



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      =    None

__lowerCamelCase      =    logging.get_logger(__name__)

__lowerCamelCase      =    {"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase      =    {
    """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      =    {
    """microsoft/deberta-v2-xlarge""": 5_1_2,
    """microsoft/deberta-v2-xxlarge""": 5_1_2,
    """microsoft/deberta-v2-xlarge-mnli""": 5_1_2,
    """microsoft/deberta-v2-xxlarge-mnli""": 5_1_2,
}

__lowerCamelCase      =    {
    """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 						_snake_case					(	a_ ):





 """simple docstring"""



 lowerCamelCase_								=	VOCAB_FILES_NAMES
 lowerCamelCase_								=	PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase_								=	PRETRAINED_INIT_CONFIGURATION
 lowerCamelCase_								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase_								=	DebertaVaTokenizer
 def __init__(  self		,			a=None		,			a=None		,			a=False		,			a=False		,			a="[CLS]"		,			a="[SEP]"		,			a="[UNK]"		,			a="[SEP]"		,			a="[PAD]"		,			a="[CLS]"		,			a="[MASK]"		,			**a		,			)		->							None:




   """simple docstring"""







   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							=			do_lower_case
   _A							=			split_by_punct
   _A							=			vocab_file
   _A							=			False if not self.vocab_file else True
 def lowercase_						(  self		,			a		,			a=None    )		->							Optional[int]:




   """simple docstring"""







   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   _A							=			[self.cls_token_id]
   _A							=			[self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep
 def lowercase_						(  self		,			a		,			a=None		,			a=False    )		->							Optional[Any]:




   """simple docstring"""







   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 lowercase_						(  self		,			a		,			a=None    )		->							Dict:




   """simple docstring"""







   _A							=			[self.sep_token_id]
   _A							=			[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		,			a		,			a = None    )		->							Tuple[str]:




   """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(a    ):
     logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
     return
   _A							=			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,)
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase							= {'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase							= [
     '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''NatForImageClassification''',
     '''NatModel''',
     '''NatPreTrainedModel''',
     '''NatBackbone''',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



