

'''simple docstring'''






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[str]							=			['image_processor', 'tokenizer']
  SCREAMING_SNAKE_CASE    :  List[Any]							=			'AutoImageProcessor'
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			'AutoTokenizer'






  def __init__(      self   :     Dict					,lowercase__   :     int					,lowercase__   :     List[Any]			):
   super().__init__(lowercase__					,lowercase__			)
   __lowercase							  = self.image_processor






  def __call__(      self   :     int					,lowercase__   :     Dict=None					,lowercase__   :     int=None					,lowercase__   :     str=None					,**lowercase__   :     str			):

   if text is None and images is None:
    raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

   if text is not None:
    __lowercase							  = self.tokenizer(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if images is not None:
    __lowercase							  = self.image_processor(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if text is not None and images is not None:
    __lowercase							  = image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**lowercase__			)					,tensor_type=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,*lowercase__   :     Union[str, Any]					,**lowercase__   :     Optional[Any]			):
   return self.tokenizer.batch_decode(*lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,*lowercase__   :     Optional[int]					,**lowercase__   :     Tuple			):
   return self.tokenizer.decode(*lowercase__					,**lowercase__			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   return ["input_ids", "attention_mask", "pixel_values"]




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}






class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[str]							=			'convnextv2'






  def __init__(      self   :     Tuple					,lowercase__   :     Dict=3					,lowercase__   :     int=4					,lowercase__   :     Optional[int]=4					,lowercase__   :     Optional[Any]=None					,lowercase__   :     List[str]=None					,lowercase__   :     Tuple="gelu"					,lowercase__   :     Dict=0.0_2					,lowercase__   :     List[Any]=1e-1_2					,lowercase__   :     Optional[int]=0.0					,lowercase__   :     int=2_2_4					,lowercase__   :     int=None					,lowercase__   :     Tuple=None					,**lowercase__   :     List[str]					,):
   super().__init__(**lowercase__			)

   __lowercase							  = num_channels
   __lowercase							  = patch_size
   __lowercase							  = num_stages
   __lowercase							  = [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
   __lowercase							  = [3, 3, 9, 3] if depths is None else depths
   __lowercase							  = hidden_act
   __lowercase							  = initializer_range
   __lowercase							  = layer_norm_eps
   __lowercase							  = drop_path_rate
   __lowercase							  = image_size
   __lowercase							  = ['''stem'''] + [F"stage{idx}" for idx in range(1					,len(self.depths			) + 1			)]
   __lowercase  ,	__lowercase							  = get_aligned_output_features_output_indices(
       out_features=lowercase__					,out_indices=lowercase__					,stage_names=self.stage_names			)

