text: import argparse
  from pathlib import Path
  
  import torch
  from rouge_score import rouge_scorer, scoring
  from tqdm import tqdm
  
  from transformers import T5ForConditionalGeneration, T5Tokenizer
  
  
  def chunks(lst, n):
      """Yield successive n-sized chunks from lst."""
      for i in range(0, len(lst), n):
          yield lst[i : i + n]
  
  
  def generate_summaries(lns, output_file_path, model_size, batch_size, device):
      output_file = Path(output_file_path).open("w")
  
      model = T5ForConditionalGeneration.from_pretrained(model_size)
      model.to(device)
  
      tokenizer = T5Tokenizer.from_pretrained(model_size)
  
      # update config with summarization specific params
      task_specific_params = model.config.task_specific_params
      if task_specific_params is not None:
          model.config.update(task_specific_params.get("summarization", {}))
  
      for batch in tqdm(list(chunks(lns, batch_size))):
          batch = [model.config.prefix + text for text in batch]
  
          dct = tokenizer.batch_encode_plus(batch, max_length=512, return_tensors="pt", pad_to_max_length=True)
          input_ids = dct["input_ids"].to(device)
          attention_mask = dct["attention_mask"].to(device)
  
          summaries = model.generate(input_ids=input_ids, attention_mask=attention_mask)
          dec = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summaries]
  
          for hypothesis in dec:
              output_file.write(hypothesis + "\n")
              output_file.flush()
  
  
  def calculate_rouge(output_lns, reference_lns, score_path):
      score_file = Path(score_path).open("w")
      scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
      aggregator = scoring.BootstrapAggregator()
  
      for reference_ln, output_ln in zip(reference_lns, output_lns):
          scores = scorer.score(reference_ln, output_ln)
          aggregator.add_scores(scores)
  
      result = aggregator.aggregate()
      score_file.write(
          "ROUGE_1: \n{} \n\n ROUGE_2: \n{} \n\n ROUGE_L: \n{} \n\n".format(
              result["rouge1"], result["rouge2"], result["rougeL"]
          )
      )
  
  
  def run_generate():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "model_size",
          type=str,
          help="T5 model size, either 't5-small', 't5-base', 't5-large', 't5-3b', 't5-11b'. Defaults to 't5-base'.",
          default="t5-base",
      )
      parser.add_argument(
          "input_path", type=str, help="like cnn_dm/test_articles_input.txt",
      )
      parser.add_argument(
          "output_path", type=str, help="where to save summaries",
      )
      parser.add_argument("reference_path", type=str, help="like cnn_dm/test_reference_summaries.txt")
      parser.add_argument(
          "score_path", type=str, help="where to save the rouge score",
      )
      parser.add_argument(
          "--batch_size", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
      )
      parser.add_argument(
          "--no_cuda", default=False, type=bool, help="Whether to force the execution on CPU.",
      )
  
      args = parser.parse_args()
      args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
  
      source_lns = [x.rstrip() for x in open(args.input_path).readlines()]
  
      generate_summaries(source_lns, args.output_path, args.model_size, args.batch_size, args.device)
  
      output_lns = [x.rstrip() for x in open(args.output_path).readlines()]
      reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()]
  
      calculate_rouge(output_lns, reference_lns, args.score_path)
  
  
  if __name__ == "__main__":
      run_generate()
  