



"""simple docstring"""






import copy
from typing import Dict, List, Optional

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


__snake_case							:     Any   =					{
    '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
}

__snake_case							:     Any   =					logging.get_logger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'mask2former'
	SCREAMING_SNAKE_CASE					   =			['swin']
	SCREAMING_SNAKE_CASE					   =			{'hidden_size': 'hidden_dim'}

	def __init__(  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[Dict] = None ,    _SCREAMING_SNAKE_CASE:	int = 256 ,    _SCREAMING_SNAKE_CASE:	int = 256 ,    _SCREAMING_SNAKE_CASE:	int = 256 ,    _SCREAMING_SNAKE_CASE:	int = 1024 ,    _SCREAMING_SNAKE_CASE:	str = "relu" ,    _SCREAMING_SNAKE_CASE:	int = 6 ,    _SCREAMING_SNAKE_CASE:	int = 10 ,    _SCREAMING_SNAKE_CASE:	int = 8 ,    _SCREAMING_SNAKE_CASE:	float = 0.0 ,    _SCREAMING_SNAKE_CASE:	int = 2048 ,    _SCREAMING_SNAKE_CASE:	bool = False ,    _SCREAMING_SNAKE_CASE:	bool = False ,    _SCREAMING_SNAKE_CASE:	int = 4 ,    _SCREAMING_SNAKE_CASE:	int = 255 ,    _SCREAMING_SNAKE_CASE:	int = 100 ,    _SCREAMING_SNAKE_CASE:	float = 0.1 ,    _SCREAMING_SNAKE_CASE:	float = 2.0 ,    _SCREAMING_SNAKE_CASE:	float = 5.0 ,    _SCREAMING_SNAKE_CASE:	float = 5.0 ,    _SCREAMING_SNAKE_CASE:	int = 1_2544 ,    _SCREAMING_SNAKE_CASE:	float = 3.0 ,    _SCREAMING_SNAKE_CASE:	float = 0.75 ,    _SCREAMING_SNAKE_CASE:	float = 0.02 ,    _SCREAMING_SNAKE_CASE:	float = 1.0 ,    _SCREAMING_SNAKE_CASE:	bool = True ,    _SCREAMING_SNAKE_CASE:	List[int] = [4, 8, 16, 32] ,    _SCREAMING_SNAKE_CASE:	bool = None ,    **_SCREAMING_SNAKE_CASE:	List[Any] ,    )       -> Union[str, Any]:

				"""simple docstring"""

				if backbone_config is None:
							logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
							__lowerCAmelCase   :						Union[str, Any]				= CONFIG_MAPPING["swin"](
							    image_size=224 ,    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=_SCREAMING_SNAKE_CASE ,    out_features=["stage1", "stage2", "stage3", "stage4"] ,    )

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Tuple				= backbone_config.pop("model_type")
							__lowerCAmelCase   :						Optional[int]				= CONFIG_MAPPING[backbone_model_type]
							__lowerCAmelCase   :						Union[str, Any]				= config_class.from_dict(_SCREAMING_SNAKE_CASE)

				# 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)}""")

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

				super().__init__(**_SCREAMING_SNAKE_CASE)

	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	PretrainedConfig ,    **_SCREAMING_SNAKE_CASE:	Optional[Any])       -> Optional[int]:

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Dict[str, any]:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= copy.deepcopy(self.__dict__)
				__lowerCAmelCase   :						Tuple				= self.backbone_config.to_dict()
				__lowerCAmelCase   :						str				= self.__class__.model_type
				return output




"""simple docstring"""






def     _lowercase       (						__snake_case = 3      ,__snake_case = 7      ,__snake_case = 1_000_000			)		->     int:
			__lowerCAmelCase   :						Optional[Any]				= 0
			__lowerCAmelCase   :						List[str]				= 1

			for current_denominator in range(1      ,limit + 1			):
						__lowerCAmelCase   :						Optional[int]				= current_denominator * numerator // denominator
						if current_denominator % denominator == 0:
									current_numerator -= 1
						if current_numerator * max_denominator > current_denominator * max_numerator:
									__lowerCAmelCase   :						Union[str, Any]				= current_numerator
									__lowerCAmelCase   :						int				= current_denominator
			return max_numerator


if __name__ == "__main__":
	print(solution(numerator=3, denominator=7, limit=1_000_000))