


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_A		:				Optional[Any]									=       {
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_A		:				Optional[Any]									=       logging.get_logger(__name__)



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Optional[Any]						      =	"maskformer"
     UpperCAmelCase__ :  Optional[Any]						      =	{"hidden_size": "mask_feature_size"}
     UpperCAmelCase__ :  int						      =	["resnet", "swin"]
     UpperCAmelCase__ :  List[str]						      =	["detr"]





     def __init__(    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int = 256    ,	SCREAMING_SNAKE_CASE_					:				int = 256    ,	SCREAMING_SNAKE_CASE_					:				float = 0.1    ,	SCREAMING_SNAKE_CASE_					:				bool = False    ,	SCREAMING_SNAKE_CASE_					:				Optional[Dict] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[Dict] = None    ,	SCREAMING_SNAKE_CASE_					:				float = 0.02    ,	SCREAMING_SNAKE_CASE_					:				float = 1.0    ,	SCREAMING_SNAKE_CASE_					:				float = 1.0    ,	SCREAMING_SNAKE_CASE_					:				float = 1.0    ,	SCREAMING_SNAKE_CASE_					:				float = 20.0    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	**SCREAMING_SNAKE_CASE_					:				List[str]    ,	):
        if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           __lowerCamelCase:				int     =	SwinConfig(
               image_size=384    ,	in_channels=3    ,	patch_size=4    ,	embed_dim=128    ,	depths=[2, 2, 18, 2]    ,	num_heads=[4, 8, 16, 32]    ,	window_size=12    ,	drop_path_rate=0.3    ,	out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    ,	)

        if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				str     =	backbone_config.pop("""model_type"""							)
           __lowerCamelCase:				Any     =	CONFIG_MAPPING[backbone_model_type]
           __lowerCamelCase:				Tuple     =	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 MaskFormer. '
               F'Supported model types: {",".join(self.backbones_supported							)}'							)

        if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           __lowerCamelCase:				Optional[Any]     =	DetrConfig()
        else:
           # verify that the decoder is supported
           __lowerCamelCase:				int     =	(
               decoder_config.pop("""model_type"""							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
              raise ValueError(
                  F'Transformer Decoder {decoder_type} not supported, please use one of'
                  F' {",".join(self.decoders_supported							)}'							)
           if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
              __lowerCamelCase:				Tuple     =	CONFIG_MAPPING[decoder_type]
              __lowerCamelCase:				List[Any]     =	config_class.from_dict(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	backbone_config
        __lowerCamelCase:				Optional[Any]     =	decoder_config
        # main feature dimension for the model
        __lowerCamelCase:				int     =	fpn_feature_size
        __lowerCamelCase:				Tuple     =	mask_feature_size
        # initializer
        __lowerCamelCase:				int     =	init_std
        __lowerCamelCase:				Optional[int]     =	init_xavier_std
        # Hungarian matcher && loss
        __lowerCamelCase:				Any     =	cross_entropy_weight
        __lowerCamelCase:				Union[str, Any]     =	dice_weight
        __lowerCamelCase:				List[str]     =	mask_weight
        __lowerCamelCase:				Optional[int]     =	use_auxiliary_loss
        __lowerCamelCase:				int     =	no_object_weight
        __lowerCamelCase:				Dict     =	output_auxiliary_logits

        __lowerCamelCase:				Any     =	self.decoder_config.encoder_attention_heads
        __lowerCamelCase:				Union[str, Any]     =	self.decoder_config.num_hidden_layers
        super().__init__(**SCREAMING_SNAKE_CASE_							)





     @classmethod
     def  SCREAMING_SNAKE_CASE__       (    cls					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				PretrainedConfig    ,	SCREAMING_SNAKE_CASE_					:				PretrainedConfig    ,	**SCREAMING_SNAKE_CASE_					:				Any							):
        return cls(
            backbone_config=SCREAMING_SNAKE_CASE_    ,	decoder_config=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_    ,	)







     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				Union[str, Any]     =	copy.deepcopy(self.__dict__							)
        __lowerCamelCase:				Union[str, Any]     =	self.backbone_config.to_dict()
        __lowerCamelCase:				Any     =	self.decoder_config.to_dict()
        __lowerCamelCase:				Optional[Any]     =	self.__class__.model_type
        return output






import math



def     __lowerCAmelCase						(							snake_case	:    int )       ->							bool:
   __lowerCamelCase:				Dict     =	math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )

   return exponent == int(snake_case )



def     __lowerCAmelCase						(							snake_case	:    float = 1 / 12345 )       ->							int:
   __lowerCamelCase:				str     =	0
   __lowerCamelCase:				Optional[Any]     =	0

   __lowerCamelCase:				str     =	3
   while True:
      __lowerCamelCase:				int     =	(integer**2 - 1) / 4
      # if candidate is an integer, then there is a partition for k
      if partition_candidate == int(snake_case ):
         __lowerCamelCase:				Union[str, Any]     =	int(snake_case )
         total_partitions += 1
         if check_partition_perfect(snake_case ):
            perfect_partitions += 1
      if perfect_partitions > 0:
         if perfect_partitions / total_partitions < max_proportion:
            return int(snake_case )
      integer += 1


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


