


"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__snake_case				=				logging.get_logger(__name__)

__snake_case				=				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case				=				{
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

__snake_case				=				{
    '''yjernite/retribert-base-uncased''': 512,
}


__snake_case				=				{
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}


class 			__lowerCamelCase     (    __snake_case ):

     '''simple docstring'''
     A_				:		Optional[Any]			=						VOCAB_FILES_NAMES
     A_				:		Any			=						PRETRAINED_VOCAB_FILES_MAP
     A_				:		Tuple			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_				:		Dict			=						PRETRAINED_INIT_CONFIGURATION
     A_				:		Optional[int]			=						RetriBertTokenizer
     A_				:		List[Any]			=						["""input_ids""", """attention_mask"""]


     def __init__(					self			, __UpperCAmelCase=None			, __UpperCAmelCase=None			, __UpperCAmelCase=True			, __UpperCAmelCase="[UNK]"			, __UpperCAmelCase="[SEP]"			, __UpperCAmelCase="[PAD]"			, __UpperCAmelCase="[CLS]"			, __UpperCAmelCase="[MASK]"			, __UpperCAmelCase=True			, __UpperCAmelCase=None			, **__UpperCAmelCase			, )				->							str:
          super().__init__(
              __UpperCAmelCase			, tokenizer_file=__UpperCAmelCase			, do_lower_case=__UpperCAmelCase			, unk_token=__UpperCAmelCase			, sep_token=__UpperCAmelCase			, pad_token=__UpperCAmelCase			, cls_token=__UpperCAmelCase			, mask_token=__UpperCAmelCase			, tokenize_chinese_chars=__UpperCAmelCase			, strip_accents=__UpperCAmelCase			, **__UpperCAmelCase			, )

          _a					= json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
          if (
              normalizer_state.get('''lowercase'''			, __UpperCAmelCase							) != do_lower_case
              or normalizer_state.get('''strip_accents'''			, __UpperCAmelCase							) != strip_accents
              or normalizer_state.get('''handle_chinese_chars'''			, __UpperCAmelCase							) != tokenize_chinese_chars
          ):
               _a					= getattr(__UpperCAmelCase			, normalizer_state.pop('''type'''							)							)
               _a					= do_lower_case
               _a					= strip_accents
               _a					= tokenize_chinese_chars
               _a					= normalizer_class(**__UpperCAmelCase							)

          _a					= do_lower_case


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase=None							)				->							Optional[Any]:
          _a					= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

          return output


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = None							)				->							List[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							)				->							Any:
          _a					= self._tokenizer.model.save(__UpperCAmelCase			, name=__UpperCAmelCase							)
          return tuple(__UpperCAmelCase							)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_			  = {
    '''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:
       snake_case_			  = [
           '''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

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

