

"""simple docstring"""




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A_							:     Optional[Any]     =	"\\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"

A_							:     Optional[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"


A_							:     Dict     =	"\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 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= [rgram for rgrams in rgramslist for rgram in rgrams]
			SCREAMING_SNAKE_CASE__									= Counter(snake_case__      )

			SCREAMING_SNAKE_CASE__									= Counter(snake_case__      )
			SCREAMING_SNAKE_CASE__									= Counter()
			for sgram, scount in sgramcounter.items():
						SCREAMING_SNAKE_CASE__									= scount * numref

			SCREAMING_SNAKE_CASE__									= Counter(snake_case__      )
			SCREAMING_SNAKE_CASE__									= Counter()
			for cgram, ccount in cgramcounter.items():
						SCREAMING_SNAKE_CASE__									= ccount * numref

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

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

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

			# ADDITION
			SCREAMING_SNAKE_CASE__									= set(snake_case__      ) - set(snake_case__      )
			SCREAMING_SNAKE_CASE__									= set(snake_case__      ) & set(snake_case__      )
			SCREAMING_SNAKE_CASE__									= set(snake_case__      ) - set(snake_case__      )

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

			return (keepscore, delscore_precision, addscore)

def 		A    (       snake_case__						,			snake_case__						,			snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= len(snake_case__      )

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

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

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

def 		A    (       snake_case__						,			snake_case__ = True						,			snake_case__ = "13a"						,			snake_case__ = True      ):




			'''simple docstring'''


			if lowercase:
						SCREAMING_SNAKE_CASE__									= sentence.lower()

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

			if not return_str:
						SCREAMING_SNAKE_CASE__									= normalized_sent.split()

			return normalized_sent





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

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      str:
						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			:  Tuple      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  List[Any]    )				->      Optional[Any]:
						if not (len(__UpperCAmelCase    ) == len(__UpperCAmelCase    ) == len(__UpperCAmelCase    )):
									raise ValueError("""Sources length must match predictions and references lengths."""    )
						SCREAMING_SNAKE_CASE__									= 0
						for src, pred, refs in zip(__UpperCAmelCase      ,			__UpperCAmelCase      ,			__UpperCAmelCase    ):
									sari_score += SARIsent(normalize(__UpperCAmelCase    )      ,			normalize(__UpperCAmelCase    )      ,			[normalize(__UpperCAmelCase    ) for sent in refs]    )
						SCREAMING_SNAKE_CASE__									= sari_score / len(__UpperCAmelCase    )
						return {"sari": 1_0_0 * sari_score}





"""simple docstring"""




def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


			return a + b


if __name__ == "__main__":
						A_							:     List[str]     =	5
						A_							:     Optional[int]     =	6
						print(F'The sum of {a} + {b} is {add(a, b)}')


