'''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


SCREAMING_SNAKE_CASE_:	Any    =logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE_:	Union[str, Any]    ={
    '''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'''
        ),
    },
}

SCREAMING_SNAKE_CASE_:	Any    ={
    '''yjernite/retribert-base-uncased''': 5_12,
}


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




class 			__A (					__lowerCAmelCase      ):
    a__			:		Union[str, Any]			  =    VOCAB_FILES_NAMES
    a__			:		Dict			  =    PRETRAINED_VOCAB_FILES_MAP
    a__			:		Dict			  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__			:		Optional[int]			  =    PRETRAINED_INIT_CONFIGURATION
    a__			:		int			  =    RetriBertTokenizer
    a__			:		Union[str, Any]			  =    ["""input_ids""", """attention_mask"""]



    def __init__(self       :       Dict  ,     __a       :       Dict=None  ,     __a       :       int=None  ,     __a       :       int=True  ,     __a       :       Union[str, Any]="[UNK]"  ,     __a       :       List[str]="[SEP]"  ,     __a       :       int="[PAD]"  ,     __a       :       List[str]="[CLS]"  ,     __a       :       str="[MASK]"  ,     __a       :       Optional[Any]=True  ,     __a       :       str=None  ,     **__a       :       Optional[Any]  ,     ):
         super().__init__(
             _SCREAMING_SNAKE_CASE  ,     tokenizer_file=_SCREAMING_SNAKE_CASE  ,     do_lower_case=_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  ,     tokenize_chinese_chars=_SCREAMING_SNAKE_CASE  ,     strip_accents=_SCREAMING_SNAKE_CASE  ,     **_SCREAMING_SNAKE_CASE  ,     )

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

         UpperCAmelCase_			       =  do_lower_case



    def    _lowercase		(self       :       str  ,     __a       :       List[str]  ,     __a       :       Union[str, 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    _lowercase		(self       :       Dict  ,     __a       :       List[str]  ,     __a       :       Union[str, Any] = 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    _lowercase		(self       :       Tuple  ,     __a       :       Dict  ,     __a       :       Optional[int] = None					):
         UpperCAmelCase_			       =  self._tokenizer.model.save(_SCREAMING_SNAKE_CASE  ,     name=_SCREAMING_SNAKE_CASE					)
         return tuple(_SCREAMING_SNAKE_CASE					)










'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_		:     Optional[int]          =		logging.get_logger(__name__)

UpperCamelCase_		:     str          =		{
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		"""ibert"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=30_522   ,_SCREAMING_SNAKE_CASE=768   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=3_072   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=512   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.0_2   ,_SCREAMING_SNAKE_CASE=1e-12   ,_SCREAMING_SNAKE_CASE=1   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE="absolute"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE="none"   ,**_SCREAMING_SNAKE_CASE   ,)				->     Dict:
          super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )

          _snake_case				= vocab_size
          _snake_case				= hidden_size
          _snake_case				= num_hidden_layers
          _snake_case				= num_attention_heads
          _snake_case				= hidden_act
          _snake_case				= intermediate_size
          _snake_case				= hidden_dropout_prob
          _snake_case				= attention_probs_dropout_prob
          _snake_case				= max_position_embeddings
          _snake_case				= type_vocab_size
          _snake_case				= initializer_range
          _snake_case				= layer_norm_eps
          _snake_case				= position_embedding_type
          _snake_case				= quant_mode
          _snake_case				= force_dequant







class   _a							(		__lowerCAmelCase				):






     @property
     def 							_lowercase      (					self       )				->     Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _snake_case				= {0: "batch", 1: "choice", 2: "sequence"}
          else:
               _snake_case				= {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]       )


