

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase							:  List[Any]            =						"""
@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\",
}
"""

lowerCAmelCase							:  int            =						"""\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


lowerCAmelCase							:  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       A_				(   _UpperCAmelCase   ):

						def remove_articles(_UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			Any  		=	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   ):
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	[any(compute_exact(_UpperCAmelCase    ,     _UpperCAmelCase   ) for ref in refs   ) for pred, refs in zip(_UpperCAmelCase    ,     _UpperCAmelCase   )]
						return (sum(_UpperCAmelCase   ) / len(_UpperCAmelCase   )) * 1_00







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	[rgram for rgrams in rgramslist for rgram in rgrams]
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	Counter(_UpperCAmelCase   )

						SCREAMING_SNAKE_CASE_:			Any  		=	Counter(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	Counter()
						for sgram, scount in sgramcounter.items():
												SCREAMING_SNAKE_CASE_:			Dict  		=	scount * numref

						SCREAMING_SNAKE_CASE_:			Dict  		=	Counter(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	Counter()
						for cgram, ccount in cgramcounter.items():
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	ccount * numref

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

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

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

						# ADDITION
						SCREAMING_SNAKE_CASE_:			Dict  		=	set(_UpperCAmelCase   ) - set(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	set(_UpperCAmelCase   ) & set(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	set(_UpperCAmelCase   ) - set(_UpperCAmelCase   )

						SCREAMING_SNAKE_CASE_:			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.
						SCREAMING_SNAKE_CASE_:			Tuple  		=	1
						SCREAMING_SNAKE_CASE_:			int  		=	1
						if len(_UpperCAmelCase   ) > 0:
												SCREAMING_SNAKE_CASE_:			Dict  		=	addtmpscore / len(_UpperCAmelCase   )
						if len(_UpperCAmelCase   ) > 0:
												SCREAMING_SNAKE_CASE_:			Tuple  		=	addtmpscore / len(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	0
						if addscore_precision > 0 or addscore_recall > 0:
												SCREAMING_SNAKE_CASE_:			str  		=	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	len(_UpperCAmelCase   )

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

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

						((SCREAMING_SNAKE_CASE_)		,   (SCREAMING_SNAKE_CASE_)		,   (SCREAMING_SNAKE_CASE_)):			Union[str, Any]  		=	SARIngram(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
						((SCREAMING_SNAKE_CASE_)		,   (SCREAMING_SNAKE_CASE_)		,   (SCREAMING_SNAKE_CASE_)):			Optional[Any]  		=	SARIngram(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
						((SCREAMING_SNAKE_CASE_)		,   (SCREAMING_SNAKE_CASE_)		,   (SCREAMING_SNAKE_CASE_)):			Any  		=	SARIngram(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
						((SCREAMING_SNAKE_CASE_)		,   (SCREAMING_SNAKE_CASE_)		,   (SCREAMING_SNAKE_CASE_)):			Union[str, Any]  		=	SARIngram(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
						SCREAMING_SNAKE_CASE_:			List[str]  		=	sum([delascore, delascore, delascore, delascore]   ) / 4
						SCREAMING_SNAKE_CASE_:			Tuple  		=	sum([addascore, addascore, addascore, addascore]   ) / 4
						SCREAMING_SNAKE_CASE_:			List[str]  		=	(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:
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	sentence.lower()

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

						if not return_str:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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."   )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	0
						for src, pred, refs in zip(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
												sari_score += SARIsent(normalize(_UpperCAmelCase   )    ,     normalize(_UpperCAmelCase   )    ,     [normalize(_UpperCAmelCase   ) for sent in refs]   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	sari_score / len(_UpperCAmelCase   )
						return 1_00 * sari_score







