





'''simple docstring'''






from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A :			Optional[Any]							   =      '\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n'

A :			int							   =      '\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n'


A :			List[str]							   =      '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\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\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 .","About 95 species are now accepted .","95 species are now accepted ."]]\n    >>> sari = datasets.load_metric("sari")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n'





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    ) ->List[str]:
						_snake_case: Optional[int]	       =		[rgram for rgrams in rgramslist for rgram in rgrams]
						_snake_case: Tuple	       =		Counter(lowercase__    )

						_snake_case: Dict	       =		Counter(lowercase__    )
						_snake_case: Dict	       =		Counter()
						for sgram, scount in sgramcounter.items():
												_snake_case: Union[str, Any]	       =		scount * numref

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

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

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

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

						# ADDITION
						_snake_case: str	       =		set(lowercase__    ) - set(lowercase__    )
						_snake_case: Optional[Any]	       =		set(lowercase__    ) & set(lowercase__    )
						_snake_case: Tuple	       =		set(lowercase__    ) - set(lowercase__    )

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

						return (keepscore, delscore_precision, addscore)





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[Any]:
						_snake_case: List[str]	       =		len(lowercase__    )

						_snake_case: int	       =		ssent.split(' '    )
						_snake_case: List[str]	       =		csent.split(' '    )
						_snake_case: Dict	       =		[]
						_snake_case: Dict	       =		[]
						_snake_case: Any	       =		[]
						_snake_case: List[str]	       =		[]
						_snake_case: Union[str, Any]	       =		[]
						_snake_case: Optional[Any]	       =		[]

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

						for i in range(0					,	len(lowercase__    ) - 1    ):
												if i < len(lowercase__    ) - 1:
																		_snake_case: Union[str, Any]	       =		sagrams[i] + ' ' + sagrams[i + 1]
																		sagrams.append(lowercase__    )
												if i < len(lowercase__    ) - 2:
																		_snake_case: Optional[Any]	       =		sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
																		sagrams.append(lowercase__    )
												if i < len(lowercase__    ) - 3:
																		_snake_case: List[str]	       =		sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
																		sagrams.append(lowercase__    )

						for i in range(0					,	len(lowercase__    ) - 1    ):
												if i < len(lowercase__    ) - 1:
																		_snake_case: Optional[Any]	       =		cagrams[i] + ' ' + cagrams[i + 1]
																		cagrams.append(lowercase__    )
												if i < len(lowercase__    ) - 2:
																		_snake_case: Any	       =		cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
																		cagrams.append(lowercase__    )
												if i < len(lowercase__    ) - 3:
																		_snake_case: Optional[int]	       =		cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
																		cagrams.append(lowercase__    )

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





def    lowercase_   (		lowercase__					,	lowercase__ = True					,	lowercase__ = "13a"					,	lowercase__ = True    ) ->Union[str, Any]:
						# 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:
												_snake_case: Dict	       =		sentence.lower()

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

						if not return_str:
												_snake_case: Tuple	       =		normalized_sent.split()

						return normalized_sent







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


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											return datasets.MetricInfo(
											    description=_DESCRIPTION							,  citation=_CITATION							,  inputs_description=_KWARGS_DESCRIPTION							,  features=datasets.Features(
											        {
											            'sources': datasets.Value('string'							,  id='sequence'							),
											            'predictions': datasets.Value('string'							,  id='sequence'							),
											            'references': datasets.Sequence(datasets.Value('string'							,  id='sequence'							)							,  id='references'							),
											        }							)							,  codebase_urls=[
											        'https://github.com/cocoxu/simplification/blob/master/SARI.py',
											        'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
											    ]							,  reference_urls=['https://www.aclweb.org/anthology/Q16-1029.pdf']							,  )




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	Dict							,  __snake_case						:	List[Any]							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											if not (len(__snake_case							) == len(__snake_case							) == len(__snake_case							)):
																	raise ValueError('Sources length must match predictions and references lengths.'							)
											_snake_case: List[Any]	       =		0
											for src, pred, refs in zip(__snake_case							,  __snake_case							,  __snake_case							):
																	sari_score += SARIsent(normalize(__snake_case							)							,  normalize(__snake_case							)							,  [normalize(__snake_case							) for sent in refs]							)
											_snake_case: List[Any]	       =		sari_score / len(__snake_case							)
											return {"sari": 1_00 * sari_score}












'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
		A :			Optional[Any]							   =      None


A :			Tuple							   =      logging.get_logger(__name__)

A :			Optional[int]							   =      {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A :			Tuple							   =      {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

A :			int							   =      {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}







class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE				 =     ["input_ids", "attention_mask"]
					_SCREAMING_SNAKE_CASE				 =     XLMRobertaTokenizer


					def __init__(       self						:	Any							,  __snake_case						:	Tuple=None							,  __snake_case						:	List[Any]=None							,  __snake_case						:	List[str]="<s>"							,  __snake_case						:	int="</s>"							,  __snake_case						:	str="</s>"							,  __snake_case						:	Any="<s>"							,  __snake_case						:	int="<unk>"							,  __snake_case						:	Optional[Any]="<pad>"							,  __snake_case						:	Optional[int]="<mask>"							,  **__snake_case						:	List[str]							,  ):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		AddedToken(__snake_case							,  lstrip=__snake_case							,  rstrip=__snake_case							) if isinstance(__snake_case							,  __snake_case							) else mask_token

											super().__init__(
											    __snake_case							,  tokenizer_file=__snake_case							,  bos_token=__snake_case							,  eos_token=__snake_case							,  sep_token=__snake_case							,  cls_token=__snake_case							,  unk_token=__snake_case							,  pad_token=__snake_case							,  mask_token=__snake_case							,  **__snake_case							,  )

											_snake_case: int	       =		vocab_file
											_snake_case: List[Any]	       =		False if not self.vocab_file else True


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	List[int]							,  __snake_case						:	Optional[List[int]] = None							):





											'''simple docstring'''





											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_snake_case: Union[str, Any]	       =		[self.cls_token_id]
											_snake_case: int	       =		[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	List[int]							,  __snake_case						:	Optional[List[int]] = None							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		[self.sep_token_id]
											_snake_case: List[str]	       =		[self.cls_token_id]

											if token_ids_a is None:
																	return len(cls + token_ids_a + sep							) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	str							,  __snake_case						:	Optional[str] = None							):





											'''simple docstring'''





											if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																	    'tokenizer.'							)

											if not os.path.isdir(__snake_case							):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''							)
																	return
											_snake_case: Optional[int]	       =		os.path.join(
											    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

											if os.path.abspath(self.vocab_file							) != os.path.abspath(__snake_case							):
																	copyfile(self.vocab_file							,  __snake_case							)

											return (out_vocab_file,)





