



"""simple docstring"""







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:
     lowercase__		:				Optional[Any]          =	None

lowercase__		:				int          =	logging.get_logger(__name__)
lowercase__		:				Union[str, Any]          =	{"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase__		:				Optional[Any]          =	{
    """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""",
    },
}

lowercase__		:				Any          =	{
    """google/rembert""": 2_5_6,
}

lowercase__		:				str          =	"""▁"""







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE  				=     PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE  				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE  				=     RemBertTokenizer



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

        super().__init__(
            SCREAMING_SNAKE_CASE_    ,			tokenizer_file=SCREAMING_SNAKE_CASE_    ,			do_lower_case=SCREAMING_SNAKE_CASE_    ,			remove_space=SCREAMING_SNAKE_CASE_    ,			keep_accents=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_    ,			**SCREAMING_SNAKE_CASE_    ,			)

        lowerCAmelCase_   :				List[Any]								= do_lower_case
        lowerCAmelCase_   :				List[Any]								= remove_space
        lowerCAmelCase_   :				List[str]								= keep_accents
        lowerCAmelCase_   :				Optional[int]								= vocab_file
        lowerCAmelCase_   :				Union[str, Any]								= False if not self.vocab_file else True



      def      SCREAMING_SNAKE_CASE__ (      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      List[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[List[int]] = None       ):
        lowerCAmelCase_   :				List[Any]								= [self.sep_token_id]
        lowerCAmelCase_   :				Optional[Any]								= [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      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      List[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[List[int]] = None    ,			SCREAMING_SNAKE_CASE_				:      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(SCREAMING_SNAKE_CASE_       )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_       )) + [1]
        return [1] + ([0] * len(SCREAMING_SNAKE_CASE_       )) + [1]



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










"""simple docstring"""







import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)





