






from typing import TYPE_CHECKING

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


__A					=	{
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __A					=	["MobileNetV2FeatureExtractor"]
   __A					=	["MobileNetV2ImageProcessor"]


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __A					=	[
       "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MobileNetV2ForImageClassification",
       "MobileNetV2ForSemanticSegmentation",
       "MobileNetV2Model",
       "MobileNetV2PreTrainedModel",
       "load_tf_weights_in_mobilenet_v2",
   ]


if TYPE_CHECKING:
   from .configuration_mobilenet_va import (
       MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileNetVaConfig,
       MobileNetVaOnnxConfig,
   )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
      from .image_processing_mobilenet_va import MobileNetVaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilenet_va import (
          MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileNetVaForImageClassification,
          MobileNetVaForSemanticSegmentation,
          MobileNetVaModel,
          MobileNetVaPreTrainedModel,
          load_tf_weights_in_mobilenet_va,
      )


else:
   import sys

   __A					=	_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     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Optional[int]			  =    DistilBertTokenizer
 __a      :    Union[str, Any]			  =    DistilBertTokenizerFast
 __a      :    Any			  =    True
 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""			)

       UpperCamelCase_					: Dict            =				tokenizer.encode("""sequence builders"""      ,  add_special_tokens=__lowerCAmelCase			)
       UpperCamelCase_					: int            =				tokenizer.encode("""multi-sequence build"""      ,  add_special_tokens=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase      ,  __lowerCAmelCase			)

       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
       ]






