





'''simple docstring'''






from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case_     			=						"""\
@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},
}
"""

snake_case_     			=						"""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


snake_case_     			=						"""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
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
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]     ,							UpperCamelCase__     :			Optional[Any]     ,							UpperCamelCase__     :			Optional[int]     ,							UpperCamelCase__     :			List[Any]		)		->	Tuple:
    A							:							Tuple				=    [rgram for rgrams in rgramslist for rgram in rgrams]
    A							:							Tuple				=    Counter(UpperCamelCase__		)

    A							:							List[Any]				=    Counter(UpperCamelCase__		)
    A							:							Union[str, Any]				=    Counter()
    for sgram, scount in sgramcounter.items():
        A							:							int				=    scount * numref

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

    # KEEP
    A							:							Optional[int]				=    sgramcounter_rep & cgramcounter_rep
    A							:							Union[str, Any]				=    keepgramcounter_rep & rgramcounter
    A							:							Optional[int]				=    sgramcounter_rep & rgramcounter

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

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

    # ADDITION
    A							:							str				=    set(UpperCamelCase__		) - set(UpperCamelCase__		)
    A							:							List[Any]				=    set(UpperCamelCase__		) & set(UpperCamelCase__		)
    A							:							str				=    set(UpperCamelCase__		) - set(UpperCamelCase__		)

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

    return (keepscore, delscore_precision, addscore)
def 				_lowerCamelCase(	UpperCamelCase__     :			List[str]     ,							UpperCamelCase__     :			Union[str, Any]     ,							UpperCamelCase__     :			List[Any]		)		->	str:
    A							:							Optional[Any]				=    len(UpperCamelCase__		)

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

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

    ((A),						(A),						(A))							:							Union[str, Any]				=    SARIngram(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__		)
    ((A),						(A),						(A))							:							List[Any]				=    SARIngram(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__		)
    ((A),						(A),						(A))							:							Tuple				=    SARIngram(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__		)
    ((A),						(A),						(A))							:							Any				=    SARIngram(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__		)
    A							:							Any				=    sum([keepascore, keepascore, keepascore, keepascore]		) / 4
    A							:							Tuple				=    sum([delascore, delascore, delascore, delascore]		) / 4
    A							:							Union[str, Any]				=    sum([addascore, addascore, addascore, addascore]		) / 4
    A							:							List[str]				=    (avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore
def 				_lowerCamelCase(	UpperCamelCase__     :			str     ,							UpperCamelCase__     :			bool = True     ,							UpperCamelCase__     :			str = "13a"     ,							UpperCamelCase__     :			bool = 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:
        A							:							int				=    sentence.lower()

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

    if not return_str:
        A							:							Dict				=    normalized_sent.split()

    return normalized_sent





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


   def        snake_case					(			self       ):
       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        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
       if not (len(_UpperCAmelCase       ) == len(_UpperCAmelCase       ) == len(_UpperCAmelCase       )):
           raise ValueError('''Sources length must match predictions and references lengths.'''       )
       A							:							Union[str, Any]				=    0
       for src, pred, refs in zip(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
           sari_score += SARIsent(normalize(_UpperCAmelCase       )     , normalize(_UpperCAmelCase       )     , [normalize(_UpperCAmelCase       ) for sent in refs]       )
       A							:							Optional[int]				=    sari_score / len(_UpperCAmelCase       )
       return {"sari": 100 * sari_score}







'''simple docstring'''






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


snake_case_     			=						logging.get_logger(__name__)


snake_case_     			=						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
snake_case_     			=						{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
}

snake_case_     			=						{
    """RUCAIBox/mvp""": 10_24,
}
@lru_cache()
def 				_lowerCamelCase(	)		->	str:
    A							:							Dict				=    (
        list(range(ord('''!'''		)     ,							ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)     ,							ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)     ,							ord('''ÿ'''		) + 1		)		)
    )
    A							:							int				=    bs[:]
    A							:							Any				=    0
    for b in range(2**8		):
        if b not in bs:
            bs.append(UpperCamelCase__		)
            cs.append(2**8 + n		)
            n += 1
    A							:							Union[str, Any]				=    [chr(UpperCamelCase__		) for n in cs]
    return dict(zip(UpperCamelCase__     ,							UpperCamelCase__		)		)
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	str:
    A							:							Tuple				=    set()
    A							:							Optional[Any]				=    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A							:							Tuple				=    char
    return pairs





class 	_lowercase    (				a					):
   _UpperCamelCase									= VOCAB_FILES_NAMES
   _UpperCamelCase									= PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase									= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase									= ["""input_ids""", """attention_mask"""]


   def __init__(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase="replace"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="<unk>"     , _UpperCAmelCase="<pad>"     , _UpperCAmelCase="<mask>"     , _UpperCAmelCase=False     , **_UpperCAmelCase     , ):
       A							:							int				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else bos_token
       A							:							List[str]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else eos_token
       A							:							List[str]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else sep_token
       A							:							Dict				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else cls_token
       A							:							str				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else unk_token
       A							:							str				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else pad_token

