




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,
)


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


a_										=       namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		List[Any]:

    """simple docstring"""


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

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

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

        import nltk
        import rouge

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

    __UpperCamelCase											=  build_data_iterator(lowercase_   ,       lowercase_ )
    __UpperCamelCase											=  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_ ):
        __UpperCamelCase											=  predictor.translate_batch(lowercase_ )
        __UpperCamelCase											=  predictor.from_batch(lowercase_ )
        __UpperCamelCase											=  [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:
        __UpperCamelCase											=  rouge_evaluator.get_scores(lowercase_   ,       lowercase_ )
        __UpperCamelCase											=  format_rouge_scores(lowercase_ )
        save_rouge_scores(lowercase_ )
        print(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Tuple:

    """simple docstring"""


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

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




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Any:

    """simple docstring"""


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

    return summary




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Union[str, Any]:

    """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    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		List[Any]:

    """simple docstring"""


    with open('''rouge_scores.txt'''   ,       '''w''' ) as output:
        output.write(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Optional[Any]:

    """simple docstring"""


    __UpperCamelCase											=  load_and_cache_examples(lowercase_   ,       lowercase_ )
    __UpperCamelCase											=  SequentialSampler(lowercase_ )

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

    __UpperCamelCase											=  DataLoader(
        lowercase_   ,       sampler=lowercase_   ,       batch_size=args.batch_size   ,       collate_fn=lowercase_   ,       )

    return iterator




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		List[str]:

    """simple docstring"""


    __UpperCamelCase											=  CNNDMDataset(args.documents_dir )
    return dataset




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		List[Any]:

    """simple docstring"""


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

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

    __UpperCamelCase											=  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    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Dict:

    """simple docstring"""


    __UpperCamelCase											=  summary_tokens.to('''cpu''' ).numpy()
    __UpperCamelCase											=  tokenizer.decode(lowercase_ )
    __UpperCamelCase											=  summary.split('''.''' )
    __UpperCamelCase											=  [s + '''.''' for s in sentences]
    return sentences




def    __SCREAMING_SNAKE_CASE				(					)    ->		Tuple:

    """simple docstring"""


    __UpperCamelCase											=  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=2_00   ,       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.'''   ,       )
    __UpperCamelCase											=  parser.parse_args()

    # Select device (distibuted not available)
    __UpperCamelCase											=  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:
        __UpperCamelCase											=  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    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		List[str]:

    """simple docstring"""


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

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

    return True


if __name__ == "__main__":
 main()












def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


    if not isinstance(lowercase_   ,       lowercase_ ):
        raise TypeError('''recursive_lucas_number accepts only integer arguments.''' )
    if n_th_number == 0:
        return 2
    if n_th_number == 1:
        return 1

    return recursive_lucas_number(n_th_number - 1 ) + recursive_lucas_number(
        n_th_number - 2 )




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


    if not isinstance(lowercase_   ,       lowercase_ ):
        raise TypeError('''dynamic_lucas_number accepts only integer arguments.''' )
    __UpperCamelCase						, __UpperCamelCase											=  2, 1
    for _ in range(lowercase_ ):
        __UpperCamelCase						, __UpperCamelCase											=  b, a + b
    return a


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 a_										=       int(input("Enter the number of terms in lucas series:\n").strip())
 print("Using recursive function to calculate lucas series:")
 print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
 print("\nUsing dynamic function to calculate lucas series:")
 print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))






