





'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


a__							:							Optional[int]			=logging.get_logger(__name__)

a__							:							Optional[int]			={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a__							:							Tuple			={
    '''vocab_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
        ),
    },
}

a__							:							int			={
    '''microsoft/deberta-base''': 512,
    '''microsoft/deberta-large''': 512,
    '''microsoft/deberta-xlarge''': 512,
    '''microsoft/deberta-base-mnli''': 512,
    '''microsoft/deberta-large-mnli''': 512,
    '''microsoft/deberta-xlarge-mnli''': 512,
}

a__							:							Dict			={
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}

class 					snake_case					(   __lowerCamelCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[int]      		=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_     : Union[str, Any]      		=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_     : Any      		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_     : Dict      		=["input_ids", "attention_mask", "token_type_ids"]
 SCREAMING_SNAKE_CASE_     : Optional[Any]      		=DebertaTokenizer






 def __init__(	self	:							List[Any]							,     __A	:							Any=None							,     __A	:							List[Any]=None							,     __A	:							int=None							,     __A	:							Optional[int]="replace"							,     __A	:							Tuple="[CLS]"							,     __A	:							Tuple="[SEP]"							,     __A	:							List[Any]="[SEP]"							,     __A	:							str="[CLS]"							,     __A	:							Tuple="[UNK]"							,     __A	:							int="[PAD]"							,     __A	:							int="[MASK]"							,     __A	:							Tuple=False							,     **__A	:							Any							,     ):
        super().__init__(
            __A							,     __A							,     tokenizer_file=__A							,     errors=__A							,     bos_token=__A							,     eos_token=__A							,     unk_token=__A							,     sep_token=__A							,     cls_token=__A							,     pad_token=__A							,     mask_token=__A							,     add_prefix_space=__A							,     **__A							,     )
        __UpperCamelCase	=  kwargs.pop('add_bos_token'							,     __A     )

        __UpperCamelCase	=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
        if pre_tok_state.get('add_prefix_space'							,     __A     ) != add_prefix_space:
               __UpperCamelCase	=  getattr(__A							,     pre_tok_state.pop('type'     )     )
               __UpperCamelCase	=  add_prefix_space
               __UpperCamelCase	=  pre_tok_class(**__A     )

        __UpperCamelCase	=  add_prefix_space






 @property
 def 	_lowerCamelCase (	self	:							List[str]     ):
        if self._mask_token is None:
               if self.verbose:
                      logger.error('Using mask_token, but it is not set yet.'     )
               return None
        return str(self._mask_token     )






 @mask_token.setter
 def 	_lowerCamelCase (	self	:							Optional[int]							,     __A	:							Dict     ):
        __UpperCamelCase	=  AddedToken(__A							,     lstrip=__A							,     rstrip=__A     ) if isinstance(__A							,     __A     ) else value
        __UpperCamelCase	=  value






 def 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							List[int]							,     __A	:							Optional[List[int]] = None     ):
        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __UpperCamelCase	=  [self.cls_token_id]
        __UpperCamelCase	=  [self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep






 def 	_lowerCamelCase (	self	:							int							,     __A	:							List[int]							,     __A	:							Optional[List[int]] = None     ):
        __UpperCamelCase	=  [self.sep_token_id]
        __UpperCamelCase	=  [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     ) * [0] + len(token_ids_a + sep     ) * [1]






 def 	_lowerCamelCase (	self	:							List[str]							,     *__A	:							Tuple							,     **__A	:							List[str]     ):
        __UpperCamelCase	=  kwargs.get('is_split_into_words'							,     __A     )
        assert self.add_prefix_space or not is_split_into_words, (
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*__A							,     **__A     )






 def 	_lowerCamelCase (	self	:							Dict							,     *__A	:							Any							,     **__A	:							Any     ):
        __UpperCamelCase	=  kwargs.get('is_split_into_words'							,     __A     )

        assert self.add_prefix_space or not is_split_into_words, (
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*__A							,     **__A     )






 def 	_lowerCamelCase (	self	:							Any							,     __A	:							str							,     __A	:							Optional[str] = None     ):
        __UpperCamelCase	=  self._tokenizer.model.save(__A							,     name=__A     )
        return tuple(__A     )






 def 	_lowerCamelCase (	self	:							str							,     __A	:							"Conversation"     ):
        __UpperCamelCase	=  []
        for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__A							,     add_special_tokens=__A     ) + [self.eos_token_id]     )

        if len(__A     ) > self.model_max_length:
               __UpperCamelCase	=  input_ids[-self.model_max_length :]
        return input_ids







'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

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


if is_sentencepiece_available():
    from .tokenization_mbartaa import MBartaaTokenizer
else:
    a__							:							Union[str, Any]			=None


a__							:							str			=logging.get_logger(__name__)

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

a__							:							List[Any]			={
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

a__							:							List[Any]			={
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_024,
}

# fmt: off
a__							:							int			=['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']

class 					snake_case					(   __lowerCamelCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Any      		=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_     : Dict      		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_     : List[str]      		=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_     : Any      		=["input_ids", "attention_mask"]
 SCREAMING_SNAKE_CASE_     : Dict      		=MBartaaTokenizer

