






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:
							A_       :Optional[int]       =						None

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

A_       :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''',
    },
}

A_       :int       =						{
    '''google/rembert''': 256,
}

A_       :Tuple       =						'''▁'''




class 				__A     (			a ):





				"""simple docstring"""







				UpperCamelCase__ :      str			 =VOCAB_FILES_NAMES
				UpperCamelCase__ :      Union[str, Any]			 =PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__ :      str			 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__ :      List[str]			 =RemBertTokenizer


				def __init__( self							,  lowerCamelCase__=None							,  lowerCamelCase__=None							,  lowerCamelCase__=True							,  lowerCamelCase__=True							,  lowerCamelCase__=False							,  lowerCamelCase__="[CLS]"							,  lowerCamelCase__="[SEP]"							,  lowerCamelCase__="<unk>"							,  lowerCamelCase__="[SEP]"							,  lowerCamelCase__="<pad>"							,  lowerCamelCase__="[CLS]"							,  lowerCamelCase__="[MASK]"							,  **lowerCamelCase__							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=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__							,  )

									__UpperCamelCase      :					str       		=do_lower_case
									__UpperCamelCase      :					List[str]       		=remove_space
									__UpperCamelCase      :					Dict       		=keep_accents
									__UpperCamelCase      :					Tuple       		=vocab_file
									__UpperCamelCase      :					Optional[int]       		=False if not self.vocab_file else True


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = None    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		=[self.sep_token_id]
									__UpperCamelCase      :					Optional[int]       		=[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							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  lowerCamelCase__ = False    ):




									"""simple docstring"""

									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       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = None    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		=[self.sep_token_id]
									__UpperCamelCase      :					Optional[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							,  lowerCamelCase__							,  lowerCamelCase__ = None    ):




									"""simple docstring"""

									if not os.path.isdir(lowerCamelCase__    ):
														logger.error('Vocabulary path ({}) should be a directory'.format(lowerCamelCase__    )    )
														return
									__UpperCamelCase      :					List[str]       		=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,)


'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase          =  {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''InstructBlipQFormerModel''',
						    '''InstructBlipPreTrainedModel''',
						    '''InstructBlipForConditionalGeneration''',
						    '''InstructBlipVisionModel''',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

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




