
import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_lowerCamelCase       :				List[Any]       =			'''
@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
},
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

_lowerCamelCase       :				Optional[Any]       =			'''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


_lowerCamelCase       :				List[str]       =			'''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
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
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known ."]]
    >>> wiki_split = datasets.load_metric("wiki_split")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''






def        a_		(       __lowercase   :	int ) ->  int:

			def remove_articles(__lowercase   :	Union[str, Any] ):
						_snake_case		=			re.compile(r'\b(a|an|the)\b'       ,					re.UNICODE )
						return re.sub(__lowercase       ,					' '       ,					__lowercase )

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

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

			def lower(__lowercase   :	Tuple ):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(__lowercase ) ) ) )






def        a_		(       __lowercase   :	List[str]       ,					__lowercase   :	Any ) ->  List[Any]:
			return int(normalize_answer(__lowercase ) == normalize_answer(__lowercase ) )






def        a_		(       __lowercase   :	List[str]       ,					__lowercase   :	int ) ->  Optional[Any]:
			_snake_case		=			[any(compute_exact(__lowercase       ,					__lowercase ) for ref in refs ) for pred, refs in zip(__lowercase       ,					__lowercase )]
			return (sum(__lowercase ) / len(__lowercase )) * 100






def        a_		(       __lowercase   :	List[str]       ,					__lowercase   :	Dict       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  List[Any]:
			_snake_case		=			[rgram for rgrams in rgramslist for rgram in rgrams]
			_snake_case		=			Counter(__lowercase )

			_snake_case		=			Counter(__lowercase )
			_snake_case		=			Counter()
			for sgram, scount in sgramcounter.items():
						_snake_case		=			scount * numref

			_snake_case		=			Counter(__lowercase )
			_snake_case		=			Counter()
			for cgram, ccount in cgramcounter.items():
						_snake_case		=			ccount * numref

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

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

			# DELETION
			_snake_case		=			sgramcounter_rep - cgramcounter_rep
			_snake_case		=			delgramcounter_rep - rgramcounter
			_snake_case		=			sgramcounter_rep - rgramcounter
			_snake_case		=			0
			_snake_case		=			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		=			1
			if len(__lowercase ) > 0:
						_snake_case		=			deltmpscorea / len(__lowercase )

			# ADDITION
			_snake_case		=			set(__lowercase ) - set(__lowercase )
			_snake_case		=			set(__lowercase ) & set(__lowercase )
			_snake_case		=			set(__lowercase ) - set(__lowercase )

			_snake_case		=			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		=			1
			_snake_case		=			1
			if len(__lowercase ) > 0:
						_snake_case		=			addtmpscore / len(__lowercase )
			if len(__lowercase ) > 0:
						_snake_case		=			addtmpscore / len(__lowercase )
			_snake_case		=			0
			if addscore_precision > 0 or addscore_recall > 0:
						_snake_case		=			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

			return (keepscore, delscore_precision, addscore)






def        a_		(       __lowercase   :	List[str]       ,					__lowercase   :	Dict       ,					__lowercase   :	Tuple ) ->  Optional[int]:
			_snake_case		=			len(__lowercase )

			_snake_case		=			ssent.split(' ' )
			_snake_case		=			csent.split(' ' )
			_snake_case		=			[]
			_snake_case		=			[]
			_snake_case		=			[]
			_snake_case		=			[]
			_snake_case		=			[]
			_snake_case		=			[]

			_snake_case		=			[]
			_snake_case		=			[]
			_snake_case		=			[]
			_snake_case		=			[]
			for rsent in rsents:
						_snake_case		=			rsent.split(' ' )
						_snake_case		=			[]
						_snake_case		=			[]
						_snake_case		=			[]
						ragramslist.append(__lowercase )
						for i in range(0       ,					len(__lowercase ) - 1 ):
									if i < len(__lowercase ) - 1:
												_snake_case		=			ragrams[i] + ' ' + ragrams[i + 1]
												ragrams.append(__lowercase )
									if i < len(__lowercase ) - 2:
												_snake_case		=			ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
												ragrams.append(__lowercase )
									if i < len(__lowercase ) - 3:
												_snake_case		=			ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
												ragrams.append(__lowercase )
						ragramslist.append(__lowercase )
						ragramslist.append(__lowercase )
						ragramslist.append(__lowercase )