 SCREAMING_SNAKE_CASE_     : List[int]      		=[]
 SCREAMING_SNAKE_CASE_     : List[int]      		=[]






 def __init__(	self	:							Any							,     __A	:							Union[str, Any]=None							,     __A	:							Tuple=None							,     __A	:							Optional[int]=None							,     __A	:							List[Any]=None							,     __A	:							Optional[Any]="</s>"							,     __A	:							Any="</s>"							,     __A	:							Optional[Any]="<s>"							,     __A	:							List[Any]="<unk>"							,     __A	:							Tuple="<pad>"							,     __A	:							Union[str, Any]="<mask>"							,     **__A	:							Any							,     ):
        # Mask token behave like a normal word, i.e. include the space before it
        __UpperCamelCase	=  AddedToken(__A							,     lstrip=__A							,     rstrip=__A     ) if isinstance(__A							,     __A     ) else mask_token

        __UpperCamelCase	=  kwargs.get('additional_special_tokens'							,     []     )
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            __A							,     src_lang=__A							,     tgt_lang=__A							,     tokenizer_file=__A							,     eos_token=__A							,     sep_token=__A							,     cls_token=__A							,     unk_token=__A							,     pad_token=__A							,     mask_token=__A							,     **__A							,     )

        __UpperCamelCase	=  vocab_file
        __UpperCamelCase	=  False if not self.vocab_file else True

        __UpperCamelCase	=  {
            lang_code: self.convert_tokens_to_ids(__A     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        __UpperCamelCase	=  src_lang if src_lang is not None else 'en_XX'
        __UpperCamelCase	=  tgt_lang
        __UpperCamelCase	=  self.lang_code_to_id[self._src_lang]
        self.set_src_lang_special_tokens(self._src_lang     )






 @property
 def 	_lowerCamelCase (	self	:							Tuple     ):
        return self._src_lang






 @src_lang.setter
 def 	_lowerCamelCase (	self	:							Optional[int]							,     __A	:							str     ):
        __UpperCamelCase	=  new_src_lang
        self.set_src_lang_special_tokens(self._src_lang     )






 def 	_lowerCamelCase (	self	:							Optional[int]							,     __A	:							List[int]							,     __A	:							Optional[List[int]] = None     ):
        if token_ids_a is None:
               return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # 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.suffix_tokens






 def 	_lowerCamelCase (	self	:							List[Any]							,     __A	:							List[str]							,     __A	:							str = "en_XX"							,     __A	:							Optional[List[str]] = None							,     __A	:							str = "ro_RO"							,     **__A	:							Optional[int]							,     ):
        __UpperCamelCase	=  src_lang
        __UpperCamelCase	=  tgt_lang
        return super().prepare_seqaseq_batch(__A							,     __A							,     **__A     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        return self.set_src_lang_special_tokens(self.src_lang     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        return self.set_tgt_lang_special_tokens(self.tgt_lang     )






 def 	_lowerCamelCase (	self	:							Optional[int]							,     __A	:							str     ):
        __UpperCamelCase	=  self.convert_tokens_to_ids(__A     )
        __UpperCamelCase	=  [self.cur_lang_code_id]
        __UpperCamelCase	=  [self.eos_token_id]

        __UpperCamelCase	=  self.convert_ids_to_tokens(self.prefix_tokens     )
        __UpperCamelCase	=  self.convert_ids_to_tokens(self.suffix_tokens     )

        __UpperCamelCase	=  processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str							,     pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str							,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,     self.prefix_tokens + self.suffix_tokens     )     )							,     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							str     ):
        __UpperCamelCase	=  self.convert_tokens_to_ids(__A     )
        __UpperCamelCase	=  [self.cur_lang_code_id]
        __UpperCamelCase	=  [self.eos_token_id]

        __UpperCamelCase	=  self.convert_ids_to_tokens(self.prefix_tokens     )
        __UpperCamelCase	=  self.convert_ids_to_tokens(self.suffix_tokens     )

        __UpperCamelCase	=  processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str							,     pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str							,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,     self.prefix_tokens + self.suffix_tokens     )     )							,     )






 def 	_lowerCamelCase (	self	:							str							,     __A	:							Union[str, Any]							,     __A	:							str							,     __A	:							Optional[str]							,     __A	:							Optional[str]							,     **__A	:							Dict     ):
        if src_lang is None or tgt_lang is None:
               raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'     )
        __UpperCamelCase	=  src_lang
        __UpperCamelCase	=  self(__A							,     add_special_tokens=__A							,     return_tensors=__A							,     **__A     )
        __UpperCamelCase	=  self.convert_tokens_to_ids(__A     )
        __UpperCamelCase	=  tgt_lang_id
        return inputs






 def 	_lowerCamelCase (	self	:							List[str]							,     __A	:							str							,     __A	:							Optional[str] = None     ):
        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(__A     ):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
               return
        __UpperCamelCase	=  os.path.join(
            __A							,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

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

        return (out_vocab_file,)
