
"""simple docstring"""






import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__ :       int						     =			{
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

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




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """mask2former"""
	UpperCamelCase				=  ["""swin"""]
	UpperCamelCase				=  {"""hidden_size""": """hidden_dim"""}





	def __init__(						self    :Optional[Any]		, lowerCamelCase_    :Optional[Dict] = None		, lowerCamelCase_    :int = 2_56		, lowerCamelCase_    :int = 2_56		, lowerCamelCase_    :int = 2_56		, lowerCamelCase_    :int = 10_24		, lowerCamelCase_    :str = "relu"		, lowerCamelCase_    :int = 6		, lowerCamelCase_    :int = 10		, lowerCamelCase_    :int = 8		, lowerCamelCase_    :float = 0.0		, lowerCamelCase_    :int = 20_48		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :int = 4		, lowerCamelCase_    :int = 2_55		, lowerCamelCase_    :int = 1_00		, lowerCamelCase_    :float = 0.1		, lowerCamelCase_    :float = 2.0		, lowerCamelCase_    :float = 5.0		, lowerCamelCase_    :float = 5.0		, lowerCamelCase_    :int = 1_25_44		, lowerCamelCase_    :float = 3.0		, lowerCamelCase_    :float = 0.7_5		, lowerCamelCase_    :float = 0.0_2		, lowerCamelCase_    :float = 1.0		, lowerCamelCase_    :bool = True		, lowerCamelCase_    :List[int] = [4, 8, 16, 32]		, lowerCamelCase_    :bool = None		, **lowerCamelCase_    :int		, )							->			List[str]:







			'''simple docstring'''




			if backbone_config is None:
					logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''					)
					SCREAMING_SNAKE_CASE       :     Any				= CONFIG_MAPPING['''swin'''](
					    image_size=2_24		, in_channels=3		, patch_size=4		, embed_dim=96		, depths=[2, 2, 18, 2]		, num_heads=[3, 6, 12, 24]		, window_size=7		, drop_path_rate=0.3		, use_absolute_embeddings=lowerCamelCase_		, out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		, )

			if isinstance(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Any				= backbone_config.pop('''model_type'''					)
					SCREAMING_SNAKE_CASE       :     str				= CONFIG_MAPPING[backbone_model_type]
					SCREAMING_SNAKE_CASE       :     List[str]				= config_class.from_dict(lowerCamelCase_					)

			# verify that the backbone is supported
			if backbone_config.model_type not in self.backbones_supported:
					logger.warning_once(
					    f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
					    f"Supported model types: {','.join(self.backbones_supported					)}"					)

			SCREAMING_SNAKE_CASE       :     Any				= backbone_config
			SCREAMING_SNAKE_CASE       :     str				= feature_size
			SCREAMING_SNAKE_CASE       :     Any				= mask_feature_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= hidden_dim
			SCREAMING_SNAKE_CASE       :     List[Any]				= encoder_feedforward_dim
			SCREAMING_SNAKE_CASE       :     str				= activation_function
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= encoder_layers
			SCREAMING_SNAKE_CASE       :     str				= decoder_layers
			SCREAMING_SNAKE_CASE       :     int				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     List[str]				= dropout
			SCREAMING_SNAKE_CASE       :     List[Any]				= dim_feedforward
			SCREAMING_SNAKE_CASE       :     Any				= pre_norm
			SCREAMING_SNAKE_CASE       :     List[str]				= enforce_input_projection
			SCREAMING_SNAKE_CASE       :     Dict				= common_stride
			SCREAMING_SNAKE_CASE       :     Dict				= ignore_value
			SCREAMING_SNAKE_CASE       :     List[str]				= num_queries
			SCREAMING_SNAKE_CASE       :     List[str]				= no_object_weight
			SCREAMING_SNAKE_CASE       :     List[Any]				= class_weight
			SCREAMING_SNAKE_CASE       :     Dict				= mask_weight
			SCREAMING_SNAKE_CASE       :     Any				= dice_weight
			SCREAMING_SNAKE_CASE       :     Dict				= train_num_points
			SCREAMING_SNAKE_CASE       :     Optional[int]				= oversample_ratio
			SCREAMING_SNAKE_CASE       :     Dict				= importance_sample_ratio
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= init_std
			SCREAMING_SNAKE_CASE       :     List[Any]				= init_xavier_std
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_auxiliary_loss
			SCREAMING_SNAKE_CASE       :     List[Any]				= feature_strides
			SCREAMING_SNAKE_CASE       :     Dict				= output_auxiliary_logits
			SCREAMING_SNAKE_CASE       :     str				= decoder_layers

			super().__init__(**lowerCamelCase_					)





	@classmethod
	def   __lowerCAmelCase (						cls    :int		, lowerCamelCase_    :PretrainedConfig		, **lowerCamelCase_    :int					)							->			Union[str, Any]:







			'''simple docstring'''




			return cls(
			    backbone_config=lowerCamelCase_		, **lowerCamelCase_		, )







	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Dict[str, any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= copy.deepcopy(self.__dict__					)
			SCREAMING_SNAKE_CASE       :     List[str]				= self.backbone_config.to_dict()
			SCREAMING_SNAKE_CASE       :     str				= self.__class__.model_type
			return output







"""simple docstring"""






from __future__ import annotations

from fractions import Fraction





def __A						(   a_ :							int		,		a_ :							int     )->							bool:




		'''simple docstring'''






		return (
		    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
		)





def __A						(   a_ :							int     )->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
		SCREAMING_SNAKE_CASE       :     List[str]				= 11
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= int('''1''' + '''0''' * digit_len     )
		for num in range(a_		,		a_     ):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(a_		,		a_     ):
										solutions.append(F"{num}/{den}"     )
						den += 1
				num += 1
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10
		return solutions





def __A						(   a_ :							int = 2     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 1.0
		for fraction in fraction_list(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= Fraction(a_     )
				result *= frac.denominator / frac.numerator
		return int(a_     )


if __name__ == "__main__":
					print(solution())





