




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


lowerCamelCase	:	str				=   logging.get_logger(__name__)

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

lowerCamelCase	:	int				=   {
    'vocab_file': {'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'},
    'tokenizer_file': {
        'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'
    },
}

lowerCamelCase	:	Tuple				=   {'mobilebert-uncased': 5_1_2}


lowerCamelCase	:	Tuple				=   {}




class 						__lowercase		(_UpperCAmelCase							):




      """simple docstring"""





      _snake_case												=						VOCAB_FILES_NAMES
      _snake_case												=						PRETRAINED_VOCAB_FILES_MAP
      _snake_case												=						PRETRAINED_INIT_CONFIGURATION
      _snake_case												=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case												=						MobileBertTokenizer

      def __init__(				self    ,				A=None    ,				A=None    ,				A=True    ,				A="[UNK]"    ,				A="[SEP]"    ,				A="[PAD]"    ,				A="[CLS]"    ,				A="[MASK]"    ,				A=True    ,				A=None    ,				**A    ,				)		->    List[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    ,				)

         snake_case				:    Optional[Any]           =	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
         ):
            snake_case				:    Optional[Any]           =	getattr(_UpperCAmelCase    ,				normalizer_state.pop("""type"""     )     )
            snake_case				:    Any           =	do_lower_case
            snake_case				:    List[Any]           =	strip_accents
            snake_case				:    List[Any]           =	tokenize_chinese_chars
            snake_case				:    Tuple           =	normalizer_class(**_UpperCAmelCase     )

         snake_case				:    Dict           =	do_lower_case

      def      UpperCAmelCase				(				self    ,				A    ,				A=None     )		->    Optional[int]:
         snake_case				:    Any           =	[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    ,				A    ,				A = None     )		->    int:
         snake_case				:    Union[str, Any]           =	[self.sep_token_id]
         snake_case				:    Optional[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    ,				A    ,				A = None     )		->    str:
         snake_case				:    Optional[Any]           =	self._tokenizer.model.save(_UpperCAmelCase    ,				name=_UpperCAmelCase     )
         return tuple(_UpperCAmelCase     )











from typing import TYPE_CHECKING

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


lowerCamelCase	:	Optional[Any]				=   {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Tuple				=   ['RobertaTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[str]				=   [
           'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaForCausalLM',
           'RobertaForMaskedLM',
           'RobertaForMultipleChoice',
           'RobertaForQuestionAnswering',
           'RobertaForSequenceClassification',
           'RobertaForTokenClassification',
           'RobertaModel',
           'RobertaPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaForCausalLM',
           'TFRobertaForMaskedLM',
           'TFRobertaForMultipleChoice',
           'TFRobertaForQuestionAnswering',
           'TFRobertaForSequenceClassification',
           'TFRobertaForTokenClassification',
           'TFRobertaMainLayer',
           'TFRobertaModel',
           'TFRobertaPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'FlaxRobertaForCausalLM',
           'FlaxRobertaForMaskedLM',
           'FlaxRobertaForMultipleChoice',
           'FlaxRobertaForQuestionAnswering',
           'FlaxRobertaForSequenceClassification',
           'FlaxRobertaForTokenClassification',
           'FlaxRobertaModel',
           'FlaxRobertaPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

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





