


'''simple docstring'''
def 			a      (			__a ,					__a   )  ->	int:



					'''simple docstring'''






					if len(__a   ) != len(__a   ):
										raise ValueError('''String lengths must match!'''   )

					UpperCamelCase__   :Union[str, Any]												= 0

					for chara, chara in zip(__a ,					__a   ):
										if chara != chara:
															count += 1

					return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()




"""simple docstring"""






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


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



class 			A__   (  __SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'maskformer-swin'

	SCREAMING_SNAKE_CASE          =	{
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		int=224       ,			_SCREAMING_SNAKE_CASE:		Tuple=4       ,			_SCREAMING_SNAKE_CASE:		int=3       ,			_SCREAMING_SNAKE_CASE:		List[Any]=96       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=[2, 2, 6, 2]       ,			_SCREAMING_SNAKE_CASE:		Any=[3, 6, 12, 24]       ,			_SCREAMING_SNAKE_CASE:		List[str]=7       ,			_SCREAMING_SNAKE_CASE:		List[str]=4.0       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=True       ,			_SCREAMING_SNAKE_CASE:		Tuple=0.0       ,			_SCREAMING_SNAKE_CASE:		Any=0.0       ,			_SCREAMING_SNAKE_CASE:		Any=0.1       ,			_SCREAMING_SNAKE_CASE:		str="gelu"       ,			_SCREAMING_SNAKE_CASE:		Tuple=False       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=0.02       ,			_SCREAMING_SNAKE_CASE:		str=1e-5       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=None       ,			_SCREAMING_SNAKE_CASE:		str=None       ,			**_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			)				->       List[str]:

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  int												=       image_size
				__lowerCAmelCase      :  Any												=       patch_size
				__lowerCAmelCase      :  Tuple												=       num_channels
				__lowerCAmelCase      :  Any												=       embed_dim
				__lowerCAmelCase      :  Any												=       depths
				__lowerCAmelCase      :  Dict												=       len(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       num_heads
				__lowerCAmelCase      :  Tuple												=       window_size
				__lowerCAmelCase      :  Dict												=       mlp_ratio
				__lowerCAmelCase      :  Any												=       qkv_bias
				__lowerCAmelCase      :  Union[str, Any]												=       hidden_dropout_prob
				__lowerCAmelCase      :  int												=       attention_probs_dropout_prob
				__lowerCAmelCase      :  Tuple												=       drop_path_rate
				__lowerCAmelCase      :  int												=       hidden_act
				__lowerCAmelCase      :  Optional[int]												=       use_absolute_embeddings
				__lowerCAmelCase      :  List[str]												=       layer_norm_eps
				__lowerCAmelCase      :  Any												=       initializer_range
				# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				__lowerCAmelCase      :  Optional[Any]												=       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE) - 1))
				__lowerCAmelCase      :  Any												=       ["stem"] + [F"""stage{idx}""" for idx in range(1       ,			len(_SCREAMING_SNAKE_CASE) + 1)]
				__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       get_aligned_output_features_output_indices(
				    out_features=_SCREAMING_SNAKE_CASE       ,			out_indices=_SCREAMING_SNAKE_CASE       ,			stage_names=self.stage_names)