



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,
)


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


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




def 					_lowerCAmelCase   (     A__:	Tuple		):


    '''simple docstring'''


    UpperCAmelCase     =    BertTokenizer.from_pretrained('''bert-base-uncased'''		,						do_lower_case=SCREAMING_SNAKE_CASE__		)
    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=SCREAMING_SNAKE_CASE__		,						length_limit=args.beam_size		,						length_limit_type='''words'''		,						apply_avg=SCREAMING_SNAKE_CASE__		,						apply_best=SCREAMING_SNAKE_CASE__		,						alpha=0.5		,						weight_factor=1.2		,						stemming=SCREAMING_SNAKE_CASE__		,						)

    # 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(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		)
    UpperCAmelCase     =    build_predictor(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		)

    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(SCREAMING_SNAKE_CASE__		):
        UpperCAmelCase     =    predictor.translate_batch(SCREAMING_SNAKE_CASE__		)
        UpperCAmelCase     =    predictor.from_batch(SCREAMING_SNAKE_CASE__		)
        UpperCAmelCase     =    [format_summary(SCREAMING_SNAKE_CASE__		) for t in translations]
        save_summaries(SCREAMING_SNAKE_CASE__		,						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(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		)
        UpperCAmelCase     =    format_rouge_scores(SCREAMING_SNAKE_CASE__		)
        save_rouge_scores(SCREAMING_SNAKE_CASE__		)
        print(SCREAMING_SNAKE_CASE__		)




def 					_lowerCAmelCase   (     A__:	Optional[Any]		,						A__:	Any		,						A__:	Optional[Any]		):


    '''simple docstring'''


    for summary, document_name in zip(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		):
        # 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(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		)
        with open(SCREAMING_SNAKE_CASE__		,						'''w'''		) as output:
            output.write(SCREAMING_SNAKE_CASE__		)




def 					_lowerCAmelCase   (     A__:	Optional[Any]		):


    '''simple docstring'''


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

    return summary




def 					_lowerCAmelCase   (     A__:	Dict		):


    '''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 					_lowerCAmelCase   (     A__:	str		):


    '''simple docstring'''


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




def 					_lowerCAmelCase   (     A__:	List[str]		,						A__:	Dict		):


    '''simple docstring'''


    UpperCAmelCase     =    load_and_cache_examples(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		)
    UpperCAmelCase     =    SequentialSampler(SCREAMING_SNAKE_CASE__		)

    def collate_fn(A__:	Union[str, Any]		):
        return collate(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						block_size=512		,						device=args.device		)

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

    return iterator




def 					_lowerCAmelCase   (     A__:	List[str]		,						A__:	List[Any]		):


    '''simple docstring'''


    UpperCAmelCase     =    CNNDMDataset(args.documents_dir		)
    return dataset




def 					_lowerCAmelCase   (     A__:	str		,						A__:	Any		,						A__:	int		,						A__:	str		):


    '''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(SCREAMING_SNAKE_CASE__		) for _, _, summary_list in data]

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

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

    return batch




def 					_lowerCAmelCase   (     A__:	Union[str, Any]		,						A__:	int		):


    '''simple docstring'''


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




def 					_lowerCAmelCase   (     ):


    '''simple docstring'''


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

    evaluate(SCREAMING_SNAKE_CASE__		)




def 					_lowerCAmelCase   (     A__:	Optional[Any]		):


    '''simple docstring'''


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

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

    return True


if __name__ == "__main__":
    main()








import doctest

import projectq
from projectq.ops import H, Measure
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int							=						quantum_engine.allocate_qubit()
 H | qubit
 Measure | qubit
 return int(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       doctest.testmod()

       # initialises a new quantum backend
       lowercase_           =	projectq.MainEngine()

       # Generate a list of 10 random numbers
       lowercase_           =	[get_random_number(quantum_engine) for _ in range(1_0)]

       # Flushes the quantum engine from memory
       quantum_engine.flush()

       print('Random numbers', random_numbers_list)

