






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__				)  ->      Dict:
 lowerCamelCase_							:    Union[str, 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_							:    List[str]							=     {
     'BOS': tokenizer.vocab['[unused0]'],
     'EOS': tokenizer.vocab['[unused1]'],
     'PAD': tokenizer.vocab['[PAD]'],
 }

 if args.compute_rouge:
  lowerCamelCase_							:    Any							=     []
  lowerCamelCase_							:    int							=     []

  import nltk
  import rouge

  nltk.download('punkt'				)
  lowerCamelCase_							:    str							=     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_							:    Tuple							=     ''

 lowerCamelCase_							:    str							=     build_data_iterator(lowerCamelCase__ ,			lowerCamelCase__				)
 lowerCamelCase_							:    List[str]							=     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_							:    str							=     predictor.from_batch(lowerCamelCase__				)
  lowerCamelCase_							:    Dict							=     [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_							:    Dict							=     rouge_evaluator.get_scores(lowerCamelCase__ ,			lowerCamelCase__				)
  lowerCamelCase_							:    str							=     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_							:    Any							=     '.'.join(document_name.split('.'				)[:-1]				)
   lowerCamelCase_							:    Dict							=     document_name.split('.'				)[-1]
   lowerCamelCase_							:    Dict							=     bare_document_name + '_summary.' + extension
  else:
   lowerCamelCase_							:    Tuple							=     document_name + '_summary'

  lowerCamelCase_							:    Any							=     os.path.join(lowerCamelCase__ ,			lowerCamelCase__				)
  with open(lowerCamelCase__ ,			'w'				) as output:
   output.write(lowerCamelCase__				)




def       _a   (							lowerCamelCase__				)  ->      Tuple:
 lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_							:    Tuple							=     translation
 lowerCamelCase_							:    List[Any]							=     (
     raw_summary.replace('[unused0]' ,			''				)
     .replace('[unused3]' ,			''				)
     .replace('[PAD]' ,			''				)
     .replace('[unused1]' ,			''				)
     .replace(r' +' ,			' '				)
     .replace(' [unused2] ' ,			'. '				)
     .replace('[unused2]' ,			''				)
     .strip()
 )

 return summary




def       _a   (							lowerCamelCase__				)  ->      Union[str, Any]:
 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__				)  ->      int:
 with open('rouge_scores.txt' ,			'w'				) as output:
  output.write(lowerCamelCase__				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      Tuple:
 lowerCamelCase_							:    List[str]							=     load_and_cache_examples(lowerCamelCase__ ,			lowerCamelCase__				)
 lowerCamelCase_							:    Optional[int]							=     SequentialSampler(lowerCamelCase__				)

 def collate_fn(lowerCamelCase__				):
  return collate(lowerCamelCase__ ,			lowerCamelCase__ ,			block_size=5_12 ,			device=args.device				)

 lowerCamelCase_							:    Any							=     DataLoader(
     lowerCamelCase__ ,			sampler=lowerCamelCase__ ,			batch_size=args.batch_size ,			collate_fn=lowerCamelCase__ ,			)

 return iterator




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      str:
 lowerCamelCase_							:    str							=     CNNDMDataset(args.documents_dir				)
 return dataset




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      str:
 lowerCamelCase_							:    List[str]							=     [x for x in data if not len(x[1]				) == 0]  # remove empty_files
 lowerCamelCase_							:    int							=     [name for name, _, _ in data]
 lowerCamelCase_							:    Tuple							=     [' '.join(lowerCamelCase__				) for _, _, summary_list in data]

 lowerCamelCase_							:    List[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_							:    int							=     compute_token_type_ids(lowerCamelCase__ ,			tokenizer.cls_token_id				)
 lowerCamelCase_							:    List[str]							=     build_mask(lowerCamelCase__ ,			tokenizer.pad_token_id				)

 lowerCamelCase_							:    Union[str, Any]							=     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__				)  ->      List[Any]:
 lowerCamelCase_							:    Dict							=     summary_tokens.to('cpu'				).numpy()
 lowerCamelCase_							:    List[Any]							=     tokenizer.decode(lowerCamelCase__				)
 lowerCamelCase_							:    Union[str, Any]							=     summary.split('.'				)
 lowerCamelCase_							:    Union[str, Any]							=     [s + '.' for s in sentences]
 return sentences




def       _a   (							)  ->      int:
 lowerCamelCase_							:    Dict							=     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=2_00 ,			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_							:    List[str]							=     parser.parse_args()

 # Select device (distibuted not available)
 lowerCamelCase_							:    List[Any]							=     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_							:    List[str]							=     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__				)  ->      List[Any]:
 if not os.path.exists(lowerCamelCase__				):
  return False

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

 return True


if __name__ == "__main__":
       main()








from __future__ import annotations




def       _a   (							lowerCamelCase__				)  ->      list[int]:
 lowerCamelCase_							:    Any							=     [0] * max_one

 for i in range(0 ,			lowerCamelCase__				):
  lowerCamelCase_							:    Union[str, Any]							=     i

 for i in range(2 ,			lowerCamelCase__				):
  if totients[i] == i:
   for j in range(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				):
    totients[j] -= totients[j] // i

 return totients




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      bool:
 return sorted(str(lowerCamelCase__				)				) == sorted(str(lowerCamelCase__				)				)




def       _a   (							lowerCamelCase__ = 10_00_00_00				)  ->      int:
 lowerCamelCase_							:    Tuple							=     1  # i
 lowerCamelCase_							:    Union[str, Any]							=     0  # φ(i)
 lowerCamelCase_							:    List[Any]							=     get_totients(max_n + 1				)

 for i in range(2 ,			max_n + 1				):
  lowerCamelCase_							:    Tuple							=     totients[i]

  if i * min_denominator < min_numerator * t and has_same_digits(lowerCamelCase__ ,			lowerCamelCase__				):
   lowerCamelCase_							:    str							=     i
   lowerCamelCase_							:    List[Any]							=     t

 return min_numerator


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