






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__)


class       __lowercase      (	lowercase_			,      lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				"maskformer-swin"

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




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


         """simple docstring"""



         super().__init__(**UpperCamelCase_				)

         __A         =	image_size
         __A         =	patch_size
         __A         =	num_channels
         __A         =	embed_dim
         __A         =	depths
         __A         =	len(UpperCamelCase_				)
         __A         =	num_heads
         __A         =	window_size
         __A         =	mlp_ratio
         __A         =	qkv_bias
         __A         =	hidden_dropout_prob
         __A         =	attention_probs_dropout_prob
         __A         =	drop_path_rate
         __A         =	hidden_act
         __A         =	use_absolute_embeddings
         __A         =	layer_norm_eps
         __A         =	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
         __A         =	int(embed_dim * 2 ** (len(UpperCamelCase_				) - 1)				)
         __A         =	["""stem"""] + [F"stage{idx}" for idx in range(1 ,	len(UpperCamelCase_				) + 1				)]
         __A    ,	__A         =	get_aligned_output_features_output_indices(
             out_features=UpperCamelCase_ ,	out_indices=UpperCamelCase_ ,	stage_names=self.stage_names				)












def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Dict			,				__lowercase   :			str    )						->      Dict:





    """simple docstring"""

    if n == 0:
        return 1

    elif n % 2 == 1:
        return (binary_exponentiation(__lowercase			,				n - 1			,				__lowercase    ) * a) % mod

    else:
        __A         =	binary_exponentiation(__lowercase			,				n / 2			,				__lowercase    )
        return (b * b) % mod


# a prime number
__a      :       Union[str, Any]				  =   701

__a      :       Dict				  =   1000000000
__a      :       Union[str, Any]				  =   10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)






print((a / b) % p == (a * b ** (p - 2)) % p)




