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:
  					UpperCAmelCase_       :				Dict		   =      None
  
  UpperCAmelCase_       :				Dict		   =      logging.get_logger(__name__)
  
  UpperCAmelCase_       :				Tuple		   =      {'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  UpperCAmelCase_       :				List[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'''
          ),
      }
  }
  
  UpperCAmelCase_       :				List[Any]		   =      {
      '''microsoft/deberta-v2-xlarge''': 512,
      '''microsoft/deberta-v2-xxlarge''': 512,
      '''microsoft/deberta-v2-xlarge-mnli''': 512,
      '''microsoft/deberta-v2-xxlarge-mnli''': 512,
  }
  
  UpperCAmelCase_       :				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     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      Optional[int]    				=							VOCAB_FILES_NAMES
  	_lowercase		:      List[str]    				=							PRETRAINED_VOCAB_FILES_MAP
  	_lowercase		:      Optional[Any]    				=							PRETRAINED_INIT_CONFIGURATION
  	_lowercase		:      Optional[Any]    				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  	_lowercase		:      Tuple    				=							DebertaVaTokenizer
  
  
  
  
  	def __init__(    self	:    Any				,			__A	:    Tuple=None				,			__A	:    Optional[int]=None				,			__A	:    List[str]=False				,			__A	:    Union[str, Any]=False				,			__A	:    List[Any]="[CLS]"				,			__A	:    Union[str, Any]="[SEP]"				,			__A	:    Any="[UNK]"				,			__A	:    List[Any]="[SEP]"				,			__A	:    str="[PAD]"				,			__A	:    List[str]="[CLS]"				,			__A	:    List[Any]="[MASK]"				,			**__A	:    int				,			):
  		super().__init__(
  		    __A				,			tokenizer_file=__A				,			do_lower_case=__A				,			bos_token=__A				,			eos_token=__A				,			unk_token=__A				,			sep_token=__A				,			pad_token=__A				,			cls_token=__A				,			mask_token=__A				,			split_by_punct=__A				,			**__A				,			)
  
  		__A   : Optional[int]								= do_lower_case
  		__A   : List[Any]								= split_by_punct
  		__A   : str								= vocab_file
  		__A   : Union[str, Any]								= False if not self.vocab_file else True
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    Optional[int]				,			__A	:    int=None       ):
  
  		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   : List[Any]								= [self.sep_token_id]
  		return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Dict				,			__A	:    Optional[int]				,			__A	:    Tuple=None				,			__A	:    List[str]=False       ):
  
  		if already_has_special_tokens:
  			return super().get_special_tokens_mask(
  			    token_ids_a=__A				,			token_ids_a=__A				,			already_has_special_tokens=__A       )
  
  		if token_ids_a is not None:
  			return [1] + ([0] * len(__A       )) + [1] + ([0] * len(__A       )) + [1]
  		return [1] + ([0] * len(__A       )) + [1]
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int				,			__A	:    List[str]				,			__A	:    Any=None       ):
  		__A   : int								= [self.sep_token_id]
  		__A   : 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	:    Union[str, Any]				,			__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
  		__A   : Union[str, Any]								= 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,)
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  UpperCAmelCase_       :				Optional[Any]		   =      {
      '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
      '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
  }
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			UpperCAmelCase_       :				List[str]		   =      [
  			    '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
  			    '''ProphetNetDecoder''',
  			    '''ProphetNetEncoder''',
  			    '''ProphetNetForCausalLM''',
  			    '''ProphetNetForConditionalGeneration''',
  			    '''ProphetNetModel''',
  			    '''ProphetNetPreTrainedModel''',
  			]
  
  
  if TYPE_CHECKING:
  			from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
  			from .tokenization_prophetnet import ProphetNetTokenizer
  
  			try:
  						if not is_torch_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .modeling_prophetnet import (
  						    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    ProphetNetDecoder,
  						    ProphetNetEncoder,
  						    ProphetNetForCausalLM,
  						    ProphetNetForConditionalGeneration,
  						    ProphetNetModel,
  						    ProphetNetPreTrainedModel,
  						)
  
  else:
  			import sys
  
  			UpperCAmelCase_       :				Any		   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  