text: import os
  import sys
  import logging
  import argparse
  from pathlib import Path
  
  from qp2q.eval.gen_pred_data import generate_predictions
  from qp2q.eval.eval_pred_latency import eval_pred_latency
  
  logging.basicConfig(
      stream=sys.stdout,
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s ",
      datefmt="%m/%d/%Y %H:%M:%S",
      level=logging.INFO,
  )
  LOGGER = logging.getLogger(__name__)
  
  
  def eval_helper(model_path, gt_file, gen_pred_data, eval_latency, out_dir):
  
      LOGGER.info("Param: GT_FILE={}".format(gt_file))
      LOGGER.info("Param: MODEL_PATH{}".format(model_path))
      LOGGER.info("Param: GEN_PRED_DATA={}".format(gen_pred_data))
      LOGGER.info("Param: EVAL_LATENCY={}".format(eval_latency))
  
      gt_filename = gt_file.split("/")[-1]
      pred_data_path = f"{out_dir}/{gt_filename}.pred_data"
  
      LOGGER.info(f"Evaluating model at : {model_path} \n")
  
      # Create config file for evaluating using PECOS Model
      config_dict = {
          "name": gt_filename,
          "driver": "PecosSuggester",
          "args": {"model_path": model_path, "beam_size": 10, "topk": 10},
      }
  
      if gen_pred_data:
          LOGGER.info(
              "\n Generating data for for model : {m} in res dir :{o} using ground-truth at {g} \n".format(
                  m=model_path, o=out_dir, g=gt_file
              )
          )
          generate_predictions(
              config_dict=config_dict, pred_data_path=pred_data_path, gt_data_file=gt_file
          )
  
      LOGGER.info(
          "\n Computing metrics for model : {m} in res dir :{o} using ground-truth at {g} \n".format(
              m=model_path, o=out_dir, g=gt_file
          )
      )
      command = f"python eval/eval_pred_data.py  --out_file {out_dir}/eval_results.json --pred_file {pred_data_path} --gt_file {gt_file} "
      LOGGER.info(command)
      os.system(command)
  
      if eval_latency:
          LOGGER.info("Evaluating latency ")
          eval_pred_latency(
              config_dict=config_dict,
              gt_data_file=gt_file,
              pred_data_path=pred_data_path,
              num_samples=100000,
              warmup_samples=1000,
          )
  
  
  def main():
      parser = argparse.ArgumentParser(description="Run evaluation using a  trained model \n ")
      parser.add_argument(
          "--model_path",
          required=True,
          type=str,
          help="Path to model dir that stores actual parameters of the model.",
      )
      parser.add_argument("--gt", required=True, type=str, help="Path to file with ground-truth data")
      parser.add_argument("--out_dir", required=True, type=str, help="Directory to save eval result")
      parser.add_argument(
          "--gen_pred_data", default=1, type=int, help="Whether to generate pred data or not"
      )
      parser.add_argument(
          "--eval_latency",
          default=0,
          type=int,
          help="Whether to evaluate latency for generation or not",
      )
  
      args = parser.parse_args()
      model_path = args.model_path
      out_dir = args.out_dir
      gt_file = args.gt
      gen_pred_data = bool(args.gen_pred_data)
      eval_latency = bool(args.eval_latency)
  
      Path(out_dir).mkdir(exist_ok=True, parents=True)
      LOGGER.info("Beginning eval")
  
      if os.path.isfile(gt_file):
          eval_helper(
              model_path=model_path,
              gt_file=gt_file,
              gen_pred_data=gen_pred_data,
              eval_latency=eval_latency,
              out_dir=out_dir,
          )
      else:
          LOGGER.info("Invalid gt file = {}".format(gt_file))
  
  
  if __name__ == "__main__":
      main()
  