





import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_A					:				List[Any]				=			"""
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

_A					:				Dict				=			"""\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


_A					:				Dict				=			"""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""


def 							__snake_case							(    lowerCAmelCase_	)				->						List[str]:

    def remove_articles(lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			re.compile(r'''\b(a|an|the)\b''' ,							re.UNICODE	)
        return re.sub(lowerCAmelCase_ ,							''' ''' ,							lowerCAmelCase_	)

    def white_space_fix(lowerCAmelCase_	):
        return " ".join(text.split()	)

    def remove_punc(lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			set(string.punctuation	)
        return "".join(ch for ch in text if ch not in exclude	)

    def lower(lowerCAmelCase_	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_	)	)	)	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:
    return int(normalize_answer(lowerCAmelCase_	) == normalize_answer(lowerCAmelCase_	)	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						int:
    SCREAMING_SNAKE_CASE__											=			[any(compute_exact(lowerCAmelCase_ ,							lowerCAmelCase_	) for ref in refs	) for pred, refs in zip(lowerCAmelCase_ ,							lowerCAmelCase_	)]
    return (sum(lowerCAmelCase_	) / len(lowerCAmelCase_	)) * 1_0_0


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[rgram for rgrams in rgramslist for rgram in rgrams]
    SCREAMING_SNAKE_CASE__											=			Counter(lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			Counter(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			Counter()
    for sgram, scount in sgramcounter.items():
        SCREAMING_SNAKE_CASE__											=			scount * numref

    SCREAMING_SNAKE_CASE__											=			Counter(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			Counter()
    for cgram, ccount in cgramcounter.items():
        SCREAMING_SNAKE_CASE__											=			ccount * numref

    # KEEP
    SCREAMING_SNAKE_CASE__											=			sgramcounter_rep & cgramcounter_rep
    SCREAMING_SNAKE_CASE__											=			keepgramcounter_rep & rgramcounter
    SCREAMING_SNAKE_CASE__											=			sgramcounter_rep & rgramcounter

    SCREAMING_SNAKE_CASE__											=			0
    SCREAMING_SNAKE_CASE__											=			0
    for keepgram in keepgramcountergood_rep:
        keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
        # Fix an alleged bug [2] in the keep score computation.
        # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
        keeptmpscorea += keepgramcountergood_rep[keepgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    #      a target exactly.
    SCREAMING_SNAKE_CASE__											=			1
    SCREAMING_SNAKE_CASE__											=			1
    if len(lowerCAmelCase_	) > 0:
        SCREAMING_SNAKE_CASE__											=			keeptmpscorea / len(lowerCAmelCase_	)
    if len(lowerCAmelCase_	) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        SCREAMING_SNAKE_CASE__											=			keeptmpscorea / sum(keepgramcounterall_rep.values()	)
    SCREAMING_SNAKE_CASE__											=			0
    if keepscore_precision > 0 or keepscore_recall > 0:
        SCREAMING_SNAKE_CASE__											=			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    SCREAMING_SNAKE_CASE__											=			sgramcounter_rep - cgramcounter_rep
    SCREAMING_SNAKE_CASE__											=			delgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE__											=			sgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE__											=			0
    SCREAMING_SNAKE_CASE__											=			0
    for delgram in delgramcountergood_rep:
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    SCREAMING_SNAKE_CASE__											=			1
    if len(lowerCAmelCase_	) > 0:
        SCREAMING_SNAKE_CASE__											=			deltmpscorea / len(lowerCAmelCase_	)

    # ADDITION
    SCREAMING_SNAKE_CASE__											=			set(lowerCAmelCase_	) - set(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			set(lowerCAmelCase_	) & set(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			set(lowerCAmelCase_	) - set(lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			0
    for addgram in addgramcountergood:
        addtmpscore += 1

