code: 
  
  
  
  
  
  from collections import Counter
  
  import sacrebleu
  import sacremoses
  from packaging import version
  
  import datasets
  
  
  _lowercase		:     str 					="""\\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"""
  
  _lowercase		:     Union[str, Any] 					="""\\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"""
  
  
  _lowercase		:     Any 					="""\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 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
  		lowerCamelCase_  :    Union[str, Any]   	=			[rgram for rgrams in rgramslist for rgram in rgrams]
  		lowerCamelCase_  :    Any   	=			Counter(UpperCamelCase__	)
  
  		lowerCamelCase_  :    List[Any]   	=			Counter(UpperCamelCase__	)
  		lowerCamelCase_  :    Optional[Any]   	=			Counter()
  		for sgram, scount in sgramcounter.items():
  				lowerCamelCase_  :    Optional[Any]   	=			scount * numref
  
  		lowerCamelCase_  :    Any   	=			Counter(UpperCamelCase__	)
  		lowerCamelCase_  :    Optional[int]   	=			Counter()
  		for cgram, ccount in cgramcounter.items():
  				lowerCamelCase_  :    Optional[int]   	=			ccount * numref
  
  		# KEEP
  		lowerCamelCase_  :    int   	=			sgramcounter_rep & cgramcounter_rep
  		lowerCamelCase_  :    Optional[Any]   	=			keepgramcounter_rep & rgramcounter
  		lowerCamelCase_  :    Optional[int]   	=			sgramcounter_rep & rgramcounter
  
  		lowerCamelCase_  :    List[str]   	=			0
  		lowerCamelCase_  :    Optional[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.
  		lowerCamelCase_  :    str   	=			1
  		lowerCamelCase_  :    str   	=			1
  		if len(UpperCamelCase__	) > 0:
  				lowerCamelCase_  :    Dict   	=			keeptmpscorea / len(UpperCamelCase__	)
  		if len(UpperCamelCase__	) > 0:
  				# Fix an alleged bug [2] in the keep score computation.
  				# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
  				lowerCamelCase_  :    Tuple   	=			keeptmpscorea / sum(keepgramcounterall_rep.values()	)
  		lowerCamelCase_  :    Optional[int]   	=			0
  		if keepscore_precision > 0 or keepscore_recall > 0:
  				lowerCamelCase_  :    Optional[int]   	=			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
  
  		# DELETION
  		lowerCamelCase_  :    Union[str, Any]   	=			sgramcounter_rep - cgramcounter_rep
  		lowerCamelCase_  :    str   	=			delgramcounter_rep - rgramcounter
  		lowerCamelCase_  :    int   	=			sgramcounter_rep - rgramcounter
  		lowerCamelCase_  :    Tuple   	=			0
  		lowerCamelCase_  :    Optional[Any]   	=			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.
  		lowerCamelCase_  :    Optional[int]   	=			1
  		if len(UpperCamelCase__	) > 0:
  				lowerCamelCase_  :    Union[str, Any]   	=			deltmpscorea / len(UpperCamelCase__	)
  
  		# ADDITION
  		lowerCamelCase_  :    Optional[Any]   	=			set(UpperCamelCase__	) - set(UpperCamelCase__	)
  		lowerCamelCase_  :    Any   	=			set(UpperCamelCase__	) & set(UpperCamelCase__	)
  		lowerCamelCase_  :    Optional[Any]   	=			set(UpperCamelCase__	) - set(UpperCamelCase__	)
  
  		lowerCamelCase_  :    Dict   	=			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.
  		lowerCamelCase_  :    int   	=			1
  		lowerCamelCase_  :    str   	=			1
  		if len(UpperCamelCase__	) > 0:
  				lowerCamelCase_  :    Dict   	=			addtmpscore / len(UpperCamelCase__	)
  		if len(UpperCamelCase__	) > 0:
  				lowerCamelCase_  :    Dict   	=			addtmpscore / len(UpperCamelCase__	)
  		lowerCamelCase_  :    str   	=			0
  		if addscore_precision > 0 or addscore_recall > 0:
  				lowerCamelCase_  :    Optional[int]   	=			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
  
  		return (keepscore, delscore_precision, addscore)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
  		lowerCamelCase_  :    Dict   	=			len(UpperCamelCase__	)
  
  		lowerCamelCase_  :    Dict   	=			ssent.split(' '	)
  		lowerCamelCase_  :    Union[str, Any]   	=			csent.split(' '	)
  		lowerCamelCase_  :    List[str]   	=			[]
  		lowerCamelCase_  :    Union[str, Any]   	=			[]
  		lowerCamelCase_  :    List[str]   	=			[]
  		lowerCamelCase_  :    Any   	=			[]
  		lowerCamelCase_  :    Dict   	=			[]
  		lowerCamelCase_  :    Dict   	=			[]
  
