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

import sacrebleu
import sacremoses
from packaging import version

import datasets


a=  '''
@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=  '''\
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=  '''
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  _UpperCamelCase (     _a	:	Optional[int]			):




    """simple docstring"""



    def remove_articles(_a	:	str			):
        __UpperCamelCase	:	Union[str, Any]		    =  re.compile(r'\b(a|an|the)\b'				,     re.UNICODE			)
        return re.sub(_lowerCAmelCase				,     ' '				,     _lowerCAmelCase			)

    def white_space_fix(_a	:	str			):
        return " ".join(text.split()			)

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

    def lower(_a	:	str			):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase			)			)			)			)







def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    return int(normalize_answer(_lowerCAmelCase			) == normalize_answer(_lowerCAmelCase			)			)







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	Tuple			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  [any(compute_exact(_lowerCAmelCase				,     _lowerCAmelCase			) for ref in refs			) for pred, refs in zip(_lowerCAmelCase				,     _lowerCAmelCase			)]
    return (sum(_lowerCAmelCase			) / len(_lowerCAmelCase			)) * 1_0_0







def  _UpperCamelCase (     _a	:	List[str]				,     _a	:	int				,     _a	:	List[str]				,     _a	:	Dict			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  [rgram for rgrams in rgramslist for rgram in rgrams]
    __UpperCamelCase	:	Tuple		    =  Counter(_lowerCAmelCase			)

    __UpperCamelCase	:	List[Any]		    =  Counter(_lowerCAmelCase			)
    __UpperCamelCase	:	Any		    =  Counter()
    for sgram, scount in sgramcounter.items():
        __UpperCamelCase	:	Any		    =  scount * numref

    __UpperCamelCase	:	Union[str, Any]		    =  Counter(_lowerCAmelCase			)
    __UpperCamelCase	:	List[Any]		    =  Counter()
    for cgram, ccount in cgramcounter.items():
        __UpperCamelCase	:	List[str]		    =  ccount * numref

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

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

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

    # ADDITION
    __UpperCamelCase	:	Optional[Any]		    =  set(_lowerCAmelCase			) - set(_lowerCAmelCase			)
    __UpperCamelCase	:	Any		    =  set(_lowerCAmelCase			) & set(_lowerCAmelCase			)
    __UpperCamelCase	:	Optional[Any]		    =  set(_lowerCAmelCase			) - set(_lowerCAmelCase			)

    __UpperCamelCase	:	List[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.
    __UpperCamelCase	:	int		    =  1
    __UpperCamelCase	:	Optional[int]		    =  1
    if len(_lowerCAmelCase			) > 0:
        __UpperCamelCase	:	List[str]		    =  addtmpscore / len(_lowerCAmelCase			)
    if len(_lowerCAmelCase			) > 0:
        __UpperCamelCase	:	List[Any]		    =  addtmpscore / len(_lowerCAmelCase			)
    __UpperCamelCase	:	Dict		    =  0
    if addscore_precision > 0 or addscore_recall > 0:
        __UpperCamelCase	:	str		    =  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)







def  _UpperCamelCase (     _a	:	Any				,     _a	:	List[Any]				,     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  len(_lowerCAmelCase			)

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

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

    for i in range(0				,     len(_lowerCAmelCase			) - 1			):
        if i < len(_lowerCAmelCase			) - 1:
            __UpperCamelCase	:	int		    =  sagrams[i] + ' ' + sagrams[i + 1]
            sagrams.append(_lowerCAmelCase			)
        if i < len(_lowerCAmelCase			) - 2:
            __UpperCamelCase	:	str		    =  sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
            sagrams.append(_lowerCAmelCase			)
        if i < len(_lowerCAmelCase			) - 3:
            __UpperCamelCase	:	List[str]		    =  sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
            sagrams.append(_lowerCAmelCase			)

