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

#include "sherpa-onnx/csrc/offline-transducer-model.h"

#include <algorithm>
#include <string>
#include <vector>

#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/offline-transducer-decoder.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
#include "sherpa-onnx/csrc/session.h"

namespace sherpa_onnx {

class OfflineTransducerModel::Impl {
 public:
  explicit Impl(const OfflineModelConfig &config)
      : config_(config),
        env_(ORT_LOGGING_LEVEL_WARNING),
        sess_opts_(GetSessionOptions(config)),
        allocator_{} {
    {
      auto buf = ReadFile(config.transducer.encoder_filename);
      InitEncoder(buf.data(), buf.size());
    }

    {
      auto buf = ReadFile(config.transducer.decoder_filename);
      InitDecoder(buf.data(), buf.size());
    }

    {
      auto buf = ReadFile(config.transducer.joiner_filename);
      InitJoiner(buf.data(), buf.size());
    }
  }

#if __ANDROID_API__ >= 9
  Impl(AAssetManager *mgr, const OfflineModelConfig &config)
      : config_(config),
        env_(ORT_LOGGING_LEVEL_WARNING),
        sess_opts_(GetSessionOptions(config)),
        allocator_{} {
    {
      auto buf = ReadFile(mgr, config.transducer.encoder_filename);
      InitEncoder(buf.data(), buf.size());
    }

    {
      auto buf = ReadFile(mgr, config.transducer.decoder_filename);
      InitDecoder(buf.data(), buf.size());
    }

    {
      auto buf = ReadFile(mgr, config.transducer.joiner_filename);
      InitJoiner(buf.data(), buf.size());
    }
  }
#endif

  std::pair<Ort::Value, Ort::Value> RunEncoder(Ort::Value features,
                                               Ort::Value features_length) {
    std::array<Ort::Value, 2> encoder_inputs = {std::move(features),
                                                std::move(features_length)};

    auto encoder_out = encoder_sess_->Run(
        {}, encoder_input_names_ptr_.data(), encoder_inputs.data(),
        encoder_inputs.size(), encoder_output_names_ptr_.data(),
        encoder_output_names_ptr_.size());

    return {std::move(encoder_out[0]), std::move(encoder_out[1])};
  }

  Ort::Value RunDecoder(Ort::Value decoder_input) {
    auto decoder_out = decoder_sess_->Run(
        {}, decoder_input_names_ptr_.data(), &decoder_input, 1,
        decoder_output_names_ptr_.data(), decoder_output_names_ptr_.size());
    return std::move(decoder_out[0]);
  }

  Ort::Value RunJoiner(Ort::Value encoder_out, Ort::Value decoder_out) {
    std::array<Ort::Value, 2> joiner_input = {std::move(encoder_out),
                                              std::move(decoder_out)};
    auto logit = joiner_sess_->Run({}, joiner_input_names_ptr_.data(),
                                   joiner_input.data(), joiner_input.size(),
                                   joiner_output_names_ptr_.data(),
                                   joiner_output_names_ptr_.size());

    return std::move(logit[0]);
  }

  int32_t VocabSize() const { return vocab_size_; }
  int32_t ContextSize() const { return context_size_; }
  int32_t SubsamplingFactor() const { return 4; }
  OrtAllocator *Allocator() const { return allocator_; }

  Ort::Value BuildDecoderInput(
      const std::vector<OfflineTransducerDecoderResult> &results,
      int32_t end_index) const {
    assert(end_index <= results.size());

    int32_t batch_size = end_index;
    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 (int32_t i = 0; i != batch_size; ++i) {
      const auto &r = results[i];
      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 BuildDecoderInput(const std::vector<Hypothesis> &results,
                               int32_t end_index) const {
    assert(end_index <= results.size());

    int32_t batch_size = end_index;
    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 (int32_t i = 0; i != batch_size; ++i) {
      const auto &r = results[i];
      const int64_t *begin = r.ys.data() + r.ys.size() - context_size;
      const int64_t *end = r.ys.data() + r.ys.size();
      std::copy(begin, end, p);
      p += context_size;
    }

    return decoder_input;
  }

 private:
  void InitEncoder(void *model_data, size_t model_data_length) {
    encoder_sess_ = std::make_unique<Ort::Session>(
        env_, model_data, model_data_length, sess_opts_);

    GetInputNames(encoder_sess_.get(), &encoder_input_names_,
                  &encoder_input_names_ptr_);

    GetOutputNames(encoder_sess_.get(), &encoder_output_names_,
                   &encoder_output_names_ptr_);

    // get meta data
    Ort::ModelMetadata meta_data = encoder_sess_->GetModelMetadata();
    if (config_.debug) {
      std::ostringstream os;
      os << "---encoder---\n";
      PrintModelMetadata(os, meta_data);
      SHERPA_ONNX_LOGE("%s\n", os.str().c_str());
    }
  }

  void InitDecoder(void *model_data, size_t model_data_length) {
    decoder_sess_ = std::make_unique<Ort::Session>(
        env_, model_data, model_data_length, sess_opts_);

