
'''simple docstring'''
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__ :		Optional[Any]					=logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


A__ :		Dict					=namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[int]			)					->							List[str]:





  """simple docstring"""

  __A		:  Any       				=		BertTokenizer.from_pretrained("""bert-base-uncased"""      ,  do_lower_case=__SCREAMING_SNAKE_CASE			)
  __A		:  str       				=		BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""			)
  model.to(args.device			)
  model.eval()

  __A		:  Any       				=		{
      """BOS""": tokenizer.vocab["""[unused0]"""],
      """EOS""": tokenizer.vocab["""[unused1]"""],
      """PAD""": tokenizer.vocab["""[PAD]"""],
  }

  if args.compute_rouge:
    __A		:  Dict       				=		[]
    __A		:  str       				=		[]

    import nltk
    import rouge

    nltk.download("""punkt"""			)
    __A		:  List[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.
  __A		:  Union[str, Any]       				=		""""""
  __A		:  List[str]       				=		""""""

  __A		:  List[Any]       				=		build_data_iterator(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
  __A		:  str       				=		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			):
    __A		:  Optional[int]       				=		predictor.translate_batch(__SCREAMING_SNAKE_CASE			)
    __A		:  Dict       				=		predictor.from_batch(__SCREAMING_SNAKE_CASE			)
    __A		:  Union[str, 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:
    __A		:  List[Any]       				=		rouge_evaluator.get_scores(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
    __A		:  Union[str, Any]       				=		format_rouge_scores(__SCREAMING_SNAKE_CASE			)
    save_rouge_scores(__SCREAMING_SNAKE_CASE			)
    print(__SCREAMING_SNAKE_CASE			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	Tuple			)					->							str:





  """simple docstring"""

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

    __A		:  str       				=		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]			)					->							Any:





  """simple docstring"""

  __A				,       __A				,       __A		:  Union[str, Any]       				=		translation
  __A		:  List[str]       				=		(
      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]			)					->							int:





  """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    :	Optional[Any]			)					->							Dict:





  """simple docstring"""

  with open("""rouge_scores.txt"""      ,  """w"""			) as output:
    output.write(__SCREAMING_SNAKE_CASE			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Any      ,  __SCREAMING_SNAKE_CASE    :	int			)					->							int:





  """simple docstring"""

  __A		:  Dict       				=		load_and_cache_examples(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
  __A		:  int       				=		SequentialSampler(__SCREAMING_SNAKE_CASE			)

  def collate_fn(__SCREAMING_SNAKE_CASE    :	str			):
    return collate(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  block_size=512      ,  device=args.device			)

  __A		:  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    :	int      ,  __SCREAMING_SNAKE_CASE    :	Dict			)					->							Tuple:





  """simple docstring"""

  __A		:  List[str]       				=		CNNDMDataset(args.documents_dir			)
  return dataset







def 							A_  (    __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]      ,  __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]			)					->							Optional[Any]:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		[x for x in data if not len(x[1]			) == 0]  # remove empty_files
  __A		:  Any       				=		[name for name, _, _ in data]
  __A		:  List[str]       				=		[""" """.join(__SCREAMING_SNAKE_CASE			) for _, _, summary_list in data]

  __A		:  List[str]       				=		[encode_for_summarization(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			) for _, story, summary in data]
  __A		:  Optional[int]       				=		torch.tensor(
      [truncate_or_pad(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  tokenizer.pad_token_id			) for story, _ in encoded_text]			)
  __A		:  List[Any]       				=		compute_token_type_ids(__SCREAMING_SNAKE_CASE      ,  tokenizer.cls_token_id			)
  __A		:  List[Any]       				=		build_mask(__SCREAMING_SNAKE_CASE      ,  tokenizer.pad_token_id			)

  __A		:  List[str]       				=		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    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	Optional[Any]			)					->							List[str]:





  """simple docstring"""

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







def 							A_  (    )					->							Optional[Any]:





  """simple docstring"""

  __A		:  Dict       				=		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=200      ,  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=0.95      ,  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."""      ,  )
  __A		:  Any       				=		parser.parse_args()

  # Select device (distibuted not available)
  __A		:  int       				=		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:
    __A		:  int       				=		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    :	Any			)					->							List[Any]:





  """simple docstring"""

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

  __A		:  int       				=		os.listdir(__SCREAMING_SNAKE_CASE			)
  if len(__SCREAMING_SNAKE_CASE			) == 0:
    return False

  return True


if __name__ == "__main__":
     main()





'''simple docstring'''
from __future__ import annotations







def 							A_  (    __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  )					->							list[float | int]:





  """simple docstring"""

  if not all((nth_term, start_term_a, common_ratio_r)			):
    return []
  __A		:  list[float | int]       				=		[]
  __A		:  Union[str, Any]       				=		1
  __A		:  List[str]       				=		common_ratio_r
  for _ in range(int(__SCREAMING_SNAKE_CASE			)			):
    if not series:
      series.append(__SCREAMING_SNAKE_CASE			)
    else:
      power += 1
      series.append(float(start_term_a * multiple			)			)
      __A		:  List[str]       				=		pow(float(__SCREAMING_SNAKE_CASE			)      ,  __SCREAMING_SNAKE_CASE			)
  return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A__ :		Optional[int]					=float(input('Enter the last number (n term) of the Geometric Series'))
     A__ :		List[Any]					=float(input('Enter the starting term (a) of the Geometric Series'))
     A__ :		List[Any]					=float(
         input('Enter the common ratio between two terms (r) of the Geometric Series')
     )
     print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
     print(geometric_series(nth_term, start_term_a, common_ratio_r))



