





'''simple docstring'''




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__UpperCAmelCase         =   """\
@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},
}
"""

__UpperCAmelCase         =   """\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


__UpperCAmelCase         =   """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
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
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	[rgram for rgrams in rgramslist for rgram in rgrams]
		SCREAMING_SNAKE_CASE				:							Tuple						=	Counter(lowerCamelCase_					)

		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	Counter(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							Tuple						=	Counter()
		for sgram, scount in sgramcounter.items():
				SCREAMING_SNAKE_CASE				:							str						=	scount * numref

		SCREAMING_SNAKE_CASE				:							Tuple						=	Counter(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							Dict						=	Counter()
		for cgram, ccount in cgramcounter.items():
				SCREAMING_SNAKE_CASE				:							int						=	ccount * numref

		# KEEP
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	sgramcounter_rep & cgramcounter_rep
		SCREAMING_SNAKE_CASE				:							str						=	keepgramcounter_rep & rgramcounter
		SCREAMING_SNAKE_CASE				:							List[Any]						=	sgramcounter_rep & rgramcounter

		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	0
		SCREAMING_SNAKE_CASE				:							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.
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	1
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	1
		if len(lowerCamelCase_					) > 0:
				SCREAMING_SNAKE_CASE				:							Optional[Any]						=	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				:							str						=	keeptmpscorea / sum(keepgramcounterall_rep.values()					)
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	0
		if keepscore_precision > 0 or keepscore_recall > 0:
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

		# DELETION
		SCREAMING_SNAKE_CASE				:							Dict						=	sgramcounter_rep - cgramcounter_rep
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	delgramcounter_rep - rgramcounter
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	sgramcounter_rep - rgramcounter
		SCREAMING_SNAKE_CASE				:							List[str]						=	0
		SCREAMING_SNAKE_CASE				:							str						=	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				:							List[Any]						=	deltmpscorea / len(lowerCamelCase_					)

		# ADDITION
		SCREAMING_SNAKE_CASE				:							Tuple						=	set(lowerCamelCase_					) - set(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							Any						=	set(lowerCamelCase_					) & set(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	set(lowerCamelCase_					) - set(lowerCamelCase_					)

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

		return (keepscore, delscore_precision, addscore)



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							str						=	len(lowerCamelCase_					)

		SCREAMING_SNAKE_CASE				:							Optional[int]						=	ssent.split(""" """					)
		SCREAMING_SNAKE_CASE				:							str						=	csent.split(""" """					)
		SCREAMING_SNAKE_CASE				:							List[Any]						=	[]
		SCREAMING_SNAKE_CASE				:							int						=	[]
		SCREAMING_SNAKE_CASE				:							Tuple						=	[]
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	[]
		SCREAMING_SNAKE_CASE				:							str						=	[]
		SCREAMING_SNAKE_CASE				:							List[Any]						=	[]

		SCREAMING_SNAKE_CASE				:							List[Any]						=	[]
		SCREAMING_SNAKE_CASE				:							Dict						=	[]
		SCREAMING_SNAKE_CASE				:							Dict						=	[]
		SCREAMING_SNAKE_CASE				:							Any						=	[]
		for rsent in rsents:
				SCREAMING_SNAKE_CASE				:							str						=	rsent.split(""" """					)
				SCREAMING_SNAKE_CASE				:							Optional[Any]						=	[]
				SCREAMING_SNAKE_CASE				:							List[str]						=	[]
				SCREAMING_SNAKE_CASE				:							str						=	[]
				ragramslist.append(lowerCamelCase_					)
				for i in range(0	,	len(lowerCamelCase_					) - 1					):
						if i < len(lowerCamelCase_					) - 1:
								SCREAMING_SNAKE_CASE				:							str						=	ragrams[i] + """ """ + ragrams[i + 1]
								ragrams.append(lowerCamelCase_					)
						if i < len(lowerCamelCase_					) - 2:
								SCREAMING_SNAKE_CASE				:							int						=	ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
								ragrams.append(lowerCamelCase_					)
						if i < len(lowerCamelCase_					) - 3:
								SCREAMING_SNAKE_CASE				:							List[str]						=	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[int]						=	sagrams[i] + """ """ + sagrams[i + 1]
						sagrams.append(lowerCamelCase_					)
				if i < len(lowerCamelCase_					) - 2:
						SCREAMING_SNAKE_CASE				:							str						=	sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
						sagrams.append(lowerCamelCase_					)
				if i < len(lowerCamelCase_					) - 3:
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	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				:							List[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				:							List[Any]						=	cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
						cagrams.append(lowerCamelCase_					)

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



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_ = True	,	lowerCamelCase_ = "13a"	,	lowerCamelCase_ = True					):


		"""simple docstring"""



		if lowercase:
				SCREAMING_SNAKE_CASE				:							Optional[Any]						=	sentence.lower()

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

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

		return normalized_sent




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




					"""simple docstring"""


					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							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 lowerCamelCase_	(		self							:	Dict     ,		lowerCamelCase_							:	Any     ,		lowerCamelCase_							:	Optional[int]     ,		lowerCamelCase_							:	int		):

							'''simple docstring'''



							if not (len(lowerCamelCase_		) == len(lowerCamelCase_		) == len(lowerCamelCase_		)):
									raise ValueError("""Sources length must match predictions and references lengths."""		)
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	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				:							List[str]						=	sari_score / len(lowerCamelCase_		)
							return {"sari": 1_00 * sari_score}










'''simple docstring'''




from __future__ import annotations

from collections import Counter



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							int						=	[True] * n
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	False
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	False
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	True

		for i in range(3	,	int(n**0.5 + 1					)	,	2					):
				SCREAMING_SNAKE_CASE				:							List[Any]						=	i * 2
				while index < n:
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	False
						SCREAMING_SNAKE_CASE				:							Optional[Any]						=	index + i

		SCREAMING_SNAKE_CASE				:							Dict						=	[2]

		for i in range(3	,	lowerCamelCase_	,	2					):
				if is_prime[i]:
						primes.append(lowerCamelCase_					)

		return primes



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							List[str]						=	str(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							List[str]						=	[]
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	["""0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9"""]

		for duplicate in Counter(lowerCamelCase_					) - Counter(set(lowerCamelCase_					)					):
				SCREAMING_SNAKE_CASE				:							Tuple						=	[int(number_str.replace(lowerCamelCase_	,	lowerCamelCase_					)					) for digit in digits]
				replacements.append(lowerCamelCase_					)

		return replacements



def 						__A						(       lowerCamelCase_ = 8					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	set()

		# Filter primes with less than 3 replaceable digits
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{
		    x for x in set(prime_sieve(1_00_00_00					)					) if len(str(lowerCamelCase_					)					) - len(set(str(lowerCamelCase_					)					)					) >= 3
		}

		for prime in primes:
				if prime in numbers_checked:
						continue

				SCREAMING_SNAKE_CASE				:							int						=	digit_replacements(lowerCamelCase_					)

				for family in replacements:
						numbers_checked.update(lowerCamelCase_					)
						SCREAMING_SNAKE_CASE				:							Dict						=	primes.intersection(lowerCamelCase_					)

						if len(lowerCamelCase_					) != family_length:
								continue

						return min(lowerCamelCase_					)

		return -1


if __name__ == "__main__":
							print(solution())



