code: #!/usr/bin/env python
  
  import argparse
  import datetime
  import json
  import time
  import warnings
  from logging import getLogger
  from pathlib import Path
  from typing import Dict, List
  
  import torch
  from tqdm import tqdm
  
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
  from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
  
  
  logger = getLogger(__name__)
  
  
  DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
  
  
  def generate_summaries_or_translations(
      examples: List[str],
      out_file: str,
      model_name: str,
      batch_size: int = 8,
      device: str = DEFAULT_DEVICE,
      fp16=False,
      task="summarization",
      prefix=None,
      **generate_kwargs,
  ) -> Dict:
      """Save model.generate results to <out_file>, and return how long it took."""
      fout = Path(out_file).open("w", encoding="utf-8")
      model_name = str(model_name)
      model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
      if fp16:
          model = model.half()
  
      tokenizer = AutoTokenizer.from_pretrained(model_name)
      logger.info(f"Inferred tokenizer type: {tokenizer.__class__}")  # if this is wrong, check config.model_type.
  
      start_time = time.time()
      # update config with task specific params
      use_task_specific_params(model, task)
      if prefix is None:
          prefix = prefix or getattr(model.config, "prefix", "") or ""
      for examples_chunk in tqdm(list(chunks(examples, batch_size))):
          examples_chunk = [prefix + text for text in examples_chunk]
          batch = tokenizer(examples_chunk, return_tensors="pt", truncation=True, padding="longest").to(device)
          summaries = model.generate(
              input_ids=batch.input_ids,
              attention_mask=batch.attention_mask,
              **generate_kwargs,
          )
          dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
          for hypothesis in dec:
              fout.write(hypothesis + "\n")
              fout.flush()
      fout.close()
      runtime = int(time.time() - start_time)  # seconds
      n_obs = len(examples)
      return dict(n_obs=n_obs, runtime=runtime, seconds_per_sample=round(runtime / n_obs, 4))
  
  
  def datetime_now():
      return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
  
  
  def run_generate(verbose=True):
      """
  
      Takes input text, generates output, and then using reference calculates the BLEU scores.
  
      The results are saved to a file and returned to the caller, and printed out unless ``verbose=False`` is passed.
  
      Args:
          verbose (:obj:`bool`, `optional`, defaults to :obj:`True`): print results to stdout
  
      Returns:
          a tuple: ``(scores, params}``
          - ``scores``: a dict of scores data ``{'bleu': 39.6501, 'n_obs': 2000, 'runtime': 186, 'seconds_per_sample': 0.093}``
          - ``params``: a dict of custom params, e.g. ``{'num_beams': 5, 'length_penalty': 0.8}``
      """
  
      parser = argparse.ArgumentParser()
      parser.add_argument("model_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.")
      parser.add_argument("input_path", type=str, help="like cnn_dm/test.source")
      parser.add_argument("save_path", type=str, help="where to save summaries")
      parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test.target")
      parser.add_argument("--score_path", type=str, required=False, default="metrics.json", help="where to save metrics")
      parser.add_argument("--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.")
      parser.add_argument(
          "--prefix", type=str, required=False, default=None, help="will be added to the begininng of src examples"
      )
      parser.add_argument("--task", type=str, default="summarization", help="used for task_specific_params + metrics")
      parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
      parser.add_argument(
          "--n_obs", type=int, default=-1, required=False, help="How many observations. Defaults to all."
      )
      parser.add_argument("--fp16", action="store_true")
      parser.add_argument("--dump-args", action="store_true", help="print the custom hparams with the results")
      parser.add_argument(
          "--info",
          nargs="?",
          type=str,
          const=datetime_now(),
          help="use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g. lang=en-ru. If no value is passed, the current datetime string will be used.",
      )
      # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
      args, rest = parser.parse_known_args()
      parsed_args = parse_numeric_n_bool_cl_kwargs(rest)
      if parsed_args and verbose:
          print(f"parsed the following generate kwargs: {parsed_args}")
      examples = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
      if args.n_obs > 0:
          examples = examples[: args.n_obs]
      Path(args.save_path).parent.mkdir(exist_ok=True)
      if args.reference_path is None and Path(args.score_path).exists():
          warnings.warn(f"score_path {args.score_path} will be overwritten unless you type ctrl-c.")
      runtime_metrics = generate_summaries_or_translations(
          examples,
          args.save_path,
          args.model_name,
          batch_size=args.bs,
          device=args.device,
          fp16=args.fp16,
          task=args.task,
          prefix=args.prefix,
          **parsed_args,
      )
  
      if args.reference_path is None:
          return {}
  
      # Compute scores
      score_fn = calculate_bleu if "translation" in args.task else calculate_rouge
      output_lns = [x.rstrip() for x in open(args.save_path).readlines()]
      reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()][: len(output_lns)]
      scores: dict = score_fn(output_lns, reference_lns)
      scores.update(runtime_metrics)
  
      if args.dump_args:
          scores.update(parsed_args)
      if args.info:
          scores["info"] = args.info
  
      if verbose:
          print(scores)
  
      if args.score_path is not None:
          json.dump(scores, open(args.score_path, "w"))
  
      return scores
  
  
  if __name__ == "__main__":
      # Usage for MT:
      # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
      run_generate(verbose=True)
  