// sherpa-onnx/csrc/features.h
//
// Copyright (c)  2023  Xiaomi Corporation

#ifndef SHERPA_ONNX_CSRC_FEATURES_H_
#define SHERPA_ONNX_CSRC_FEATURES_H_

#include <memory>
#include <string>
#include <vector>

#include "sherpa-onnx/csrc/parse-options.h"

namespace sherpa_onnx {

struct FeatureExtractorConfig {
  // Sampling rate used by the feature extractor. If it is different from
  // the sampling rate of the input waveform, we will do resampling inside.
  int32_t sampling_rate = 16000;

  // Feature dimension
  int32_t feature_dim = 80;

  // Set internally by some models, e.g., paraformer sets it to false.
  // This parameter is not exposed to users from the commandline
  // If true, the feature extractor expects inputs to be normalized to
  // the range [-1, 1].
  // If false, we will multiply the inputs by 32768
  bool normalize_samples = true;

  std::string ToString() const;

  void Register(ParseOptions *po);
};

class FeatureExtractor {
 public:
  explicit FeatureExtractor(const FeatureExtractorConfig &config = {});
  ~FeatureExtractor();

  /**
     @param sampling_rate The sampling_rate of the input waveform. If it does
                          not equal to  config.sampling_rate, we will do
                          resampling inside.
     @param waveform Pointer to a 1-D array of size n. It must be normalized to
                     the range [-1, 1].
     @param n Number of entries in waveform
   */
  void AcceptWaveform(int32_t sampling_rate, const float *waveform,
                      int32_t n) const;

  /**
   * InputFinished() tells the class you won't be providing any
   * more waveform.  This will help flush out the last frame or two
   * of features, in the case where snip-edges == false; it also
   * affects the return value of IsLastFrame().
   */
  void InputFinished() const;

  int32_t NumFramesReady() const;

  /** Note: IsLastFrame() will only ever return true if you have called
   * InputFinished() (and this frame is the last frame).
   */
  bool IsLastFrame(int32_t frame) const;

  /** Get n frames starting from the given frame index.
   *
   * @param frame_index  The starting frame index
   * @param n  Number of frames to get.
   * @return Return a 2-D tensor of shape (n, feature_dim).
   *         which is flattened into a 1-D vector (flattened in in row major)
   */
  std::vector<float> GetFrames(int32_t frame_index, int32_t n) const;

  /// Return feature dim of this extractor
  int32_t FeatureDim() const;

 private:
  class Impl;
  std::unique_ptr<Impl> impl_;
};

}  // namespace sherpa_onnx

#endif  // SHERPA_ONNX_CSRC_FEATURES_H_