






"""simple docstring"""







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a			:    Dict				 =		"""
@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\",
}
"""

a			:    List[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.
"""


a			:    Optional[int]				 =		"""
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 						lowercase__(A						)    ->Tuple:







						def remove_articles(A						):
												lowercase__    :				List[Any]=	re.compile(R"\b(a|an|the)\b" ,		re.UNICODE						)
												return re.sub(UpperCamelCase__ ,		" " ,		UpperCamelCase__						)

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

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

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

						return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__						)						)						)						)





def 						lowercase__(A ,		A						)    ->List[str]:
						return int(normalize_answer(UpperCamelCase__						) == normalize_answer(UpperCamelCase__						)						)





def 						lowercase__(A ,		A						)    ->List[Any]:
						lowercase__    :				Any=	[any(compute_exact(UpperCamelCase__ ,		UpperCamelCase__						) for ref in refs						) for pred, refs in zip(UpperCamelCase__ ,		UpperCamelCase__						)]
						return (sum(UpperCamelCase__						) / len(UpperCamelCase__						)) * 100





def 						lowercase__(A ,		A ,		A ,		A						)    ->int:
						lowercase__    :				Dict=	[rgram for rgrams in rgramslist for rgram in rgrams]
						lowercase__    :				List[Any]=	Counter(UpperCamelCase__						)

						lowercase__    :				Dict=	Counter(UpperCamelCase__						)
						lowercase__    :				Tuple=	Counter()
						for sgram, scount in sgramcounter.items():
												lowercase__    :				Any=	scount * numref

						lowercase__    :				Union[str, Any]=	Counter(UpperCamelCase__						)
						lowercase__    :				str=	Counter()
						for cgram, ccount in cgramcounter.items():
												lowercase__    :				Optional[int]=	ccount * numref

						# KEEP
						lowercase__    :				Dict=	sgramcounter_rep & cgramcounter_rep
						lowercase__    :				List[str]=	keepgramcounter_rep & rgramcounter
						lowercase__    :				Any=	sgramcounter_rep & rgramcounter

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

						# DELETION
						lowercase__    :				Optional[int]=	sgramcounter_rep - cgramcounter_rep
						lowercase__    :				Union[str, Any]=	delgramcounter_rep - rgramcounter
						lowercase__    :				Optional[Any]=	sgramcounter_rep - rgramcounter
						lowercase__    :				str=	0
						lowercase__    :				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.
						lowercase__    :				Any=	1
						if len(UpperCamelCase__						) > 0:
												lowercase__    :				str=	deltmpscorea / len(UpperCamelCase__						)

						# ADDITION
						lowercase__    :				int=	set(UpperCamelCase__						) - set(UpperCamelCase__						)
						lowercase__    :				Optional[Any]=	set(UpperCamelCase__						) & set(UpperCamelCase__						)
						lowercase__    :				int=	set(UpperCamelCase__						) - set(UpperCamelCase__						)

						lowercase__    :				Dict=	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__    :				Union[str, Any]=	1
						lowercase__    :				Optional[Any]=	1
						if len(UpperCamelCase__						) > 0:
												lowercase__    :				List[str]=	addtmpscore / len(UpperCamelCase__						)
						if len(UpperCamelCase__						) > 0:
												lowercase__    :				Any=	addtmpscore / len(UpperCamelCase__						)
						lowercase__    :				str=	0
						if addscore_precision > 0 or addscore_recall > 0:
												lowercase__    :				str=	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)





def 						lowercase__(A ,		A ,		A						)    ->List[str]:
						lowercase__    :				List[Any]=	len(UpperCamelCase__						)

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

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

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





def 						lowercase__(A ,		A = True ,		A = "13a" ,		A = True						)    ->Tuple:







						if lowercase:
												lowercase__    :				List[Any]=	sentence.lower()

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

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

						return normalized_sent





def 						lowercase__(A ,		A ,		A						)    ->Union[str, Any]:







						if not (len(UpperCamelCase__						) == len(UpperCamelCase__						) == len(UpperCamelCase__						)):
												raise ValueError("Sources length must match predictions and references lengths."						)
						lowercase__    :				List[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__    :				Optional[Any]=	sari_score / len(UpperCamelCase__						)
						return 100 * sari_score





def 						lowercase__(A ,		A ,		A="exp" ,		A=None ,		A=False ,		A=False ,		A=False ,		)    ->List[Any]:
						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__    :				Union[str, Any]=	[[refs[i] for refs in references] for i in range(UpperCamelCase__						)]
						lowercase__    :				List[str]=	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    __UpperCAmelCase(       datasets.Metric							):




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												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	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	{}
												result.update({"sari": compute_sari(sources=_a	,  predictions=_a	,  references=_a      )}      )
												result.update({"sacrebleu": compute_sacrebleu(predictions=_a	,  references=_a      )}      )
												result.update({"exact": compute_em(predictions=_a	,  references=_a      )}      )
												return result














"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a			:    Union[str, Any]				 =		{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    int				 =		[
			    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PegasusXForConditionalGeneration""",
			    """PegasusXModel""",
			    """PegasusXPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_pegasus_x import (
						    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PegasusXForConditionalGeneration,
						    PegasusXModel,
						    PegasusXPreTrainedModel,
						)


else:
			import sys

			a			:    Optional[Any]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






