code: import argparse
  import logging
  import os
  import sys
  from collections import namedtuple
  
  import torch
  from modeling_bertabs import BertAbs, build_predictor
  from torch.utils.data import DataLoader, SequentialSampler
  from tqdm import tqdm
  
  from transformers import BertTokenizer
  
  from .utils_summarization import (
      CNNDMDataset,
      build_mask,
      compute_token_type_ids,
      encode_for_summarization,
      truncate_or_pad,
  )
  
  
  UpperCAmelCase					=				logging.getLogger(__name__)
  logging.basicConfig(stream=sys.stdout, level=logging.INFO)
  
  
  UpperCAmelCase					=				namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	str				):
         _UpperCamelCase     :       Any  						=  BertTokenizer.from_pretrained('bert-base-uncased'		,						do_lower_case=lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'				)
         model.to(args.device				)
         model.eval()
  
         _UpperCamelCase     :       int  						=  {
             'BOS': tokenizer.vocab['[unused0]'],
             'EOS': tokenizer.vocab['[unused1]'],
             'PAD': tokenizer.vocab['[PAD]'],
         }
  
         if args.compute_rouge:
                _UpperCamelCase     :       int  						=  []
                _UpperCamelCase     :       int  						=  []
  
                import nltk
                import rouge
  
                nltk.download('punkt'				)
                _UpperCamelCase     :       Tuple  						=  rouge.Rouge(
                    metrics=['rouge-n', 'rouge-l']		,						max_n=2		,						limit_length=lowercase_		,						length_limit=args.beam_size		,						length_limit_type='words'		,						apply_avg=lowercase_		,						apply_best=lowercase_		,						alpha=0.5		,						weight_factor=1.2		,						stemming=lowercase_		,						)
  
         # these (unused) arguments are defined to keep the compatibility
         # with the legacy code and will be deleted in a next iteration.
         _UpperCamelCase     :       List[Any]  						=  ''
         _UpperCamelCase     :       Tuple  						=  ''
  
         _UpperCamelCase     :       int  						=  build_data_iterator(lowercase_		,						lowercase_				)
         _UpperCamelCase     :       Union[str, Any]  						=  build_predictor(lowercase_		,						lowercase_		,						lowercase_		,						lowercase_				)
  
         logger.info('***** Running evaluation *****'				)
         logger.info('  Number examples = %d'		,						len(data_iterator.dataset				)				)
         logger.info('  Batch size = %d'		,						args.batch_size				)
         logger.info(''				)
         logger.info('***** Beam Search parameters *****'				)
         logger.info('  Beam size = %d'		,						args.beam_size				)
         logger.info('  Minimum length = %d'		,						args.min_length				)
         logger.info('  Maximum length = %d'		,						args.max_length				)
         logger.info('  Alpha (length penalty) = %.2f'		,						args.alpha				)
         logger.info('  Trigrams %s be blocked'		,						('will' if args.block_trigram else 'will NOT')				)
  
         for batch in tqdm(lowercase_				):
                _UpperCamelCase     :       Optional[int]  						=  predictor.translate_batch(lowercase_				)
                _UpperCamelCase     :       Any  						=  predictor.from_batch(lowercase_				)
                _UpperCamelCase     :       Union[str, Any]  						=  [format_summary(lowercase_				) for t in translations]
                save_summaries(lowercase_		,						args.summaries_output_dir		,						batch.document_names				)
  
                if args.compute_rouge:
                       reference_summaries += batch.tgt_str
                       generated_summaries += summaries
  
         if args.compute_rouge:
                _UpperCamelCase     :       List[str]  						=  rouge_evaluator.get_scores(lowercase_		,						lowercase_				)
                _UpperCamelCase     :       int  						=  format_rouge_scores(lowercase_				)
                save_rouge_scores(lowercase_				)
                print(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]		,						lowercase_:	Union[str, Any]		,						lowercase_:	List[str]				):
         for summary, document_name in zip(lowercase_		,						lowercase_				):
                # Prepare the summary file's name
                if "." in document_name:
                       _UpperCamelCase     :       int  						=  '.'.join(document_name.split('.'				)[:-1]				)
                       _UpperCamelCase     :       List[Any]  						=  document_name.split('.'				)[-1]
                       _UpperCamelCase     :       Optional[int]  						=  bare_document_name + '_summary.' + extension
                else:
                       _UpperCamelCase     :       List[str]  						=  document_name + '_summary'
  
                _UpperCamelCase     :       Union[str, Any]  						=  os.path.join(lowercase_		,						lowercase_				)
                with open(lowercase_		,						'w'				) as output:
                       output.write(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple				):
         _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       List[str]  						=  translation
         _UpperCamelCase     :       Dict  						=  (
             raw_summary.replace('[unused0]'		,						''				)
             .replace('[unused3]'		,						''				)
             .replace('[PAD]'		,						''				)
             .replace('[unused1]'		,						''				)
             .replace(R' +'		,						' '				)
             .replace(' [unused2] '		,						'. '				)
             .replace('[unused2]'		,						''				)
             .strip()
         )
  
         return summary
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Any				):
         return """\n
  ****** ROUGE SCORES ******
  
