// Copyright (c) 2020 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 <include/ocr_rec.h>

namespace PaddleOCR {
    
void CRNNRecognizer::Run(std::vector<cv::Mat> img_list, std::vector<double> *times) {
    std::chrono::duration<float> preprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
    std::chrono::duration<float> inference_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
    std::chrono::duration<float> postprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();

    int img_num = img_list.size();
    std::vector<float> width_list;
    for (int i = 0; i < img_num; i++) {
        width_list.push_back(float(img_list[i].cols) / img_list[i].rows);
    }
    std::vector<int> indices = Utility::argsort(width_list);

    for (int beg_img_no = 0; beg_img_no < img_num; beg_img_no += this->rec_batch_num_) {
        auto preprocess_start = std::chrono::steady_clock::now();
        int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_);
        float max_wh_ratio = 0;
        for (int ino = beg_img_no; ino < end_img_no; ino ++) {
            int h = img_list[indices[ino]].rows;
            int w = img_list[indices[ino]].cols;
            float wh_ratio = w * 1.0 / h;
            max_wh_ratio = max(max_wh_ratio, wh_ratio);
        }
//         cout << "max_wh_ratio: " << max_wh_ratio << endl;
        std::vector<cv::Mat> norm_img_batch;
        for (int ino = beg_img_no; ino < end_img_no; ino ++) {
            cv::Mat srcimg;
            img_list[indices[ino]].copyTo(srcimg);
            cv::Mat resize_img;
            this->resize_op_.Run(srcimg, resize_img, max_wh_ratio, this->use_tensorrt_);
            this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, this->is_scale_);
            norm_img_batch.push_back(resize_img);
        }
        
        int batch_width = int(ceilf(32 * max_wh_ratio)) - 1;
        std::vector<float> input(this->rec_batch_num_ * 3 * 32 * batch_width, 0.0f);
        this->permute_op_.Run(norm_img_batch, input.data());
        auto preprocess_end = std::chrono::steady_clock::now();
        preprocess_diff += preprocess_end - preprocess_start;

        // Inference.
        auto input_names = this->predictor_->GetInputNames();
        auto input_t = this->predictor_->GetInputHandle(input_names[0]);
        input_t->Reshape({this->rec_batch_num_, 3, 32, batch_width});
        auto inference_start = std::chrono::steady_clock::now();
        input_t->CopyFromCpu(input.data());
        this->predictor_->Run();

        std::vector<float> predict_batch;
        auto output_names = this->predictor_->GetOutputNames();
        auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
        auto predict_shape = output_t->shape();

        int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
                                std::multiplies<int>());
        predict_batch.resize(out_num);

        output_t->CopyToCpu(predict_batch.data());
        auto inference_end = std::chrono::steady_clock::now();
        inference_diff += inference_end - inference_start;
        
        // ctc decode
        auto postprocess_start = std::chrono::steady_clock::now();
        for (int m = 0; m < predict_shape[0]; m++) {
            std::vector<std::string> str_res;
            int argmax_idx;
            int last_index = 0;
            float score = 0.f;
            int count = 0;
            float max_value = 0.0f;

            for (int n = 0; n < predict_shape[1]; n++) {
                argmax_idx =
                    int(Utility::argmax(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
                                        &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
                max_value =
                    float(*std::max_element(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
                                            &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));

                if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
                    score += max_value;
                    count += 1;
                    str_res.push_back(label_list_[argmax_idx]);
                }
                last_index = argmax_idx;
            }
            score /= count;
            if (isnan(score))
                continue;
            for (int i = 0; i < str_res.size(); i++) {
                std::cout << str_res[i];
            }
            std::cout << "\tscore: " << score << std::endl;
        }
        auto postprocess_end = std::chrono::steady_clock::now();
        postprocess_diff += postprocess_end - postprocess_start;
    }
    times->push_back(double(preprocess_diff.count() * 1000));
    times->push_back(double(inference_diff.count() * 1000));
    times->push_back(double(postprocess_diff.count() * 1000));
}

    
void CRNNRecognizer::LoadModel(const std::string &model_dir) {
  //   AnalysisConfig config;
  paddle_infer::Config config;
  config.SetModel(model_dir + "/inference.pdmodel",
                  model_dir + "/inference.pdiparams");

  if (this->use_gpu_) {
    config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
    if (this->use_tensorrt_) {
      auto precision = paddle_infer::Config::Precision::kFloat32;
      if (this->precision_ == "fp16") {
        precision = paddle_infer::Config::Precision::kHalf;
      }
     if (this->precision_ == "int8") {
        precision = paddle_infer::Config::Precision::kInt8;
      } 
      config.EnableTensorRtEngine(
          1 << 20, 10, 3,
          precision,
          false, false);

      std::map<std::string, std::vector<int>> min_input_shape = {
          {"x", {1, 3, 32, 10}},
          {"lstm_0.tmp_0", {10, 1, 96}}};
      std::map<std::string, std::vector<int>> max_input_shape = {
          {"x", {1, 3, 32, 2000}},
          {"lstm_0.tmp_0", {1000, 1, 96}}};
      std::map<std::string, std::vector<int>> opt_input_shape = {
          {"x", {1, 3, 32, 320}},
          {"lstm_0.tmp_0", {25, 1, 96}}};

      config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
                                    opt_input_shape);
    }
  } else {
    config.DisableGpu();
    if (this->use_mkldnn_) {
      config.EnableMKLDNN();
      // cache 10 different shapes for mkldnn to avoid memory leak
      config.SetMkldnnCacheCapacity(10);
    }
    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
  }

  config.SwitchUseFeedFetchOps(false);
  // true for multiple input
  config.SwitchSpecifyInputNames(true);

  config.SwitchIrOptim(true);

  config.EnableMemoryOptim();
//   config.DisableGlogInfo();

  this->predictor_ = CreatePredictor(config);
}

} // namespace PaddleOCR
