


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


A	:		Tuple    =       logging.get_logger(__name__)

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



class 							A   (	A__			,  A__							):
					'''simple docstring'''






					A__      =			"convnextv2"

					def __init__(self   :						List[Any]				, _UpperCAmelCase   :						int=3				, _UpperCAmelCase   :						str=4				, _UpperCAmelCase   :						Optional[int]=4				, _UpperCAmelCase   :						Optional[Any]=None				, _UpperCAmelCase   :						str=None				, _UpperCAmelCase   :						Optional[Any]="gelu"				, _UpperCAmelCase   :						Tuple=0.02				, _UpperCAmelCase   :						List[Any]=1E-1_2				, _UpperCAmelCase   :						Dict=0.0				, _UpperCAmelCase   :						Optional[Any]=224				, _UpperCAmelCase   :						Tuple=None				, _UpperCAmelCase   :						List[str]=None				, **_UpperCAmelCase   :						List[Any]				, )						->       Any:




							"""simple docstring"""


							super().__init__(**__A		)

							lowercase__					      =		num_channels
							lowercase__					      =		patch_size
							lowercase__					      =		num_stages
							lowercase__					      =		[96, 192, 384, 768] 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__					      =		get_aligned_output_features_output_indices(
							    out_features=__A				, out_indices=__A				, stage_names=self.stage_names		)





import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


A	:		List[Any]    =       logging.get_logger(__name__)



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self   :						List[Any]				, *_UpperCAmelCase   :						int				, **_UpperCAmelCase   :						List[str]		)						->       None:




							"""simple docstring"""


							warnings.warn(
							    """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
							    """ use CLIPImageProcessor instead."""				, _UpperCAmelCase				, )
							super().__init__(*_UpperCAmelCase				, **_UpperCAmelCase		)

