
import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase_						:     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\",
}
"""

UpperCamelCase_						:     str	       =							"""\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


UpperCamelCase_						:     Dict	       =							"""
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 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[Any]  )   ->			Union[str, Any]:






						'''simple docstring'''

						def remove_articles(_UpperCAmelCase  :   int  ):
												_lowercase       :	Any           =   re.compile(R"\b(a|an|the)\b" ,			re.UNICODE  )
												return re.sub(_UpperCAmelCase ,			" " ,			_UpperCAmelCase  )

						def white_space_fix(_UpperCAmelCase  :   Tuple  ):
												return " ".join(text.split()  )

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

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

						return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase  )  )  )  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   Dict ,			_UpperCAmelCase  :   List[Any]  )   ->			Optional[Any]:






						'''simple docstring'''

						return int(normalize_answer(_UpperCAmelCase  ) == normalize_answer(_UpperCAmelCase  )  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   List[Any]  )   ->			List[str]:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   [any(compute_exact(_UpperCAmelCase ,			_UpperCAmelCase  ) for ref in refs  ) for pred, refs in zip(_UpperCAmelCase ,			_UpperCAmelCase  )]
						return (sum(_UpperCAmelCase  ) / len(_UpperCAmelCase  )) * 100



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   str ,			_UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   Dict  )   ->			Optional[Any]:






						'''simple docstring'''

						_lowercase       :	List[str]           =   [rgram for rgrams in rgramslist for rgram in rgrams]
						_lowercase       :	List[str]           =   Counter(_UpperCAmelCase  )

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

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

						# KEEP
						_lowercase       :	Any           =   sgramcounter_rep & cgramcounter_rep
						_lowercase       :	str           =   keepgramcounter_rep & rgramcounter
						_lowercase       :	Optional[Any]           =   sgramcounter_rep & rgramcounter

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

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

						# ADDITION
						_lowercase       :	str           =   set(_UpperCAmelCase  ) - set(_UpperCAmelCase  )
						_lowercase       :	List[str]           =   set(_UpperCAmelCase  ) & set(_UpperCAmelCase  )
						_lowercase       :	str           =   set(_UpperCAmelCase  ) - set(_UpperCAmelCase  )

						_lowercase       :	Optional[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.
						_lowercase       :	Dict           =   1
						_lowercase       :	Any           =   1
						if len(_UpperCAmelCase  ) > 0:
												_lowercase       :	Any           =   addtmpscore / len(_UpperCAmelCase  )
						if len(_UpperCAmelCase  ) > 0:
												_lowercase       :	Any           =   addtmpscore / len(_UpperCAmelCase  )
						_lowercase       :	Optional[Any]           =   0
						if addscore_precision > 0 or addscore_recall > 0:
												_lowercase       :	List[str]           =   2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   Dict  )   ->			Dict:






						'''simple docstring'''

						_lowercase       :	Any           =   len(_UpperCAmelCase  )

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

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

						for i in range(0 ,			len(_UpperCAmelCase  ) - 1  ):
												if i < len(_UpperCAmelCase  ) - 1:
																		_lowercase       :	Any           =   sagrams[i] + " " + sagrams[i + 1]
																		sagrams.append(_UpperCAmelCase  )
												if i < len(_UpperCAmelCase  ) - 2:
																		_lowercase       :	List[Any]           =   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
																		sagrams.append(_UpperCAmelCase  )
												if i < len(_UpperCAmelCase  ) - 3:
																		_lowercase       :	Tuple           =   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
																		sagrams.append(_UpperCAmelCase  )

						for i in range(0 ,			len(_UpperCAmelCase  ) - 1  ):
												if i < len(_UpperCAmelCase  ) - 1:
																		_lowercase       :	List[str]           =   cagrams[i] + " " + cagrams[i + 1]
																		cagrams.append(_UpperCAmelCase  )
												if i < len(_UpperCAmelCase  ) - 2:
																		_lowercase       :	List[Any]           =   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
																		cagrams.append(_UpperCAmelCase  )
												if i < len(_UpperCAmelCase  ) - 3:
																		_lowercase       :	int           =   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
																		cagrams.append(_UpperCAmelCase  )

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



def 		UpperCamelCase     (       _UpperCAmelCase  :   Any ,			_UpperCAmelCase  :   bool = True ,			_UpperCAmelCase  :   str = "13a" ,			_UpperCAmelCase  :   bool = True  )   ->			str:






						'''simple docstring'''

						if lowercase:
												_lowercase       :	Any           =   sentence.lower()

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

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

						return normalized_sent



def 		UpperCamelCase     (       _UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   Dict ,			_UpperCAmelCase  :   Optional[int]  )   ->			List[str]:






						'''simple docstring'''

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



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   List[Any]="exp" ,			_UpperCAmelCase  :   Dict=None ,			_UpperCAmelCase  :   Optional[Any]=False ,			_UpperCAmelCase  :   Union[str, Any]=False ,			_UpperCAmelCase  :   int=False ,			)   ->			List[Any]:






						'''simple docstring'''

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




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






	def       _a(self				: Union[str, Any]    ) ->			Union[str, 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       _a(self				: List[str]      ,	snake_case				: List[str]      ,	snake_case				: List[Any]      ,	snake_case				: List[str]    ) ->			Dict:
							_lowercase       :	int           =   {}
							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






import math




class   __lowercase						:






	def       _a(self				: Dict      ,	snake_case				: list[list[float]]      ,	snake_case				: list[int]    ) ->			int:
							_lowercase       :	List[Any]           =   0.0
							_lowercase       :	Any           =   0.0
							for i in range(len(snake_case    )    ):
													da += math.pow((sample[i] - weights[0][i])      ,	2    )
													da += math.pow((sample[i] - weights[1][i])      ,	2    )
													return 0 if da > da else 1
							return 0






	def       _a(self				: Dict      ,	snake_case				: list[list[int | float]]      ,	snake_case				: list[int]      ,	snake_case				: int      ,	snake_case				: float    ) ->			list[list[int | float]]:
							for i in range(len(snake_case    )    ):
													weights[j][i] += alpha * (sample[i] - weights[j][i])
							return weights



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

						# weight initialization ( n, C )
						_lowercase       :	Tuple           =   [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

						# training
						_lowercase       :	List[Any]           =   SelfOrganizingMap()
						_lowercase       :	Any           =   3
						_lowercase       :	Optional[Any]           =   0.5

						for _ in range(_UpperCAmelCase  ):
												for j in range(len(_UpperCAmelCase  )  ):
																		# training sample
																		_lowercase       :	Optional[int]           =   training_samples[j]

																		# Compute the winning vector
																		_lowercase       :	Tuple           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

																		# Update the winning vector
																		_lowercase       :	str           =   self_organizing_map.update(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

    # classify test sample
						_lowercase       :	Any           =   [0, 0, 0, 1]
						_lowercase       :	Dict           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

						# results
						print(f"""Clusters that the test sample belongs to : {winner}"""  )
						print(f"""Weights that have been trained : {weights}"""  )


# running the main() function
if __name__ == "__main__":
							main()




