


"""simple docstring"""







from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCamelCase_  :  Optional[int]     		=    """\
@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},
}
"""

lowerCamelCase_  :  Dict     		=    """\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


lowerCamelCase_  :  Union[str, Any]     		=    """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
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
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""
def _A		(		lowercase			,       lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    a											=[rgram for rgrams in rgramslist for rgram in rgrams]
    a											=Counter(lowercase						)

    a											=Counter(lowercase						)
    a											=Counter()
    for sgram, scount in sgramcounter.items():
        a											=scount * numref

    a											=Counter(lowercase						)
    a											=Counter()
    for cgram, ccount in cgramcounter.items():
        a											=ccount * numref

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

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

    # DELETION
    a											=sgramcounter_rep - cgramcounter_rep
    a											=delgramcounter_rep - rgramcounter
    a											=sgramcounter_rep - rgramcounter
    a											=0
    a											=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.
    a											=1
    if len(lowercase						) > 0:
        a											=deltmpscorea / len(lowercase						)

    # ADDITION
    a											=set(lowercase						) - set(lowercase						)
    a											=set(lowercase						) & set(lowercase						)
    a											=set(lowercase						) - set(lowercase						)

    a											=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.
    a											=1
    a											=1
    if len(lowercase						) > 0:
        a											=addtmpscore / len(lowercase						)
    if len(lowercase						) > 0:
        a											=addtmpscore / len(lowercase						)
    a											=0
    if addscore_precision > 0 or addscore_recall > 0:
        a											=2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)
def _A		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    a											=len(lowercase						)

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

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

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

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

    ((a)						, (a)						, (a))											=SARIngram(lowercase			,       lowercase			,       lowercase			,       lowercase						)
    ((a)						, (a)						, (a))											=SARIngram(lowercase			,       lowercase			,       lowercase			,       lowercase						)
    ((a)						, (a)						, (a))											=SARIngram(lowercase			,       lowercase			,       lowercase			,       lowercase						)
    ((a)						, (a)						, (a))											=SARIngram(lowercase			,       lowercase			,       lowercase			,       lowercase						)
    a											=sum([keepascore, keepascore, keepascore, keepascore]						) / 4
    a											=sum([delascore, delascore, delascore, delascore]						) / 4
    a											=sum([addascore, addascore, addascore, addascore]						) / 4
    a											=(avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore
def _A		(		lowercase			,       lowercase = True			,       lowercase = "13a"			,       lowercase = True						):







    """simple docstring"""
    # 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:
        a											=sentence.lower()

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

    if not return_str:
        a											=normalized_sent.split()

    return normalized_sent






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






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     return datasets.MetricInfo(
         description=_DESCRIPTION					,       citation=_CITATION					,       inputs_description=_KWARGS_DESCRIPTION					,       features=datasets.Features(
             {
                 '''sources''': datasets.Value('''string'''					,       id='''sequence'''		),
                 '''predictions''': datasets.Value('''string'''					,       id='''sequence'''		),
                 '''references''': datasets.Sequence(datasets.Value('''string'''					,       id='''sequence'''		)					,       id='''references'''		),
             }		)					,       codebase_urls=[
             '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
             '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
         ]					,       reference_urls=['''https://www.aclweb.org/anthology/Q16-1029.pdf''']					,       )





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A					,       __A		)   ->	Any:
     if not (len(__A		) == len(__A		) == len(__A		)):
         raise ValueError('''Sources length must match predictions and references lengths.'''		)
     a											=0
     for src, pred, refs in zip(__A					,       __A					,       __A		):
         sari_score += SARIsent(normalize(__A		)					,       normalize(__A		)					,       [normalize(__A		) for sent in refs]		)
     a											=sari_score / len(__A		)
     return {"sari": 100 * sari_score}



"""simple docstring"""







from .stack import Stack
def _A		(		lowercase						):







    """simple docstring"""
    a											=Stack()
    a											={'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
    for bracket in parentheses:
        if bracket in bracket_pairs:
            stack.push(lowercase						)
        elif bracket in (")", "]", "}"):
            if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                return False
    return stack.is_empty()


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

       testmod()

       lowerCamelCase_  :  List[str]     		=    ["""((()))""", """((())""", """(()))"""]
       print("""Balanced parentheses demonstration:\n""")
       for example in examples:
              lowerCamelCase_  :  str     		=    """""" if balanced_parentheses(example) else """not """
              print(F'{example} is {not_str}balanced')