#include <structure_table.h>
namespace ANSCENTER {
    void StructureTableRecognizer::Run(std::vector<cv::Mat> img_list,std::vector<std::vector<std::string>> &structure_html_tags,
                                   std::vector<float> &structure_scores,std::vector<std::vector<std::vector<int>>> &structure_boxes) 
{
  int img_num = img_list.size();
  for (int beg_img_no = 0; beg_img_no < img_num;
       beg_img_no += this->table_batch_num_) {
    // preprocess
    int end_img_no = std::min(img_num, beg_img_no + this->table_batch_num_);
    int batch_num = end_img_no - beg_img_no;
    std::vector<cv::Mat> norm_img_batch;
    std::vector<int> width_list;
    std::vector<int> height_list;
    for (int ino = beg_img_no; ino < end_img_no; ino++) {
      cv::Mat srcimg;
      img_list[ino].copyTo(srcimg);
      cv::Mat resize_img;
      cv::Mat pad_img;
      this->resize_op_.Run(srcimg, resize_img, this->table_max_len_);
      this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
                              this->is_scale_);
      this->pad_op_.Run(resize_img, pad_img, this->table_max_len_);
      norm_img_batch.push_back(pad_img);
      width_list.push_back(srcimg.cols);
      height_list.push_back(srcimg.rows);
    }

    std::vector<float> input(
        batch_num * 3 * this->table_max_len_ * this->table_max_len_, 0.0f);
    this->permute_op_.Run(norm_img_batch, input.data());
    // inference.
    auto input_names = this->predictor_->GetInputNames();
    auto input_t = this->predictor_->GetInputHandle(input_names[0]);
    input_t->Reshape(
        {batch_num, 3, this->table_max_len_, this->table_max_len_});
    auto inference_start = std::chrono::steady_clock::now();
    input_t->CopyFromCpu(input.data());
    this->predictor_->Run();
    auto output_names = this->predictor_->GetOutputNames();
    auto output_tensor0 = this->predictor_->GetOutputHandle(output_names[0]);
    auto output_tensor1 = this->predictor_->GetOutputHandle(output_names[1]);
    std::vector<int> predict_shape0 = output_tensor0->shape();
    std::vector<int> predict_shape1 = output_tensor1->shape();

    int out_num0 = std::accumulate(predict_shape0.begin(), predict_shape0.end(),
                                   1, std::multiplies<int>());
    int out_num1 = std::accumulate(predict_shape1.begin(), predict_shape1.end(),
                                   1, std::multiplies<int>());
    std::vector<float> loc_preds;
    std::vector<float> structure_probs;
    loc_preds.resize(out_num0);
    structure_probs.resize(out_num1);

    output_tensor0->CopyToCpu(loc_preds.data());
    output_tensor1->CopyToCpu(structure_probs.data());
    // postprocess
    auto postprocess_start = std::chrono::steady_clock::now();
    std::vector<std::vector<std::string>> structure_html_tag_batch;
    std::vector<float> structure_score_batch;
    std::vector<std::vector<std::vector<int>>> structure_boxes_batch;
    this->post_processor_.Run(loc_preds, structure_probs, structure_score_batch,
                              predict_shape0, predict_shape1,
                              structure_html_tag_batch, structure_boxes_batch,
                              width_list, height_list);
    for (int m = 0; m < predict_shape0[0]; m++) {

      structure_html_tag_batch[m].insert(structure_html_tag_batch[m].begin(),
                                         "<table>");
      structure_html_tag_batch[m].insert(structure_html_tag_batch[m].begin(),
                                         "<body>");
      structure_html_tag_batch[m].insert(structure_html_tag_batch[m].begin(),
                                         "<html>");
      structure_html_tag_batch[m].push_back("</table>");
      structure_html_tag_batch[m].push_back("</body>");
      structure_html_tag_batch[m].push_back("</html>");
      structure_html_tags.push_back(structure_html_tag_batch[m]);
      structure_scores.push_back(structure_score_batch[m]);
      structure_boxes.push_back(structure_boxes_batch[m]);
    }
  }
}
 bool StructureTableRecognizer::LoadModel(const std::string &model_dir) {
     try {
         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);
                 if (!ANSOCRUtility::PathExists("./trt_table_shape.txt")) {
                     config.CollectShapeRangeInfo("./trt_table_shape.txt");
                 }
                 else {
                     config.EnableTunedTensorRtDynamicShape("./trt_table_shape.txt", true);
                 }
             }
         }
         else {
             config.DisableGpu();
             if (this->use_mkldnn_) {
                 config.EnableMKLDNN();
             }
             config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
         }

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

         config.SwitchIrOptim(true);

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

         this->predictor_ = paddle_infer::CreatePredictor(config);
         return true;
     }
     catch (std::exception& e) {
         std::cout << "StructureTableRecognizer::LoadModel" << e.what() << std::endl;
         return false;
     }
}
} // namespace ANSCENTER
