




"""simple docstring"""







from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


SCREAMING_SNAKE_CASE_      =					"\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n"

SCREAMING_SNAKE_CASE_      =					"\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow's tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\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.\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\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 .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]\n    >>> sari = datasets.load_metric(\"sari\")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 26.953601953601954}\n"





def     __snake_case   (							_lowercase     :   Any ,_lowercase     :   List[Any] ,_lowercase     :   int ,_lowercase     :   Optional[Any]						):


    """simple docstring"""







    UpperCamelCase										=  [rgram for rgrams in rgramslist for rgram in rgrams]
    UpperCamelCase										=  Counter(snake_case_						)

    UpperCamelCase										=  Counter(snake_case_						)
    UpperCamelCase										=  Counter()
    for sgram, scount in sgramcounter.items():
        UpperCamelCase										=  scount * numref

    UpperCamelCase										=  Counter(snake_case_						)
    UpperCamelCase										=  Counter()
    for cgram, ccount in cgramcounter.items():
        UpperCamelCase										=  ccount * numref

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

    UpperCamelCase										=  0
    UpperCamelCase										=  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.
    UpperCamelCase										=  1
    UpperCamelCase										=  1
    if len(snake_case_						) > 0:
        UpperCamelCase										=  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)
        UpperCamelCase										=  keeptmpscorea / sum(keepgramcounterall_rep.values()						)
    UpperCamelCase										=  0
    if keepscore_precision > 0 or keepscore_recall > 0:
        UpperCamelCase										=  2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

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

    # ADDITION
    UpperCamelCase										=  set(snake_case_						) - set(snake_case_						)
    UpperCamelCase										=  set(snake_case_						) & set(snake_case_						)
    UpperCamelCase										=  set(snake_case_						) - set(snake_case_						)

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

    return (keepscore, delscore_precision, addscore)





def     __snake_case   (							_lowercase     :   int ,_lowercase     :   Tuple ,_lowercase     :   Optional[int]						):


    """simple docstring"""







    UpperCamelCase										=  len(snake_case_						)

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

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

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





def     __snake_case   (							_lowercase     :   Optional[Any] ,_lowercase     :   Tuple = True ,_lowercase     :   Union[str, Any] = "13a" ,_lowercase     :   Dict = True						):


    """simple docstring"""







    if lowercase:
        UpperCamelCase										=  sentence.lower()

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

    if not return_str:
        UpperCamelCase										=  normalized_sent.split()

    return normalized_sent


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







   """simple docstring"""




   def      UpperCAmelCase__ (		self)							->      List[Any]:
       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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Optional[Any]:


       if not (len(__UpperCamelCase) == len(__UpperCamelCase) == len(__UpperCamelCase)):
           raise ValueError('''Sources length must match predictions and references lengths.''')
       UpperCamelCase										=  0
       for src, pred, refs in zip(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase):
           sari_score += SARIsent(normalize(__UpperCamelCase)					,				normalize(__UpperCamelCase)					,				[normalize(__UpperCamelCase) for sent in refs])
       UpperCamelCase										=  sari_score / len(__UpperCamelCase)
       return {"sari": 1_0_0 * sari_score}





"""simple docstring"""







from math import pi





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    return degree / (180 / pi)


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

  testmod()