code: 
  
  
  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:
  						__lowerCamelCase       :       Optional[int]				   =			None
  
  __lowerCamelCase       :       Any				   =			logging.get_logger(__name__)
  
  __lowerCamelCase       :       List[str]				   =			{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  __lowerCamelCase       :       str				   =			{
      '''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'''
          ),
      }
  }
  
  __lowerCamelCase       :       List[str]				   =			{
      '''microsoft/deberta-v2-xlarge''': 512,
      '''microsoft/deberta-v2-xxlarge''': 512,
      '''microsoft/deberta-v2-xlarge-mnli''': 512,
      '''microsoft/deberta-v2-xxlarge-mnli''': 512,
  }
  
  __lowerCamelCase       :       Optional[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      (      A__   ):
  		__lowercase													=		VOCAB_FILES_NAMES
  		__lowercase													=		PRETRAINED_VOCAB_FILES_MAP
  		__lowercase													=		PRETRAINED_INIT_CONFIGURATION
  		__lowercase													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  		__lowercase													=		DebertaVaTokenizer
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=False	,     __UpperCamelCase=False	,     __UpperCamelCase="[CLS]"	,     __UpperCamelCase="[SEP]"	,     __UpperCamelCase="[UNK]"	,     __UpperCamelCase="[SEP]"	,     __UpperCamelCase="[PAD]"	,     __UpperCamelCase="[CLS]"	,     __UpperCamelCase="[MASK]"	,     **__UpperCamelCase	,     )->			Any:
  						'''simple docstring'''
  
  
  						super().__init__(
  						    __UpperCamelCase	,     tokenizer_file=__UpperCamelCase	,     do_lower_case=__UpperCamelCase	,     bos_token=__UpperCamelCase	,     eos_token=__UpperCamelCase	,     unk_token=__UpperCamelCase	,     sep_token=__UpperCamelCase	,     pad_token=__UpperCamelCase	,     cls_token=__UpperCamelCase	,     mask_token=__UpperCamelCase	,     split_by_punct=__UpperCamelCase	,     **__UpperCamelCase	,     )
  
  						A__     :							Union[str, Any]   				=do_lower_case
  						A__     :							Optional[int]   				=split_by_punct
  						A__     :							Optional[Any]   				=vocab_file
  						A__     :							List[Any]   				=False if not self.vocab_file else True
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase=None							)->			Dict:
  						'''simple docstring'''
  
  
  						if token_ids_a is None:
  										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  						A__     :							Tuple   				=[self.cls_token_id]
  						A__     :							Any   				=[self.sep_token_id]
  						return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase=None	,     __UpperCamelCase=False							)->			List[str]:
  						'''simple docstring'''
  
  
  						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 not None:
  										return [1] + ([0] * len(__UpperCamelCase							)) + [1] + ([0] * len(__UpperCamelCase							)) + [1]
  						return [1] + ([0] * len(__UpperCamelCase							)) + [1]
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase=None							)->			Tuple:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				=[self.sep_token_id]
  						A__     :							Optional[int]   				=[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	,     __UpperCamelCase	,     __UpperCamelCase = None							)->			Optional[int]:
  						'''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(__UpperCamelCase							):
  										logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
  										return
  						A__     :							Union[str, Any]   				=os.path.join(
  						    __UpperCamelCase	,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
  
  						if os.path.abspath(self.vocab_file							) != os.path.abspath(__UpperCamelCase							):
  										copyfile(self.vocab_file	,     __UpperCamelCase							)
  
  						return (out_vocab_file,)
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  __lowerCamelCase       :       str				   =			{
      "configuration_align": [
          "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "AlignConfig",
          "AlignTextConfig",
          "AlignVisionConfig",
      ],
      "processing_align": ["AlignProcessor"],
  }
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						__lowerCamelCase       :       Dict				   =			[
  						    "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "AlignModel",
  						    "AlignPreTrainedModel",
  						    "AlignTextModel",
  						    "AlignVisionModel",
  						]
  
  if TYPE_CHECKING:
  						from .configuration_align import (
  						    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
  						    AlignConfig,
  						    AlignTextConfig,
  						    AlignVisionConfig,
  						)
  						from .processing_align import AlignProcessor
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_align import (
  												    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    AlignModel,
  												    AlignPreTrainedModel,
  												    AlignTextModel,
  												    AlignVisionModel,
  												)
  
  else:
  						import sys
  
  						__lowerCamelCase       :       List[str]				   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  