



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase_							:      Tuple      =  "\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"

UpperCAmelCase_							:      int      =  "\\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"


UpperCAmelCase_							:      Tuple      =  "\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  UpperCamelCase						(       _A			:				Tuple    )->       List[str]:





						"""simple docstring"""





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

						def white_space_fix(_A			:				Optional[int]    ):
												return " ".join(text.split()    )

						def remove_punc(_A			:				List[Any]    ):
												A__							=       set(string.punctuation    )
												return "".join(ch for ch in text if ch not in exclude    )

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

						return white_space_fix(remove_articles(remove_punc(lower(_A    )    )    )    )
def  UpperCamelCase						(       _A			:				List[str]			,							_A			:				Tuple    )->       Tuple:





						"""simple docstring"""





						return int(normalize_answer(_A    ) == normalize_answer(_A    )    )
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				List[Any]    )->       List[Any]:





						"""simple docstring"""





						A__							=       [any(compute_exact(_A			,							_A    ) for ref in refs    ) for pred, refs in zip(_A			,							_A    )]
						return (sum(_A    ) / len(_A    )) * 100
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				Any			,							_A			:				List[Any]			,							_A			:				Dict    )->       Optional[int]:





						"""simple docstring"""





						A__							=       [rgram for rgrams in rgramslist for rgram in rgrams]
						A__							=       Counter(_A    )

						A__							=       Counter(_A    )
						A__							=       Counter()
						for sgram, scount in sgramcounter.items():
												A__							=       scount * numref

						A__							=       Counter(_A    )
						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(_A    ) > 0:
												A__							=       keeptmpscorea / len(_A    )
						if len(_A    ) > 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(_A    ) > 0:
												A__							=       deltmpscorea / len(_A    )

						# ADDITION
						A__							=       set(_A    ) - set(_A    )
						A__							=       set(_A    ) & set(_A    )
						A__							=       set(_A    ) - set(_A    )

						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(_A    ) > 0:
												A__							=       addtmpscore / len(_A    )
						if len(_A    ) > 0:
												A__							=       addtmpscore / len(_A    )
						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  UpperCamelCase						(       _A			:				str			,							_A			:				Optional[int]			,							_A			:				Any    )->       Optional[int]:





						"""simple docstring"""





						A__							=       len(_A    )

						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(_A    )
												for i in range(0			,							len(_A    ) - 1    ):
																		if i < len(_A    ) - 1:
																								A__							=       ragrams[i] + " " + ragrams[i + 1]
																								ragrams.append(_A    )
																		if i < len(_A    ) - 2:
																								A__							=       ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
																								ragrams.append(_A    )
																		if i < len(_A    ) - 3:
																								A__							=       ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
																								ragrams.append(_A    )
												ragramslist.append(_A    )
												ragramslist.append(_A    )
												ragramslist.append(_A    )

						for i in range(0			,							len(_A    ) - 1    ):
												if i < len(_A    ) - 1:
																		A__							=       sagrams[i] + " " + sagrams[i + 1]
																		sagrams.append(_A    )
												if i < len(_A    ) - 2:
																		A__							=       sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
																		sagrams.append(_A    )
												if i < len(_A    ) - 3:
																		A__							=       sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
																		sagrams.append(_A    )

						for i in range(0			,							len(_A    ) - 1    ):
												if i < len(_A    ) - 1:
																		A__							=       cagrams[i] + " " + cagrams[i + 1]
																		cagrams.append(_A    )
												if i < len(_A    ) - 2:
																		A__							=       cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
																		cagrams.append(_A    )
												if i < len(_A    ) - 3:
																		A__							=       cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
																		cagrams.append(_A    )

						((A__)						,       (A__)						,       (A__))							=       SARIngram(_A			,							_A			,							_A			,							_A    )
						((A__)						,       (A__)						,       (A__))							=       SARIngram(_A			,							_A			,							_A			,							_A    )
						((A__)						,       (A__)						,       (A__))							=       SARIngram(_A			,							_A			,							_A			,							_A    )
						((A__)						,       (A__)						,       (A__))							=       SARIngram(_A			,							_A			,							_A			,							_A    )
						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  UpperCamelCase						(       _A			:				Tuple			,							_A			:				bool = True			,							_A			:				str = "13a"			,							_A			:				bool = True    )->       Dict:





						"""simple docstring"""





						if lowercase:
												A__							=       sentence.lower()

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

						if not return_str:
												A__							=       normalized_sent.split()

						return normalized_sent
def  UpperCamelCase						(       _A			:				Union[str, Any]			,							_A			:				Tuple			,							_A			:				str    )->       str:





						"""simple docstring"""





						if not (len(_A    ) == len(_A    ) == len(_A    )):
												raise ValueError("Sources length must match predictions and references lengths."    )
						A__							=       0
						for src, pred, refs in zip(_A			,							_A			,							_A    ):
												sari_score += SARIsent(normalize(_A    )			,							normalize(_A    )			,							[normalize(_A    ) for sent in refs]    )
						A__							=       sari_score / len(_A    )
						return 100 * sari_score
def  UpperCamelCase						(       _A			:				Any			,							_A			:				Union[str, Any]			,							_A			:				Union[str, Any]="exp"			,							_A			:				Dict=None			,							_A			:				Dict=False			,							_A			:				Union[str, Any]=False			,							_A			:				Union[str, Any]=False			,							)->       Union[str, Any]:





						"""simple docstring"""





						A__							=       len(references[0]    )
						if any(len(_A    ) != 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(_A    )]
						A__							=       sacrebleu.corpus_bleu(
						    _A			,							_A			,							smooth_method=_A			,							smooth_value=_A			,							force=_A			,							lowercase=_A			,							use_effective_order=_A			,							)
						return output.score






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



	def 			__A							(       self		):
							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__		):
							A__							=       {}
							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










import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def  UpperCamelCase						(       _A			:				Optional[int]    )->       List[Any]:





						"""simple docstring"""





						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       0.01
						with locka.acquire():
												with pytest.raises(_A    ):
																		A__							=       time.time()
																		locka.acquire(_A    )
																		assert time.time() - _start > timeout
def  UpperCamelCase						(       _A			:				str    )->       List[Any]:





						"""simple docstring"""





						A__							=       "a" * 1000 + ".lock"
						A__							=       FileLock(str(tmpdir / filename    )    )
						assert locka._lock_file.endswith(".lock"    )
						assert not locka._lock_file.endswith(_A    )
						assert len(os.path.basename(locka._lock_file    )    ) <= 255
						A__							=       FileLock(tmpdir / filename    )
						with locka.acquire():
												with pytest.raises(_A    ):
																		locka.acquire(0    )





