// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "lite/api/light_api.h"
#include <string>
#include "lite/api/paddle_api.h"
#include "lite/core/version.h"
#include "lite/model_parser/model_parser.h"
#ifndef LITE_ON_TINY_PUBLISH
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#endif
#include "lite/core/parallel_defines.h"
#include "lite/core/thread_pool.h"

#if (defined LITE_WITH_X86) && (defined PADDLE_WITH_MKLML) && \
    !(defined LITE_ON_MODEL_OPTIMIZE_TOOL)
#include "lite/backends/x86/mklml.h"
#endif

namespace paddle {
namespace lite {

void LightPredictorImpl::Init(const lite_api::MobileConfig& config) {
  // LightPredictor Only support NaiveBuffer backend in publish lib
  auto use_low_precision =
      config.precision_mode() == lite_api::LITE_PRECISION_LOW ? true : false;
  if (config.lite_model_file().empty() && !config.lite_model_buffer_ptr()) {
    raw_predictor_.reset(
        new LightPredictor(config.model_dir(),
                           config.model_buffer(),
                           config.param_buffer(),
                           config.is_model_from_memory(),
                           lite_api::LiteModelType::kNaiveBuffer,
                           use_low_precision));
  } else if (!config.lite_model_file().empty() &&
             !config.is_model_from_memory()) {
    raw_predictor_.reset(
        new LightPredictor(config.lite_model_file(), use_low_precision));
  } else if (!config.lite_model_file().empty() &&
             config.is_model_from_memory()) {
    raw_predictor_.reset(new LightPredictor(config.lite_model_file().c_str(),
                                            config.lite_model_file().length(),
                                            use_low_precision));
  } else {
    raw_predictor_.reset(new LightPredictor(config.lite_model_buffer_ptr(),
                                            config.lite_model_buffer_size(),
                                            use_low_precision));
  }

  mode_ = config.power_mode();
  threads_ = config.threads();
  raw_predictor_->SetTargetConfigs(config.target_configs());
#ifdef LITE_WITH_XPU
  CHECK(config.target_configs().count(TARGET(kXPU)))
      << "XPU runtime option is not initialized!";
  XPULoadRunTimeOptionGuard xpu_load_runtime_option_guard(
      reinterpret_cast<paddle::lite::XPURunTimeOption*>(
          config.target_configs().at(TARGET(kXPU)).get()));
#endif
#ifdef LITE_USE_THREAD_POOL
  int thread_num = ThreadPool::Init(threads_);
  if (thread_num > 1) {
    ThreadPool::AcquireThreadPool();
  }
#endif

#ifdef LITE_WITH_METAL
  raw_predictor_->ConfigMetalContext(config);
#endif

#if defined(LITE_ON_MODEL_OPTIMIZE_TOOL) || defined(LITE_WITH_PYTHON) || \
    defined(LITE_WITH_NNADAPTER)
  // Use scope to store the model-level configuration for the subgraph kernel
  Context<TargetType::kNNAdapter>::SetNNAdapterDeviceNames(
      raw_predictor_->scope(), config.nnadapter_device_names());
  Context<TargetType::kNNAdapter>::SetNNAdapterContextProperties(
      raw_predictor_->scope(), config.nnadapter_context_properties());
  Context<TargetType::kNNAdapter>::SetNNAdapterContextCallback(
      raw_predictor_->scope(), config.nnadapter_context_callback());
  Context<TargetType::kNNAdapter>::SetNNAdapterModelCacheDir(
      raw_predictor_->scope(), config.nnadapter_model_cache_dir());
  Context<TargetType::kNNAdapter>::SetNNAdapterModelCacheBuffers(
      raw_predictor_->scope(), config.nnadapter_model_cache_buffers());
  Context<TargetType::kNNAdapter>::SetNNAdapterDynamicShapeInfo(
      raw_predictor_->scope(), config.nnadapter_dynamic_shape_info());
#endif

#if (defined LITE_WITH_X86) && (defined PADDLE_WITH_MKLML) && \
    !(defined LITE_ON_MODEL_OPTIMIZE_TOOL)
  int num_threads = config.x86_math_num_threads();
  int real_num_threads = num_threads > 1 ? num_threads : 1;
#ifdef LITE_WITH_STATIC_MKL
  MKL_Set_Num_Threads(real_num_threads);
#else
  x86::MKL_Set_Num_Threads(real_num_threads);
#endif
  VLOG(3) << "x86_math_num_threads() is set successfully and the "
             "number of threads is:"
          << real_num_threads;
#endif
}

LightPredictorImpl::~LightPredictorImpl() {
#ifdef LITE_USE_THREAD_POOL
  ThreadPool::ReleaseThreadPool();
#endif
}

std::unique_ptr<lite_api::Tensor> LightPredictorImpl::GetInputByName(
    const std::string& name) {
  return std::unique_ptr<lite_api::Tensor>(
      new lite_api::Tensor(raw_predictor_->GetInputByName(name)));
}

std::unique_ptr<const lite_api::Tensor> LightPredictorImpl::GetOutputByName(
    const std::string& name) const {
  return std::unique_ptr<lite_api::Tensor>(
      new lite_api::Tensor(raw_predictor_->GetOutputByName(name)));
}

std::unique_ptr<lite_api::Tensor> LightPredictorImpl::GetInput(int i) {
  return std::unique_ptr<lite_api::Tensor>(
      new lite_api::Tensor(raw_predictor_->GetInput(i)));
}

std::unique_ptr<const lite_api::Tensor> LightPredictorImpl::GetOutput(
    int i) const {
  return std::unique_ptr<lite_api::Tensor>(
      new lite_api::Tensor(raw_predictor_->GetOutput(i)));
}

void LightPredictorImpl::Run() {
#ifdef LITE_WITH_ARM
  lite::DeviceInfo::Global().SetRunMode(mode_, threads_);
#endif
  raw_predictor_->Run();
}

std::shared_ptr<lite_api::PaddlePredictor> LightPredictorImpl::Clone() {
  LOG(FATAL) << "The Clone API is not supported in LigthPredictor";
  return nullptr;
}

std::shared_ptr<lite_api::PaddlePredictor> LightPredictorImpl::Clone(
    const std::vector<std::string>& var_names) {
  LOG(FATAL) << "The Clone API is not supported in LigthPredictor";
  return nullptr;
}

std::string LightPredictorImpl::GetVersion() const { return lite::version(); }

std::unique_ptr<const lite_api::Tensor> LightPredictorImpl::GetTensor(
    const std::string& name) const {
  return std::unique_ptr<const lite_api::Tensor>(
      new lite_api::Tensor(raw_predictor_->GetTensor(name)));
}

std::vector<std::string> LightPredictorImpl::GetInputNames() {
  return raw_predictor_->GetInputNames();
}

std::vector<std::string> LightPredictorImpl::GetOutputNames() {
  return raw_predictor_->GetOutputNames();
}

bool LightPredictorImpl::TryShrinkMemory() {
  return raw_predictor_->TryShrinkMemory();
}

void LightPredictorImpl::SetStream(TargetType target, void* stream) {
  raw_predictor_->SetStream(target, stream);
}

}  // namespace lite

namespace lite_api {

template <>
std::shared_ptr<PaddlePredictor> CreatePaddlePredictor(
    const MobileConfig& config) {
  auto x = std::make_shared<lite::LightPredictorImpl>();
  x->Init(config);
  return x;
}

}  // namespace lite_api
}  // namespace paddle
