
"""simple docstring"""

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_A						=			'\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

_A						=			'\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


_A						=			'\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\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\n    sacrebleu: sacrebleu score\n    exact: exact score\n\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 ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		def remove_articles(a_      ):
				lowerCamelCase					:      Dict          =   re.compile(r'\b(a|an|the)\b',						re.UNICODE      )
				return re.sub(a_,						' ',						a_      )

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

		def remove_punc(a_      ):
				lowerCamelCase					:      Any          =   set(string.punctuation      )
				return "".join(ch for ch in text if ch not in exclude      )

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

		return white_space_fix(remove_articles(remove_punc(lower(a_      )      )      )      )
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		return int(normalize_answer(a_      ) == normalize_answer(a_      )      )
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   [any(compute_exact(a_,						a_      ) for ref in refs      ) for pred, refs in zip(a_,						a_      )]
		return (sum(a_      ) / len(a_      )) * 100
def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Union[str, Any]          =   [rgram for rgrams in rgramslist for rgram in rgrams]
		lowerCamelCase					:      Union[str, Any]          =   Counter(a_      )

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

		lowerCamelCase					:      Optional[Any]          =   Counter(a_      )
		lowerCamelCase					:      int          =   Counter()
		for cgram, ccount in cgramcounter.items():
				lowerCamelCase					:      Optional[int]          =   ccount * numref

		# KEEP
		lowerCamelCase					:      Optional[Any]          =   sgramcounter_rep & cgramcounter_rep
		lowerCamelCase					:      Any          =   keepgramcounter_rep & rgramcounter
		lowerCamelCase					:      int          =   sgramcounter_rep & rgramcounter

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

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

		# ADDITION
		lowerCamelCase					:      List[Any]          =   set(a_      ) - set(a_      )
		lowerCamelCase					:      List[str]          =   set(a_      ) & set(a_      )
		lowerCamelCase					:      Tuple          =   set(a_      ) - set(a_      )

		lowerCamelCase					:      List[str]          =   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					:      Tuple          =   1
		lowerCamelCase					:      int          =   1
		if len(a_      ) > 0:
				lowerCamelCase					:      Tuple          =   addtmpscore / len(a_      )
		if len(a_      ) > 0:
				lowerCamelCase					:      int          =   addtmpscore / len(a_      )
		lowerCamelCase					:      Any          =   0
		if addscore_precision > 0 or addscore_recall > 0:
				lowerCamelCase					:      Tuple          =   2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

		return (keepscore, delscore_precision, addscore)
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      List[Any]          =   len(a_      )

		lowerCamelCase					:      str          =   ssent.split(' '      )
		lowerCamelCase					:      Union[str, Any]          =   csent.split(' '      )
		lowerCamelCase					:      Any          =   []
		lowerCamelCase					:      List[Any]          =   []
		lowerCamelCase					:      Union[str, Any]          =   []
		lowerCamelCase					:      Any          =   []
		lowerCamelCase					:      Any          =   []
		lowerCamelCase					:      Tuple          =   []

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

		for i in range(0,						len(a_      ) - 1      ):
				if i < len(a_      ) - 1:
						lowerCamelCase					:      Tuple          =   sagrams[i] + ' ' + sagrams[i + 1]
						sagrams.append(a_      )
				if i < len(a_      ) - 2:
						lowerCamelCase					:      int          =   sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
						sagrams.append(a_      )
				if i < len(a_      ) - 3:
						lowerCamelCase					:      Optional[Any]          =   sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
						sagrams.append(a_      )

		for i in range(0,						len(a_      ) - 1      ):
				if i < len(a_      ) - 1:
						lowerCamelCase					:      Tuple          =   cagrams[i] + ' ' + cagrams[i + 1]
						cagrams.append(a_      )
				if i < len(a_      ) - 2:
						lowerCamelCase					:      Optional[int]          =   cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
						cagrams.append(a_      )
				if i < len(a_      ) - 3:
						lowerCamelCase					:      Union[str, Any]          =   cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
						cagrams.append(a_      )

