



'''simple docstring'''





import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


SCREAMING_SNAKE_CASE__          =  "\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__          =  "\\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__          =  "\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 							lowerCamelCase	(					_snake_case	:    List[Any]							):





    '''simple docstring'''


    def remove_articles(_snake_case	:    List[Any]							):
        lowercase__									=    re.compile(R"\b(a|an|the)\b"  ,re.UNICODE							)
        return re.sub(_snake_case  ," "  ,_snake_case							)

    def white_space_fix(_snake_case	:    Dict							):
        return " ".join(text.split()							)

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

    def lower(_snake_case	:    Optional[Any]							):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_snake_case							)							)							)							)




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    List[Any]							):





    '''simple docstring'''


    return int(normalize_answer(_snake_case							) == normalize_answer(_snake_case							)							)




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Optional[int]							):





    '''simple docstring'''


    lowercase__									=    [any(compute_exact(_snake_case  ,_snake_case							) for ref in refs							) for pred, refs in zip(_snake_case  ,_snake_case							)]
    return (sum(_snake_case							) / len(_snake_case							)) * 100




def 							lowerCamelCase	(					_snake_case	:    Union[str, Any]  ,_snake_case	:    Tuple  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    str							):





    '''simple docstring'''


    lowercase__									=    [rgram for rgrams in rgramslist for rgram in rgrams]
    lowercase__									=    Counter(_snake_case							)

    lowercase__									=    Counter(_snake_case							)
    lowercase__									=    Counter()
    for sgram, scount in sgramcounter.items():
        lowercase__									=    scount * numref

    lowercase__									=    Counter(_snake_case							)
    lowercase__									=    Counter()
    for cgram, ccount in cgramcounter.items():
        lowercase__									=    ccount * numref

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

    lowercase__									=    0
    lowercase__									=    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__									=    1
    lowercase__									=    1
    if len(_snake_case							) > 0:
        lowercase__									=    keeptmpscorea / len(_snake_case							)
    if len(_snake_case							) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        lowercase__									=    keeptmpscorea / sum(keepgramcounterall_rep.values()							)
    lowercase__									=    0
    if keepscore_precision > 0 or keepscore_recall > 0:
        lowercase__									=    2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    lowercase__									=    sgramcounter_rep - cgramcounter_rep
    lowercase__									=    delgramcounter_rep - rgramcounter
    lowercase__									=    sgramcounter_rep - rgramcounter
    lowercase__									=    0
    lowercase__									=    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__									=    1
    if len(_snake_case							) > 0:
        lowercase__									=    deltmpscorea / len(_snake_case							)

    # ADDITION
    lowercase__									=    set(_snake_case							) - set(_snake_case							)
    lowercase__									=    set(_snake_case							) & set(_snake_case							)
    lowercase__									=    set(_snake_case							) - set(_snake_case							)

    lowercase__									=    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__									=    1
    lowercase__									=    1
    if len(_snake_case							) > 0:
        lowercase__									=    addtmpscore / len(_snake_case							)
    if len(_snake_case							) > 0:
        lowercase__									=    addtmpscore / len(_snake_case							)
    lowercase__									=    0
    if addscore_precision > 0 or addscore_recall > 0:
        lowercase__									=    2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)




def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    Optional[Any]  ,_snake_case	:    Dict							):





    '''simple docstring'''


    lowercase__									=    len(_snake_case							)

    lowercase__									=    ssent.split(" "							)
    lowercase__									=    csent.split(" "							)
    lowercase__									=    []
    lowercase__									=    []
    lowercase__									=    []
    lowercase__									=    []
    lowercase__									=    []
    lowercase__									=    []

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

    for i in range(0  ,len(_snake_case							) - 1							):
        if i < len(_snake_case							) - 1:
            lowercase__									=    sagrams[i] + " " + sagrams[i + 1]
            sagrams.append(_snake_case							)
        if i < len(_snake_case							) - 2:
            lowercase__									=    sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
            sagrams.append(_snake_case							)
        if i < len(_snake_case							) - 3:
            lowercase__									=    sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
            sagrams.append(_snake_case							)

    for i in range(0  ,len(_snake_case							) - 1							):
        if i < len(_snake_case							) - 1:
            lowercase__									=    cagrams[i] + " " + cagrams[i + 1]
            cagrams.append(_snake_case							)
        if i < len(_snake_case							) - 2:
            lowercase__									=    cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
            cagrams.append(_snake_case							)
        if i < len(_snake_case							) - 3:
            lowercase__									=    cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
            cagrams.append(_snake_case							)

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




def 							lowerCamelCase	(					_snake_case	:    Union[str, Any]  ,_snake_case	:    bool = True  ,_snake_case	:    str = "13a"  ,_snake_case	:    bool = True							):





    '''simple docstring'''


    if lowercase:
        lowercase__									=    sentence.lower()

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

    if not return_str:
        lowercase__									=    normalized_sent.split()

    return normalized_sent




def 							lowerCamelCase	(					_snake_case	:    List[Any]  ,_snake_case	:    Optional[Any]  ,_snake_case	:    List[Any]							):





    '''simple docstring'''


    if not (len(_snake_case							) == len(_snake_case							) == len(_snake_case							)):
        raise ValueError("Sources length must match predictions and references lengths."							)
    lowercase__									=    0
    for src, pred, refs in zip(_snake_case  ,_snake_case  ,_snake_case							):
        sari_score += SARIsent(normalize(_snake_case							)  ,normalize(_snake_case							)  ,[normalize(_snake_case							) for sent in refs]							)
    lowercase__									=    sari_score / len(_snake_case							)
    return 100 * sari_score




def 							lowerCamelCase	(					_snake_case	:    List[Any]  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    Optional[Any]="exp"  ,_snake_case	:    int=None  ,_snake_case	:    Any=False  ,_snake_case	:    List[str]=False  ,_snake_case	:    int=False  ,):





    '''simple docstring'''


    lowercase__									=    len(references[0]							)
    if any(len(_snake_case							) != references_per_prediction for refs in references							):
        raise ValueError("Sacrebleu requires the same number of references for each prediction"							)
    lowercase__									=    [[refs[i] for refs in references] for i in range(_snake_case							)]
    lowercase__									=    sacrebleu.corpus_bleu(
        _snake_case  ,_snake_case  ,smooth_method=_snake_case  ,smooth_value=_snake_case  ,force=_snake_case  ,lowercase=_snake_case  ,use_effective_order=_snake_case  ,)
    return output.score



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



       def 				_a   (					self   )	->				Tuple:
           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 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				int:
           lowercase__									=    {}
           result.update({"sari": compute_sari(sources=UpperCAmelCase_  ,predictions=UpperCAmelCase_  ,references=UpperCAmelCase_   )}   )
           result.update({"sacrebleu": compute_sacrebleu(predictions=UpperCAmelCase_  ,references=UpperCAmelCase_   )}   )
           result.update({"exact": compute_em(predictions=UpperCAmelCase_  ,references=UpperCAmelCase_   )}   )
           return result









'''simple docstring'''





from __future__ import annotations




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    if b == 0:
        return (1, 0)
    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,a % b							)
    lowercase__									=    a // b
    return (y, x - k * y)




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    if b < 0:
        lowercase__									=    (b % n + n) % n
    return b




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__							,							lowercase__									=    invert_modulo(_snake_case  ,_snake_case							), invert_modulo(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m


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

 testmod(name="chinese_remainder_theorem", verbose=True)
 testmod(name="chinese_remainder_theorem2", verbose=True)
 testmod(name="invert_modulo", verbose=True)
 testmod(name="extended_euclid", verbose=True)