  		lowerCamelCase_  :    List[Any]   	=			[]
  		lowerCamelCase_  :    Optional[int]   	=			[]
  		lowerCamelCase_  :    List[Any]   	=			[]
  		lowerCamelCase_  :    str   	=			[]
  		for rsent in rsents:
  				lowerCamelCase_  :    Any   	=			rsent.split(' '	)
  				lowerCamelCase_  :    int   	=			[]
  				lowerCamelCase_  :    Dict   	=			[]
  				lowerCamelCase_  :    Any   	=			[]
  				ragramslist.append(UpperCamelCase__	)
  				for i in range(0       ,len(UpperCamelCase__	) - 1	):
  						if i < len(UpperCamelCase__	) - 1:
  								lowerCamelCase_  :    Optional[int]   	=			ragrams[i] + " " + ragrams[i + 1]
  								ragrams.append(UpperCamelCase__	)
  						if i < len(UpperCamelCase__	) - 2:
  								lowerCamelCase_  :    str   	=			ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
  								ragrams.append(UpperCamelCase__	)
  						if i < len(UpperCamelCase__	) - 3:
  								lowerCamelCase_  :    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:
  						lowerCamelCase_  :    Union[str, Any]   	=			sagrams[i] + " " + sagrams[i + 1]
  						sagrams.append(UpperCamelCase__	)
  				if i < len(UpperCamelCase__	) - 2:
  						lowerCamelCase_  :    List[str]   	=			sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
  						sagrams.append(UpperCamelCase__	)
  				if i < len(UpperCamelCase__	) - 3:
  						lowerCamelCase_  :    List[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:
  						lowerCamelCase_  :    List[Any]   	=			cagrams[i] + " " + cagrams[i + 1]
  						cagrams.append(UpperCamelCase__	)
  				if i < len(UpperCamelCase__	) - 2:
  						lowerCamelCase_  :    Any   	=			cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
  						cagrams.append(UpperCamelCase__	)
  				if i < len(UpperCamelCase__	) - 3:
  						lowerCamelCase_  :    List[str]   	=			cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
  						cagrams.append(UpperCamelCase__	)
  
  		(lowerCamelCase_)  :    Optional[int]   	=			SARIngram(UpperCamelCase__       ,UpperCamelCase__       ,UpperCamelCase__       ,UpperCamelCase__	)
  		(lowerCamelCase_)  :    int   	=			SARIngram(UpperCamelCase__       ,UpperCamelCase__       ,UpperCamelCase__       ,UpperCamelCase__	)
  		(lowerCamelCase_)  :    Optional[Any]   	=			SARIngram(UpperCamelCase__       ,UpperCamelCase__       ,UpperCamelCase__       ,UpperCamelCase__	)
  		(lowerCamelCase_)  :    Tuple   	=			SARIngram(UpperCamelCase__       ,UpperCamelCase__       ,UpperCamelCase__       ,UpperCamelCase__	)
  		lowerCamelCase_  :    Union[str, Any]   	=			sum([keepascore, keepascore, keepascore, keepascore]	) / 4
  		lowerCamelCase_  :    Union[str, Any]   	=			sum([delascore, delascore, delascore, delascore]	) / 4
  		lowerCamelCase_  :    Optional[int]   	=			sum([addascore, addascore, addascore, addascore]	) / 4
  		lowerCamelCase_  :    Optional[int]   	=			(avgkeepscore + avgdelscore + avgaddscore) / 3
  		return finalscore
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__ = True       ,lowerCAmelCase__ = "13a"       ,lowerCAmelCase__ = 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:
  				lowerCamelCase_  :    Optional[int]   	=			sentence.lower()
  
  		if tokenizer in ["13a", "intl"]:
  				if version.parse(sacrebleu.__version__	).major >= 2:
  						lowerCamelCase_  :    Dict   	=			sacrebleu.metrics.bleu._get_tokenizer(UpperCamelCase__	)()(UpperCamelCase__	)
  				else:
  						lowerCamelCase_  :    Any   	=			sacrebleu.TOKENIZERS[tokenizer]()(UpperCamelCase__	)
  		elif tokenizer == "moses":
  				lowerCamelCase_  :    Dict   	=			sacremoses.MosesTokenizer().tokenize(UpperCamelCase__       ,return_str=UpperCamelCase__       ,escape=UpperCamelCase__	)
  		elif tokenizer == "penn":
  				lowerCamelCase_  :    Dict   	=			sacremoses.MosesTokenizer().penn_tokenize(UpperCamelCase__       ,return_str=UpperCamelCase__	)
  		else:
  				lowerCamelCase_  :    int   	=			sentence
  
  		if not return_str:
  				lowerCamelCase_  :    Dict   	=			normalized_sent.split()
  
  		return normalized_sent
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,    _KWARGS_DESCRIPTION       )
  class 	UpperCamelCase_     (     datasets.Metric       ):
  
  
  
  					def      __a    (		self    :    Union[str, 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      __a    (		self    :    Any					,			lowerCamelCase    :    str					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    List[str]	):
  							if not (len(__lowerCamelCase	) == len(__lowerCamelCase	) == len(__lowerCamelCase	)):
  									raise ValueError('Sources length must match predictions and references lengths.'	)
  							lowerCamelCase_  :    List[Any]   	=			0
  							for src, pred, refs in zip(__lowerCamelCase					,			__lowerCamelCase					,			__lowerCamelCase	):
  									sari_score += SARIsent(normalize(__lowerCamelCase	)					,			normalize(__lowerCamelCase	)					,			[normalize(__lowerCamelCase	) for sent in refs]	)
  							lowerCamelCase_  :    Union[str, Any]   	=			sari_score / len(__lowerCamelCase	)
  							return {"sari": 1_00 * sari_score}
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		lowerCamelCase_  :    int
  		lowerCamelCase_  :    list[str]   	=			list(lowerCAmelCase__	)
  		lowerCamelCase_	,			lowerCamelCase_  :    List[str]   	=			_vector[:-1], int(_vector[-1]	)
  		lowerCamelCase_  :    list[int]   	=			[int(lowerCAmelCase__	) for digit in __vector]
  
  		vector.reverse()
  		for i, digit in enumerate(lowerCAmelCase__	):
  				if i & 1 == 0:
  						lowerCamelCase_  :    int   	=			digit * 2
  						if doubled > 9:
  								doubled -= 9
  						check_digit += doubled
  				else:
  						check_digit += digit
  
  		return check_digit % 10 == 0
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  		assert is_luhn("""79927398713""")
  		assert not is_luhn("""79927398714""")
  