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

       super().__init__(
           errors=_UpperCAmelCase     , bos_token=_UpperCAmelCase     , eos_token=_UpperCAmelCase     , unk_token=_UpperCAmelCase     , sep_token=_UpperCAmelCase     , cls_token=_UpperCAmelCase     , pad_token=_UpperCAmelCase     , mask_token=_UpperCAmelCase     , add_prefix_space=_UpperCAmelCase     , **_UpperCAmelCase     , )
       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as vocab_handle:
           A							:							List[str]				=    json.load(_UpperCAmelCase       )
       A							:							Optional[int]				=    {v: k for k, v in self.encoder.items()}
       A							:							List[str]				=    errors  # how to handle errors in decoding
       A							:							Optional[Any]				=    bytes_to_unicode()
       A							:							Dict				=    {v: k for k, v in self.byte_encoder.items()}
       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as merges_handle:
           A							:							Union[str, Any]				=    merges_handle.read().split('''\n'''       )[1:-1]
       A							:							int				=    [tuple(merge.split()       ) for merge in bpe_merges]
       A							:							int				=    dict(zip(_UpperCAmelCase     , range(len(_UpperCAmelCase       )       )       )       )
       A							:							Tuple				=    {}
       A							:							Tuple				=    add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       A							:							int				=    re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''       )


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


   def        snake_case					(			self       ):
       return dict(self.encoder     , **self.added_tokens_encoder       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       if token in self.cache:
           return self.cache[token]
       A							:							Dict				=    tuple(_UpperCAmelCase       )
       A							:							Dict				=    get_pairs(_UpperCAmelCase       )

       if not pairs:
           return token

       while True:
           A							:							Optional[int]				=    min(_UpperCAmelCase     , key=lambda _UpperCAmelCase       : self.bpe_ranks.get(_UpperCAmelCase     , float('''inf'''       )       )       )
           if bigram not in self.bpe_ranks:
               break
           A,						A							:							Optional[int]				=    bigram
           A							:							str				=    []
           A							:							Any				=    0
           while i < len(_UpperCAmelCase       ):
               try:
                   A							:							List[Any]				=    word.index(_UpperCAmelCase     , _UpperCAmelCase       )
               except ValueError:
                   new_word.extend(word[i:]       )
                   break
               else:
                   new_word.extend(word[i:j]       )
                   A							:							str				=    j

               if word[i] == first and i < len(_UpperCAmelCase       ) - 1 and word[i + 1] == second:
                   new_word.append(first + second       )
                   i += 2
               else:
                   new_word.append(word[i]       )
                   i += 1
           A							:							Optional[Any]				=    tuple(_UpperCAmelCase       )
           A							:							int				=    new_word
           if len(_UpperCAmelCase       ) == 1:
               break
           else:
               A							:							Union[str, Any]				=    get_pairs(_UpperCAmelCase       )
       A							:							int				=    ''' '''.join(_UpperCAmelCase       )
       A							:							List[Any]				=    word
       return word


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							str				=    []
       for token in re.findall(self.pat     , _UpperCAmelCase       ):
           A							:							Optional[int]				=    ''''''.join(
               self.byte_encoder[b] for b in token.encode('''utf-8'''       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCAmelCase       ).split(''' '''       )       )
       return bpe_tokens


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.encoder.get(_UpperCAmelCase     , self.encoder.get(self.unk_token       )       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.decoder.get(_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							int				=    ''''''.join(_UpperCAmelCase       )
       A							:							Any				=    bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     , errors=self.errors       )
       return text


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if not os.path.isdir(_UpperCAmelCase       ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
       A							:							Optional[Any]				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
       A							:							Tuple				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

       with open(_UpperCAmelCase     , '''w'''     , encoding='''utf-8'''       ) as f:
           f.write(json.dumps(self.encoder     , indent=2     , sort_keys=_UpperCAmelCase     , ensure_ascii=_UpperCAmelCase       ) + '''\n'''       )

       A							:							str				=    0
       with open(_UpperCAmelCase     , '''w'''     , encoding='''utf-8'''       ) as writer:
           writer.write('''#version: 0.2\n'''       )
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     , key=lambda _UpperCAmelCase       : kv[1]       ):
               if index != token_index:
                   logger.warning(
                       f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                       ''' Please check that the tokenizer is not corrupted!'''       )
                   A							:							int				=    token_index
               writer.write(''' '''.join(_UpperCAmelCase       ) + '''\n'''       )
               index += 1

       return vocab_file, merge_file


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       A							:							Union[str, Any]				=    [self.cls_token_id]
       A							:							Any				=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = False       ):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCAmelCase     , token_ids_a=_UpperCAmelCase     , already_has_special_tokens=_UpperCAmelCase       )

       if token_ids_a is None:
           return [1] + ([0] * len(_UpperCAmelCase       )) + [1]
       return [1] + ([0] * len(_UpperCAmelCase       )) + [1, 1] + ([0] * len(_UpperCAmelCase       )) + [1]


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       A							:							Union[str, Any]				=    [self.sep_token_id]
       A							:							Tuple				=    [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        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=False     , **_UpperCAmelCase       ):
       A							:							Optional[int]				=    kwargs.pop('''add_prefix_space'''     , self.add_prefix_space       )
       if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase       ) > 0 and not text[0].isspace()):
           A							:							Tuple				=    ''' ''' + text
       return (text, kwargs)