			for i in range(0       ,					len(__lowercase ) - 1 ):
						if i < len(__lowercase ) - 1:
									_snake_case		=			sagrams[i] + ' ' + sagrams[i + 1]
									sagrams.append(__lowercase )
						if i < len(__lowercase ) - 2:
									_snake_case		=			sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
									sagrams.append(__lowercase )
						if i < len(__lowercase ) - 3:
									_snake_case		=			sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
									sagrams.append(__lowercase )

			for i in range(0       ,					len(__lowercase ) - 1 ):
						if i < len(__lowercase ) - 1:
									_snake_case		=			cagrams[i] + ' ' + cagrams[i + 1]
									cagrams.append(__lowercase )
						if i < len(__lowercase ) - 2:
									_snake_case		=			cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
									cagrams.append(__lowercase )
						if i < len(__lowercase ) - 3:
									_snake_case		=			cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
									cagrams.append(__lowercase )

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






def        a_		(       __lowercase   :	List[str]       ,					__lowercase   :	bool = True       ,					__lowercase   :	str = "13a"       ,					__lowercase   :	bool = True ) ->  Tuple:
			# 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		=			sentence.lower()

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

			if not return_str:
						_snake_case		=			normalized_sent.split()

			return normalized_sent






def        a_		(       __lowercase   :	Union[str, Any]       ,					__lowercase   :	List[str]       ,					__lowercase   :	Tuple ) ->  Dict:
			if not (len(__lowercase ) == len(__lowercase ) == len(__lowercase )):
						raise ValueError('Sources length must match predictions and references lengths.' )
			_snake_case		=			0
			for src, pred, refs in zip(__lowercase       ,					__lowercase       ,					__lowercase ):
						sari_score += SARIsent(normalize(__lowercase )       ,					normalize(__lowercase )       ,					[normalize(__lowercase ) for sent in refs] )
			_snake_case		=			sari_score / len(__lowercase )
			return 100 * sari_score






def        a_		(       __lowercase   :	Any       ,					__lowercase   :	Union[str, Any]       ,					__lowercase   :	Tuple="exp"       ,					__lowercase   :	Union[str, Any]=None       ,					__lowercase   :	Optional[Any]=False       ,					__lowercase   :	Any=False       ,					__lowercase   :	Tuple=False       ,					) ->  List[str]:
			_snake_case		=			len(references[0] )
			if any(len(__lowercase ) != references_per_prediction for refs in references ):
						raise ValueError('Sacrebleu requires the same number of references for each prediction' )
			_snake_case		=			[[refs[i] for refs in references] for i in range(__lowercase )]
			_snake_case		=			sacrebleu.corpus_bleu(
			    __lowercase       ,					__lowercase       ,					smooth_method=__lowercase       ,					smooth_value=__lowercase       ,					force=__lowercase       ,					lowercase=__lowercase       ,					use_effective_order=__lowercase       ,					)
			return output.score




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





	'''simple docstring'''



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


				'''simple docstring'''


				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	:    Any			,  lowercase	:    List[str]			,  lowercase	:    Optional[int]			,  lowercase	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			{}
				result.update({'sari': compute_sari(sources=lowercase			,  predictions=lowercase			,  references=lowercase	)}	)
				result.update({'sacrebleu': compute_sacrebleu(predictions=lowercase			,  references=lowercase	)}	)
				result.update({'exact': compute_em(predictions=lowercase			,  references=lowercase	)}	)
				return result

class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    List[str]			,  lowercase	:    list[int]	):


				'''simple docstring'''


				_snake_case		=			len(lowercase	)
				_snake_case		=			[0] * len_array

				if len_array > 0:
							_snake_case		=			array[0]

				for i in range(1			,  lowercase	):
							_snake_case		=			self.prefix_sum[i - 1] + array[i]



	def  A							(  self	:    Optional[Any]			,  lowercase	:    int			,  lowercase	:    int	):


				'''simple docstring'''


				if start == 0:
							return self.prefix_sum[end]

				return self.prefix_sum[end] - self.prefix_sum[start - 1]





	def  A							(  self	:    Union[str, Any]			,  lowercase	:    int	):


				'''simple docstring'''


				_snake_case		=			{0}
				for sum_item in self.prefix_sum:
							if sum_item - target_sum in sums:
										return True

							sums.add(lowercase	)

				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()