


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,
)


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


lowercase_           =	namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]							=						BertTokenizer.from_pretrained('bert-base-uncased'			,       do_lower_case=_UpperCAmelCase						)
 __lowerCamelCase		:							Dict							=						BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'						)
 model.to(args.device						)
 model.eval()

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

 if args.compute_rouge:
  __lowerCamelCase		:							Dict							=						[]
  __lowerCamelCase		:							List[str]							=						[]

  import nltk
  import rouge

  nltk.download('punkt'						)
  __lowerCamelCase		:							Optional[Any]							=						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.
 __lowerCamelCase		:							Optional[Any]							=						''
 __lowerCamelCase		:							Optional[Any]							=						''

 __lowerCamelCase		:							int							=						build_data_iterator(_UpperCAmelCase			,       _UpperCAmelCase						)
 __lowerCamelCase		:							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						):
  __lowerCamelCase		:							Union[str, Any]							=						predictor.translate_batch(_UpperCAmelCase						)
  __lowerCamelCase		:							str							=						predictor.from_batch(_UpperCAmelCase						)
  __lowerCamelCase		:							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:
  __lowerCamelCase		:							List[Any]							=						rouge_evaluator.get_scores(_UpperCAmelCase			,       _UpperCAmelCase						)
  __lowerCamelCase		:							Optional[Any]							=						format_rouge_scores(_UpperCAmelCase						)
  save_rouge_scores(_UpperCAmelCase						)
  print(_UpperCAmelCase						)
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 for summary, document_name in zip(_UpperCAmelCase			,       _UpperCAmelCase						):
  # Prepare the summary file's name
  if "." in document_name:
   __lowerCamelCase		:							List[Any]							=						'.'.join(document_name.split('.'						)[:-1]						)
   __lowerCamelCase		:							Dict							=						document_name.split('.'						)[-1]
   __lowerCamelCase		:							Any							=						bare_document_name + '_summary.' + extension
  else:
   __lowerCamelCase		:							List[str]							=						document_name + '_summary'

  __lowerCamelCase		:							List[str]							=						os.path.join(_UpperCAmelCase			,       _UpperCAmelCase						)
  with open(_UpperCAmelCase			,       'w'						) as output:
   output.write(_UpperCAmelCase						)
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase					,     __lowerCamelCase					,     __lowerCamelCase		:							Optional[Any]							=						translation
 __lowerCamelCase		:							Dict							=						(
     raw_summary.replace('[unused0]'			,       ''						)
     .replace('[unused3]'			,       ''						)
     .replace('[PAD]'			,       ''						)
     .replace('[unused1]'			,       ''						)
     .replace(r' +'			,       ' '						)
     .replace(' [unused2] '			,       '. '						)
     .replace('[unused2]'			,       ''						)
     .strip()
 )

 return summary
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 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       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 with open('rouge_scores.txt'			,       'w'						) as output:
  output.write(_UpperCAmelCase						)
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]							=						load_and_cache_examples(_UpperCAmelCase			,       _UpperCAmelCase						)
 __lowerCamelCase		:							Tuple							=						SequentialSampler(_UpperCAmelCase						)

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

 __lowerCamelCase		:							Optional[int]							=						DataLoader(
     _UpperCAmelCase			,       sampler=_UpperCAmelCase			,       batch_size=args.batch_size			,       collate_fn=_UpperCAmelCase			,       )

 return iterator
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]							=						CNNDMDataset(args.documents_dir						)
 return dataset
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict							=						[x for x in data if not len(x[1]						) == 0]  # remove empty_files
 __lowerCamelCase		:							str							=						[name for name, _, _ in data]
 __lowerCamelCase		:							Dict							=						[' '.join(_UpperCAmelCase						) for _, _, summary_list in data]

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

 __lowerCamelCase		:							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       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[str]							=						summary_tokens.to('cpu'						).numpy()
 __lowerCamelCase		:							Dict							=						tokenizer.decode(_UpperCAmelCase						)
 __lowerCamelCase		:							List[str]							=						summary.split('.'						)
 __lowerCamelCase		:							Union[str, Any]							=						[s + '.' for s in sentences]
 return sentences
def       UpperCamelCase__     (  ):
 __lowerCamelCase		:							int							=						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=50			,       type=_UpperCAmelCase			,       help='Minimum number of tokens for the summaries.'			,       )
 parser.add_argument(
     '--max_length'			,       default=200			,       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.'			,       )
 __lowerCamelCase		:							Tuple							=						parser.parse_args()

 # Select device (distibuted not available)
 __lowerCamelCase		:							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:
  __lowerCamelCase		:							Optional[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=_UpperCAmelCase						)

 evaluate(_UpperCAmelCase						)
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 if not os.path.exists(_UpperCAmelCase						):
  return False

 __lowerCamelCase		:							Any							=						os.listdir(_UpperCAmelCase						)
 if len(_UpperCAmelCase						) == 0:
  return False

 return True


if __name__ == "__main__":
       main()








"""simple docstring"""
__UpperCamelCase			:		Tuple		    =   10**8

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  int       ):
 if n % 10 == 0:
  return False
 lowerCAmelCase  							=      str(_UpperCAmelCase       )
 return s == s[::-1]

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      set()
 lowerCAmelCase  							=      1
 lowerCAmelCase  							=      5
 while sum_squares < LIMIT:
  lowerCAmelCase  							=      first_square + 1
  while sum_squares < LIMIT:
   if is_palindrome(_UpperCAmelCase       ):
    answer.add(_UpperCAmelCase       )
   last_square += 1
   sum_squares += last_square**2
  first_square += 1
  lowerCAmelCase  							=      first_square**2 + (first_square + 1) ** 2

 return sum(_UpperCAmelCase       )


if __name__ == "__main__":
       print(solution())





