






from __future__ import annotations

import random
from collections.abc import Iterable


class _lowerCAmelCase	:







						def __init__(						self      ,							lowerCAmelCase_       )		->       None:
										_SCREAMING_SNAKE_CASE      :		str							=						{literal: None for literal in literals}







						def __str__(						self       )		->       str:
										return "{" + " , ".join(self.literals       ) + "}"







						def __len__(						self       )		->       int:
										return len(self.literals       )







						def  A			(						self      ,							lowerCAmelCase_       )		->       None:






										for literal in self.literals:
														_SCREAMING_SNAKE_CASE      :		List[str]							=						literal[:2]
														if symbol in model:
																		_SCREAMING_SNAKE_CASE      :		str							=						model[symbol]
														else:
																		continue
														if value is not None:
																		# Complement assignment if literal is in complemented form
																		if literal.endswith('\''       ):
																						_SCREAMING_SNAKE_CASE      :		int							=						not value
														_SCREAMING_SNAKE_CASE      :		List[Any]							=						value







						def  A			(						self      ,							lowerCAmelCase_       )		->       bool | None:






										for literal in self.literals:
														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						literal.rstrip('\''       ) if literal.endswith('\''       ) else literal + '\''
														if symbol in self.literals:
																		return True

										self.assign(lowerCAmelCase_       )
										for value in self.literals.values():
														if value in (True, None):
																		return value
										return any(self.literals.values()       )







class _lowerCAmelCase	:







						def __init__(						self      ,							lowerCAmelCase_       )		->       None:
										_SCREAMING_SNAKE_CASE      :		List[str]							=						list(lowerCAmelCase_       )







						def __str__(						self       )		->       str:
										return "{" + " , ".join(str(lowerCAmelCase_       ) for clause in self.clauses       ) + "}"







def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		Any							=						[]
				_SCREAMING_SNAKE_CASE      :		List[str]							=						random.randint(1,	5					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						'A'
				_SCREAMING_SNAKE_CASE      :		str							=						0
				while i < no_of_literals:
								_SCREAMING_SNAKE_CASE      :		Any							=						random.randint(1,	5					)
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						base_var + str(lowerCamelCase					)
								_SCREAMING_SNAKE_CASE      :		Dict							=						random.randint(0,	1					)
								if var_complement == 1:
												var_name += "'"
								if var_name in literals:
												i -= 1
								else:
												literals.append(lowerCamelCase					)
								i += 1
				return Clause(lowerCamelCase					)





def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		int							=						set()
				_SCREAMING_SNAKE_CASE      :		Any							=						random.randint(1,	10					)
				while len(lowerCamelCase					) < no_of_clauses:
								clauses.add(generate_clause()					)
				return Formula(lowerCamelCase					)





def 							lowercase__		(				lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		List[str]							=						formula.clauses
				_SCREAMING_SNAKE_CASE      :		Tuple							=						[]
				for clause in formula.clauses:
								for literal in clause.literals:
												_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						literal[:2]
												if symbol not in symbols_set:
																symbols_set.append(lowerCamelCase					)
				return clauses, symbols_set





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		Dict							=						{}
				_SCREAMING_SNAKE_CASE      :		Any							=						[]

				for clause in clauses:
								if clause.evaluate(lowerCamelCase					):
												continue
								for literal in clause.literals:
												literals.append(lowerCamelCase					)

				for s in symbols:
								_SCREAMING_SNAKE_CASE      :		Tuple							=						s + '\''
								if (s in literals and sym not in literals) or (
								    s not in literals and sym in literals
								):
												pure_symbols.append(lowerCamelCase					)
				for p in pure_symbols:
								_SCREAMING_SNAKE_CASE      :		str							=						None
				for s in pure_symbols:
								_SCREAMING_SNAKE_CASE      :		str							=						s + '\''
								if s in literals:
												_SCREAMING_SNAKE_CASE      :		str							=						True
								elif sym in literals:
												_SCREAMING_SNAKE_CASE      :		List[str]							=						False
				return pure_symbols, assignment