  ** ROUGE 1
  F1        >> {:.3f}
  Precision >> {:.3f}
  Recall    >> {:.3f}
  
  ** ROUGE 2
  F1        >> {:.3f}
  Precision >> {:.3f}
  Recall    >> {:.3f}
  
  ** ROUGE L
  F1        >> {:.3f}
  Precision >> {:.3f}
  Recall    >> {:.3f}""".format(
             scores['rouge-1']['f']		,						scores['rouge-1']['p']		,						scores['rouge-1']['r']		,						scores['rouge-2']['f']		,						scores['rouge-2']['p']		,						scores['rouge-2']['r']		,						scores['rouge-l']['f']		,						scores['rouge-l']['p']		,						scores['rouge-l']['r']		,						)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
         with open('rouge_scores.txt'		,						'w'				) as output:
                output.write(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]		,						lowercase_:	str				):
         _UpperCamelCase     :       Optional[Any]  						=  load_and_cache_examples(lowercase_		,						lowercase_				)
         _UpperCamelCase     :       Dict  						=  SequentialSampler(lowercase_				)
  
         def collate_fn(lowercase_:	Optional[int]				):
                return collate(lowercase_		,						lowercase_		,						block_size=512		,						device=args.device				)
  
         _UpperCamelCase     :       Optional[int]  						=  DataLoader(
             lowercase_		,						sampler=lowercase_		,						batch_size=args.batch_size		,						collate_fn=lowercase_		,						)
  
         return iterator
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[Any]		,						lowercase_:	int				):
         _UpperCamelCase     :       Tuple  						=  CNNDMDataset(args.documents_dir				)
         return dataset
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[str]		,						lowercase_:	Any		,						lowercase_:	int		,						lowercase_:	int				):
         _UpperCamelCase     :       Dict  						=  [x for x in data if not len(x[1]				) == 0]  # remove empty_files
         _UpperCamelCase     :       str  						=  [name for name, _, _ in data]
         _UpperCamelCase     :       Optional[Any]  						=  [' '.join(lowercase_				) for _, _, summary_list in data]
  
         _UpperCamelCase     :       Any  						=  [encode_for_summarization(lowercase_		,						lowercase_		,						lowercase_				) for _, story, summary in data]
         _UpperCamelCase     :       int  						=  torch.tensor(
             [truncate_or_pad(lowercase_		,						lowercase_		,						tokenizer.pad_token_id				) for story, _ in encoded_text]				)
         _UpperCamelCase     :       Tuple  						=  compute_token_type_ids(lowercase_		,						tokenizer.cls_token_id				)
         _UpperCamelCase     :       List[str]  						=  build_mask(lowercase_		,						tokenizer.pad_token_id				)
  
         _UpperCamelCase     :       List[Any]  						=  Batch(
             document_names=lowercase_		,						batch_size=len(lowercase_				)		,						src=encoded_stories.to(lowercase_				)		,						segs=encoder_token_type_ids.to(lowercase_				)		,						mask_src=encoder_mask.to(lowercase_				)		,						tgt_str=lowercase_		,						)
  
         return batch
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	Any				):
         _UpperCamelCase     :       Dict  						=  summary_tokens.to('cpu'				).numpy()
         _UpperCamelCase     :       int  						=  tokenizer.decode(lowercase_				)
         _UpperCamelCase     :       str  						=  summary.split('.'				)
         _UpperCamelCase     :       List[str]  						=  [s + '.' for s in sentences]
         return sentences
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         _UpperCamelCase     :       Any  						=  argparse.ArgumentParser()
         parser.add_argument(
             '--documents_dir'		,						default=lowercase_		,						type=lowercase_		,						required=lowercase_		,						help='The folder where the documents to summarize are located.'		,						)
         parser.add_argument(
             '--summaries_output_dir'		,						default=lowercase_		,						type=lowercase_		,						required=lowercase_		,						help='The folder in wich the summaries should be written. Defaults to the folder where the documents are'		,						)
         parser.add_argument(
             '--compute_rouge'		,						default=lowercase_		,						type=lowercase_		,						required=lowercase_		,						help='Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'		,						)
         # EVALUATION options
         parser.add_argument(
             '--no_cuda'		,						default=lowercase_		,						type=lowercase_		,						help='Whether to force the execution on CPU.'		,						)
         parser.add_argument(
             '--batch_size'		,						default=4		,						type=lowercase_		,						help='Batch size per GPU/CPU for training.'		,						)
         # BEAM SEARCH arguments
         parser.add_argument(
             '--min_length'		,						default=50		,						type=lowercase_		,						help='Minimum number of tokens for the summaries.'		,						)
         parser.add_argument(
             '--max_length'		,						default=200		,						type=lowercase_		,						help='Maixmum number of tokens for the summaries.'		,						)
         parser.add_argument(
             '--beam_size'		,						default=5		,						type=lowercase_		,						help='The number of beams to start with for each example.'		,						)
         parser.add_argument(
             '--alpha'		,						default=0.95		,						type=lowercase_		,						help='The value of alpha for the length penalty in the beam search.'		,						)
         parser.add_argument(
             '--block_trigram'		,						default=lowercase_		,						type=lowercase_		,						help='Whether to block the existence of repeating trigrams in the text generated by beam search.'		,						)
         _UpperCamelCase     :       Dict  						=  parser.parse_args()
  
