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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
     from .tokenization_deberta_va import DebertaVaTokenizer
else:
     A__ :		Optional[Any]					=None

A__ :		Optional[Any]					=logging.get_logger(__name__)

A__ :		Dict					={'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

A__ :		Union[str, Any]					={
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

A__ :		int					={
    '''microsoft/deberta-v2-xlarge''': 512,
    '''microsoft/deberta-v2-xxlarge''': 512,
    '''microsoft/deberta-v2-xlarge-mnli''': 512,
    '''microsoft/deberta-v2-xxlarge-mnli''': 512,
}

A__ :		int					={
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=VOCAB_FILES_NAMES
 lowerCamelCase												=PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase												=PRETRAINED_INIT_CONFIGURATION
 lowerCamelCase												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase												=DebertaVaTokenizer
 def __init__( self      :     Union[str, Any]					, lowerCamelCase      :     Optional[int]=None					, lowerCamelCase      :     int=None					, lowerCamelCase      :     Tuple=False					, lowerCamelCase      :     List[Any]=False					, lowerCamelCase      :     List[str]="[CLS]"					, lowerCamelCase      :     Optional[Any]="[SEP]"					, lowerCamelCase      :     Dict="[UNK]"					, lowerCamelCase      :     List[Any]="[SEP]"					, lowerCamelCase      :     Tuple="[PAD]"					, lowerCamelCase      :     str="[CLS]"					, lowerCamelCase      :     Union[str, Any]="[MASK]"					, **lowerCamelCase      :     Optional[Any]					, ):



   """simple docstring"""





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

   __A		:  Tuple       				=		do_lower_case
   __A		:  int       				=		split_by_punct
   __A		:  Any       				=		vocab_file
   __A		:  List[str]       				=		False if not self.vocab_file else True
 def 		lowercase_( self      :     Tuple					, lowerCamelCase      :     Any					, lowerCamelCase      :     Optional[int]=None							):



   """simple docstring"""





   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   __A		:  Any       				=		[self.cls_token_id]
   __A		:  Any       				=		[self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep
 def 		lowercase_( self      :     Optional[Any]					, lowerCamelCase      :     Optional[Any]					, lowerCamelCase      :     int=None					, lowerCamelCase      :     Tuple=False							):



   """simple docstring"""





   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=lowerCamelCase					, token_ids_a=lowerCamelCase					, already_has_special_tokens=lowerCamelCase							)

   if token_ids_a is not None:
     return [1] + ([0] * len(lowerCamelCase							)) + [1] + ([0] * len(lowerCamelCase							)) + [1]
   return [1] + ([0] * len(lowerCamelCase							)) + [1]
 def 		lowercase_( self      :     Optional[int]					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     str=None							):



   """simple docstring"""





   __A		:  int       				=		[self.sep_token_id]
   __A		:  List[Any]       				=		[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 		lowercase_( self      :     List[Any]					, lowerCamelCase      :     str					, lowerCamelCase      :     Optional[str] = None							):



   """simple docstring"""





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

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

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

   return (out_vocab_file,)











'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_					:			str     						=				{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''HubertForCTC''',
					    '''HubertForSequenceClassification''',
					    '''HubertModel''',
					    '''HubertPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFHubertForCTC''',
					    '''TFHubertModel''',
					    '''TFHubertPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase_					:			Dict     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
