



'''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_mobilebert import MobileBertTokenizer


A_							: Dict						 =	logging.get_logger(__name__)

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

A_							: Optional[Any]						 =	{
    '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'
    },
}

A_							: Any						 =	{'mobilebert-uncased': 5_1_2}


A_							: str						 =	{}



class 							lowercase       (  UpperCamelCase_				):


 """simple docstring"""


 UpperCAmelCase							=  VOCAB_FILES_NAMES
 UpperCAmelCase							=  PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase							=  PRETRAINED_INIT_CONFIGURATION
 UpperCAmelCase							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase							=  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_  ,)		->   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__  ,)

        _UpperCAmelCase  :						Dict			     = 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
        ):
               _UpperCAmelCase  :						Optional[int]			     = getattr(UpperCamelCase__  ,normalizer_state.pop("""type"""							)							)
               _UpperCAmelCase  :						Dict			     = do_lower_case
               _UpperCAmelCase  :						List[str]			     = strip_accents
               _UpperCAmelCase  :						List[Any]			     = tokenize_chinese_chars
               _UpperCAmelCase  :						Any			     = normalizer_class(**UpperCamelCase__							)

        _UpperCAmelCase  :						Any			     = do_lower_case







 def _snake_case (							self  ,a_  ,a_=None							)		->   Any:
        _UpperCAmelCase  :						int			     = [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 _snake_case (							self  ,a_  ,a_ = None							)		->   str:
        _UpperCAmelCase  :						Optional[Any]			     = [self.sep_token_id]
        _UpperCAmelCase  :						Any			     = [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 _snake_case (							self  ,a_  ,a_ = None							)		->   str:
        _UpperCAmelCase  :						Dict			     = self._tokenizer.model.save(UpperCamelCase__  ,name=UpperCamelCase__							)
        return tuple(UpperCamelCase__							)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__UpperCamelCase		:       Tuple									=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     [
					    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SwiftFormerForImageClassification',
					    'SwiftFormerModel',
					    'SwiftFormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       List[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





