


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


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

SCREAMING_SNAKE_CASE			: int				   =					"\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


SCREAMING_SNAKE_CASE			: Tuple				   =					"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Tuple:

    def remove_articles(lowerCamelCase_					):
        _lowercase			:				Any 						=						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_					):
        _lowercase			:				Tuple 						=						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 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    return int(normalize_answer(lowerCamelCase_					) == normalize_answer(lowerCamelCase_					)					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				str 						=						[any(compute_exact(lowerCamelCase_				,					lowerCamelCase_					) for ref in refs					) for pred, refs in zip(lowerCamelCase_				,					lowerCamelCase_					)]
    return (sum(lowerCamelCase_					) / len(lowerCamelCase_					)) * 100



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Union[str, Any]:
    _lowercase			:				Tuple 						=						[rgram for rgrams in rgramslist for rgram in rgrams]
    _lowercase			:				Any 						=						Counter(lowerCamelCase_					)

    _lowercase			:				Optional[Any] 						=						Counter(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						Counter()
    for sgram, scount in sgramcounter.items():
        _lowercase			:				Optional[Any] 						=						scount * numref

    _lowercase			:				Union[str, Any] 						=						Counter(lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						Counter()
    for cgram, ccount in cgramcounter.items():
        _lowercase			:				List[str] 						=						ccount * numref

    # KEEP
    _lowercase			:				Tuple 						=						sgramcounter_rep & cgramcounter_rep
    _lowercase			:				int 						=						keepgramcounter_rep & rgramcounter
    _lowercase			:				List[Any] 						=						sgramcounter_rep & rgramcounter

    _lowercase			:				str 						=						0
    _lowercase			:				int 						=						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.
    _lowercase			:				Union[str, Any] 						=						1
    _lowercase			:				Tuple 						=						1
    if len(lowerCamelCase_					) > 0:
        _lowercase			:				Optional[int] 						=						keeptmpscorea / len(lowerCamelCase_					)
    if len(lowerCamelCase_					) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        _lowercase			:				Optional[Any] 						=						keeptmpscorea / sum(keepgramcounterall_rep.values()					)
    _lowercase			:				str 						=						0
    if keepscore_precision > 0 or keepscore_recall > 0:
        _lowercase			:				Optional[int] 						=						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    _lowercase			:				Optional[int] 						=						sgramcounter_rep - cgramcounter_rep
    _lowercase			:				Optional[int] 						=						delgramcounter_rep - rgramcounter
    _lowercase			:				Dict 						=						sgramcounter_rep - rgramcounter
    _lowercase			:				List[str] 						=						0
    _lowercase			:				Optional[int] 						=						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.
    _lowercase			:				int 						=						1
    if len(lowerCamelCase_					) > 0:
        _lowercase			:				Tuple 						=						deltmpscorea / len(lowerCamelCase_					)

    # ADDITION
    _lowercase			:				Any 						=						set(lowerCamelCase_					) - set(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						set(lowerCamelCase_					) & set(lowerCamelCase_					)
    _lowercase			:				int 						=						set(lowerCamelCase_					) - set(lowerCamelCase_					)

    _lowercase			:				int 						=						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.
    _lowercase			:				Union[str, Any] 						=						1
    _lowercase			:				Tuple 						=						1
    if len(lowerCamelCase_					) > 0:
        _lowercase			:				Optional[int] 						=						addtmpscore / len(lowerCamelCase_					)
    if len(lowerCamelCase_					) > 0:
        _lowercase			:				Any 						=						addtmpscore / len(lowerCamelCase_					)
    _lowercase			:				Optional[int] 						=						0
    if addscore_precision > 0 or addscore_recall > 0:
        _lowercase			:				Union[str, Any] 						=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    _lowercase			:				List[str] 						=						len(lowerCamelCase_					)

    _lowercase			:				List[str] 						=						ssent.split(' '					)
    _lowercase			:				Optional[int] 						=						csent.split(' '					)
    _lowercase			:				Dict 						=						[]
    _lowercase			:				Union[str, Any] 						=						[]
    _lowercase			:				Optional[Any] 						=						[]
    _lowercase			:				Union[str, Any] 						=						[]
    _lowercase			:				Union[str, Any] 						=						[]
    _lowercase			:				Any 						=						[]

