
'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase			:				Any    		=			{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Any    		=			['SqueezeBertTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Optional[Any]    		=			[
          'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SqueezeBertForMaskedLM',
          'SqueezeBertForMultipleChoice',
          'SqueezeBertForQuestionAnswering',
          'SqueezeBertForSequenceClassification',
          'SqueezeBertForTokenClassification',
          'SqueezeBertModel',
          'SqueezeBertModule',
          'SqueezeBertPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_squeezebert import (
          SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SqueezeBertConfig,
          SqueezeBertOnnxConfig,
      )
      from .tokenization_squeezebert import SqueezeBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_squeezebert import (
                SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SqueezeBertForMaskedLM,
                SqueezeBertForMultipleChoice,
                SqueezeBertForQuestionAnswering,
                SqueezeBertForSequenceClassification,
                SqueezeBertForTokenClassification,
                SqueezeBertModel,
                SqueezeBertModule,
                SqueezeBertPreTrainedModel,
            )

else:
      import sys

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






'''simple docstring'''




from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class  A				(							__snake_case						):
     __magic_name__						=		DistilBertTokenizer
     __magic_name__						=		DistilBertTokenizerFast
     __magic_name__						=		True





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   List[Any]					=						DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''	)

         A       :   Dict					=						tokenizer.encode('''sequence builders'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						tokenizer.encode('''multi-sequence build'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]




