
"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A      :	Optional[int]       		=	logging.get_logger(__name__)

_A      :	List[Any]       		=	{
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """regnet"""
   __lowerCAmelCase						=    ["""x""", """y"""]
   def __init__(      self     ,   _a=3     ,   _a=32     ,   _a=[128, 192, 512, 1_088]     ,   _a=[2, 6, 12, 2]     ,   _a=64     ,   _a="y"     ,   _a="relu"     ,   **_a     ,   ):
          super().__init__(**_a    )
          if layer_type not in self.layer_types:
                 raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types    )}"""    )
          lowercase   :    int			=							num_channels
          lowercase   :    str			=							embedding_size
          lowercase   :    Dict			=							hidden_sizes
          lowercase   :    Tuple			=							depths
          lowercase   :    Union[str, Any]			=							groups_width
          lowercase   :    List[Any]			=							layer_type
          lowercase   :    List[Any]			=							hidden_act
          # always downsample in the first stage
          lowercase   :    Any			=							True




"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_A      :	List[str]       		=	logging.get_logger(__name__)

class 		a__						(				a_						):
   def __init__(      self     ,   *_a     ,   **_a    ):
          warnings.warn(
              "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use ConvNextImageProcessor instead."     ,   _a     ,   )
          super().__init__(*_a     ,   **_a    )


