// sherpa-onnx/csrc/online-transducer-model.cc
//
// Copyright (c)  2023  Xiaomi Corporation
// Copyright (c)  2023  Pingfeng Luo
#include "sherpa-onnx/csrc/online-transducer-model.h"

#if __ANDROID_API__ >= 9
#include "android/asset_manager.h"
#include "android/asset_manager_jni.h"
#endif

#include <algorithm>
#include <memory>
#include <sstream>
#include <string>

#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/online-conformer-transducer-model.h"
#include "sherpa-onnx/csrc/online-lstm-transducer-model.h"
#include "sherpa-onnx/csrc/online-zipformer-transducer-model.h"
#include "sherpa-onnx/csrc/online-zipformer2-transducer-model.h"
#include "sherpa-onnx/csrc/onnx-utils.h"

namespace {

enum class ModelType {
  kConformer,
  kLstm,
  kZipformer,
  kZipformer2,
  kUnkown,
};

}  // namespace

namespace sherpa_onnx {

static ModelType GetModelType(char *model_data, size_t model_data_length,
                              bool debug) {
  Ort::Env env(ORT_LOGGING_LEVEL_WARNING);
  Ort::SessionOptions sess_opts;

  auto sess = std::make_unique<Ort::Session>(env, model_data, model_data_length,
                                             sess_opts);

  Ort::ModelMetadata meta_data = sess->GetModelMetadata();
  if (debug) {
    std::ostringstream os;
    PrintModelMetadata(os, meta_data);
    SHERPA_ONNX_LOGE("%s", os.str().c_str());
  }

  Ort::AllocatorWithDefaultOptions allocator;
  auto model_type =
      meta_data.LookupCustomMetadataMapAllocated("model_type", allocator);
  if (!model_type) {
    SHERPA_ONNX_LOGE(
        "No model_type in the metadata!\n"
        "Please make sure you are using the latest export-onnx.py from icefall "
        "to export your transducer models");
    return ModelType::kUnkown;
  }

  if (model_type.get() == std::string("conformer")) {
    return ModelType::kConformer;
  } else if (model_type.get() == std::string("lstm")) {
    return ModelType::kLstm;
  } else if (model_type.get() == std::string("zipformer")) {
    return ModelType::kZipformer;
  } else if (model_type.get() == std::string("zipformer2")) {
    return ModelType::kZipformer2;
  } else {
    SHERPA_ONNX_LOGE("Unsupported model_type: %s", model_type.get());
    return ModelType::kUnkown;
  }
}

std::unique_ptr<OnlineTransducerModel> OnlineTransducerModel::Create(
    const OnlineTransducerModelConfig &config) {
  ModelType model_type = ModelType::kUnkown;

  {
    auto buffer = ReadFile(config.encoder_filename);

    model_type = GetModelType(buffer.data(), buffer.size(), config.debug);
  }

  switch (model_type) {
    case ModelType::kConformer:
      return std::make_unique<OnlineConformerTransducerModel>(config);
    case ModelType::kLstm:
      return std::make_unique<OnlineLstmTransducerModel>(config);
    case ModelType::kZipformer:
      return std::make_unique<OnlineZipformerTransducerModel>(config);
    case ModelType::kZipformer2:
      return std::make_unique<OnlineZipformer2TransducerModel>(config);
    case ModelType::kUnkown:
      SHERPA_ONNX_LOGE("Unknown model type in online transducer!");
      return nullptr;
  }

  // unreachable code
  return nullptr;
}

Ort::Value OnlineTransducerModel::BuildDecoderInput(
    const std::vector<OnlineTransducerDecoderResult> &results) {
  int32_t batch_size = static_cast<int32_t>(results.size());
  int32_t context_size = ContextSize();
  std::array<int64_t, 2> shape{batch_size, context_size};
  Ort::Value decoder_input = Ort::Value::CreateTensor<int64_t>(
      Allocator(), shape.data(), shape.size());
  int64_t *p = decoder_input.GetTensorMutableData<int64_t>();

  for (const auto &r : results) {
    const int64_t *begin = r.tokens.data() + r.tokens.size() - context_size;
    const int64_t *end = r.tokens.data() + r.tokens.size();
    std::copy(begin, end, p);
    p += context_size;
  }
  return decoder_input;
}

Ort::Value OnlineTransducerModel::BuildDecoderInput(
    const std::vector<Hypothesis> &hyps) {
  int32_t batch_size = static_cast<int32_t>(hyps.size());
  int32_t context_size = ContextSize();
  std::array<int64_t, 2> shape{batch_size, context_size};
  Ort::Value decoder_input = Ort::Value::CreateTensor<int64_t>(
      Allocator(), shape.data(), shape.size());
  int64_t *p = decoder_input.GetTensorMutableData<int64_t>();

  for (const auto &h : hyps) {
    std::copy(h.ys.end() - context_size, h.ys.end(), p);
    p += context_size;
  }
  return decoder_input;
}

#if __ANDROID_API__ >= 9
std::unique_ptr<OnlineTransducerModel> OnlineTransducerModel::Create(
    AAssetManager *mgr, const OnlineTransducerModelConfig &config) {
  auto buffer = ReadFile(mgr, config.encoder_filename);
  auto model_type = GetModelType(buffer.data(), buffer.size(), config.debug);

  switch (model_type) {
    case ModelType::kConformer:
      return std::make_unique<OnlineConformerTransducerModel>(mgr, config);
    case ModelType::kLstm:
      return std::make_unique<OnlineLstmTransducerModel>(mgr, config);
    case ModelType::kZipformer:
      return std::make_unique<OnlineZipformerTransducerModel>(mgr, config);
    case ModelType::kZipformer2:
      return std::make_unique<OnlineZipformer2TransducerModel>(mgr, config);
    case ModelType::kUnkown:
      SHERPA_ONNX_LOGE("Unknown model type in online transducer!");
      return nullptr;
  }

  // unreachable code
  return nullptr;
}
#endif

}  // namespace sherpa_onnx