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

    ((__UpperCamelCase)    ,					(__UpperCamelCase)    ,					(__UpperCamelCase))	:	str		    =  SARIngram(_lowerCAmelCase				,     _lowerCAmelCase				,     _lowerCAmelCase				,     _lowerCAmelCase			)
    ((__UpperCamelCase)    ,					(__UpperCamelCase)    ,					(__UpperCamelCase))	:	Optional[Any]		    =  SARIngram(_lowerCAmelCase				,     _lowerCAmelCase				,     _lowerCAmelCase				,     _lowerCAmelCase			)
    ((__UpperCamelCase)    ,					(__UpperCamelCase)    ,					(__UpperCamelCase))	:	Optional[Any]		    =  SARIngram(_lowerCAmelCase				,     _lowerCAmelCase				,     _lowerCAmelCase				,     _lowerCAmelCase			)
    ((__UpperCamelCase)    ,					(__UpperCamelCase)    ,					(__UpperCamelCase))	:	Dict		    =  SARIngram(_lowerCAmelCase				,     _lowerCAmelCase				,     _lowerCAmelCase				,     _lowerCAmelCase			)
    __UpperCamelCase	:	List[str]		    =  sum([keepascore, keepascore, keepascore, keepascore]			) / 4
    __UpperCamelCase	:	Tuple		    =  sum([delascore, delascore, delascore, delascore]			) / 4
    __UpperCamelCase	:	str		    =  sum([addascore, addascore, addascore, addascore]			) / 4
    __UpperCamelCase	:	Optional[int]		    =  (avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore







def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	List[Any] = True				,     _a	:	Any = "13a"				,     _a	:	str = True			):




    """simple docstring"""



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

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

    if not return_str:
        __UpperCamelCase	:	List[Any]		    =  normalized_sent.split()

    return normalized_sent







def  _UpperCamelCase (     _a	:	Any				,     _a	:	Optional[int]				,     _a	:	Dict			):




    """simple docstring"""



    if not (len(_lowerCAmelCase			) == len(_lowerCAmelCase			) == len(_lowerCAmelCase			)):
        raise ValueError('Sources length must match predictions and references lengths.'			)
    __UpperCamelCase	:	Tuple		    =  0
    for src, pred, refs in zip(_lowerCAmelCase				,     _lowerCAmelCase				,     _lowerCAmelCase			):
        sari_score += SARIsent(normalize(_lowerCAmelCase			)				,     normalize(_lowerCAmelCase			)				,     [normalize(_lowerCAmelCase			) for sent in refs]			)
    __UpperCamelCase	:	Optional[int]		    =  sari_score / len(_lowerCAmelCase			)
    return 1_0_0 * sari_score







def  _UpperCamelCase (     _a	:	int				,     _a	:	Dict				,     _a	:	Tuple="exp"				,     _a	:	Union[str, Any]=None				,     _a	:	Optional[Any]=False				,     _a	:	str=False				,     _a	:	Optional[int]=False				,     ):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  len(references[0]			)
    if any(len(_lowerCAmelCase			) != references_per_prediction for refs in references			):
        raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
    __UpperCamelCase	:	str		    =  [[refs[i] for refs in references] for i in range(_lowerCAmelCase			)]
    __UpperCamelCase	:	Dict		    =  sacrebleu.corpus_bleu(
        _lowerCAmelCase				,     _lowerCAmelCase				,     smooth_method=_lowerCAmelCase				,     smooth_value=_lowerCAmelCase				,     force=_lowerCAmelCase				,     lowercase=_lowerCAmelCase				,     use_effective_order=_lowerCAmelCase				,     )
    return output.score






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







     """simple docstring"""






     def lowerCAmelCase     (		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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Any		    =  {}
         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 json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a=  logging.get_logger(__name__)

a=  '''▁'''

a=  {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

a=  {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

a=  {
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

a=  ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

a=  {'''mustc''': MUSTC_LANGS}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						MAX_MODEL_INPUT_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']

     SCREAMING_SNAKE_CASE__		   =						[]







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	List[Any]		    =  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCamelCase     ,			eos_token=_lowerCamelCase     ,			unk_token=_lowerCamelCase     ,			pad_token=_lowerCamelCase     ,			do_upper_case=_lowerCamelCase     ,			do_lower_case=_lowerCamelCase     ,			tgt_lang=_lowerCamelCase     ,			lang_codes=_lowerCamelCase     ,			sp_model_kwargs=self.sp_model_kwargs     ,			**_lowerCamelCase     ,			)
         __UpperCamelCase	:	Union[str, Any]		    =  do_upper_case
         __UpperCamelCase	:	Dict		    =  do_lower_case

