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

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

     if args.compute_rouge:
          snake_case_   			=   []
          snake_case_   			=   []

          import nltk
          import rouge

          nltk.download('punkt'   )
          snake_case_   			=   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.
     snake_case_   			=   ''
     snake_case_   			=   ''

     snake_case_   			=   build_data_iterator(UpperCAmelCase		,   UpperCAmelCase   )
     snake_case_   			=   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   ):
          snake_case_   			=   predictor.translate_batch(UpperCAmelCase   )
          snake_case_   			=   predictor.from_batch(UpperCAmelCase   )
          snake_case_   			=   [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:
          snake_case_   			=   rouge_evaluator.get_scores(UpperCAmelCase		,   UpperCAmelCase   )
          snake_case_   			=   format_rouge_scores(UpperCAmelCase   )
          save_rouge_scores(UpperCAmelCase   )
          print(UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[int]:
     for summary, document_name in zip(UpperCAmelCase		,   UpperCAmelCase   ):
          # Prepare the summary file's name
          if "." in document_name:
               snake_case_   			=   '.'.join(document_name.split('.'   )[:-1]   )
               snake_case_   			=   document_name.split('.'   )[-1]
               snake_case_   			=   bare_document_name + '_summary.' + extension
          else:
               snake_case_   			=   document_name + '_summary'

          snake_case_   			=   os.path.join(UpperCAmelCase		,   UpperCAmelCase   )
          with open(UpperCAmelCase		,   'w'   ) as output:
               output.write(UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase   )							-> Any:
     snake_case_			,      snake_case_			,      snake_case_   			=   translation
     snake_case_   			=   (
         raw_summary.replace('[unused0]'		,   ''   )
         .replace('[unused3]'		,   ''   )
         .replace('[PAD]'		,   ''   )
         .replace('[unused1]'		,   ''   )
         .replace(R' +'		,   ' '   )
         .replace(' [unused2] '		,   '. '   )
         .replace('[unused2]'		,   ''   )
         .strip()
     )

     return summary







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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Any:
     snake_case_   			=   load_and_cache_examples(UpperCAmelCase		,   UpperCAmelCase   )
     snake_case_   			=   SequentialSampler(UpperCAmelCase   )

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

     snake_case_   			=   DataLoader(
         UpperCAmelCase		,   sampler=UpperCAmelCase		,   batch_size=args.batch_size		,   collate_fn=UpperCAmelCase		,   )

     return iterator







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> List[Any]:
     snake_case_   			=   CNNDMDataset(args.documents_dir   )
     return dataset







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> Any:
     snake_case_   			=   [x for x in data if not len(x[1]   ) == 0]  # remove empty_files
     snake_case_   			=   [name for name, _, _ in data]
     snake_case_   			=   [' '.join(UpperCAmelCase   ) for _, _, summary_list in data]

     snake_case_   			=   [encode_for_summarization(UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   ) for _, story, summary in data]
     snake_case_   			=   torch.tensor(
         [truncate_or_pad(UpperCAmelCase		,   UpperCAmelCase		,   tokenizer.pad_token_id   ) for story, _ in encoded_text]   )
     snake_case_   			=   compute_token_type_ids(UpperCAmelCase		,   tokenizer.cls_token_id   )
     snake_case_   			=   build_mask(UpperCAmelCase		,   tokenizer.pad_token_id   )

     snake_case_   			=   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       (						UpperCAmelCase		,   UpperCAmelCase   )							-> List[str]:
     snake_case_   			=   summary_tokens.to('cpu'   ).numpy()
     snake_case_   			=   tokenizer.decode(UpperCAmelCase   )
     snake_case_   			=   summary.split('.'   )
     snake_case_   			=   [s + '.' for s in sentences]
     return sentences







def    UpperCAmelCase       (						)							-> List[Any]:
     snake_case_   			=   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.'		,   )
     snake_case_   			=   parser.parse_args()

     # Select device (distibuted not available)
     snake_case_   			=   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:
          snake_case_   			=   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       (						UpperCAmelCase   )							-> str:
     if not os.path.exists(UpperCAmelCase   ):
          return False

     snake_case_   			=   os.listdir(UpperCAmelCase   )
     if len(UpperCAmelCase   ) == 0:
          return False

     return True


if __name__ == "__main__":
    main()


"""simple docstring"""


from math import factorial







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> List[str]:
     return factorial(UpperCAmelCase   ) / (factorial(UpperCAmelCase   ) * factorial(n - r   ))







def    UpperCAmelCase       (						)							-> Optional[Any]:
     snake_case_   			=   0

     for i in range(1		,   101   ):
          for j in range(1		,   i + 1   ):
               if combinations(UpperCAmelCase		,   UpperCAmelCase   ) > 1e6:
                    total += 1
     return total


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

