
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      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[Any]       ):




			"""simple docstring"""


			def remove_articles(__lowerCAmelCase						:						List[Any]       ):
						lowerCAmelCase_      			=			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						:						List[Any]       ):
						lowerCAmelCase_      			=			set(string.punctuation       )
						return "".join(ch for ch in text if ch not in exclude       )

			def lower(__lowerCAmelCase						:						Optional[int]       ):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase       )       )       )       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[Any]					,							__lowerCAmelCase						:						Optional[int]       ):




			"""simple docstring"""


			return int(normalize_answer(__lowerCAmelCase       ) == normalize_answer(__lowerCAmelCase       )       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int       ):




			"""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      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						Dict       ):




			"""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      lowerCamelCase__       (       __lowerCAmelCase						:						int					,							__lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						Optional[Any]       ):




			"""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      lowerCamelCase__       (       __lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						bool = True					,							__lowerCAmelCase						:						str = "13a"					,							__lowerCAmelCase						:						bool = 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      lowerCamelCase__       (       __lowerCAmelCase						:						Dict					,							__lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						int       ):




			"""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      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[Any]					,							__lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						Union[str, Any]="exp"					,							__lowerCAmelCase						:						List[Any]=None					,							__lowerCAmelCase						:						Tuple=False					,							__lowerCAmelCase						:						Optional[Any]=False					,							__lowerCAmelCase						:						List[str]=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 			_lowerCAmelCase      (			datasets.Metric ):




				def   __a		(      self  )       ->   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       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[str]:
							lowerCAmelCase_      			=			{}
							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





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_A     =  datasets.load_iris()

_A     =  np.array(data["data"])
_A     =  np.array(data["target"])
_A     =  data["target_names"]

_A,   _A,   _A,   _A     =  train_test_split(X, y)






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			return np.linalg.norm(np.array(__lowerCAmelCase       ) - np.array(__lowerCAmelCase       )       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any=5       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			zip(__lowerCAmelCase					,							__lowerCAmelCase       )
			# List of distances of all points from the point to be classified
			lowerCAmelCase_      			=			[]
			for data_point in data:
						lowerCAmelCase_      			=			euclidean_distance(data_point[0]					,							__lowerCAmelCase       )
						distances.append((distance, data_point[1])       )
			# Choosing 'k' points with the least distances.
			lowerCAmelCase_      			=			[i[1] for i in sorted(__lowerCAmelCase       )[:k]]
			# Most commonly occurring class among them
			# is the class into which the point is classified
			lowerCAmelCase_      			=			Counter(__lowerCAmelCase       ).most_common(1       )[0][0]
			return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



