






"""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,
)


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


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

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			BertTokenizer.from_pretrained("""bert-base-uncased"""  ,     do_lower_case=UpperCamelCase__       )
  _a						: Optional[int]    =			BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""       )
  model.to(args.device       )
  model.eval()

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

  if args.compute_rouge:
    _a						: Optional[int]    =			[]
    _a						: Tuple    =			[]

    import nltk
    import rouge

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

  # these (unused) arguments are defined to keep the compatibility
  # with the legacy code and will be deleted in a next iteration.
  _a						: Optional[Any]    =			""""""
  _a						: Any    =			""""""

  _a						: Tuple    =			build_data_iterator(UpperCamelCase__  ,     UpperCamelCase__       )
  _a						: int    =			build_predictor(UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       )

  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(UpperCamelCase__       ):
    _a						: List[str]    =			predictor.translate_batch(UpperCamelCase__       )
    _a						: Optional[int]    =			predictor.from_batch(UpperCamelCase__       )
    _a						: int    =			[format_summary(UpperCamelCase__       ) for t in translations]
    save_summaries(UpperCamelCase__  ,     args.summaries_output_dir  ,     batch.document_names       )

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

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

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  for summary, document_name in zip(UpperCamelCase__  ,     UpperCamelCase__       ):
    # Prepare the summary file's name
    if "." in document_name:
      _a						: List[str]    =			""".""".join(document_name.split("""."""       )[:-1]       )
      _a						: Optional[int]    =			document_name.split("""."""       )[-1]
      _a						: Optional[int]    =			bare_document_name + """_summary.""" + extension
    else:
      _a						: Optional[int]    =			document_name + """_summary"""

    _a						: Dict    =			os.path.join(UpperCamelCase__  ,     UpperCamelCase__       )
    with open(UpperCamelCase__  ,     """w"""       ) as output:
      output.write(UpperCamelCase__       )

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a   ,						_a   ,						_a						: Any    =			translation
  _a						: Dict    =			(
      raw_summary.replace("""[unused0]"""  ,     """"""       )
      .replace("""[unused3]"""  ,     """"""       )
      .replace("""[PAD]"""  ,     """"""       )
      .replace("""[unused1]"""  ,     """"""       )
      .replace(R""" +"""  ,     """ """       )
      .replace(""" [unused2] """  ,     """. """       )
      .replace("""[unused2]"""  ,     """"""       )
      .strip()
  )

  return summary

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''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     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  with open("""rouge_scores.txt"""  ,     """w"""       ) as output:
    output.write(UpperCamelCase__       )

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: Any    =			load_and_cache_examples(UpperCamelCase__  ,     UpperCamelCase__       )
  _a						: List[Any]    =			SequentialSampler(UpperCamelCase__       )

  def collate_fn(UpperCamelCase__       ):
    return collate(UpperCamelCase__  ,     UpperCamelCase__  ,     block_size=5_1_2  ,     device=args.device       )

  _a						: List[str]    =			DataLoader(
      UpperCamelCase__  ,     sampler=UpperCamelCase__  ,     batch_size=args.batch_size  ,     collate_fn=UpperCamelCase__  ,     )

  return iterator

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			CNNDMDataset(args.documents_dir       )
  return dataset

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: str    =			[x for x in data if not len(x[1]       ) == 0]  # remove empty_files
  _a						: Any    =			[name for name, _, _ in data]
  _a						: List[Any]    =			[""" """.join(UpperCamelCase__       ) for _, _, summary_list in data]

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

  _a						: Tuple    =			Batch(
      document_names=UpperCamelCase__  ,     batch_size=len(UpperCamelCase__       )  ,     src=encoded_stories.to(UpperCamelCase__       )  ,     segs=encoder_token_type_ids.to(UpperCamelCase__       )  ,     mask_src=encoder_mask.to(UpperCamelCase__       )  ,     tgt_str=UpperCamelCase__  ,     )

  return batch

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			summary_tokens.to("""cpu"""       ).numpy()
  _a						: List[Any]    =			tokenizer.decode(UpperCamelCase__       )
  _a						: Dict    =			summary.split("""."""       )
  _a						: List[Any]    =			[s + """.""" for s in sentences]
  return sentences

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Dict    =			argparse.ArgumentParser()
  parser.add_argument(
      """--documents_dir"""  ,     default=UpperCamelCase__  ,     type=UpperCamelCase__  ,     required=UpperCamelCase__  ,     help="""The folder where the documents to summarize are located."""  ,     )
  parser.add_argument(
      """--summaries_output_dir"""  ,     default=UpperCamelCase__  ,     type=UpperCamelCase__  ,     required=UpperCamelCase__  ,     help="""The folder in wich the summaries should be written. Defaults to the folder where the documents are"""  ,     )
  parser.add_argument(
      """--compute_rouge"""  ,     default=UpperCamelCase__  ,     type=UpperCamelCase__  ,     required=UpperCamelCase__  ,     help="""Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."""  ,     )
  # EVALUATION options
  parser.add_argument(
      """--no_cuda"""  ,     default=UpperCamelCase__  ,     type=UpperCamelCase__  ,     help="""Whether to force the execution on CPU."""  ,     )
  parser.add_argument(
      """--batch_size"""  ,     default=4  ,     type=UpperCamelCase__  ,     help="""Batch size per GPU/CPU for training."""  ,     )
  # BEAM SEARCH arguments
  parser.add_argument(
      """--min_length"""  ,     default=5_0  ,     type=UpperCamelCase__  ,     help="""Minimum number of tokens for the summaries."""  ,     )
  parser.add_argument(
      """--max_length"""  ,     default=2_0_0  ,     type=UpperCamelCase__  ,     help="""Maixmum number of tokens for the summaries."""  ,     )
  parser.add_argument(
      """--beam_size"""  ,     default=5  ,     type=UpperCamelCase__  ,     help="""The number of beams to start with for each example."""  ,     )
  parser.add_argument(
      """--alpha"""  ,     default=0.95  ,     type=UpperCamelCase__  ,     help="""The value of alpha for the length penalty in the beam search."""  ,     )
  parser.add_argument(
      """--block_trigram"""  ,     default=UpperCamelCase__  ,     type=UpperCamelCase__  ,     help="""Whether to block the existence of repeating trigrams in the text generated by beam search."""  ,     )
  _a						: Optional[int]    =			parser.parse_args()

  # Select device (distibuted not available)
  _a						: Tuple    =			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						: 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=UpperCamelCase__       )

  evaluate(UpperCamelCase__       )

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if not os.path.exists(UpperCamelCase__       ):
    return False

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

  return True


if __name__ == "__main__":
 main()









"""simple docstring"""




from __future__ import annotations

import sys

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if n < 0:
    raise ValueError("""Negative arguments are not supported"""       )
  return _fib(UpperCamelCase__       )[0]

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if n == 0:  # (F(0), F(1))
    return (0, 1)

  # F(2n) = F(n)[2F(n+1) − F(n)]
  # F(2n+1) = F(n+1)^2+F(n)^2
  _a   ,						_a						: Dict    =			_fib(n // 2       )
  _a						: Dict    =			a * (b * 2 - a)
  _a						: Optional[Any]    =			a * a + b * b
  return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
 _snake_case					   =     int(sys.argv[1])
 print(F'''fibonacci({n}) is {fibonacci(n)}''')

