





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCamelCase_           =logging.get_logger(__name__)

UpperCamelCase_           ={
    """facebook/convnext-tiny-224""": """https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json""",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}




class  _a						(      _lowerCAmelCase	,				_lowerCAmelCase				):
				UpperCamelCase								=    '''convnext'''






				def __init__( self		:    List[Any], lowerCAmelCase__		:    Optional[int]=3, lowerCAmelCase__		:    Any=4, lowerCAmelCase__		:    Optional[Any]=4, lowerCAmelCase__		:    Optional[Any]=None, lowerCAmelCase__		:    int=None, lowerCAmelCase__		:    str="gelu", lowerCAmelCase__		:    Any=0.02, lowerCAmelCase__		:    Tuple=1e-1_2, lowerCAmelCase__		:    Optional[Any]=1e-6, lowerCAmelCase__		:    Optional[Any]=0.0, lowerCAmelCase__		:    Tuple=2_2_4, lowerCAmelCase__		:    Optional[int]=None, lowerCAmelCase__		:    List[str]=None, **lowerCAmelCase__		:    Optional[int], )	->      Any:




										'''simple docstring'''



										super().__init__(**lowerCAmelCase__							)

										_UpperCamelCase			:					Tuple     =    num_channels
										_UpperCamelCase			:					Dict     =    patch_size
										_UpperCamelCase			:					Optional[int]     =    num_stages
										_UpperCamelCase			:					List[Any]     =    [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
										_UpperCamelCase			:					Any     =    [3, 3, 9, 3] if depths is None else depths
										_UpperCamelCase			:					List[str]     =    hidden_act
										_UpperCamelCase			:					List[str]     =    initializer_range
										_UpperCamelCase			:					Union[str, Any]     =    layer_norm_eps
										_UpperCamelCase			:					List[str]     =    layer_scale_init_value
										_UpperCamelCase			:					Optional[int]     =    drop_path_rate
										_UpperCamelCase			:					List[Any]     =    image_size
										_UpperCamelCase			:					Tuple     =    ['''stem'''] + [f"""stage{idx}""" for idx in range(1, len(self.depths							) + 1							)]
										_UpperCamelCase   ,							_UpperCamelCase			:					Tuple     =    get_aligned_output_features_output_indices(
										    out_features=lowerCAmelCase__, out_indices=lowerCAmelCase__, stage_names=self.stage_names							)




class  _a						(      _lowerCAmelCase				):
				UpperCamelCase								=    version.parse('''1.11'''				)






				@property
				def 				snake_case	( self		:    Dict							)	->      Mapping[str, Mapping[int, str]]:




										'''simple docstring'''



										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										    ]							)






				@property
				def 				snake_case	( self		:    List[str]							)	->      float:




										'''simple docstring'''



										return 1e-5










"""simple docstring"""




from __future__ import annotations





def        a_				(							_lowercase   ):
						assert (
						    isinstance(_lowercase       ,						_lowercase   ) and mask > 0
						), F"""mask needs to be positive integer, your input {mask}"""
						_UpperCamelCase			:					Union[str, Any]     =    []
						_UpperCamelCase			:					str     =    mask

						while submask:
												all_submasks.append(_lowercase   )
												_UpperCamelCase			:					int     =    (submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()