         __UpperCamelCase	:	List[str]		    =  load_json(_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  {v: k for k, v in self.encoder.items()}
         __UpperCamelCase	:	int		    =  spm_file
         __UpperCamelCase	:	List[Any]		    =  load_spm(_lowerCamelCase     ,			self.sp_model_kwargs    )

         if lang_codes is not None:
             __UpperCamelCase	:	Any		    =  lang_codes
             __UpperCamelCase	:	Any		    =  LANGUAGES[lang_codes]
             __UpperCamelCase	:	str		    =  [f"""<lang:{lang}>""" for lang in self.langs]
             __UpperCamelCase	:	List[str]		    =  {lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""    ) for lang in self.langs}

             __UpperCamelCase	:	str		    =  self.lang_tokens
             __UpperCamelCase	:	str		    =  tgt_lang if tgt_lang is not None else self.langs[0]

             self.set_tgt_lang_special_tokens(self._tgt_lang    )
         else:
             __UpperCamelCase	:	Dict		    =  {}







     @property
     def lowerCAmelCase     (		self    ):
         return len(self.encoder    )







     @property
     def lowerCAmelCase     (		self    ):
         return self._tgt_lang







     @tgt_lang.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[int]		    =  new_tgt_lang
         self.set_tgt_lang_special_tokens(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  self.lang_code_to_id[tgt_lang]
         __UpperCamelCase	:	List[str]		    =  [lang_code_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.sp_model.encode(_lowerCamelCase     ,			out_type=_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.encoder.get(_lowerCamelCase     ,			self.encoder[self.unk_token]    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.decoder.get(_lowerCamelCase     ,			self.unk_token    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  []
         __UpperCamelCase	:	List[str]		    =  ''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 __UpperCamelCase	:	int		    =  self.sp_model.decode(_lowerCamelCase    )
                 out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                 __UpperCamelCase	:	int		    =  []
             else:
                 current_sub_tokens.append(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  self.sp_model.decode(_lowerCamelCase    )
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase=None    ):
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = 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    )

         __UpperCamelCase	:	Union[str, Any]		    =  [1] * len(self.prefix_tokens    )
         __UpperCamelCase	:	Any		    =  [1]
         if token_ids_a is None:
             return prefix_ones + ([0] * len(_lowerCamelCase    )) + suffix_ones
         return prefix_ones + ([0] * len(_lowerCamelCase    )) + ([0] * len(_lowerCamelCase    )) + suffix_ones







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder.copy()
         vocab.update(self.added_tokens_encoder    )
         return vocab







     def __getstate__(		self    ):
         __UpperCamelCase	:	int		    =  self.__dict__.copy()
         __UpperCamelCase	:	Dict		    =  None
         return state







     def __setstate__(		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  d

         # for backward compatibility
         if not hasattr(self     ,			'sp_model_kwargs'    ):
             __UpperCamelCase	:	Optional[int]		    =  {}

         __UpperCamelCase	:	Union[str, Any]		    =  load_spm(self.spm_file     ,			self.sp_model_kwargs    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  Path(_lowerCamelCase    )
         assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
         __UpperCamelCase	:	Optional[int]		    =  save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
         )
         __UpperCamelCase	:	Union[str, Any]		    =  save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
         )

         save_json(self.encoder     ,			_lowerCamelCase    )

         if os.path.abspath(self.spm_file    ) != os.path.abspath(_lowerCamelCase    ) and os.path.isfile(self.spm_file    ):
             copyfile(self.spm_file     ,			_lowerCamelCase    )
         elif not os.path.isfile(self.spm_file    ):
             with open(_lowerCamelCase     ,			'wb'    ) as fi:
                 __UpperCamelCase	:	Dict		    =  self.sp_model.serialized_model_proto()
                 fi.write(_lowerCamelCase    )

         return (str(_lowerCamelCase    ), str(_lowerCamelCase    ))







def  _UpperCamelCase (     _a	:	str				,     _a	:	Dict[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  sentencepiece.SentencePieceProcessor(**_a			)
    spm.Load(str(_a			)			)
    return spm







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    with open(_a				,     'r'			) as f:
        return json.load(_a			)







def  _UpperCamelCase (     _a	:	Any				,     _a	:	str			):




    """simple docstring"""



    with open(_a				,     'w'			) as f:
        json.dump(_a				,     _a				,     indent=2			)



