

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,
)


_a									=  logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


_a									=  namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]						)       ->       int:

 """simple docstring"""






 __lowerCAmelCase:  str        = BertTokenizer.from_pretrained('bert-base-uncased'       ,   do_lower_case=SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  int        = BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'						)
 model.to(args.device						)
 model.eval()

 __lowerCAmelCase:  Union[str, Any]        = {
     'BOS': tokenizer.vocab['[unused0]'],
     'EOS': tokenizer.vocab['[unused1]'],
     'PAD': tokenizer.vocab['[PAD]'],
 }

 if args.compute_rouge:
  __lowerCAmelCase:  Union[str, Any]        = []
  __lowerCAmelCase:  str        = []

  import nltk
  import rouge

  nltk.download('punkt'						)
  __lowerCAmelCase:  Any        = rouge.Rouge(
      metrics=['rouge-n', 'rouge-l']       ,   max_n=2       ,   limit_length=SCREAMING_SNAKE_CASE       ,   length_limit=args.beam_size       ,   length_limit_type='words'       ,   apply_avg=SCREAMING_SNAKE_CASE       ,   apply_best=SCREAMING_SNAKE_CASE       ,   alpha=0.5       ,   weight_factor=1.2       ,   stemming=SCREAMING_SNAKE_CASE       ,   )

 # these (unused) arguments are defined to keep the compatibility
 # with the legacy code and will be deleted in a next iteration.
 __lowerCAmelCase:  List[Any]        = ''
 __lowerCAmelCase:  Union[str, Any]        = ''

 __lowerCAmelCase:  Optional[Any]        = build_data_iterator(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Any        = build_predictor(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 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(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Dict        = predictor.translate_batch(SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  int        = predictor.from_batch(SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Any        = [format_summary(SCREAMING_SNAKE_CASE						) for t in translations]
  save_summaries(SCREAMING_SNAKE_CASE       ,   args.summaries_output_dir       ,   batch.document_names						)

  if args.compute_rouge:
   reference_summaries += batch.tgt_str
   generated_summaries += summaries

 if args.compute_rouge:
  __lowerCAmelCase:  Union[str, Any]        = rouge_evaluator.get_scores(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Optional[int]        = format_rouge_scores(SCREAMING_SNAKE_CASE						)
  save_rouge_scores(SCREAMING_SNAKE_CASE						)
  print(SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     int       ,   SCREAMING_SNAKE_CASE						:     Dict						)       ->       Any:

 """simple docstring"""






 for summary, document_name in zip(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						):
  # Prepare the summary file's name
  if "." in document_name:
   __lowerCAmelCase:  Tuple        = '.'.join(document_name.split('.'						)[:-1]						)
   __lowerCAmelCase:  Any        = document_name.split('.'						)[-1]
   __lowerCAmelCase:  str        = bare_document_name + '_summary.' + extension
  else:
   __lowerCAmelCase:  List[Any]        = document_name + '_summary'

  __lowerCAmelCase:  List[Any]        = os.path.join(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  with open(SCREAMING_SNAKE_CASE       ,   'w'						) as output:
   output.write(SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]						)       ->       Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase:  List[str]        = translation
 __lowerCAmelCase:  Optional[int]        = (
     raw_summary.replace('[unused0]'       ,   ''						)
     .replace('[unused3]'       ,   ''						)
     .replace('[PAD]'       ,   ''						)
     .replace('[unused1]'       ,   ''						)
     .replace(R' +'       ,   ' '						)
     .replace(' [unused2] '       ,   '. '						)
     .replace('[unused2]'       ,   ''						)
     .strip()
 )

 return summary







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[Any]						)       ->       Tuple:

 """simple docstring"""






 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 _a					(			SCREAMING_SNAKE_CASE						:     int						)       ->       Tuple:

 """simple docstring"""






 with open('rouge_scores.txt'       ,   'w'						) as output:
  output.write(SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  int        = load_and_cache_examples(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  int        = SequentialSampler(SCREAMING_SNAKE_CASE						)

 def collate_fn(SCREAMING_SNAKE_CASE						:     Optional[Any]						):
  return collate(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   block_size=5_12       ,   device=args.device						)

 __lowerCAmelCase:  Union[str, Any]        = DataLoader(
     SCREAMING_SNAKE_CASE       ,   sampler=SCREAMING_SNAKE_CASE       ,   batch_size=args.batch_size       ,   collate_fn=SCREAMING_SNAKE_CASE       ,   )

 return iterator







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  List[Any]        = CNNDMDataset(args.documents_dir						)
 return dataset







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]						)       ->       Dict:

 """simple docstring"""






 __lowerCAmelCase:  Tuple        = [x for x in data if not len(x[1]						) == 0]  # remove empty_files
 __lowerCAmelCase:  Optional[Any]        = [name for name, _, _ in data]
 __lowerCAmelCase:  List[str]        = [' '.join(SCREAMING_SNAKE_CASE						) for _, _, summary_list in data]

 __lowerCAmelCase:  Tuple        = [encode_for_summarization(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) for _, story, summary in data]
 __lowerCAmelCase:  int        = torch.tensor(
     [truncate_or_pad(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   tokenizer.pad_token_id						) for story, _ in encoded_text]						)
 __lowerCAmelCase:  Optional[int]        = compute_token_type_ids(SCREAMING_SNAKE_CASE       ,   tokenizer.cls_token_id						)
 __lowerCAmelCase:  Optional[Any]        = build_mask(SCREAMING_SNAKE_CASE       ,   tokenizer.pad_token_id						)

