code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import glob
  import os
  
  from get_test_info import get_tester_classes
  
  
  if __name__ == "__main__":
      A_			  =    []
  
      A_			  =    os.path.join("tests", "models", "**", "test_modeling_*.py")
      A_			  =    glob.glob(pattern)
      # TODO: deal with TF/Flax too
      A_			  =    [
          x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
      ]
  
      for test_file in test_files:
          A_			  =    get_tester_classes(test_file)
          for tester_class in tester_classes:
              # A few tester classes don't have `parent` parameter in `__init__`.
              # TODO: deal this better
              try:
                  A_			  =    tester_class(parent=None)
              except Exception:
                  continue
              if hasattr(tester, "get_config"):
                  A_			  =    tester.get_config()
                  for k, v in config.to_dict().items():
                      if isinstance(v, int):
                          A_			  =    None
                          if k in ["vocab_size"]:
                              A_			  =    100
                          elif k in ["max_position_embeddings"]:
                              A_			  =    128
                          elif k in ["hidden_size", "d_model"]:
                              A_			  =    40
                          elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                              A_			  =    5
                          if target is not None and v > target:
                              failures.append(
                                  f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
                                  f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
                                  f''' than {target}.'''
                              )
  
      if len(failures) > 0:
          raise Exception(f'''There were {len(failures)} failures:\n''' + "\n".join(failures))
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import warnings
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    Optional[Any]     	=			["image_processor", "tokenizer"]
    __lowerCamelCase       :    List[str]     	=			"VideoMAEImageProcessor"
    __lowerCamelCase       :    Dict     	=			("CLIPTokenizer", "CLIPTokenizerFast")
    def __init__(      self:		str				,			UpperCamelCase_:		Dict=None				,			UpperCamelCase_:		str=None				,			**UpperCamelCase_:		str					):
       UpperCamelCase_     =None
       if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."				,			UpperCamelCase_				,			)
          UpperCamelCase_     =kwargs.pop("feature_extractor"					)
  
       UpperCamelCase_     =image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
          raise ValueError("You need to specify an `image_processor`."					)
       if tokenizer is None:
          raise ValueError("You need to specify a `tokenizer`."					)
  
       super().__init__(UpperCamelCase_				,			UpperCamelCase_					)
       UpperCamelCase_     =self.image_processor
    def __call__(      self:		Tuple				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		Optional[Any]=None				,			**UpperCamelCase_:		int					):
  
       if text is None and videos is None:
          raise ValueError("You have to specify either text or videos. Both cannot be none."					)
  
       if text is not None:
          UpperCamelCase_     =self.tokenizer(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)
  
       if videos is not None:
          UpperCamelCase_     =self.image_processor(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)
  
       if text is not None and videos is not None:
          UpperCamelCase_     =image_features.pixel_values
          return encoding
       elif text is not None:
          return encoding
       else:
          return BatchEncoding(data=dict(**UpperCamelCase_					)				,			tensor_type=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Any				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Optional[int]					):
       return self.tokenizer.batch_decode(*UpperCamelCase_				,			**UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Optional[Any]				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Tuple					):
       return self.tokenizer.decode(*UpperCamelCase_				,			**UpperCamelCase_					)
    @property
    def 					UpperCamelCase__   (      self:		List[Any]					):
       return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
    @property
    def 					UpperCamelCase__   (      self:		str					):
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			UpperCamelCase_				,			)
       return self.image_processor_class
  
  
  
  
  
    @property
    def 					UpperCamelCase__   (      self:		Optional[Any]					):
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			UpperCamelCase_				,			)
       return self.image_processor
  