"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = []
		if len(lowerCamelCase__  ) == 1:
				return [nums.copy()]
		for _ in range(len(lowerCamelCase__  )  ):
				lowerCAmelCase__   = nums.pop(0  )
				lowerCAmelCase__   = permute(lowerCamelCase__  )
				for perm in permutations:
						perm.append(lowerCamelCase__  )
				result.extend(lowerCamelCase__  )
				nums.append(lowerCamelCase__  )
		return result







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		def backtrack(lowerCamelCase__  ):
				if start == len(lowerCamelCase__  ) - 1:
						output.append(nums[:]  )
				else:
						for i in range(lowerCamelCase__      ,	len(lowerCamelCase__  )  ):
								lowerCAmelCase__ ,       lowerCAmelCase__   = nums[i], nums[start]
								backtrack(start + 1  )
								lowerCAmelCase__ ,       lowerCAmelCase__   = nums[i], nums[start]  # backtrack

		lowerCAmelCase__   = []
		backtrack(0  )
		return output


if __name__ == "__main__":
			import doctest

			# use res to print the data in permute2 function
			__lowerCAmelCase     :				Optional[int]    				=    permutea([1, 2, 3])
			print(res)
			doctest.testmod()





"""simple docstring"""




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase     :				Optional[Any]    				=    "\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"

__lowerCAmelCase     :				str    				=    "\\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"


__lowerCAmelCase     :				int    				=    "\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		(							lowerCamelCase__  ):





		"""simple docstring"""




		def remove_articles(lowerCamelCase__  ):
				lowerCAmelCase__   = re.compile(r"""\b(a|an|the)\b"""      ,	re.UNICODE  )
				return re.sub(lowerCamelCase__      ,	""" """      ,	lowerCamelCase__  )

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

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

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

		return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase__  )  )  )  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return int(normalize_answer(lowerCamelCase__  ) == normalize_answer(lowerCamelCase__  )  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [any(compute_exact(lowerCamelCase__      ,	lowerCamelCase__  ) for ref in refs  ) for pred, refs in zip(lowerCamelCase__      ,	lowerCamelCase__  )]
		return (sum(lowerCamelCase__  ) / len(lowerCamelCase__  )) * 100







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [rgram for rgrams in rgramslist for rgram in rgrams]
		lowerCAmelCase__   = Counter(lowerCamelCase__  )

		lowerCAmelCase__   = Counter(lowerCamelCase__  )
		lowerCAmelCase__   = Counter()
		for sgram, scount in sgramcounter.items():
				lowerCAmelCase__   = scount * numref

		lowerCAmelCase__   = Counter(lowerCamelCase__  )
		lowerCAmelCase__   = Counter()
		for cgram, ccount in cgramcounter.items():
				lowerCAmelCase__   = ccount * numref

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

		lowerCAmelCase__   = 0
		lowerCAmelCase__   = 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__   = 1
		lowerCAmelCase__   = 1
		if len(lowerCamelCase__  ) > 0:
				lowerCAmelCase__   = keeptmpscorea / len(lowerCamelCase__  )
		if len(lowerCamelCase__  ) > 0:
				# Fix an alleged bug [2] in the keep score computation.
				# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
				lowerCAmelCase__   = keeptmpscorea / sum(keepgramcounterall_rep.values()  )
		lowerCAmelCase__   = 0
		if keepscore_precision > 0 or keepscore_recall > 0:
				lowerCAmelCase__   = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

		# DELETION
		lowerCAmelCase__   = sgramcounter_rep - cgramcounter_rep
		lowerCAmelCase__   = delgramcounter_rep - rgramcounter
		lowerCAmelCase__   = sgramcounter_rep - rgramcounter
		lowerCAmelCase__   = 0
		lowerCAmelCase__   = 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__   = 1
		if len(lowerCamelCase__  ) > 0:
				lowerCAmelCase__   = deltmpscorea / len(lowerCamelCase__  )

		# ADDITION
		lowerCAmelCase__   = set(lowerCamelCase__  ) - set(lowerCamelCase__  )
		lowerCAmelCase__   = set(lowerCamelCase__  ) & set(lowerCamelCase__  )
		lowerCAmelCase__   = set(lowerCamelCase__  ) - set(lowerCamelCase__  )

		lowerCAmelCase__   = 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__   = 1
		lowerCAmelCase__   = 1
		if len(lowerCamelCase__  ) > 0:
				lowerCAmelCase__   = addtmpscore / len(lowerCamelCase__  )
		if len(lowerCamelCase__  ) > 0:
				lowerCAmelCase__   = addtmpscore / len(lowerCamelCase__  )
		lowerCAmelCase__   = 0
		if addscore_precision > 0 or addscore_recall > 0:
				lowerCAmelCase__   = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

		return (keepscore, delscore_precision, addscore)







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = len(lowerCamelCase__  )

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

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

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__ = True      ,	lowerCamelCase__ = "13a"      ,	lowerCamelCase__ = True  ):





		"""simple docstring"""




		if lowercase:
				lowerCAmelCase__   = sentence.lower()

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

		if not return_str:
				lowerCAmelCase__   = normalized_sent.split()

		return normalized_sent







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		if not (len(lowerCamelCase__  ) == len(lowerCamelCase__  ) == len(lowerCamelCase__  )):
				raise ValueError("""Sources length must match predictions and references lengths."""  )
		lowerCAmelCase__   = 0
		for src, pred, refs in zip(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):
				sari_score += SARIsent(normalize(lowerCamelCase__  )      ,	normalize(lowerCamelCase__  )      ,	[normalize(lowerCamelCase__  ) for sent in refs]  )
		lowerCAmelCase__   = sari_score / len(lowerCamelCase__  )
		return 100 * sari_score







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__="exp"      ,	lowerCamelCase__=None      ,	lowerCamelCase__=False      ,	lowerCamelCase__=False      ,	lowerCamelCase__=False      ,	):





		"""simple docstring"""




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






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




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[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     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]	,    snake_case__    :  Dict	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  Union[str, Any]  ):
							lowerCAmelCase__   = {}
							result.update({"""sari""": compute_sari(sources=snake_case__	,    predictions=snake_case__	,    references=snake_case__  )}  )
							result.update({"""sacrebleu""": compute_sacrebleu(predictions=snake_case__	,    references=snake_case__  )}  )
							result.update({"""exact""": compute_em(predictions=snake_case__	,    references=snake_case__  )}  )
							return result




