


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


_A		:				Union[str, Any]									=       logging.get_logger(__name__)

_A		:				List[Any]									=       {
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}



class        a					(			_UpperCAmelCase     ,_UpperCAmelCase   ):
     UpperCAmelCase__ :  Dict						      =	"focalnet"





     def __init__(    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Dict=224    ,	SCREAMING_SNAKE_CASE_					:				List[str]=4    ,	SCREAMING_SNAKE_CASE_					:				Any=3    ,	SCREAMING_SNAKE_CASE_					:				Any=96    ,	SCREAMING_SNAKE_CASE_					:				Tuple=False    ,	SCREAMING_SNAKE_CASE_					:				Any=[192, 384, 768, 768]    ,	SCREAMING_SNAKE_CASE_					:				Any=[2, 2, 6, 2]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=[2, 2, 2, 2]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=[3, 3, 3, 3]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]="gelu"    ,	SCREAMING_SNAKE_CASE_					:				int=4.0    ,	SCREAMING_SNAKE_CASE_					:				Any=0.0    ,	SCREAMING_SNAKE_CASE_					:				List[str]=0.1    ,	SCREAMING_SNAKE_CASE_					:				str=False    ,	SCREAMING_SNAKE_CASE_					:				Tuple=1E-4    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=False    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=False    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=False    ,	SCREAMING_SNAKE_CASE_					:				str=0.02    ,	SCREAMING_SNAKE_CASE_					:				int=1E-5    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=32    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=None    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=None    ,	**SCREAMING_SNAKE_CASE_					:				Any    ,	):
        super().__init__(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Dict     =	image_size
        __lowerCamelCase:				Optional[Any]     =	patch_size
        __lowerCamelCase:				List[str]     =	num_channels
        __lowerCamelCase:				int     =	embed_dim
        __lowerCamelCase:				int     =	use_conv_embed
        __lowerCamelCase:				List[str]     =	hidden_sizes
        __lowerCamelCase:				Optional[int]     =	depths
        __lowerCamelCase:				Tuple     =	focal_levels
        __lowerCamelCase:				Any     =	focal_windows
        __lowerCamelCase:				Optional[Any]     =	hidden_act
        __lowerCamelCase:				Tuple     =	mlp_ratio
        __lowerCamelCase:				List[str]     =	hidden_dropout_prob
        __lowerCamelCase:				Tuple     =	drop_path_rate
        __lowerCamelCase:				Optional[int]     =	use_layerscale
        __lowerCamelCase:				Optional[Any]     =	layerscale_value
        __lowerCamelCase:				Tuple     =	use_post_layernorm
        __lowerCamelCase:				List[str]     =	use_post_layernorm_in_modulation
        __lowerCamelCase:				str     =	normalize_modulator
        __lowerCamelCase:				Union[str, Any]     =	initializer_range
        __lowerCamelCase:				Tuple     =	layer_norm_eps
        __lowerCamelCase:				List[Any]     =	encoder_stride
        __lowerCamelCase:				List[Any]     =	["""stem"""] + [F'stage{idx}' for idx in range(1    ,	len(self.depths							) + 1							)]
        __lowerCamelCase:				Dict     =	get_aligned_output_features_output_indices(
            out_features=SCREAMING_SNAKE_CASE_    ,	out_indices=SCREAMING_SNAKE_CASE_    ,	stage_names=self.stage_names							)






def     __lowerCAmelCase						(							snake_case	:    int = 100 )       ->							int:
   __lowerCamelCase:				List[Any]     =	0
   __lowerCamelCase:				int     =	0
   for i in range(1						,      n + 1 ):
      sum_of_squares += i**2
      sum_of_ints += i
   return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
   print(F"""{solution() = }""")


