


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


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

_snake_case     :		Tuple						=				{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}

class 				a		(lowercase_     ,				lowercase_      ):




							"""simple docstring"""


							__UpperCAmelCase    :							List[str]       =  '''bit'''
							__UpperCAmelCase    :							Dict       =  ['''preactivation''', '''bottleneck''']
							__UpperCAmelCase    :							Any       =  ['''SAME''', '''VALID''']

							def __init__(							self       :  Optional[Any]    ,	lowerCamelCase       :  Union[str, Any]=3    ,	lowerCamelCase       :  Any=64    ,	lowerCamelCase       :  Union[str, Any]=[256, 512, 1024, 2048]    ,	lowerCamelCase       :  int=[3, 4, 6, 3]    ,	lowerCamelCase       :  Tuple="preactivation"    ,	lowerCamelCase       :  List[Any]="relu"    ,	lowerCamelCase       :  Any=None    ,	lowerCamelCase       :  str=32    ,	lowerCamelCase       :  List[Any]=0.0    ,	lowerCamelCase       :  List[Any]=False    ,	lowerCamelCase       :  str=32    ,	lowerCamelCase       :  str=1    ,	lowerCamelCase       :  Optional[Any]=None    ,	lowerCamelCase       :  List[Any]=None    ,	**lowerCamelCase       :  str    ,	)	->    Union[str, Any]:
														super().__init__(**lowerCamelCase				)
														if layer_type not in self.layer_types:
																					raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types				)}'				)
														if global_padding is not None:
																					if global_padding.upper() in self.supported_padding:
																												__snake_case		:			Any							 =   global_padding.upper()
																					else:
																												raise ValueError(F'Padding strategy {global_padding} not supported'				)
														__snake_case		:			Any							 =   num_channels
														__snake_case		:			Union[str, Any]							 =   embedding_size
														__snake_case		:			Optional[Any]							 =   hidden_sizes
														__snake_case		:			int							 =   depths
														__snake_case		:			Dict							 =   layer_type
														__snake_case		:			Tuple							 =   hidden_act
														__snake_case		:			Any							 =   global_padding
														__snake_case		:			str							 =   num_groups
														__snake_case		:			Optional[Any]							 =   drop_path_rate
														__snake_case		:			Any							 =   embedding_dynamic_padding
														__snake_case		:			Optional[int]							 =   output_stride
														__snake_case		:			Dict							 =   width_factor

														__snake_case		:			List[str]							 =   ["stem"] + [F'stage{idx}' for idx in range(1    ,	len(lowerCamelCase				) + 1				)]
														__snake_case ,			__snake_case		:			int							 =   get_aligned_output_features_output_indices(
														    out_features=lowerCamelCase    ,	out_indices=lowerCamelCase    ,	stage_names=self.stage_names				)




from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  lowercase				(       lowercase_			):




				@staticmethod
				@abstractmethod
				def     a							(					snake_case       ):
											raise NotImplementedError()




				@abstractmethod
				def     a							(					self       ):
											raise NotImplementedError()




