



'''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:
   _UpperCamelCase											=		None

_UpperCamelCase											=		logging.get_logger(__name__)
_UpperCamelCase											=		{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase											=		{
    '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',
    },
}

_UpperCamelCase											=		{
    'google/rembert': 256,
}

_UpperCamelCase											=		'▁'






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =VOCAB_FILES_NAMES
  a_				   =PRETRAINED_VOCAB_FILES_MAP
  a_				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_				   =RemBertTokenizer




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

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

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




  def 		_lowercase	(					self :      List[str]     ,      _a :      List[int]     ,      _a :      Optional[List[int]] = None  )   ->   List[int]:
    __lowerCamelCase			:			int       			=					[self.sep_token_id]
    __lowerCamelCase			:			List[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 		_lowercase	(					self :      List[str]     ,      _a :      List[int]     ,      _a :      Optional[List[int]] = None     ,      _a :      bool = False  )   ->   List[int]:

    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(_a  )) + [1] + ([0] * len(_a  )) + [1]
    return [1] + ([0] * len(_a  )) + [1]




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      List[int]     ,      _a :      Optional[List[int]] = None  )   ->   List[int]:
    __lowerCamelCase			:			Any       			=					[self.sep_token_id]
    __lowerCamelCase			:			Union[str, 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 		_lowercase	(					self :      Optional[int]     ,      _a :      str     ,      _a :      Optional[str] = None  )   ->   Tuple[str]:
    if not os.path.isdir(_a  ):
      logger.error('Vocabulary path ({}) should be a directory'.format(_a  )  )
      return
    __lowerCamelCase			:			List[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,)











'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase											=		{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		['BridgeTowerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		[
       'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BridgeTowerForContrastiveLearning',
       'BridgeTowerForImageAndTextRetrieval',
       'BridgeTowerForMaskedLM',
       'BridgeTowerModel',
       'BridgeTowerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=		_LazyModule(__name__, globals()['__file__'], _import_structure)