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Dict							=						[]
				for clause in clauses:
								if len(lowerCamelCase					) == 1:
												unit_symbols.append(list(clause.literals.keys()					)[0]					)
								else:
												_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Dict							=						0, 0
												for literal, value in clause.literals.items():
																if value is False:
																				f_count += 1
																elif value is None:
																				_SCREAMING_SNAKE_CASE      :		Dict							=						literal
																				n_count += 1
												if f_count == len(lowerCamelCase					) - 1 and n_count == 1:
																unit_symbols.append(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						{}
				for i in unit_symbols:
								_SCREAMING_SNAKE_CASE      :		Dict							=						i[:2]
								_SCREAMING_SNAKE_CASE      :		Dict							=						len(lowerCamelCase					) == 2
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						[i[:2] for i in unit_symbols]

				return unit_symbols, assignment





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						True
				for clause in clauses:
								_SCREAMING_SNAKE_CASE      :		Tuple							=						clause.evaluate(lowerCamelCase					)
								if clause_check is False:
												return False, None
								elif clause_check is None:
												_SCREAMING_SNAKE_CASE      :		Dict							=						False
												continue

				if check_clause_all_true:
								return True, model

				try:
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						find_pure_symbols(lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				except RecursionError:
								print('raises a RecursionError and is'					)
								return None, {}
				_SCREAMING_SNAKE_CASE      :		List[str]							=						None
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						pure_symbols[0], assignment[pure_symbols[0]]

				if p:
								_SCREAMING_SNAKE_CASE      :		List[Any]							=						model
								_SCREAMING_SNAKE_CASE      :		List[Any]							=						value
								_SCREAMING_SNAKE_CASE      :		str							=						list(lowerCamelCase					)
								if p in tmp_symbols:
												tmp_symbols.remove(lowerCamelCase					)
								return dpll_algorithm(lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)

				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Optional[int]							=						find_unit_clauses(lowerCamelCase,	lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						None
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		int							=						unit_symbols[0], assignment[unit_symbols[0]]
				if p:
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						model
								_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						value
								_SCREAMING_SNAKE_CASE      :		List[str]							=						list(lowerCamelCase					)
								if p in tmp_symbols:
												tmp_symbols.remove(lowerCamelCase					)
								return dpll_algorithm(lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Any							=						symbols[0]
				_SCREAMING_SNAKE_CASE      :		List[str]							=						symbols[1:]
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Dict							=						model, model
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Dict							=						True, False

				return dpll_algorithm(lowerCamelCase,	lowerCamelCase,	lowerCamelCase					) or dpll_algorithm(lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			lowerCAmelCase__							= generate_formula()
			print(F'The formula {formula} is', end=' ')

			lowerCAmelCase__	,   lowerCAmelCase__							= generate_parameters(formula)
			lowerCAmelCase__	,   lowerCAmelCase__							= dpll_algorithm(clauses, symbols, {})

			if solution:
						print(F'satisfiable with the assignment {model}.')
			else:
						print('not satisfiable.')









"""simple docstring"""
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase__							= '\\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'

lowerCAmelCase__							= '\\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'


lowerCAmelCase__							= '\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 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		int							=						[rgram for rgrams in rgramslist for rgram in rgrams]
				_SCREAMING_SNAKE_CASE      :		Any							=						Counter(lowerCamelCase					)

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

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

				# KEEP
				_SCREAMING_SNAKE_CASE      :		List[str]							=						sgramcounter_rep & cgramcounter_rep
				_SCREAMING_SNAKE_CASE      :		str							=						keepgramcounter_rep & rgramcounter
				_SCREAMING_SNAKE_CASE      :		Tuple							=						sgramcounter_rep & rgramcounter

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

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

				# ADDITION
				_SCREAMING_SNAKE_CASE      :		Any							=						set(lowerCamelCase					) - set(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						set(lowerCamelCase					) & set(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						set(lowerCamelCase					) - set(lowerCamelCase					)

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

				return (keepscore, delscore_precision, addscore)





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						len(lowerCamelCase					)

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

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

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

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

				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		Tuple							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		int							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		Optional[int]							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		Union[str, Any]							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		int							=						sum([keepascore, keepascore, keepascore, keepascore]					) / 4
				_SCREAMING_SNAKE_CASE      :		List[str]							=						sum([delascore, delascore, delascore, delascore]					) / 4
				_SCREAMING_SNAKE_CASE      :		List[str]							=						sum([addascore, addascore, addascore, addascore]					) / 4
				_SCREAMING_SNAKE_CASE      :		Dict							=						(avgkeepscore + avgdelscore + avgaddscore) / 3
				return finalscore





def 							lowercase__		(				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:
								_SCREAMING_SNAKE_CASE      :		List[str]							=						sentence.lower()

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

				if not return_str:
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						normalized_sent.split()

				return normalized_sent






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







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

