



'''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,
)


_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  a_  (							_lowerCAmelCase )   ->   Optional[int]:
  __lowerCamelCase : List[Any]					 =				BertTokenizer.from_pretrained('bert-base-uncased' ,do_lower_case=_lowerCAmelCase )
  __lowerCamelCase : int					 =				BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization' )
  model.to(args.device )
  model.eval()

  __lowerCamelCase : int					 =				{
      'BOS': tokenizer.vocab['[unused0]'],
      'EOS': tokenizer.vocab['[unused1]'],
      'PAD': tokenizer.vocab['[PAD]'],
  }

  if args.compute_rouge:
    __lowerCamelCase : List[str]					 =				[]
    __lowerCamelCase : Optional[int]					 =				[]

    import nltk
    import rouge

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

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

  __lowerCamelCase : List[Any]					 =				build_data_iterator(_lowerCAmelCase ,_lowerCAmelCase )
  __lowerCamelCase : Any					 =				build_predictor(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )

  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(_lowerCAmelCase ):
    __lowerCamelCase : Union[str, Any]					 =				predictor.translate_batch(_lowerCAmelCase )
    __lowerCamelCase : Union[str, Any]					 =				predictor.from_batch(_lowerCAmelCase )
    __lowerCamelCase : Optional[int]					 =				[format_summary(_lowerCAmelCase ) for t in translations]
    save_summaries(_lowerCAmelCase ,args.summaries_output_dir ,batch.document_names )

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

  if args.compute_rouge:
    __lowerCamelCase : Any					 =				rouge_evaluator.get_scores(_lowerCAmelCase ,_lowerCAmelCase )
    __lowerCamelCase : Dict					 =				format_rouge_scores(_lowerCAmelCase )
    save_rouge_scores(_lowerCAmelCase )
    print(_lowerCAmelCase )

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )   ->   int:
  for summary, document_name in zip(_lowerCAmelCase ,_lowerCAmelCase ):
    # Prepare the summary file's name
    if "." in document_name:
      __lowerCamelCase : str					 =				'.'.join(document_name.split('.' )[:-1] )
      __lowerCamelCase : Tuple					 =				document_name.split('.' )[-1]
      __lowerCamelCase : str					 =				bare_document_name + '_summary.' + extension
    else:
      __lowerCamelCase : Union[str, Any]					 =				document_name + '_summary'

    __lowerCamelCase : Dict					 =				os.path.join(_lowerCAmelCase ,_lowerCAmelCase )
    with open(_lowerCAmelCase ,'w' ) as output:
      output.write(_lowerCAmelCase )

def  a_  (							_lowerCAmelCase )   ->   List[str]:
  __lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase : Optional[int]					 =				translation
  __lowerCamelCase : Dict					 =				(
      raw_summary.replace('[unused0]' ,'' )
      .replace('[unused3]' ,'' )
      .replace('[PAD]' ,'' )
      .replace('[unused1]' ,'' )
      .replace(r' +' ,' ' )
      .replace(' [unused2] ' ,'. ' )
      .replace('[unused2]' ,'' )
      .strip()
  )

  return summary

def  a_  (							_lowerCAmelCase )   ->   Dict:
  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_  (							_lowerCAmelCase )   ->   Tuple:
  with open('rouge_scores.txt' ,'w' ) as output:
    output.write(_lowerCAmelCase )

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   str:
  __lowerCamelCase : Optional[Any]					 =				load_and_cache_examples(_lowerCAmelCase ,_lowerCAmelCase )
  __lowerCamelCase : List[str]					 =				SequentialSampler(_lowerCAmelCase )

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

  __lowerCamelCase : Tuple					 =				DataLoader(
      _lowerCAmelCase ,sampler=_lowerCAmelCase ,batch_size=args.batch_size ,collate_fn=_lowerCAmelCase ,)

  return iterator

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   Tuple:
  __lowerCamelCase : Dict					 =				CNNDMDataset(args.documents_dir )
  return dataset

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )   ->   Any:
  __lowerCamelCase : Optional[Any]					 =				[x for x in data if not len(x[1] ) == 0]  # remove empty_files
  __lowerCamelCase : Union[str, Any]					 =				[name for name, _, _ in data]
  __lowerCamelCase : Optional[int]					 =				[' '.join(_lowerCAmelCase ) for _, _, summary_list in data]

