
"""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,
)


a		:					Optional[int]    	=					logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


a		:					List[Any]    	=					namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])



def 						__magic_name__		(				UpperCamelCase    :		Dict    )    ->	Union[str, Any]:
    a__    = BertTokenizer.from_pretrained('bert-base-uncased'       ,		do_lower_case=UpperCamelCase    )
    a__    = BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'    )
    model.to(args.device    )
    model.eval()

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

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

        import nltk
        import rouge

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

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



def 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		str       ,		UpperCamelCase    :		int    )    ->	List[Any]:
    for summary, document_name in zip(UpperCamelCase       ,		UpperCamelCase    ):
        # Prepare the summary file's name
        if "." in document_name:
            a__    = '.'.join(document_name.split('.'    )[:-1]    )
            a__    = document_name.split('.'    )[-1]
            a__    = bare_document_name + '_summary.' + extension
        else:
            a__    = document_name + '_summary'

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



def 						__magic_name__		(				UpperCamelCase    :		Optional[Any]    )    ->	str:
    a__  ,    a__  ,    a__    = translation
    a__    = (
        raw_summary.replace('[unused0]'       ,		''    )
        .replace('[unused3]'       ,		''    )
        .replace('[PAD]'       ,		''    )
        .replace('[unused1]'       ,		''    )
        .replace(r' +'       ,		' '    )
        .replace(' [unused2] '       ,		'. '    )
        .replace('[unused2]'       ,		''    )
        .strip()
    )

    return summary



def 						__magic_name__		(				UpperCamelCase    :		Optional[int]    )    ->	Dict:
    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 						__magic_name__		(				UpperCamelCase    :		str    )    ->	List[str]:
    with open('rouge_scores.txt'       ,		'w'    ) as output:
        output.write(UpperCamelCase    )



def 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		Tuple    )    ->	Optional[int]:
    a__    = load_and_cache_examples(UpperCamelCase       ,		UpperCamelCase    )
    a__    = SequentialSampler(UpperCamelCase    )

    def collate_fn(UpperCamelCase    :		List[Any]    ):
        return collate(UpperCamelCase       ,		UpperCamelCase       ,		block_size=512       ,		device=args.device    )

    a__    = DataLoader(
        UpperCamelCase       ,		sampler=UpperCamelCase       ,		batch_size=args.batch_size       ,		collate_fn=UpperCamelCase       ,		)

    return iterator



def 						__magic_name__		(				UpperCamelCase    :		Dict       ,		UpperCamelCase    :		Tuple    )    ->	List[str]:
    a__    = CNNDMDataset(args.documents_dir    )
    return dataset



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

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

    a__    = 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 						__magic_name__		(				UpperCamelCase    :		Dict       ,		UpperCamelCase    :		Tuple    )    ->	Optional[Any]:
    a__    = summary_tokens.to('cpu'    ).numpy()
    a__    = tokenizer.decode(UpperCamelCase    )
    a__    = summary.split('.'    )
    a__    = [s + '.' for s in sentences]
    return sentences



def 						__magic_name__		(				)    ->	Optional[int]:
    a__    = 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.'       ,		)
    a__    = parser.parse_args()

    # Select device (distibuted not available)
    a__    = 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:
        a__    = 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 						__magic_name__		(				UpperCamelCase    :		Any    )    ->	Union[str, Any]:
    if not os.path.exists(UpperCamelCase    ):
        return False

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

    return True


if __name__ == "__main__":
    main()




"""simple docstring"""




a		:					str    	=					(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
a		:					tuple    	=					(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False

    # Initialization of sum and number of digits.
    a__    = 0
    a__    = 0
    a__    = n
    # Calculation of digits of the number
    while temp > 0:
        number_of_digits += 1
        temp //= 10
    # Dividing number into separate digits and find Armstrong number
    a__    = n
    while temp > 0:
        a__    = temp % 10
        total += rem**number_of_digits
        temp //= 10
    return n == total



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False

    # Init a "histogram" of the digits
    a__    = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    a__    = 0
    a__    = 0
    a__    = n
    while temp > 0:
        a__  ,    a__    = divmod(UpperCamelCase       ,		10    )
        digit_histogram[rem] += 1
        digit_total += 1

    for cnt, i in zip(UpperCamelCase       ,		range(len(UpperCamelCase    )    )    ):
        total += cnt * i**digit_total

    return n == total



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False
    a__    = len(str(UpperCamelCase    )    )  # the power that all digits will be raised to
    # check if sum of each digit multiplied expo times is equal to number
    return n == sum(int(UpperCamelCase    ) ** expo for i in str(UpperCamelCase    )    )



def 						__magic_name__		(				)    ->	List[Any]:
    a__    = int(input('Enter an integer to see if it is an Armstrong number: '    ).strip()    )
    print(f'{num} is {"" if armstrong_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )
    print(f'{num} is {"" if narcissistic_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )
    print(f'{num} is {"" if pluperfect_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


