// 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 <gflags/gflags.h>
#include <sys/time.h>
#include <time.h>
#include <algorithm>
#include <cstdio>
#include <fstream>
#include <iomanip>
#include <numeric>
#include <string>
#include <vector>
#include "lite/api/paddle_api.h"
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/api/paddle_use_passes.h"
#include "lite/core/device_info.h"
#include "lite/utils/cp_logging.h"
#include "lite/utils/string.h"

DEFINE_string(model_dir,
              "",
              "the path of the model, set model_dir when the model is no "
              "combined formate. This option will be ignored if model_file "
              "and param_file are exist.");
DEFINE_string(model_file,
              "",
              "the path of model file, set model_file when the model is "
              "combined formate.");
DEFINE_string(param_file,
              "",
              "the path of param file, set param_file when the model is "
              "combined formate.");
DEFINE_string(input_shape,
              "1,3,224,224",
              "set input shapes according to the model, "
              "separated by colon and comma, "
              "such as 1,3,244,244:1,3,300,300.");
DEFINE_int32(warmup, 0, "warmup times");
DEFINE_int32(repeats, 1, "repeats times");
DEFINE_int32(power_mode,
             3,
             "arm power mode: "
             "0 for big cluster, "
             "1 for little cluster, "
             "2 for all cores, "
             "3 for no bind");
DEFINE_int32(threads, 1, "threads num");
DEFINE_string(result_filename,
              "result.txt",
              "save benchmark "
              "result to the file");
DEFINE_bool(run_model_optimize,
            false,
            "if set true, apply model_optimize_tool to "
            "model and use optimized model to test. ");
DEFINE_bool(is_quantized_model,
            false,
            "if set true, "
            "test the performance of the quantized model. ");

namespace paddle {
namespace lite_api {

inline double GetCurrentUS() {
  struct timeval time;
  gettimeofday(&time, NULL);
  return 1e+6 * time.tv_sec + time.tv_usec;
}

void OutputOptModel(const std::string& save_optimized_model_dir,
                    const std::vector<std::vector<int64_t>>& input_shapes) {
  lite_api::CxxConfig config;
  config.set_model_dir(FLAGS_model_dir);
  config.set_model_file(FLAGS_model_file);
  config.set_param_file(FLAGS_param_file);
  std::vector<Place> vaild_places = {
      Place{TARGET(kARM), PRECISION(kFloat)},
  };
  if (FLAGS_is_quantized_model) {
    vaild_places.insert(vaild_places.begin(),
                        Place{TARGET(kARM), PRECISION(kInt8)});
  }
  config.set_valid_places(vaild_places);
  auto predictor = lite_api::CreatePaddlePredictor(config);

  int ret = system(
      paddle::lite::string_format("rm -rf %s", save_optimized_model_dir.c_str())
          .c_str());
  if (ret == 0) {
    LOG(INFO) << "Delete old optimized model " << save_optimized_model_dir;
  }
  predictor->SaveOptimizedModel(save_optimized_model_dir,
                                LiteModelType::kNaiveBuffer);
  LOG(INFO) << "Load model from " << FLAGS_model_dir;
  LOG(INFO) << "Save optimized model to " << save_optimized_model_dir;
}

#ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
void Run(const std::vector<std::vector<int64_t>>& input_shapes,
         const std::string& model_dir,
         const std::string model_name) {
  // set config and create predictor
  lite_api::MobileConfig config;
  config.set_threads(FLAGS_threads);
  config.set_power_mode(static_cast<PowerMode>(FLAGS_power_mode));
  config.set_model_from_file(model_dir + ".nb");

  auto predictor = lite_api::CreatePaddlePredictor(config);

  // set input
  for (int j = 0; j < input_shapes.size(); ++j) {
    auto input_tensor = predictor->GetInput(j);
    input_tensor->Resize(input_shapes[j]);
    auto input_data = input_tensor->mutable_data<float>();
    int input_num = 1;
    for (size_t i = 0; i < input_shapes[j].size(); ++i) {
      input_num *= input_shapes[j][i];
    }
    for (int i = 0; i < input_num; ++i) {
      input_data[i] = 1.f;
    }
  }

  // warmup
  for (int i = 0; i < FLAGS_warmup; ++i) {
    predictor->Run();
  }

  // run
  std::vector<float> perf_vct;
  for (int i = 0; i < FLAGS_repeats; ++i) {
    auto start = GetCurrentUS();
    predictor->Run();
    auto end = GetCurrentUS();
    perf_vct.push_back((end - start) / 1000.0);
  }
  std::sort(perf_vct.begin(), perf_vct.end());
  float min_res = perf_vct.back();
  float max_res = perf_vct.front();
  float total_res = accumulate(perf_vct.begin(), perf_vct.end(), 0.0);
  float avg_res = total_res / FLAGS_repeats;

  // save result
  std::ofstream ofs(FLAGS_result_filename, std::ios::app);
  if (!ofs.is_open()) {
    LOG(FATAL) << "open result file failed";
  }
  ofs.precision(5);
  ofs << std::setw(30) << std::fixed << std::left << model_name;
  ofs << "min = " << std::setw(12) << min_res;
  ofs << "max = " << std::setw(12) << max_res;
  ofs << "average = " << std::setw(12) << avg_res;
  ofs << std::endl;
  ofs.close();
}
#endif

}  // namespace lite_api
}  // namespace paddle

int main(int argc, char** argv) {
  gflags::ParseCommandLineFlags(&argc, &argv, true);
  if (FLAGS_model_dir == "" || FLAGS_result_filename == "") {
    LOG(INFO) << "please run ./benchmark_bin --help to obtain usage.";
    exit(0);
  }

  std::size_t found = FLAGS_model_dir.find_last_of("/");
  std::string model_name = FLAGS_model_dir.substr(found + 1);
  std::string save_optimized_model_dir = FLAGS_model_dir + "opt2";

  auto split_string =
      [](const std::string& str_in) -> std::vector<std::string> {
    std::vector<std::string> str_out;
    std::string tmp_str = str_in;
    while (!tmp_str.empty()) {
      size_t next_offset = tmp_str.find(":");
      str_out.push_back(tmp_str.substr(0, next_offset));
      if (next_offset == std::string::npos) {
        break;
      } else {
        tmp_str = tmp_str.substr(next_offset + 1);
      }
    }
    return str_out;
  };

  auto get_shape = [](const std::string& str_shape) -> std::vector<int64_t> {
    std::vector<int64_t> shape;
    std::string tmp_str = str_shape;
    while (!tmp_str.empty()) {
      int dim = atoi(tmp_str.data());
      shape.push_back(dim);
      size_t next_offset = tmp_str.find(",");
      if (next_offset == std::string::npos) {
        break;
      } else {
        tmp_str = tmp_str.substr(next_offset + 1);
      }
    }
    return shape;
  };

  std::vector<std::string> str_input_shapes = split_string(FLAGS_input_shape);
  std::vector<std::vector<int64_t>> input_shapes;
  for (size_t i = 0; i < str_input_shapes.size(); ++i) {
    input_shapes.push_back(get_shape(str_input_shapes[i]));
  }

  // Output optimized model if needed
  if (FLAGS_run_model_optimize) {
    paddle::lite_api::OutputOptModel(save_optimized_model_dir, input_shapes);
  }

#ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
  // Run inference using optimized model
  std::string run_model_dir =
      FLAGS_run_model_optimize ? save_optimized_model_dir : FLAGS_model_dir;
  paddle::lite_api::Run(input_shapes, run_model_dir, model_name);
#endif
  return 0;
}