


"""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:
   __snake_case												=							None

__snake_case												=							logging.get_logger(__name__)

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

__snake_case												=							{
    '''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'''
        ),
    }
}

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

__snake_case												=							{
    '''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					):

     '''simple docstring'''
     A_							:						int     =		VOCAB_FILES_NAMES
     A_							:						Any     =		PRETRAINED_VOCAB_FILES_MAP
     A_							:						Optional[Any]     =		PRETRAINED_INIT_CONFIGURATION
     A_							:						Tuple     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_							:						Optional[int]     =		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     ,					)							->				None:
          super().__init__(
              _SCREAMING_SNAKE_CASE     ,					tokenizer_file=_SCREAMING_SNAKE_CASE     ,					do_lower_case=_SCREAMING_SNAKE_CASE     ,					bos_token=_SCREAMING_SNAKE_CASE     ,					eos_token=_SCREAMING_SNAKE_CASE     ,					unk_token=_SCREAMING_SNAKE_CASE     ,					sep_token=_SCREAMING_SNAKE_CASE     ,					pad_token=_SCREAMING_SNAKE_CASE     ,					cls_token=_SCREAMING_SNAKE_CASE     ,					mask_token=_SCREAMING_SNAKE_CASE     ,					split_by_punct=_SCREAMING_SNAKE_CASE     ,					**_SCREAMING_SNAKE_CASE     ,					)

          _a     						=					do_lower_case
          _a     						=					split_by_punct
          _a     						=					vocab_file
          _a     						=					False if not self.vocab_file else True


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None				)							->				Optional[Any]:

          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 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=False				)							->				Optional[Any]:

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

          if token_ids_a is not None:
               return [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]
          return [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None				)							->				Union[str, Any]:
          _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 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				)							->				Tuple[str]:
          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(_SCREAMING_SNAKE_CASE				):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'				)
               return
          _a     						=					os.path.join(
              _SCREAMING_SNAKE_CASE     ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

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

          return (out_vocab_file,)



import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


lowerCamelCase		=						logging.get_logger(__name__)





class    _a				(							_lowercase):




	def __init__(   self		:						Any						,   *_SCREAMING_SNAKE_CASE		:						int						,   **_SCREAMING_SNAKE_CASE		:						Dict						)->			None:
			warnings.warn(
			    '''The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
			    ''' Please use MaskFormerImageProcessor instead.'''						,   _SCREAMING_SNAKE_CASE						,   )
			super().__init__(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