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

  __lowerCamelCase : int					 =				Batch(
      document_names=_lowerCAmelCase ,batch_size=len(_lowerCAmelCase ) ,src=encoded_stories.to(_lowerCAmelCase ) ,segs=encoder_token_type_ids.to(_lowerCAmelCase ) ,mask_src=encoder_mask.to(_lowerCAmelCase ) ,tgt_str=_lowerCAmelCase ,)

  return batch

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   Optional[Any]:
  __lowerCamelCase : Optional[Any]					 =				summary_tokens.to('cpu' ).numpy()
  __lowerCamelCase : Tuple					 =				tokenizer.decode(_lowerCAmelCase )
  __lowerCamelCase : str					 =				summary.split('.' )
  __lowerCamelCase : Any					 =				[s + '.' for s in sentences]
  return sentences

def  a_  (							)   ->   Optional[Any]:
  __lowerCamelCase : str					 =				argparse.ArgumentParser()
  parser.add_argument(
      '--documents_dir' ,default=_lowerCAmelCase ,type=_lowerCAmelCase ,required=_lowerCAmelCase ,help='The folder where the documents to summarize are located.' ,)
  parser.add_argument(
      '--summaries_output_dir' ,default=_lowerCAmelCase ,type=_lowerCAmelCase ,required=_lowerCAmelCase ,help='The folder in wich the summaries should be written. Defaults to the folder where the documents are' ,)
  parser.add_argument(
      '--compute_rouge' ,default=_lowerCAmelCase ,type=_lowerCAmelCase ,required=_lowerCAmelCase ,help='Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.' ,)
  # EVALUATION options
  parser.add_argument(
      '--no_cuda' ,default=_lowerCAmelCase ,type=_lowerCAmelCase ,help='Whether to force the execution on CPU.' ,)
  parser.add_argument(
      '--batch_size' ,default=4 ,type=_lowerCAmelCase ,help='Batch size per GPU/CPU for training.' ,)
  # BEAM SEARCH arguments
  parser.add_argument(
      '--min_length' ,default=50 ,type=_lowerCAmelCase ,help='Minimum number of tokens for the summaries.' ,)
  parser.add_argument(
      '--max_length' ,default=200 ,type=_lowerCAmelCase ,help='Maixmum number of tokens for the summaries.' ,)
  parser.add_argument(
      '--beam_size' ,default=5 ,type=_lowerCAmelCase ,help='The number of beams to start with for each example.' ,)
  parser.add_argument(
      '--alpha' ,default=0.95 ,type=_lowerCAmelCase ,help='The value of alpha for the length penalty in the beam search.' ,)
  parser.add_argument(
      '--block_trigram' ,default=_lowerCAmelCase ,type=_lowerCAmelCase ,help='Whether to block the existence of repeating trigrams in the text generated by beam search.' ,)
  __lowerCamelCase : int					 =				parser.parse_args()

  # Select device (distibuted not available)
  __lowerCamelCase : Dict					 =				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 : 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=_lowerCAmelCase )

  evaluate(_lowerCAmelCase )

def  a_  (							_lowerCAmelCase )   ->   int:
  if not os.path.exists(_lowerCAmelCase ):
    return False

  __lowerCamelCase : int					 =				os.listdir(_lowerCAmelCase )
  if len(_lowerCAmelCase ) == 0:
    return False

  return True


if __name__ == "__main__":
   main()











'''simple docstring'''





import decimal

def  a_  (							)   ->   int:
  __lowerCamelCase : int					 =				0
  __lowerCamelCase : Any					 =				decimal.Context(prec=105 )
  for i in range(2 ,100 ):
    __lowerCamelCase : str					 =				decimal.Decimal(_lowerCAmelCase )
    __lowerCamelCase : int					 =				number.sqrt(_lowerCAmelCase )
    if len(str(_lowerCAmelCase ) ) > 1:
      answer += int(str(_lowerCAmelCase )[0] )
      __lowerCamelCase : List[str]					 =				str(_lowerCAmelCase )[2:101]
      answer += sum(int(_lowerCAmelCase ) for x in sqrt_number_str )
  return answer


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(f'''{solution() = }''')