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase="exp"    ,     _UpperCAmelCase=None    ,     _UpperCAmelCase=False    ,     _UpperCAmelCase=False    ,     _UpperCAmelCase=False    ,     ):
						SCREAMING_SNAKE_CASE_:			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"   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	[[refs[i] for refs in references] for i in range(_UpperCAmelCase   )]
						SCREAMING_SNAKE_CASE_:			Tuple  		=	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   __lowercase    (			datasets.Metric     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									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 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					str):
									SCREAMING_SNAKE_CASE_:			str  		=	{}
									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









from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase							:  List[str]            =						logging.get_logger(__name__)


lowerCAmelCase							:  Tuple            =						{
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
lowerCAmelCase							:  Any            =						1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
lowerCAmelCase							:  Dict            =						0

lowerCAmelCase							:  Dict            =						0xe000
lowerCAmelCase							:  str            =						0xe001
lowerCAmelCase							:  str            =						0xe002
lowerCAmelCase							:  Optional[int]            =						0xe003
lowerCAmelCase							:  List[Any]            =						0xe004

# Maps special codepoints to human-readable names.
lowerCAmelCase							:  Dict[int, str]            =						{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
lowerCAmelCase							:  Dict[str, int]            =						{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							List[str]      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Tuple=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					str=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					List[Any]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Optional[int]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Union[str, Any]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Union[str, Any]=False       ,						lowerCAmelCase__  :					int=2048       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else bos_token
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else eos_token
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else sep_token
									SCREAMING_SNAKE_CASE_:			Any  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else cls_token
									SCREAMING_SNAKE_CASE_:			str  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									SCREAMING_SNAKE_CASE_:			Dict  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else mask_token

									super().__init__(
									    bos_token=lowerCAmelCase__       ,						eos_token=lowerCAmelCase__       ,						sep_token=lowerCAmelCase__       ,						cls_token=lowerCAmelCase__       ,						pad_token=lowerCAmelCase__       ,						mask_token=lowerCAmelCase__       ,						add_prefix_space=lowerCAmelCase__       ,						model_max_length=lowerCAmelCase__       ,						**lowerCAmelCase__       ,						)

									# Creates a mapping for looking up the IDs of special symbols.
									SCREAMING_SNAKE_CASE_:			Dict[str, int]  		=	{}
									for codepoint, name in SPECIAL_CODEPOINTS.items():
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	codepoint

									# Creates a mapping for looking up the string forms of special symbol IDs.
									SCREAMING_SNAKE_CASE_:			Dict[int, str]  		=	{
									    codepoint: name for name, codepoint in self._special_codepoints.items()
									}

									SCREAMING_SNAKE_CASE_:			List[str]  		=	UNICODE_VOCAB_SIZE
									SCREAMING_SNAKE_CASE_:			Tuple  		=	len(self._special_codepoints)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self._unicode_vocab_size



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					str):
									return list(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					str):
									try:
															return ord(lowerCAmelCase__)
									except TypeError:
															raise ValueError(F"invalid token: '{token}'")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					int):
									try:
															if index in SPECIAL_CODEPOINTS:
																					return SPECIAL_CODEPOINTS[index]
															return chr(lowerCAmelCase__)
									except TypeError:
															raise ValueError(F"invalid id: {index}")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					Dict):
									return "".join(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	[self.cls_token_id]

									SCREAMING_SNAKE_CASE_:			int  		=	cls + token_ids_a + sep
									if token_ids_a is not None:
															result += token_ids_a + sep
									return result



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None       ,						lowerCAmelCase__  :					bool = False):
									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCAmelCase__       ,						token_ids_a=lowerCAmelCase__       ,						already_has_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Any  		=	[1] + ([0] * len(lowerCAmelCase__)) + [1]
									if token_ids_a is not None:
															result += ([0] * len(lowerCAmelCase__)) + [1]
									return result



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			Dict  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			int  		=	[self.cls_token_id]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	len(cls + token_ids_a + sep) * [0]
									if token_ids_a is not None:
															result += len(token_ids_a + sep) * [1]
									return result




			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[str] = None):
									return ()