    _lowercase			:				Optional[Any] 						=						[]
    _lowercase			:				Dict 						=						[]
    _lowercase			:				Optional[Any] 						=						[]
    _lowercase			:				int 						=						[]
    for rsent in rsents:
        _lowercase			:				Optional[int] 						=						rsent.split(' '					)
        _lowercase			:				List[Any] 						=						[]
        _lowercase			:				str 						=						[]
        _lowercase			:				Dict 						=						[]
        ragramslist.append(lowerCamelCase_					)
        for i in range(0				,					len(lowerCamelCase_					) - 1					):
            if i < len(lowerCamelCase_					) - 1:
                _lowercase			:				Optional[int] 						=						ragrams[i] + ' ' + ragrams[i + 1]
                ragrams.append(lowerCamelCase_					)
            if i < len(lowerCamelCase_					) - 2:
                _lowercase			:				List[Any] 						=						ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                ragrams.append(lowerCamelCase_					)
            if i < len(lowerCamelCase_					) - 3:
                _lowercase			:				List[str] 						=						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:
            _lowercase			:				Optional[Any] 						=						sagrams[i] + ' ' + sagrams[i + 1]
            sagrams.append(lowerCamelCase_					)
        if i < len(lowerCamelCase_					) - 2:
            _lowercase			:				Dict 						=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
            sagrams.append(lowerCamelCase_					)
        if i < len(lowerCamelCase_					) - 3:
            _lowercase			:				Tuple 						=						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:
            _lowercase			:				str 						=						cagrams[i] + ' ' + cagrams[i + 1]
            cagrams.append(lowerCamelCase_					)
        if i < len(lowerCamelCase_					) - 2:
            _lowercase			:				Optional[Any] 						=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
            cagrams.append(lowerCamelCase_					)
        if i < len(lowerCamelCase_					) - 3:
            _lowercase			:				List[str] 						=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
            cagrams.append(lowerCamelCase_					)

    ((_lowercase)						,							(_lowercase)						,							(_lowercase))			:				Union[str, Any] 						=						SARIngram(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    ((_lowercase)						,							(_lowercase)						,							(_lowercase))			:				Union[str, Any] 						=						SARIngram(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    ((_lowercase)						,							(_lowercase)						,							(_lowercase))			:				int 						=						SARIngram(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    ((_lowercase)						,							(_lowercase)						,							(_lowercase))			:				Optional[int] 						=						SARIngram(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						sum([keepascore, keepascore, keepascore, keepascore]					) / 4
    _lowercase			:				Tuple 						=						sum([delascore, delascore, delascore, delascore]					) / 4
    _lowercase			:				Optional[int] 						=						sum([addascore, addascore, addascore, addascore]					) / 4
    _lowercase			:				str 						=						(avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore



def 							UpperCamelCase_( 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:
        _lowercase			:				int 						=						sentence.lower()

    if tokenizer in ["13a", "intl"]:
        if version.parse(sacrebleu.__version__					).major >= 2:
            _lowercase			:				List[Any] 						=						sacrebleu.metrics.bleu._get_tokenizer(lowerCamelCase_					)()(lowerCamelCase_					)
        else:
            _lowercase			:				Tuple 						=						sacrebleu.TOKENIZERS[tokenizer]()(lowerCamelCase_					)
    elif tokenizer == "moses":
        _lowercase			:				Union[str, Any] 						=						sacremoses.MosesTokenizer().tokenize(lowerCamelCase_				,					return_str=lowerCamelCase_				,					escape=lowerCamelCase_					)
    elif tokenizer == "penn":
        _lowercase			:				Dict 						=						sacremoses.MosesTokenizer().penn_tokenize(lowerCamelCase_				,					return_str=lowerCamelCase_					)
    else:
        _lowercase			:				Any 						=						sentence

    if not return_str:
        _lowercase			:				str 						=						normalized_sent.split()

    return normalized_sent



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		str:
    if not (len(lowerCamelCase_					) == len(lowerCamelCase_					) == len(lowerCamelCase_					)):
        raise ValueError('Sources length must match predictions and references lengths.'					)
    _lowercase			:				Optional[Any] 						=						0
    for src, pred, refs in zip(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					):
        sari_score += SARIsent(normalize(lowerCamelCase_					)				,					normalize(lowerCamelCase_					)				,					[normalize(lowerCamelCase_					) for sent in refs]					)
    _lowercase			:				Dict 						=						sari_score / len(lowerCamelCase_					)
    return 100 * sari_score



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_="exp"				,					lowerCamelCase_=None				,					lowerCamelCase_=False				,					lowerCamelCase_=False				,					lowerCamelCase_=False				,					)       ->		List[str]:
    _lowercase			:				Tuple 						=						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'					)
    _lowercase			:				int 						=						[[refs[i] for refs in references] for i in range(lowerCamelCase_					)]
    _lowercase			:				List[Any] 						=						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 _lowerCamelCase(		datasets.Metric  ):
       def      UpperCamelCase	(   self)			->  Any:




           """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      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  List[str]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						{}
           result.update({'sari': compute_sari(sources=lowerCamelCase, predictions=lowerCamelCase, references=lowerCamelCase)})
           result.update({'sacrebleu': compute_sacrebleu(predictions=lowerCamelCase, references=lowerCamelCase)})
           result.update({'exact': compute_em(predictions=lowerCamelCase, references=lowerCamelCase)})
           return result






from __future__ import annotations



def 							UpperCamelCase_( lowerCamelCase_					)       ->		float:
    if not nums:
        raise ValueError('List is empty'					)
    return sum(lowerCamelCase_					) / len(lowerCamelCase_					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


