts.num_bins = config.feature_dim;

    fbank_ = std::make_unique<knf::OnlineFbank>(opts_);
  }

  Impl(WhisperTag /*tag*/, ContextGraphPtr context_graph)
      : context_graph_(context_graph) {
    config_.normalize_samples = true;
    opts_.frame_opts.samp_freq = 16000;
    opts_.mel_opts.num_bins = 80;
    whisper_fbank_ =
        std::make_unique<knf::OnlineWhisperFbank>(opts_.frame_opts);
  }

  void AcceptWaveform(int32_t sampling_rate, const float *waveform, int32_t n) {
    if (config_.normalize_samples) {
      AcceptWaveformImpl(sampling_rate, waveform, n);
    } else {
      std::vector<float> buf(n);
      for (int32_t i = 0; i != n; ++i) {
        buf[i] = waveform[i] * 32768;
      }
      AcceptWaveformImpl(sampling_rate, buf.data(), n);
    }
  }

  void AcceptWaveformImpl(int32_t sampling_rate, const float *waveform,
                          int32_t n) {
    if (sampling_rate != opts_.frame_opts.samp_freq) {
      SHERPA_ONNX_LOGE(
          "Creating a resampler:\n"
          "   in_sample_rate: %d\n"
          "   output_sample_rate: %d\n",
          sampling_rate, static_cast<int32_t>(opts_.frame_opts.samp_freq));

      float min_freq =
          std::min<int32_t>(sampling_rate, opts_.frame_opts.samp_freq);
      float lowpass_cutoff = 0.99 * 0.5 * min_freq;

      int32_t lowpass_filter_width = 6;
      auto resampler = std::make_unique<LinearResample>(
          sampling_rate, opts_.frame_opts.samp_freq, lowpass_cutoff,
          lowpass_filter_width);
      std::vector<float> samples;
      resampler->Resample(waveform, n, true, &samples);

      if (fbank_) {
        fbank_->AcceptWaveform(opts_.frame_opts.samp_freq, samples.data(),
                               samples.size());
        fbank_->InputFinished();
      } else {
        whisper_fbank_->AcceptWaveform(opts_.frame_opts.samp_freq,
                                       samples.data(), samples.size());
        whisper_fbank_->InputFinished();
      }

      return;
    }  // if (sampling_rate != opts_.frame_opts.samp_freq)

    if (fbank_) {
      fbank_->AcceptWaveform(sampling_rate, waveform, n);
      fbank_->InputFinished();
    } else {
      whisper_fbank_->AcceptWaveform(sampling_rate, waveform, n);
      whisper_fbank_->InputFinished();
    }
  }

  int32_t FeatureDim() const { return opts_.mel_opts.num_bins; }

  std::vector<float> GetFrames() const {
    int32_t n =
        fbank_ ? fbank_->NumFramesReady() : whisper_fbank_->NumFramesReady();

    assert(n > 0 && "Please first call AcceptWaveform()");

    int32_t feature_dim = FeatureDim();

    std::vector<float> features(n * feature_dim);

    float *p = features.data();

    for (int32_t i = 0; i != n; ++i) {
      const float *f =
          fbank_ ? fbank_->GetFrame(i) : whisper_fbank_->GetFrame(i);
      std::copy(f, f + feature_dim, p);
      p += feature_dim;
    }

    NemoNormalizeFeatures(features.data(), n, feature_dim);

    return features;
  }

  void SetResult(const OfflineRecognitionResult &r) { r_ = r; }

  const OfflineRecognitionResult &GetResult() const { return r_; }

  const ContextGraphPtr &GetContextGraph() const { return context_graph_; }

 private:
  void NemoNormalizeFeatures(float *p, int32_t num_frames,
                             int32_t feature_dim) const {
    if (config_.nemo_normalize_type.empty()) {
      return;
    }

    if (config_.nemo_normalize_type != "per_feature") {
      SHERPA_ONNX_LOGE(
          "Only normalize_type=per_feature is implemented. Given: %s",
          config_.nemo_normalize_type.c_str());
      exit(-1);
    }

    NemoNormalizePerFeature(p, num_frames, feature_dim);
  }

  static void NemoNormalizePerFeature(float *p, int32_t num_frames,
                                      int32_t feature_dim) {
    std::vector<float> mean;
    std::vector<float> inv_stddev;

    ComputeMeanAndInvStd(p, num_frames, feature_dim, &mean, &inv_stddev);

    for (int32_t n = 0; n != num_frames; ++n) {
      for (int32_t i = 0; i != feature_dim; ++i) {
        p[i] = (p[i] - mean[i]) * inv_stddev[i];
      }
      p += feature_dim;
    }
  }

 private:
  OfflineFeatureExtractorConfig config_;
  std::unique_ptr<knf::OnlineFbank> fbank_;
  std::unique_ptr<knf::OnlineWhisperFbank> whisper_fbank_;
  knf::FbankOptions opts_;
  OfflineRecognitionResult r_;
  ContextGraphPtr context_graph_;
};

OfflineStream::OfflineStream(
    const OfflineFeatureExtractorConfig &config /*= {}*/,
    ContextGraphPtr context_graph /*= nullptr*/)
    : impl_(std::make_unique<Impl>(config, context_graph)) {}

OfflineStream::OfflineStream(WhisperTag tag,
                             ContextGraphPtr context_graph /*= nullptr*/)
    : impl_(std::make_unique<Impl>(tag, context_graph)) {}

OfflineStream::~OfflineStream() = default;

void OfflineStream::AcceptWaveform(int32_t sampling_rate, const float *waveform,
                                   int32_t n) const {
  impl_->AcceptWaveform(sampling_rate, waveform, n);
}

int32_t OfflineStream::FeatureDim() const { return impl_->FeatureDim(); }

std::vector<float> OfflineStream::GetFrames() const {
  return impl_->GetFrames();
}

void OfflineStream::SetResult(const OfflineRecognitionResult &r) {
  impl_->SetResult(r);
}

const ContextGraphPtr &OfflineStream::GetContextGraph() const {
  return impl_->GetContextGraph();
}

const OfflineRecognitionResult &OfflineStream::GetResult() const {
  return impl_->GetResult();
}
std::string OfflineRecognitionResult::AsJsonString() const {
  std::ostringstream os;
  os << "{";
  os << "\"text\""
     << ": ";
  os << "\"" << text << "\""
     << ", ";

  os << "\""
     << "timestamps"
     << "\""
     << ": ";
  os << "[";

  std::string sep = "";
  for (auto t : timestamps) {
    os << sep << std::fixed << std::setprecision(2) << t;
    sep = ", ";
  }
  os << "], ";

  os << "\""
     << "tokens"
     << "\""
     << ":";
  os << "[";

  sep = "";
  auto oldFlags = os.flags();
  for (const auto &t : tokens) {
    if (t.size() == 1 && static_cast<uint8_t>(t[0]) > 0x7f) {
      const uint8_t *p = reinterpret_cast<const uint8_t *>(t.c_str());
      os << sep << "\""
         << "<0x" << std::hex << std::uppercase << static_cast<uint32_t>(p[0])
         << ">"
         << "\"";
      os.flags(oldFlags);
    } else {
      os << sep << "\"" << t << "\"";
    }
    sep = ", ";
  }
  os << "]";
  os << "}";

  return os.str();
}
}  // namespace sherpa_onnx