


'''simple docstring'''
import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__SCREAMING_SNAKE_CASE					:				str									=		'''
@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",
}
'''

__SCREAMING_SNAKE_CASE					:				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.
'''


__SCREAMING_SNAKE_CASE					:				Any									=		'''
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_				(	UpperCamelCase_ ):

							def remove_articles(UpperCamelCase_ ):
														A_			    =		re.compile(R"\b(a|an|the)\b"						,  re.UNICODE )
														return re.sub(UpperCamelCase_						,  " "						,  UpperCamelCase_ )

							def white_space_fix(UpperCamelCase_ ):
														return " ".join(text.split() )

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

							def lower(UpperCamelCase_ ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_ ) ) ) )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return int(normalize_answer(UpperCamelCase_ ) == normalize_answer(UpperCamelCase_ ) )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		[any(compute_exact(UpperCamelCase_						,  UpperCamelCase_ ) for ref in refs ) for pred, refs in zip(UpperCamelCase_						,  UpperCamelCase_ )]
							return (sum(UpperCamelCase_ ) / len(UpperCamelCase_ )) * 1_0_0







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		[rgram for rgrams in rgramslist for rgram in rgrams]
							A_			    =		Counter(UpperCamelCase_ )

							A_			    =		Counter(UpperCamelCase_ )
							A_			    =		Counter()
							for sgram, scount in sgramcounter.items():
														A_			    =		scount * numref

							A_			    =		Counter(UpperCamelCase_ )
							A_			    =		Counter()
							for cgram, ccount in cgramcounter.items():
														A_			    =		ccount * numref

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

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

							# DELETION
							A_			    =		sgramcounter_rep - cgramcounter_rep
							A_			    =		delgramcounter_rep - rgramcounter
							A_			    =		sgramcounter_rep - rgramcounter
							A_			    =		0
							A_			    =		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.
							A_			    =		1
							if len(UpperCamelCase_ ) > 0:
														A_			    =		deltmpscorea / len(UpperCamelCase_ )

							# ADDITION
							A_			    =		set(UpperCamelCase_ ) - set(UpperCamelCase_ )
							A_			    =		set(UpperCamelCase_ ) & set(UpperCamelCase_ )
							A_			    =		set(UpperCamelCase_ ) - set(UpperCamelCase_ )

							A_			    =		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.
							A_			    =		1
							A_			    =		1
							if len(UpperCamelCase_ ) > 0:
														A_			    =		addtmpscore / len(UpperCamelCase_ )
							if len(UpperCamelCase_ ) > 0:
														A_			    =		addtmpscore / len(UpperCamelCase_ )
							A_			    =		0
							if addscore_precision > 0 or addscore_recall > 0:
														A_			    =		2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		len(UpperCamelCase_ )

							A_			    =		ssent.split(" " )
							A_			    =		csent.split(" " )
							A_			    =		[]
							A_			    =		[]
							A_			    =		[]
							A_			    =		[]
							A_			    =		[]
							A_			    =		[]

							A_			    =		[]
							A_			    =		[]
							A_			    =		[]
							A_			    =		[]
							for rsent in rsents:
														A_			    =		rsent.split(" " )
														A_			    =		[]
														A_			    =		[]
														A_			    =		[]
														ragramslist.append(UpperCamelCase_ )
														for i in range(0						,  len(UpperCamelCase_ ) - 1 ):
																					if i < len(UpperCamelCase_ ) - 1:
																												A_			    =		ragrams[i] + " " + ragrams[i + 1]
																												ragrams.append(UpperCamelCase_ )
																					if i < len(UpperCamelCase_ ) - 2:
																												A_			    =		ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
																												ragrams.append(UpperCamelCase_ )
																					if i < len(UpperCamelCase_ ) - 3:
																												A_			    =		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:
																					A_			    =		sagrams[i] + " " + sagrams[i + 1]
																					sagrams.append(UpperCamelCase_ )
														if i < len(UpperCamelCase_ ) - 2:
																					A_			    =		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
																					sagrams.append(UpperCamelCase_ )
														if i < len(UpperCamelCase_ ) - 3:
																					A_			    =		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:
																					A_			    =		cagrams[i] + " " + cagrams[i + 1]
																					cagrams.append(UpperCamelCase_ )
														if i < len(UpperCamelCase_ ) - 2:
																					A_			    =		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
																					cagrams.append(UpperCamelCase_ )
														if i < len(UpperCamelCase_ ) - 3:
																					A_			    =		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
																					cagrams.append(UpperCamelCase_ )

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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ = True						,  UpperCamelCase_ = "13a"						,  UpperCamelCase_ = True ):
							# 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:
														A_			    =		sentence.lower()

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

							if not return_str:
														A_			    =		normalized_sent.split()

							return normalized_sent







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							if not (len(UpperCamelCase_ ) == len(UpperCamelCase_ ) == len(UpperCamelCase_ )):
														raise ValueError("Sources length must match predictions and references lengths." )
							A_			    =		0
							for src, pred, refs in zip(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
														sari_score += SARIsent(normalize(UpperCamelCase_ )						,  normalize(UpperCamelCase_ )						,  [normalize(UpperCamelCase_ ) for sent in refs] )
							A_			    =		sari_score / len(UpperCamelCase_ )
							return 1_0_0 * sari_score







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_="exp"						,  UpperCamelCase_=None						,  UpperCamelCase_=False						,  UpperCamelCase_=False						,  UpperCamelCase_=False						,  ):
							A_			    =		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" )
							A_			    =		[[refs[i] for refs in references] for i in range(UpperCamelCase_ )]
							A_			    =		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 					__lowerCAmelCase   (   datasets.Metric						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  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 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      List[Any]	):
													A_			    =		{}
													result.update({"sari": compute_sari(sources=lowerCAmelCase ,  predictions=lowerCAmelCase ,  references=lowerCAmelCase	)}	)
													result.update({"sacrebleu": compute_sacrebleu(predictions=lowerCAmelCase ,  references=lowerCAmelCase	)}	)
													result.update({"exact": compute_em(predictions=lowerCAmelCase ,  references=lowerCAmelCase	)}	)
													return result









'''simple docstring'''
import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline







@pytest.mark.integration
def       a_				(	):
							with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
														with pytest.raises(UpperCamelCase_ ):
																					requests.request("GET"						,  "https://huggingface.co" )
														with pytest.raises(requests.exceptions.ConnectTimeout ):
																					requests.request("GET"						,  "https://huggingface.co"						,  timeout=1.0 )







@pytest.mark.integration
def       a_				(	):
							with offline(OfflineSimulationMode.CONNECTION_FAILS ):
														with pytest.raises(requests.exceptions.ConnectionError ):
																					requests.request("GET"						,  "https://huggingface.co" )







def       a_				(	):
							with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
														with pytest.raises(UpperCamelCase_ ):
																					http_head("https://huggingface.co" )