         # Select device (distibuted not available)
         _UpperCamelCase     :       Optional[Any]  						=  torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'				)
  
         # Check the existence of directories
         if not args.summaries_output_dir:
                _UpperCamelCase     :       Dict  						=  args.documents_dir
  
         if not documents_dir_is_valid(args.documents_dir				):
                raise FileNotFoundError(
                    'We could not find the directory you specified for the documents to summarize, or it was empty. Please'
                    ' specify a valid path.'				)
         os.makedirs(args.summaries_output_dir		,						exist_ok=lowercase_				)
  
         evaluate(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Dict				):
         if not os.path.exists(lowercase_				):
                return False
  
         _UpperCamelCase     :       str  						=  os.listdir(lowercase_				)
         if len(lowercase_				) == 0:
                return False
  
         return True
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
style_context: import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
  
         def remove_articles(lowercase_:	Dict				):
                return re.sub(R'\b(a|an|the)\b'		,						' '		,						lowercase_				)
  
         def white_space_fix(lowercase_:	Optional[Any]				):
                return " ".join(text.split()				)
  
         def remove_punc(lowercase_:	List[str]				):
                _UpperCamelCase     :       str  						=  set(string.punctuation				)
                return "".join(ch for ch in text if ch not in exclude				)
  
         def lower(lowercase_:	List[str]				):
                return text.lower()
  
         return white_space_fix(remove_articles(remove_punc(lower(lowercase_				)				)				)				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]		,						lowercase_:	Tuple				):
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       Union[str, Any]  						=  Counter(lowercase_				) & Counter(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  sum(common.values()				)
         if num_same == 0:
                return 0
         _UpperCamelCase     :       Optional[Any]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       List[str]  						=  (2 * precision * recall) / (precision + recall)
         return fa
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	Union[str, Any]				):
         return normalize_answer(lowercase_				) == normalize_answer(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	int		,						lowercase_:	Union[str, Any]				):
         _UpperCamelCase     :       Any  						=  []
         for ground_truth in ground_truths:
                _UpperCamelCase     :       Optional[Any]  						=  metric_fn(lowercase_		,						lowercase_				)
                scores_for_ground_truths.append(lowercase_				)
         return max(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]		,						lowercase_:	List[Any]				):
         _UpperCamelCase     :       List[Any]  						=  0
         for article in dataset:
                for paragraph in article["paragraphs"]:
                       for qa in paragraph["qas"]:
                              total += 1
                              if qa["id"] not in predictions:
                                     _UpperCamelCase     :       Union[str, Any]  						=  'Unanswered question ' + qa['id'] + ' will receive score 0.'
                                     print(lowercase_		,						file=sys.stderr				)
                                     continue
                              _UpperCamelCase     :       Dict  						=  [x['text'] for x in qa['answers']]
                              _UpperCamelCase     :       List[str]  						=  predictions[qa['id']]
                              exact_match += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
                              fa += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
  
         _UpperCamelCase     :       Dict  						=  100.0 * exact_match / total
         _UpperCamelCase     :       Optional[Any]  						=  100.0 * fa / total
  
         return {"exact_match": exact_match, "f1": fa}
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				'''1.1'''
   UpperCAmelCase					=				argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
   parser.add_argument('''dataset_file''', help='''Dataset file''')
   parser.add_argument('''prediction_file''', help='''Prediction File''')
   UpperCAmelCase					=				parser.parse_args()
   with open(args.dataset_file) as dataset_file:
    UpperCAmelCase					=				json.load(dataset_file)
    if dataset_json["version"] != expected_version:
     print(
         '''Evaluation expects v-''' + expected_version + ''', but got dataset with v-''' + dataset_json['''version'''],
         file=sys.stderr,
     )
    UpperCAmelCase					=				dataset_json['''data''']
   with open(args.prediction_file) as prediction_file:
    UpperCAmelCase					=				json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))
  
  
  
  