





'''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


a__							:							Union[str, Any] =logging.get_logger(__name__)

a__							:							Optional[int] ={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a__							:							Dict ={
    '''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'''
        ),
    },
}

a__							:							List[str] ={
    '''yjernite/retribert-base-uncased''': 512,
}


a__							:							Optional[int] ={
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_	:					List[Any]					=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_	:					Dict					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_	:					Any					=PRETRAINED_INIT_CONFIGURATION
 SCREAMING_SNAKE_CASE_	:					Tuple					=RetriBertTokenizer
 SCREAMING_SNAKE_CASE_	:					List[str]					=["input_ids", "attention_mask"]






 def __init__(       self       :							str							,	__A       :							int=None							,	__A       :							int=None							,	__A       :							int=True							,	__A       :							Dict="[UNK]"							,	__A       :							List[str]="[SEP]"							,	__A       :							Optional[Any]="[PAD]"							,	__A       :							Optional[int]="[CLS]"							,	__A       :							int="[MASK]"							,	__A       :							List[Any]=True							,	__A       :							Dict=None							,	**__A       :							int							,	):
        super().__init__(
            __A							,	tokenizer_file=__A							,	do_lower_case=__A							,	unk_token=__A							,	sep_token=__A							,	pad_token=__A							,	cls_token=__A							,	mask_token=__A							,	tokenize_chinese_chars=__A							,	strip_accents=__A							,	**__A							,	)

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

        __UpperCamelCase       =  do_lower_case






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							Dict							,	__A       :							Any=None	):
        __UpperCamelCase       =  [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        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None	):
        __UpperCamelCase       =  [self.sep_token_id]
        __UpperCamelCase       =  [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       :							List[Any]							,	__A       :							str							,	__A       :							Optional[str] = None	):
        __UpperCamelCase       =  self._tokenizer.model.save(__A							,	name=__A	)
        return tuple(__A	)







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__							:							List[str] ={
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__							:							Any =[
        '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''BigBirdPegasusForCausalLM''',
        '''BigBirdPegasusForConditionalGeneration''',
        '''BigBirdPegasusForQuestionAnswering''',
        '''BigBirdPegasusForSequenceClassification''',
        '''BigBirdPegasusModel''',
        '''BigBirdPegasusPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

    a__							:							str =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
