'''simple docstring'''

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__a: 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
},
@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: Any          =   """\
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: Tuple          =   """
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 			__UpperCamelCase       (   UpperCAmelCase     ):

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

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

 def remove_punc(UpperCAmelCase     ):
  lowercase__				:    Optional[int]  =					set(string.punctuation     )
  return "".join(ch for ch in text if ch not in exclude     )

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

 return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase     )     )     )     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 return int(normalize_answer(UpperCAmelCase     ) == normalize_answer(UpperCAmelCase     )     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Tuple  =					[any(compute_exact(UpperCAmelCase		,     UpperCAmelCase     ) for ref in refs     ) for pred, refs in zip(UpperCAmelCase		,     UpperCAmelCase     )]
 return (sum(UpperCAmelCase     ) / len(UpperCAmelCase     )) * 100
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Tuple  =					[rgram for rgrams in rgramslist for rgram in rgrams]
 lowercase__				:    List[Any]  =					Counter(UpperCAmelCase     )

 lowercase__				:    Optional[Any]  =					Counter(UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					Counter()
 for sgram, scount in sgramcounter.items():
  lowercase__				:    int  =					scount * numref

 lowercase__				:    Dict  =					Counter(UpperCAmelCase     )
 lowercase__				:    Optional[Any]  =					Counter()
 for cgram, ccount in cgramcounter.items():
  lowercase__				:    Optional[Any]  =					ccount * numref

 # KEEP
 lowercase__				:    Tuple  =					sgramcounter_rep & cgramcounter_rep
 lowercase__				:    Union[str, Any]  =					keepgramcounter_rep & rgramcounter
 lowercase__				:    Dict  =					sgramcounter_rep & rgramcounter

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

 # DELETION
 lowercase__				:    str  =					sgramcounter_rep - cgramcounter_rep
 lowercase__				:    Optional[int]  =					delgramcounter_rep - rgramcounter
 lowercase__				:    Optional[Any]  =					sgramcounter_rep - rgramcounter
 lowercase__				:    int  =					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__				:    Tuple  =					1
 if len(UpperCAmelCase     ) > 0:
  lowercase__				:    Any  =					deltmpscorea / len(UpperCAmelCase     )

 # ADDITION
 lowercase__				:    str  =					set(UpperCAmelCase     ) - set(UpperCAmelCase     )
 lowercase__				:    Dict  =					set(UpperCAmelCase     ) & set(UpperCAmelCase     )
 lowercase__				:    List[str]  =					set(UpperCAmelCase     ) - set(UpperCAmelCase     )

 lowercase__				:    Union[str, Any]  =					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__				:    Any  =					1
 lowercase__				:    Tuple  =					1
 if len(UpperCAmelCase     ) > 0:
  lowercase__				:    Any  =					addtmpscore / len(UpperCAmelCase     )
 if len(UpperCAmelCase     ) > 0:
  lowercase__				:    Any  =					addtmpscore / len(UpperCAmelCase     )
 lowercase__				:    List[str]  =					0
 if addscore_precision > 0 or addscore_recall > 0:
  lowercase__				:    Optional[int]  =					2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Any  =					len(UpperCAmelCase     )

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

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

 for i in range(0		,     len(UpperCAmelCase     ) - 1     ):
  if i < len(UpperCAmelCase     ) - 1:
   lowercase__				:    Tuple  =					sagrams[i] + ''' ''' + sagrams[i + 1]
   sagrams.append(UpperCAmelCase     )
  if i < len(UpperCAmelCase     ) - 2:
   lowercase__				:    List[str]  =					sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
   sagrams.append(UpperCAmelCase     )
  if i < len(UpperCAmelCase     ) - 3:
   lowercase__				:    Any  =					sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
   sagrams.append(UpperCAmelCase     )

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

 ((lowercase__) ,							(lowercase__) ,							(lowercase__))				:    Optional[int]  =					SARIngram(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
 ((lowercase__) ,							(lowercase__) ,							(lowercase__))				:    Tuple  =					SARIngram(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
 ((lowercase__) ,							(lowercase__) ,							(lowercase__))				:    Optional[int]  =					SARIngram(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
 ((lowercase__) ,							(lowercase__) ,							(lowercase__))				:    Optional[int]  =					SARIngram(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
 lowercase__				:    List[str]  =					sum([keepascore, keepascore, keepascore, keepascore]     ) / 4
 lowercase__				:    str  =					sum([delascore, delascore, delascore, delascore]     ) / 4
 lowercase__				:    Any  =					sum([addascore, addascore, addascore, addascore]     ) / 4
 lowercase__				:    Any  =					(avgkeepscore + avgdelscore + avgaddscore) / 3
 return finalscore
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase = True		,     UpperCAmelCase = "13a"		,     UpperCAmelCase = True     ):
 # 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__				:    Optional[Any]  =					sentence.lower()

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

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

 return normalized_sent
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 if not (len(UpperCAmelCase     ) == len(UpperCAmelCase     ) == len(UpperCAmelCase     )):
  raise ValueError('''Sources length must match predictions and references lengths.'''     )
 lowercase__				:    int  =					0
 for src, pred, refs in zip(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
  sari_score += SARIsent(normalize(UpperCAmelCase     )		,     normalize(UpperCAmelCase     )		,     [normalize(UpperCAmelCase     ) for sent in refs]     )
 lowercase__				:    Optional[Any]  =					sari_score / len(UpperCAmelCase     )
 return 100 * sari_score
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase="exp"		,     UpperCAmelCase=None		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     ):
 lowercase__				:    Optional[int]  =					len(references[0]     )
 if any(len(UpperCAmelCase     ) != references_per_prediction for refs in references     ):
  raise ValueError('''Sacrebleu requires the same number of references for each prediction'''     )
 lowercase__				:    Tuple  =					[[refs[i] for refs in references] for i in range(UpperCAmelCase     )]
 lowercase__				:    Union[str, Any]  =					sacrebleu.corpus_bleu(
     UpperCAmelCase		,     UpperCAmelCase		,     smooth_method=UpperCAmelCase		,     smooth_value=UpperCAmelCase		,     force=UpperCAmelCase		,     lowercase=UpperCAmelCase		,     use_effective_order=UpperCAmelCase		,     )
 return output.score





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





       '''simple docstring'''



       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        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 _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  List[Any]:
        lowercase__				:    int  =					{}
        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


'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase = 1		,     UpperCAmelCase = 1000     ):
 lowercase__				:    Dict  =					1
 lowercase__				:    Dict  =					0

 for divide_by_number in range(UpperCAmelCase		,     digit + 1     ):
  lowercase__				:    list[int]  =					[]
  lowercase__				:    Union[str, Any]  =					numerator
  for _ in range(1		,     digit + 1     ):
   if now_divide in has_been_divided:
    if longest_list_length < len(UpperCAmelCase     ):
     lowercase__				:    Dict  =					len(UpperCAmelCase     )
     lowercase__				:    Optional[Any]  =					divide_by_number
   else:
    has_been_divided.append(UpperCAmelCase     )
    lowercase__				:    int  =					now_divide * 10 % divide_by_number

 return the_digit


# Tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()