		(lowerCamelCase)					:      Optional[int]          =   SARIngram(a_,						a_,						a_,						a_      )
		(lowerCamelCase)					:      str          =   SARIngram(a_,						a_,						a_,						a_      )
		(lowerCamelCase)					:      str          =   SARIngram(a_,						a_,						a_,						a_      )
		(lowerCamelCase)					:      Dict          =   SARIngram(a_,						a_,						a_,						a_      )
		lowerCamelCase					:      Dict          =   sum([keepascore, keepascore, keepascore, keepascore]      ) / 4
		lowerCamelCase					:      List[str]          =   sum([delascore, delascore, delascore, delascore]      ) / 4
		lowerCamelCase					:      Optional[int]          =   sum([addascore, addascore, addascore, addascore]      ) / 4
		lowerCamelCase					:      int          =   (avgkeepscore + avgdelscore + avgaddscore) / 3
		return finalscore
def 		UpperCAmelCase (		a_,						a_ = True,						a_ = "13a",						a_ = True      ):





		'''simple docstring'''


		if lowercase:
				lowerCamelCase					:      Optional[int]          =   sentence.lower()

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

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

		return normalized_sent
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		if not (len(a_      ) == len(a_      ) == len(a_      )):
				raise ValueError('Sources length must match predictions and references lengths.'      )
		lowerCamelCase					:      List[str]          =   0
		for src, pred, refs in zip(a_,						a_,						a_      ):
				sari_score += SARIsent(normalize(a_      ),						normalize(a_      ),						[normalize(a_      ) for sent in refs]      )
		lowerCamelCase					:      Dict          =   sari_score / len(a_      )
		return 100 * sari_score
def 		UpperCAmelCase (		a_,						a_,						a_="exp",						a_=None,						a_=False,						a_=False,						a_=False,						):





		'''simple docstring'''


		lowerCamelCase					:      Union[str, Any]          =   len(references[0]      )
		if any(len(a_      ) != references_per_prediction for refs in references      ):
				raise ValueError('Sacrebleu requires the same number of references for each prediction'      )
		lowerCamelCase					:      Any          =   [[refs[i] for refs in references] for i in range(a_      )]
		lowerCamelCase					:      str          =   sacrebleu.corpus_bleu(
		    a_,						a_,						smooth_method=a_,						smooth_value=a_,						force=a_,						lowercase=a_,						use_effective_order=a_,						)
		return output.score






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




	def       _UpperCamelCase   (			self )		->							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       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							int:
			lowerCamelCase					:      int          =   {}
			result.update({'sari': compute_sari(sources=UpperCAmelCase_		,			predictions=UpperCAmelCase_		,			references=UpperCAmelCase_ )} )
			result.update({'sacrebleu': compute_sacrebleu(predictions=UpperCAmelCase_		,			references=UpperCAmelCase_ )} )
			result.update({'exact': compute_em(predictions=UpperCAmelCase_		,			references=UpperCAmelCase_ )} )
			return result








"""simple docstring"""

# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


_A						=			re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')






@total_ordering
@dataclass
class 	_lowercase       :
	lowercase_				=     42
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   _str_to_version_tuple(self.version_str )




	def __repr__(			self )		->							int:
			return F"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""




	@property
	def       _UpperCamelCase   (			self )		->							Dict:
			return self.major, self.minor, self.patch




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
					return Version(UpperCAmelCase_ )
			elif isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
					return other
			raise TypeError(F"""{other} (type {type(UpperCAmelCase_ )}) cannot be compared to version.""" )




	def __eq__(			self		,			UpperCAmelCase_ )		->							Optional[Any]:
			try:
					lowerCamelCase					:      List[str]          =   self._validate_operand(UpperCAmelCase_ )
			except (TypeError, ValueError):
					return False
			else:
					return self.tuple == other.tuple




	def __lt__(			self		,			UpperCAmelCase_ )		->							Optional[int]:
			lowerCamelCase					:      Optional[int]          =   self._validate_operand(UpperCAmelCase_ )
			return self.tuple < other.tuple




	def __hash__(			self )		->							Optional[Any]:
			return hash(_version_tuple_to_str(self.tuple ) )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_ )		->							Union[str, Any]:
			lowerCamelCase					:      str          =   {f.name for f in dataclasses.fields(cls )}
			return cls(**{k: v for k, v in dic.items() if k in field_names} )




	def       _UpperCamelCase   (			self )		->							str:
			return self.version_str


def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   _VERSION_REG.match(a_      )
		if not res:
				raise ValueError(F"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."""      )
		return tuple(int(a_      ) for v in [res.group('major'      ), res.group('minor'      ), res.group('patch'      )]      )





def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return ".".join(str(a_      ) for v in version_tuple      )