    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    SCREAMING_SNAKE_CASE__											=			1
    SCREAMING_SNAKE_CASE__											=			1
    if len(lowerCAmelCase_	) > 0:
        SCREAMING_SNAKE_CASE__											=			addtmpscore / len(lowerCAmelCase_	)
    if len(lowerCAmelCase_	) > 0:
        SCREAMING_SNAKE_CASE__											=			addtmpscore / len(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			0
    if addscore_precision > 0 or addscore_recall > 0:
        SCREAMING_SNAKE_CASE__											=			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			len(lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			ssent.split(''' '''	)
    SCREAMING_SNAKE_CASE__											=			csent.split(''' '''	)
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]

    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    for rsent in rsents:
        SCREAMING_SNAKE_CASE__											=			rsent.split(''' '''	)
        SCREAMING_SNAKE_CASE__											=			[]
        SCREAMING_SNAKE_CASE__											=			[]
        SCREAMING_SNAKE_CASE__											=			[]
        ragramslist.append(lowerCAmelCase_	)
        for i in range(0 ,							len(lowerCAmelCase_	) - 1	):
            if i < len(lowerCAmelCase_	) - 1:
                SCREAMING_SNAKE_CASE__											=			ragrams[i] + ''' ''' + ragrams[i + 1]
                ragrams.append(lowerCAmelCase_	)
            if i < len(lowerCAmelCase_	) - 2:
                SCREAMING_SNAKE_CASE__											=			ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
                ragrams.append(lowerCAmelCase_	)
            if i < len(lowerCAmelCase_	) - 3:
                SCREAMING_SNAKE_CASE__											=			ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
                ragrams.append(lowerCAmelCase_	)
        ragramslist.append(lowerCAmelCase_	)
        ragramslist.append(lowerCAmelCase_	)
        ragramslist.append(lowerCAmelCase_	)

    for i in range(0 ,							len(lowerCAmelCase_	) - 1	):
        if i < len(lowerCAmelCase_	) - 1:
            SCREAMING_SNAKE_CASE__											=			sagrams[i] + ''' ''' + sagrams[i + 1]
            sagrams.append(lowerCAmelCase_	)
        if i < len(lowerCAmelCase_	) - 2:
            SCREAMING_SNAKE_CASE__											=			sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
            sagrams.append(lowerCAmelCase_	)
        if i < len(lowerCAmelCase_	) - 3:
            SCREAMING_SNAKE_CASE__											=			sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
            sagrams.append(lowerCAmelCase_	)

    for i in range(0 ,							len(lowerCAmelCase_	) - 1	):
        if i < len(lowerCAmelCase_	) - 1:
            SCREAMING_SNAKE_CASE__											=			cagrams[i] + ''' ''' + cagrams[i + 1]
            cagrams.append(lowerCAmelCase_	)
        if i < len(lowerCAmelCase_	) - 2:
            SCREAMING_SNAKE_CASE__											=			cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
            cagrams.append(lowerCAmelCase_	)
        if i < len(lowerCAmelCase_	) - 3:
            SCREAMING_SNAKE_CASE__											=			cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
            cagrams.append(lowerCAmelCase_	)

    ((SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__))											=			SARIngram(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    ((SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__))											=			SARIngram(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    ((SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__))											=			SARIngram(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    ((SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__))											=			SARIngram(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			sum([keepascore, keepascore, keepascore, keepascore]	) / 4
    SCREAMING_SNAKE_CASE__											=			sum([delascore, delascore, delascore, delascore]	) / 4
    SCREAMING_SNAKE_CASE__											=			sum([addascore, addascore, addascore, addascore]	) / 4
    SCREAMING_SNAKE_CASE__											=			(avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ = True ,							lowerCAmelCase_ = "13a" ,							lowerCAmelCase_ = True	)				->						Optional[Any]:
    # Normalization is requried for the ASSET dataset (one of the primary
    # datasets in sentence simplification) to allow using space
    # to split the sentence. Even though Wiki-Auto and TURK datasets,
    # do not require normalization, we do it for consistency.
    # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
    # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

    if lowercase:
        SCREAMING_SNAKE_CASE__											=			sentence.lower()

