

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a						="\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						="\\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						="\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   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__     )    ->	Tuple:

	def remove_articles(lowerCamelCase__     ):
		__lowerCamelCase							:							Any   			=					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							:							Tuple   			=					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   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__     )    ->	List[str]:
	return int(normalize_answer(lowerCamelCase_     ) == normalize_answer(lowerCamelCase_     )     )






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__     )    ->	int:
	__lowerCamelCase							:							str   			=					[any(compute_exact(lowerCamelCase_      ,					lowerCamelCase_     ) for ref in refs     ) for pred, refs in zip(lowerCamelCase_      ,					lowerCamelCase_     )]
	return (sum(lowerCamelCase_     ) / len(lowerCamelCase_     )) * 1_0_0






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__     )    ->	Union[str, Any]:
	__lowerCamelCase							:							Tuple   			=					[rgram for rgrams in rgramslist for rgram in rgrams]
	__lowerCamelCase							:							Any   			=					Counter(lowerCamelCase_     )

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

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

	# KEEP
	__lowerCamelCase							:							Tuple   			=					sgramcounter_rep & cgramcounter_rep
	__lowerCamelCase							:							int   			=					keepgramcounter_rep & rgramcounter
	__lowerCamelCase							:							List[Any]   			=					sgramcounter_rep & rgramcounter

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

	# DELETION
	__lowerCamelCase							:							Optional[int]   			=					sgramcounter_rep - cgramcounter_rep
	__lowerCamelCase							:							Optional[int]   			=					delgramcounter_rep - rgramcounter
	__lowerCamelCase							:							Dict   			=					sgramcounter_rep - rgramcounter
	__lowerCamelCase							:							List[str]   			=					0
	__lowerCamelCase							:							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.
	__lowerCamelCase							:							int   			=					1
	if len(lowerCamelCase_     ) > 0:
		__lowerCamelCase							:							Tuple   			=					deltmpscorea / len(lowerCamelCase_     )

	# ADDITION
	__lowerCamelCase							:							Any   			=					set(lowerCamelCase_     ) - set(lowerCamelCase_     )
	__lowerCamelCase							:							Optional[Any]   			=					set(lowerCamelCase_     ) & set(lowerCamelCase_     )
	__lowerCamelCase							:							int   			=					set(lowerCamelCase_     ) - set(lowerCamelCase_     )

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

	return (keepscore, delscore_precision, addscore)






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__     )    ->	List[str]:
	__lowerCamelCase							:							List[str]   			=					len(lowerCamelCase_     )

	__lowerCamelCase							:							List[str]   			=					ssent.split(' '     )
	__lowerCamelCase							:							Optional[int]   			=					csent.split(' '     )
	__lowerCamelCase							:							Dict   			=					[]
	__lowerCamelCase							:							Union[str, Any]   			=					[]
	__lowerCamelCase							:							Optional[Any]   			=					[]
	__lowerCamelCase							:							Union[str, Any]   			=					[]
	__lowerCamelCase							:							Union[str, Any]   			=					[]
	__lowerCamelCase							:							Any   			=					[]

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

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






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__ = True      ,					lowerCamelCase__ = "13a"      ,					lowerCamelCase__ = True     )    ->	Optional[Any]:
	# 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:
		__lowerCamelCase							:							int   			=					sentence.lower()

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

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

	return normalized_sent






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__     )    ->	str:
	if not (len(lowerCamelCase_     ) == len(lowerCamelCase_     ) == len(lowerCamelCase_     )):
		raise ValueError('Sources length must match predictions and references lengths.'     )
	__lowerCamelCase							:							Optional[Any]   			=					0
	for src, pred, refs in zip(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ):
		sari_score += SARIsent(normalize(lowerCamelCase_     )      ,					normalize(lowerCamelCase_     )      ,					[normalize(lowerCamelCase_     ) for sent in refs]     )
	__lowerCamelCase							:							Dict   			=					sari_score / len(lowerCamelCase_     )
	return 1_0_0 * sari_score






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__="exp"      ,					lowerCamelCase__=None      ,					lowerCamelCase__=False      ,					lowerCamelCase__=False      ,					lowerCamelCase__=False      ,					)    ->	List[str]:
	__lowerCamelCase							:							Tuple   			=					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							:							int   			=					[[refs[i] for refs in references] for i in range(lowerCamelCase_     )]
	__lowerCamelCase							:							List[Any]   			=					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        lowerCAmelCase	(						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        lowerCAmelCase	(						self						:     Optional[Any]  ,SCREAMING_SNAKE_CASE__						:     str  ,SCREAMING_SNAKE_CASE__						:     Dict  ,SCREAMING_SNAKE_CASE__						:     List[Any]):
								__lowerCamelCase							:							Union[str, Any]   			=					{}
								result.update({'sari': compute_sari(sources=SCREAMING_SNAKE_CASE__  ,predictions=SCREAMING_SNAKE_CASE__  ,references=SCREAMING_SNAKE_CASE__)})
								result.update({'sacrebleu': compute_sacrebleu(predictions=SCREAMING_SNAKE_CASE__  ,references=SCREAMING_SNAKE_CASE__)})
								result.update({'exact': compute_em(predictions=SCREAMING_SNAKE_CASE__  ,references=SCREAMING_SNAKE_CASE__)})
								return result








from maths.prime_factors import prime_factors



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    if not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        _lowercase			:				str 						=						F'''Input value of [number={number}] must be an integer'''
        raise TypeError(lowerCamelCase_					)
    if number < 1:
        raise ValueError('Input must be a positive integer'					)
    return -1 if len(prime_factors(lowerCamelCase_					)					) % 2 else 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()