 __lowerCAmelCase:  Optional[int]        = Batch(
     document_names=SCREAMING_SNAKE_CASE       ,   batch_size=len(SCREAMING_SNAKE_CASE						)       ,   src=encoded_stories.to(SCREAMING_SNAKE_CASE						)       ,   segs=encoder_token_type_ids.to(SCREAMING_SNAKE_CASE						)       ,   mask_src=encoder_mask.to(SCREAMING_SNAKE_CASE						)       ,   tgt_str=SCREAMING_SNAKE_CASE       ,   )

 return batch







def _a					(			SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     Tuple						)       ->       Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:  int        = summary_tokens.to('cpu'						).numpy()
 __lowerCAmelCase:  Tuple        = tokenizer.decode(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  str        = summary.split('.'						)
 __lowerCAmelCase:  int        = [s + '.' for s in sentences]
 return sentences







def _a					(			)       ->       List[Any]:

 """simple docstring"""






 __lowerCAmelCase:  List[Any]        = argparse.ArgumentParser()
 parser.add_argument(
     '--documents_dir'       ,   default=SCREAMING_SNAKE_CASE       ,   type=SCREAMING_SNAKE_CASE       ,   required=SCREAMING_SNAKE_CASE       ,   help='The folder where the documents to summarize are located.'       ,   )
 parser.add_argument(
     '--summaries_output_dir'       ,   default=SCREAMING_SNAKE_CASE       ,   type=SCREAMING_SNAKE_CASE       ,   required=SCREAMING_SNAKE_CASE       ,   help='The folder in wich the summaries should be written. Defaults to the folder where the documents are'       ,   )
 parser.add_argument(
     '--compute_rouge'       ,   default=SCREAMING_SNAKE_CASE       ,   type=SCREAMING_SNAKE_CASE       ,   required=SCREAMING_SNAKE_CASE       ,   help='Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'       ,   )
 # EVALUATION options
 parser.add_argument(
     '--no_cuda'       ,   default=SCREAMING_SNAKE_CASE       ,   type=SCREAMING_SNAKE_CASE       ,   help='Whether to force the execution on CPU.'       ,   )
 parser.add_argument(
     '--batch_size'       ,   default=4       ,   type=SCREAMING_SNAKE_CASE       ,   help='Batch size per GPU/CPU for training.'       ,   )
 # BEAM SEARCH arguments
 parser.add_argument(
     '--min_length'       ,   default=50       ,   type=SCREAMING_SNAKE_CASE       ,   help='Minimum number of tokens for the summaries.'       ,   )
 parser.add_argument(
     '--max_length'       ,   default=2_00       ,   type=SCREAMING_SNAKE_CASE       ,   help='Maixmum number of tokens for the summaries.'       ,   )
 parser.add_argument(
     '--beam_size'       ,   default=5       ,   type=SCREAMING_SNAKE_CASE       ,   help='The number of beams to start with for each example.'       ,   )
 parser.add_argument(
     '--alpha'       ,   default=0.9_5       ,   type=SCREAMING_SNAKE_CASE       ,   help='The value of alpha for the length penalty in the beam search.'       ,   )
 parser.add_argument(
     '--block_trigram'       ,   default=SCREAMING_SNAKE_CASE       ,   type=SCREAMING_SNAKE_CASE       ,   help='Whether to block the existence of repeating trigrams in the text generated by beam search.'       ,   )
 __lowerCAmelCase:  str        = parser.parse_args()

 # Select device (distibuted not available)
 __lowerCAmelCase:  str        = 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:
  __lowerCAmelCase:  Tuple        = 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=SCREAMING_SNAKE_CASE						)

 evaluate(SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]						)       ->       Dict:

 """simple docstring"""






 if not os.path.exists(SCREAMING_SNAKE_CASE						):
  return False

 __lowerCAmelCase:  Union[str, Any]        = os.listdir(SCREAMING_SNAKE_CASE						)
 if len(SCREAMING_SNAKE_CASE						) == 0:
  return False

 return True


if __name__ == "__main__":
  main()








def _a					(			SCREAMING_SNAKE_CASE						:     int						)       ->       int:

 """simple docstring"""






 return n * (2 * n - 1)







def _a					(			SCREAMING_SNAKE_CASE						:     int						)       ->       bool:

 """simple docstring"""






 __lowerCAmelCase:  List[Any]        = (1 + 24 * n) ** 0.5
 return ((1 + root) / 6) % 1 == 0







def _a					(			SCREAMING_SNAKE_CASE						:     int = 1_44						)       ->       int:

 """simple docstring"""






 __lowerCAmelCase:  Dict        = start
 __lowerCAmelCase:  List[str]        = hexagonal_num(SCREAMING_SNAKE_CASE						)
 while not is_pentagonal(SCREAMING_SNAKE_CASE						):
  n += 1
  __lowerCAmelCase:  Any        = hexagonal_num(SCREAMING_SNAKE_CASE						)
 return num


if __name__ == "__main__":
  print(f"{solution()} = ")