    if tokenizer in ["13a", "intl"]:
        if version.parse(sacrebleu.__version__	).major >= 2:
            SCREAMING_SNAKE_CASE__											=			sacrebleu.metrics.bleu._get_tokenizer(lowerCAmelCase_	)()(lowerCAmelCase_	)
        else:
            SCREAMING_SNAKE_CASE__											=			sacrebleu.TOKENIZERS[tokenizer]()(lowerCAmelCase_	)
    elif tokenizer == "moses":
        SCREAMING_SNAKE_CASE__											=			sacremoses.MosesTokenizer().tokenize(lowerCAmelCase_ ,							return_str=lowerCAmelCase_ ,							escape=lowerCAmelCase_	)
    elif tokenizer == "penn":
        SCREAMING_SNAKE_CASE__											=			sacremoses.MosesTokenizer().penn_tokenize(lowerCAmelCase_ ,							return_str=lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			sentence

    if not return_str:
        SCREAMING_SNAKE_CASE__											=			normalized_sent.split()

    return normalized_sent


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[Any]:
    if not (len(lowerCAmelCase_	) == len(lowerCAmelCase_	) == len(lowerCAmelCase_	)):
        raise ValueError('''Sources length must match predictions and references lengths.'''	)
    SCREAMING_SNAKE_CASE__											=			0
    for src, pred, refs in zip(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	):
        sari_score += SARIsent(normalize(lowerCAmelCase_	) ,							normalize(lowerCAmelCase_	) ,							[normalize(lowerCAmelCase_	) for sent in refs]	)
    SCREAMING_SNAKE_CASE__											=			sari_score / len(lowerCAmelCase_	)
    return 1_0_0 * sari_score


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_="exp" ,							lowerCAmelCase_=None ,							lowerCAmelCase_=False ,							lowerCAmelCase_=False ,							lowerCAmelCase_=False ,							)				->						Any:
    SCREAMING_SNAKE_CASE__											=			len(references[0]	)
    if any(len(lowerCAmelCase_	) != references_per_prediction for refs in references	):
        raise ValueError('''Sacrebleu requires the same number of references for each prediction'''	)
    SCREAMING_SNAKE_CASE__											=			[[refs[i] for refs in references] for i in range(lowerCAmelCase_	)]
    SCREAMING_SNAKE_CASE__											=			sacrebleu.corpus_bleu(
        lowerCAmelCase_ ,							lowerCAmelCase_ ,							smooth_method=lowerCAmelCase_ ,							smooth_value=lowerCAmelCase_ ,							force=lowerCAmelCase_ ,							lowercase=lowerCAmelCase_ ,							use_effective_order=lowerCAmelCase_ ,							)
    return output.score



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,     _KWARGS_DESCRIPTION    )
class 					__snake_case	(  datasets.Metric    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        return datasets.MetricInfo(
            description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
                {
                    '''predictions''': datasets.Value('''string'''   ,				id='''sequence'''     ),
                    '''references''': datasets.Sequence(datasets.Value('''string'''   ,				id='''sequence'''     )   ,				id='''references'''     ),
                }     )   ,				codebase_urls=[
                '''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
                '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
                '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
                '''https://github.com/mjpost/sacreBLEU''',
            ]   ,				reference_urls=[
                '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
                '''https://github.com/mjpost/sacreBLEU''',
                '''https://en.wikipedia.org/wiki/BLEU''',
                '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
            ]   ,				)




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			{}
        result.update({'''sari''': compute_sari(sources=A_   ,				predictions=A_   ,				references=A_     )}     )
        result.update({'''sacrebleu''': compute_sacrebleu(predictions=A_   ,				references=A_     )}     )
        result.update({'''exact''': compute_em(predictions=A_   ,				references=A_     )}     )
        return result



def 							__SCREAMING_SNAKE_CASE     (   a__    :   int       )					->				int:
	if not isinstance(a__			,a__       ):
		raise TypeError("""Input value must be an 'int' type"""       )

	__A :			Union[str, Any]		    =			0
	while number:
		position += 1
		number >>= 1

	return position


if __name__ == "__main__":
			import doctest

			doctest.testmod()