    GetInputNames(decoder_sess_.get(), &decoder_input_names_,
                  &decoder_input_names_ptr_);

    GetOutputNames(decoder_sess_.get(), &decoder_output_names_,
                   &decoder_output_names_ptr_);

    // get meta data
    Ort::ModelMetadata meta_data = decoder_sess_->GetModelMetadata();
    if (config_.debug) {
      std::ostringstream os;
      os << "---decoder---\n";
      PrintModelMetadata(os, meta_data);
      SHERPA_ONNX_LOGE("%s\n", os.str().c_str());
    }

    Ort::AllocatorWithDefaultOptions allocator;  // used in the macro below
    SHERPA_ONNX_READ_META_DATA(vocab_size_, "vocab_size");
    SHERPA_ONNX_READ_META_DATA(context_size_, "context_size");
  }

  void InitJoiner(void *model_data, size_t model_data_length) {
    joiner_sess_ = std::make_unique<Ort::Session>(
        env_, model_data, model_data_length, sess_opts_);

    GetInputNames(joiner_sess_.get(), &joiner_input_names_,
                  &joiner_input_names_ptr_);

    GetOutputNames(joiner_sess_.get(), &joiner_output_names_,
                   &joiner_output_names_ptr_);

    // get meta data
    Ort::ModelMetadata meta_data = joiner_sess_->GetModelMetadata();
    if (config_.debug) {
      std::ostringstream os;
      os << "---joiner---\n";
      PrintModelMetadata(os, meta_data);
      SHERPA_ONNX_LOGE("%s\n", os.str().c_str());
    }
  }

 private:
  OfflineModelConfig config_;
  Ort::Env env_;
  Ort::SessionOptions sess_opts_;
  Ort::AllocatorWithDefaultOptions allocator_;

  std::unique_ptr<Ort::Session> encoder_sess_;
  std::unique_ptr<Ort::Session> decoder_sess_;
  std::unique_ptr<Ort::Session> joiner_sess_;

  std::vector<std::string> encoder_input_names_;
  std::vector<const char *> encoder_input_names_ptr_;

  std::vector<std::string> encoder_output_names_;
  std::vector<const char *> encoder_output_names_ptr_;

  std::vector<std::string> decoder_input_names_;
  std::vector<const char *> decoder_input_names_ptr_;

  std::vector<std::string> decoder_output_names_;
  std::vector<const char *> decoder_output_names_ptr_;

  std::vector<std::string> joiner_input_names_;
  std::vector<const char *> joiner_input_names_ptr_;

  std::vector<std::string> joiner_output_names_;
  std::vector<const char *> joiner_output_names_ptr_;

  int32_t vocab_size_ = 0;    // initialized in InitDecoder
  int32_t context_size_ = 0;  // initialized in InitDecoder
};

OfflineTransducerModel::OfflineTransducerModel(const OfflineModelConfig &config)
    : impl_(std::make_unique<Impl>(config)) {}

#if __ANDROID_API__ >= 9
OfflineTransducerModel::OfflineTransducerModel(AAssetManager *mgr,
                                               const OfflineModelConfig &config)
    : impl_(std::make_unique<Impl>(mgr, config)) {}
#endif

OfflineTransducerModel::~OfflineTransducerModel() = default;

std::pair<Ort::Value, Ort::Value> OfflineTransducerModel::RunEncoder(
    Ort::Value features, Ort::Value features_length) {
  return impl_->RunEncoder(std::move(features), std::move(features_length));
}

Ort::Value OfflineTransducerModel::RunDecoder(Ort::Value decoder_input) {
  return impl_->RunDecoder(std::move(decoder_input));
}

Ort::Value OfflineTransducerModel::RunJoiner(Ort::Value encoder_out,
                                             Ort::Value decoder_out) {
  return impl_->RunJoiner(std::move(encoder_out), std::move(decoder_out));
}

int32_t OfflineTransducerModel::VocabSize() const { return impl_->VocabSize(); }

int32_t OfflineTransducerModel::ContextSize() const {
  return impl_->ContextSize();
}

int32_t OfflineTransducerModel::SubsamplingFactor() const {
  return impl_->SubsamplingFactor();
}

OrtAllocator *OfflineTransducerModel::Allocator() const {
  return impl_->Allocator();
}

Ort::Value OfflineTransducerModel::BuildDecoderInput(
    const std::vector<OfflineTransducerDecoderResult> &results,
    int32_t end_index) const {
  return impl_->BuildDecoderInput(results, end_index);
}

Ort::Value OfflineTransducerModel::BuildDecoderInput(
    const std::vector<Hypothesis> &results, int32_t end_index) const {
  return impl_->BuildDecoderInput(results, end_index);
}

}  // namespace sherpa_onnx