






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,
)


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


lowerCamelCase					    =	namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						BertTokenizer.from_pretrained('bert-base-uncased',   do_lower_case=_lowercase   )
    _lowercase     			=						BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'   )
    model.to(args.device   )
    model.eval()

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

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

        import nltk
        import rouge

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

    # these (unused) arguments are defined to keep the compatibility
    # with the legacy code and will be deleted in a next iteration.
    _lowercase     			=						''
    _lowercase     			=						''

    _lowercase     			=						build_data_iterator(_lowercase,   _lowercase   )
    _lowercase     			=						build_predictor(_lowercase,   _lowercase,   _lowercase,   _lowercase   )

    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(_lowercase   ):
        _lowercase     			=						predictor.translate_batch(_lowercase   )
        _lowercase     			=						predictor.from_batch(_lowercase   )
        _lowercase     			=						[format_summary(_lowercase   ) for t in translations]
        save_summaries(_lowercase,   args.summaries_output_dir,   batch.document_names   )

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

    if args.compute_rouge:
        _lowercase     			=						rouge_evaluator.get_scores(_lowercase,   _lowercase   )
        _lowercase     			=						format_rouge_scores(_lowercase   )
        save_rouge_scores(_lowercase   )
        print(_lowercase   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



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

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


    '''simple docstring'''



    _lowercase			,     _lowercase			,     _lowercase     			=						translation
    _lowercase     			=						(
        raw_summary.replace('[unused0]',   ''   )
        .replace('[unused3]',   ''   )
        .replace('[PAD]',   ''   )
        .replace('[unused1]',   ''   )
        .replace(r' +',   ' '   )
        .replace(' [unused2] ',   '. '   )
        .replace('[unused2]',   ''   )
        .strip()
    )

    return summary
def 							UpperCAmelCase   ( _lowercase   ):


    '''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 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    with open('rouge_scores.txt',   'w'   ) as output:
        output.write(_lowercase   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						load_and_cache_examples(_lowercase,   _lowercase   )
    _lowercase     			=						SequentialSampler(_lowercase   )

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

    _lowercase     			=						DataLoader(
        _lowercase,   sampler=_lowercase,   batch_size=args.batch_size,   collate_fn=_lowercase,   )

    return iterator
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						CNNDMDataset(args.documents_dir   )
    return dataset
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						[x for x in data if not len(x[1]   ) == 0]  # remove empty_files
    _lowercase     			=						[name for name, _, _ in data]
    _lowercase     			=						[' '.join(_lowercase   ) for _, _, summary_list in data]

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

    _lowercase     			=						Batch(
        document_names=_lowercase,   batch_size=len(_lowercase   ),   src=encoded_stories.to(_lowercase   ),   segs=encoder_token_type_ids.to(_lowercase   ),   mask_src=encoder_mask.to(_lowercase   ),   tgt_str=_lowercase,   )

    return batch
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						summary_tokens.to('cpu'   ).numpy()
    _lowercase     			=						tokenizer.decode(_lowercase   )
    _lowercase     			=						summary.split('.'   )
    _lowercase     			=						[s + '.' for s in sentences]
    return sentences
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



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

    # Select device (distibuted not available)
    _lowercase     			=						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:
        _lowercase     			=						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=_lowercase   )

    evaluate(_lowercase   )
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    if not os.path.exists(_lowercase   ):
        return False

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

    return True


if __name__ == "__main__":
   main()




from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase     =       logging.get_logger(__name__)







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 __snake_case				=    ["input_ids", "attention_mask"]



 def __init__(      self						, _UpperCAmelCase="[PAD]"						, _UpperCAmelCase="[BOS]"						, _UpperCAmelCase="[EOS]"						, _UpperCAmelCase="[MASK]"						, _UpperCAmelCase="[CLS]"						, _UpperCAmelCase="[SEP]"						, _UpperCAmelCase=20_48						, **_UpperCAmelCase						, ):
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else pad_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else bos_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else eos_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else mask_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else cls_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else sep_token

       super().__init__(
           pad_token=_UpperCAmelCase						, bos_token=_UpperCAmelCase						, eos_token=_UpperCAmelCase						, mask_token=_UpperCAmelCase						, cls_token=_UpperCAmelCase						, sep_token=_UpperCAmelCase						, model_max_length=_UpperCAmelCase						, **_UpperCAmelCase						, )

       snake_case_			= 2**8  # utf is 8 bits

       # define special tokens dict
       snake_case_			= {
           self.pad_token: 0,
           self.bos_token: 1,
           self.eos_token: 2,
           self.mask_token: 3,
           self.cls_token: 4,
           self.sep_token: 5,
       }
       snake_case_			= len(self.special_tokens_encoder   )
       snake_case_			= {v: k for k, v in self.special_tokens_encoder.items()}



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.special_tokens_encoder.copy()
       vocab.update(self.added_tokens_encoder   )
       for i in range(self._utf_vocab_size   ):
             snake_case_			= chr(_UpperCAmelCase   )
             snake_case_			= i + len(self.special_tokens_encoder   )
       return vocab



 @property
 def 		UpperCamelCase__	(      self   ):
       return self._utf_vocab_size + self._num_special_tokens



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None						, _UpperCAmelCase = False   ):
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCAmelCase						, token_ids_a=_UpperCAmelCase						, already_has_special_tokens=_UpperCAmelCase   )

       # normal case: some special tokens
       if token_ids_a is None:
             return [1] + [0] * len(_UpperCAmelCase   ) + [1]
       return [1] + ([0] * len(_UpperCAmelCase   )) + [1] + ([0] * len(_UpperCAmelCase   )) + [1]



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None   ):
       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       else:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= [chr(_UpperCAmelCase   ) for i in text.encode('''utf-8'''   )]
       return tokens



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       if token in self.special_tokens_encoder:
             snake_case_			= self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
             snake_case_			= self.added_tokens_encoder[token]
       elif len(_UpperCAmelCase   ) != 1:
             snake_case_			= self.unk_token_id
       else:
             snake_case_			= ord(_UpperCAmelCase   ) + self._num_special_tokens
       return token_id



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       if index in self.special_tokens_decoder:
             snake_case_			= self.special_tokens_decoder[index]
       elif index in self.added_tokens_decoder:
             snake_case_			= self.added_tokens_decoder[index]
       else:
             snake_case_			= chr(index - self._num_special_tokens   )
       return token



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= b''''''
       for token in tokens:
             if token in self.special_tokens_decoder:
                   snake_case_			= self.special_tokens_decoder[token].encode('''utf-8'''   )
             elif token in self.added_tokens_decoder:
                   snake_case_			= self.special_tokens_decoder[token].encode('''utf-8'''   )
             elif token in self.special_tokens_encoder:
                   snake_case_			= token.encode('''utf-8'''   )
             elif token in self.added_tokens_encoder:
                   snake_case_			= token.encode('''utf-8'''   )
             else:
                   snake_case_			= bytes([ord(_UpperCAmelCase   )]   )
             bstring += tok_string
       snake_case_			= bstring.decode('''utf-8'''						, errors='''replace'''   )
       return string





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None   ):
       return ()