




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_rembert import RemBertTokenizer
else:
   lowerCamelCase__ :   Tuple					=							None

lowerCamelCase__ :   Union[str, Any]					=							logging.get_logger(__name__)
lowerCamelCase__ :   List[str]					=							{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__ :   Tuple					=							{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

lowerCamelCase__ :   Any					=							{
    'google/rembert': 256,
}

lowerCamelCase__ :   Optional[Any]					=							'▁'





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    VOCAB_FILES_NAMES
 lowercase_				    =    PRETRAINED_VOCAB_FILES_MAP
 lowercase_				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				    =    RemBertTokenizer



 def __init__(							self  :		Any				,				_lowerCAmelCase  :		Union[str, Any]=None				,				_lowerCAmelCase  :		Optional[Any]=None				,				_lowerCAmelCase  :		List[Any]=True				,				_lowerCAmelCase  :		Optional[int]=True				,				_lowerCAmelCase  :		Dict=False				,				_lowerCAmelCase  :		List[str]="[CLS]"				,				_lowerCAmelCase  :		Optional[Any]="[SEP]"				,				_lowerCAmelCase  :		List[Any]="<unk>"				,				_lowerCAmelCase  :		Optional[int]="[SEP]"				,				_lowerCAmelCase  :		Any="<pad>"				,				_lowerCAmelCase  :		List[str]="[CLS]"				,				_lowerCAmelCase  :		List[str]="[MASK]"				,				**_lowerCAmelCase  :		int				,				):
       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE_							=			AddedToken(_lowerCAmelCase				,				lstrip=_lowerCAmelCase				,				rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase				,				_lowerCAmelCase						) else mask_token

       super().__init__(
           _lowerCAmelCase				,				tokenizer_file=_lowerCAmelCase				,				do_lower_case=_lowerCAmelCase				,				remove_space=_lowerCAmelCase				,				keep_accents=_lowerCAmelCase				,				bos_token=_lowerCAmelCase				,				eos_token=_lowerCAmelCase				,				unk_token=_lowerCAmelCase				,				sep_token=_lowerCAmelCase				,				pad_token=_lowerCAmelCase				,				cls_token=_lowerCAmelCase				,				mask_token=_lowerCAmelCase				,				**_lowerCAmelCase				,				)

       SCREAMING_SNAKE_CASE_							=			do_lower_case
       SCREAMING_SNAKE_CASE_							=			remove_space
       SCREAMING_SNAKE_CASE_							=			keep_accents
       SCREAMING_SNAKE_CASE_							=			vocab_file
       SCREAMING_SNAKE_CASE_							=			False if not self.vocab_file else True



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		List[int]				,				_lowerCAmelCase  :		Optional[List[int]] = None						):
       SCREAMING_SNAKE_CASE_							=			[self.sep_token_id]
       SCREAMING_SNAKE_CASE_							=			[self.cls_token_id]
       if token_ids_a is None:
             return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		List[int]				,				_lowerCAmelCase  :		Optional[List[int]] = None				,				_lowerCAmelCase  :		bool = False						):

       if already_has_special_tokens:
             if token_ids_a is not None:
                   raise ValueError(
                       'You should not supply a second sequence if the provided sequence of '
                       'ids is already formatted with special tokens for the model.'						)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		List[int]				,				_lowerCAmelCase  :		Optional[List[int]] = None						):
       SCREAMING_SNAKE_CASE_							=			[self.sep_token_id]
       SCREAMING_SNAKE_CASE_							=			[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  :		Any				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str] = None						):
       if not os.path.isdir(_lowerCAmelCase						):
             logger.error('Vocabulary path ({}) should be a directory'.format(_lowerCAmelCase						)						)
             return
       SCREAMING_SNAKE_CASE_							=			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,)





from typing import TYPE_CHECKING

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


lowerCamelCase__ :   str					=							{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   int					=							['Mask2FormerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Dict					=							[
       'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Mask2FormerForUniversalSegmentation',
       'Mask2FormerModel',
       'Mask2FormerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__ :   Tuple					=							_LazyModule(__name__, globals()['__file__'], _import_structure)