


'''simple docstring'''






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__A	:	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'

__A	:	Dict     =     '\\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	:	Union[str, Any]     =     '\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_  :Union[str, Any] ):







		'''simple docstring'''

		def remove_articles(lowerCamelCase_  :Tuple ):
				snake_case_		:							Optional[Any]			       =						re.compile(R"""\b(a|an|the)\b"""				,      re.UNICODE )
				return re.sub(lowerCamelCase_				,      """ """				,      lowerCamelCase_ )

		def white_space_fix(lowerCamelCase_  :Union[str, Any] ):
				return " ".join(text.split() )

		def remove_punc(lowerCamelCase_  :str ):
				snake_case_		:							Optional[int]			       =						set(string.punctuation )
				return "".join(ch for ch in text if ch not in exclude )

		def lower(lowerCamelCase_  :Any ):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_ ) ) ) )





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		return int(normalize_answer(lowerCamelCase_ ) == normalize_answer(lowerCamelCase_ ) )





def      UpperCAmelCase      (		lowerCamelCase_  :Dict				,      lowerCamelCase_  :Tuple ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						[any(compute_exact(lowerCamelCase_				,      lowerCamelCase_ ) for ref in refs ) for pred, refs in zip(lowerCamelCase_				,      lowerCamelCase_ )]
		return (sum(lowerCamelCase_ ) / len(lowerCamelCase_ )) * 1_00





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :str ):







		'''simple docstring'''
		snake_case_		:							Optional[Any]			       =						[rgram for rgrams in rgramslist for rgram in rgrams]
		snake_case_		:							Optional[int]			       =						Counter(lowerCamelCase_ )

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

		snake_case_		:							List[str]			       =						Counter(lowerCamelCase_ )
		snake_case_		:							Optional[int]			       =						Counter()
		for cgram, ccount in cgramcounter.items():
				snake_case_		:							List[Any]			       =						ccount * numref

		# KEEP
		snake_case_		:							Tuple			       =						sgramcounter_rep & cgramcounter_rep
		snake_case_		:							Any			       =						keepgramcounter_rep & rgramcounter
		snake_case_		:							int			       =						sgramcounter_rep & rgramcounter

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

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

		# ADDITION
		snake_case_		:							Optional[Any]			       =						set(lowerCamelCase_ ) - set(lowerCamelCase_ )
		snake_case_		:							Union[str, Any]			       =						set(lowerCamelCase_ ) & set(lowerCamelCase_ )
		snake_case_		:							int			       =						set(lowerCamelCase_ ) - set(lowerCamelCase_ )

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

		return (keepscore, delscore_precision, addscore)





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							List[str]			       =						len(lowerCamelCase_ )

		snake_case_		:							Tuple			       =						ssent.split(""" """ )
		snake_case_		:							Union[str, Any]			       =						csent.split(""" """ )
		snake_case_		:							int			       =						[]
		snake_case_		:							Any			       =						[]
		snake_case_		:							Union[str, Any]			       =						[]
		snake_case_		:							Optional[Any]			       =						[]
		snake_case_		:							Any			       =						[]
		snake_case_		:							Tuple			       =						[]

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

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





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :bool = True				,      lowerCamelCase_  :str = "13a"				,      lowerCamelCase_  :bool = True ):







		'''simple docstring'''
		# 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:
				snake_case_		:							str			       =						sentence.lower()

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

		if not return_str:
				snake_case_		:							Dict			       =						normalized_sent.split()

		return normalized_sent





def      UpperCAmelCase      (		lowerCamelCase_  :int				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		if not (len(lowerCamelCase_ ) == len(lowerCamelCase_ ) == len(lowerCamelCase_ )):
				raise ValueError("""Sources length must match predictions and references lengths.""" )
		snake_case_		:							int			       =						0
		for src, pred, refs in zip(lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ ):
				sari_score += SARIsent(normalize(lowerCamelCase_ )				,      normalize(lowerCamelCase_ )				,      [normalize(lowerCamelCase_ ) for sent in refs] )
		snake_case_		:							List[Any]			       =						sari_score / len(lowerCamelCase_ )
		return 1_00 * sari_score





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :Tuple				,      lowerCamelCase_  :List[str]="exp"				,      lowerCamelCase_  :Optional[int]=None				,      lowerCamelCase_  :List[str]=False				,      lowerCamelCase_  :Tuple=False				,      lowerCamelCase_  :int=False				,      ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						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""" )
		snake_case_		:							int			       =						[[refs[i] for refs in references] for i in range(lowerCamelCase_ )]
		snake_case_		:							List[str]			       =						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 						__UpperCamelCase	(						datasets.Metric							):





							def 		a__   (  self		:Tuple			):
									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 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:List[str]			):
									snake_case_		:							Tuple			       =						{}
									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'''






def      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		return " ".join(
		    """""".join(word[::-1] ) if len(lowerCamelCase_ ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(reverse_long_words('Hey wollef sroirraw'))