#include <ocr_rec.h>

namespace ANSCENTER
{
  void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
                           std::vector<std::string> &rec_texts,
                           std::vector<float> &rec_text_scores)
  {
      try {
         

          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 = ANSOCRUtility::argsort(width_list);

          for (int beg_img_no = 0; beg_img_no < img_num;
              beg_img_no += this->rec_batch_num_)
          {
              int end_img_no = std::min(img_num, beg_img_no + this->rec_batch_num_);
              int batch_num = end_img_no - beg_img_no;
              int imgH = this->rec_image_shape_[1];
              int imgW = this->rec_image_shape_[2];
              float max_wh_ratio = float(imgW * 1.0 / imgH);
              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 = float(w * 1.0 / h);
                  max_wh_ratio = std::max(max_wh_ratio, wh_ratio);
              }

              int batch_width = imgW;
              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->rec_image_shape_);
                  this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
                      this->is_scale_);
                  norm_img_batch.push_back(resize_img);
                  batch_width = std::max(resize_img.cols, batch_width);
              }

              std::vector<float> input(batch_num * 3 * imgH * batch_width, 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, imgH, 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);
              // predict_batch is the result of Last FC with softmax
              output_t->CopyToCpu(predict_batch.data());
      
              // ctc decode
              for (int m = 0; m < predict_shape[0]; m++)
              {
                  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++)
                  {
                      // get idx
                      argmax_idx = int(ANSOCRUtility::argmax(
                          &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
                          &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
                      // get score
                      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 += label_list_[argmax_idx];

                          // TODO: Update structure format support default extract serial emboss metal
                          std::cout << "Debug res label: " << label_list_[argmax_idx] << std::endl;
                          // Check index 3
                          if (str_res[3] == '1')
                          {
                              str_res[3] = 'I';
                          }
                          else if (str_res[3] == '2')
                          {
                              str_res[3] = 'Z';
                          }
                          else if (str_res[3] == '6')
                          {
                              str_res[3] = 'G';
                          }
                          else if (str_res[3] == '8')
                          {
                              str_res[3] = 'B';
                          }
                          else if (str_res[3] == 'O')
                          {
                              str_res[3] = '0';
                          }
                          // Check index 5
                          if (str_res[4] == 'G')
                          {
                              str_res[4] = '6';
                          }
                          else if (str_res[4] == 'B')
                          {
                              str_res[4] = '8';
                          }
                          // Check index 6
                          if (str_res[5] == '?')
                          {
                              str_res[5] = '7';
                          }
                          else if (str_res[5] == 'B')
                          {
                              str_res[5] = '8';
                          }
                          else if (str_res[5] == 'a')
                          {
                              str_res[5] = '2';
                          }
                      }
                      last_index = argmax_idx;
                  }
                  score /= count;
                  if (std::isnan(score))
                  {
                      continue;
                  }
                  rec_texts[indices[beg_img_no + m]] = str_res;
                  rec_text_scores[indices[beg_img_no + m]] = score;
              }
    
          }

      
      }
      catch (std::exception& e) {
          std::cout << "CRNNRecognizer::Run" << e.what() << std::endl;
      }
  }

  bool CRNNRecognizer::LoadModel(const std::string &model_dir)
  {
      try {
          paddle_infer::Config config;
          config.EnableONNXRuntime();
          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;
                  }
                  if (!ANSOCRUtility::PathExists("./trt_rec_shape.txt"))
                  {
                      config.CollectShapeRangeInfo("./trt_rec_shape.txt");
                  }
                  else
                  {
                      config.EnableTunedTensorRtDynamicShape("./trt_rec_shape.txt", true);
                  }
              }
          }
          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_);
          }

          // get pass_builder object
          auto pass_builder = config.pass_builder();
          // delete "matmul_transpose_reshape_fuse_pass"
          pass_builder->DeletePass("matmul_transpose_reshape_fuse_pass");
          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 << "CRNNRecognizer::LoadModel" << e.what() << std::endl;
          return false;
      }
  }

} // namespace ANSCENTER
