






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						:			List[str]     	=					logging.get_logger(__name__)

a						:			Dict     	=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a						:			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"
        ),
    },
}

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


a						:			int     	=					{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}
class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					int							=      VOCAB_FILES_NAMES
      a    :					List[str]							=      PRETRAINED_VOCAB_FILES_MAP
      a    :					Optional[Any]							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a    :					List[Any]							=      PRETRAINED_INIT_CONFIGURATION
      a    :					Dict							=      RetriBertTokenizer
      a    :					Any							=      ['input_ids', 'attention_mask']
      def __init__( self   :     Dict						, __lowercase   :     Optional[Any]=None						, __lowercase   :     Any=None						, __lowercase   :     Optional[int]=True						, __lowercase   :     Tuple="[UNK]"						, __lowercase   :     List[str]="[SEP]"						, __lowercase   :     Any="[PAD]"						, __lowercase   :     str="[CLS]"						, __lowercase   :     List[Any]="[MASK]"						, __lowercase   :     Tuple=True						, __lowercase   :     Optional[Any]=None						, **__lowercase   :     List[str]						, )				-> Optional[int]:
        super().__init__(
            __lowercase						, tokenizer_file=__lowercase						, do_lower_case=__lowercase						, unk_token=__lowercase						, sep_token=__lowercase						, pad_token=__lowercase						, cls_token=__lowercase						, mask_token=__lowercase						, tokenize_chinese_chars=__lowercase						, strip_accents=__lowercase						, **__lowercase						, )

        __UpperCAmelCase				:      List[str]	 =    json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
        if (
            normalizer_state.get("""lowercase"""						, __lowercase   ) != do_lower_case
            or normalizer_state.get("""strip_accents"""						, __lowercase   ) != strip_accents
            or normalizer_state.get("""handle_chinese_chars"""						, __lowercase   ) != tokenize_chinese_chars
        ):
          __UpperCAmelCase				:      List[str]	 =    getattr(__lowercase						, normalizer_state.pop("""type"""   )   )
          __UpperCAmelCase				:      str	 =    do_lower_case
          __UpperCAmelCase				:      Optional[int]	 =    strip_accents
          __UpperCAmelCase				:      Optional[int]	 =    tokenize_chinese_chars
          __UpperCAmelCase				:      List[str]	 =    normalizer_class(**__lowercase   )

        __UpperCAmelCase				:      str	 =    do_lower_case
      def       UpperCAmelCase ( self   :     Union[str, Any]						, __lowercase   :     Any						, __lowercase   :     Optional[int]=None   )				-> Union[str, Any]:
        __UpperCAmelCase				:      str	 =    [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   :     int						, __lowercase   :     List[int]						, __lowercase   :     Optional[List[int]] = None   )				-> List[int]:
        __UpperCAmelCase				:      str	 =    [self.sep_token_id]
        __UpperCAmelCase				:      int	 =    [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   :     List[Any]						, __lowercase   :     str						, __lowercase   :     Optional[str] = None   )				-> Tuple[str]:
        __UpperCAmelCase				:      Any	 =    self._tokenizer.model.save(__lowercase						, name=__lowercase   )
        return tuple(__lowercase   )



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		["""BertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						List[Any]				  =		[
      """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BertForMaskedLM""",
      """BertForMultipleChoice""",
      """BertForNextSentencePrediction""",
      """BertForPreTraining""",
      """BertForQuestionAnswering""",
      """BertForSequenceClassification""",
      """BertForTokenClassification""",
      """BertLayer""",
      """BertLMHeadModel""",
      """BertModel""",
      """BertPreTrainedModel""",
      """load_tf_weights_in_bert""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		[
      """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFBertEmbeddings""",
      """TFBertForMaskedLM""",
      """TFBertForMultipleChoice""",
      """TFBertForNextSentencePrediction""",
      """TFBertForPreTraining""",
      """TFBertForQuestionAnswering""",
      """TFBertForSequenceClassification""",
      """TFBertForTokenClassification""",
      """TFBertLMHeadModel""",
      """TFBertMainLayer""",
      """TFBertModel""",
      """TFBertPreTrainedModel""",
  ]
try:
  if not is_tensorflow_text_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		["""TFBertTokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		[
      """FlaxBertForCausalLM""",
      """FlaxBertForMaskedLM""",
      """FlaxBertForMultipleChoice""",
      """FlaxBertForNextSentencePrediction""",
      """FlaxBertForPreTraining""",
      """FlaxBertForQuestionAnswering""",
      """FlaxBertForSequenceClassification""",
      """FlaxBertForTokenClassification""",
      """FlaxBertModel""",
      """FlaxBertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
  from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_fast import BertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bert import (
        BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForNextSentencePrediction,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
        BertLayer,
        BertLMHeadModel,
        BertModel,
        BertPreTrainedModel,
        load_tf_weights_in_bert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bert import (
        TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBertEmbeddings,
        TFBertForMaskedLM,
        TFBertForMultipleChoice,
        TFBertForNextSentencePrediction,
        TFBertForPreTraining,
        TFBertForQuestionAnswering,
        TFBertForSequenceClassification,
        TFBertForTokenClassification,
        TFBertLMHeadModel,
        TFBertMainLayer,
        TFBertModel,
        TFBertPreTrainedModel,
    )

  try:
    if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_tf import TFBertTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bert import (
        FlaxBertForCausalLM,
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
        FlaxBertPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


