






"""simple docstring"""

import copy
from typing import Dict, List, Optional

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


lowercase    			=       {
    '''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
}

lowercase    			=       logging.get_logger(__name__)

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''mask2former'''
				UpperCAmelCase__        =				['''swin''']
				UpperCAmelCase__        =				{'''hidden_size''': '''hidden_dim'''}







				def __init__(							self							:						Optional[int]						,    __lowerCamelCase							:						Optional[Dict] = None						,    __lowerCamelCase							:						int = 2_5_6						,    __lowerCamelCase							:						int = 2_5_6						,    __lowerCamelCase							:						int = 2_5_6						,    __lowerCamelCase							:						int = 1_0_2_4						,    __lowerCamelCase							:						str = "relu"						,    __lowerCamelCase							:						int = 6						,    __lowerCamelCase							:						int = 1_0						,    __lowerCamelCase							:						int = 8						,    __lowerCamelCase							:						float = 0.0						,    __lowerCamelCase							:						int = 2_0_4_8						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						int = 4						,    __lowerCamelCase							:						int = 2_5_5						,    __lowerCamelCase							:						int = 1_0_0						,    __lowerCamelCase							:						float = 0.1						,    __lowerCamelCase							:						float = 2.0						,    __lowerCamelCase							:						float = 5.0						,    __lowerCamelCase							:						float = 5.0						,    __lowerCamelCase							:						int = 1_2_5_4_4						,    __lowerCamelCase							:						float = 3.0						,    __lowerCamelCase							:						float = 0.75						,    __lowerCamelCase							:						float = 0.02						,    __lowerCamelCase							:						float = 1.0						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						List[int] = [4, 8, 1_6, 3_2]						,    __lowerCamelCase							:						bool = None						,    **__lowerCamelCase							:						Tuple						,    )				->					str:
										if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."	)
																__magic_name__            =     CONFIG_MAPPING["swin"](
																    image_size=2_2_4						,    in_channels=3						,    patch_size=4						,    embed_dim=9_6						,    depths=[2, 2, 1_8, 2]						,    num_heads=[3, 6, 1_2, 2_4]						,    window_size=7						,    drop_path_rate=0.3						,    use_absolute_embeddings=__lowerCamelCase						,    out_features=["stage1", "stage2", "stage3", "stage4"]						,    )

										if isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																__magic_name__            =     backbone_config.pop("model_type"	)
																__magic_name__            =     CONFIG_MAPPING[backbone_model_type]
																__magic_name__            =     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	)}'''	)

										__magic_name__            =     backbone_config
										__magic_name__            =     feature_size
										__magic_name__            =     mask_feature_size
										__magic_name__            =     hidden_dim
										__magic_name__            =     encoder_feedforward_dim
										__magic_name__            =     activation_function
										__magic_name__            =     encoder_layers
										__magic_name__            =     decoder_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     dropout
										__magic_name__            =     dim_feedforward
										__magic_name__            =     pre_norm
										__magic_name__            =     enforce_input_projection
										__magic_name__            =     common_stride
										__magic_name__            =     ignore_value
										__magic_name__            =     num_queries
										__magic_name__            =     no_object_weight
										__magic_name__            =     class_weight
										__magic_name__            =     mask_weight
										__magic_name__            =     dice_weight
										__magic_name__            =     train_num_points
										__magic_name__            =     oversample_ratio
										__magic_name__            =     importance_sample_ratio
										__magic_name__            =     init_std
										__magic_name__            =     init_xavier_std
										__magic_name__            =     use_auxiliary_loss
										__magic_name__            =     feature_strides
										__magic_name__            =     output_auxiliary_logits
										__magic_name__            =     decoder_layers

										super().__init__(**__lowerCamelCase	)







				@classmethod
				def     _snake_case					(							cls							:						Optional[int]						,    __lowerCamelCase							:						PretrainedConfig						,    **__lowerCamelCase							:						Optional[int]	)				->					Dict:
										return cls(
										    backbone_config=__lowerCamelCase						,    **__lowerCamelCase						,    )







				def     _snake_case					(							self							:						int	)				->					Dict[str, any]:
										__magic_name__            =     copy.deepcopy(self.__dict__	)
										__magic_name__            =     self.backbone_config.to_dict()
										__magic_name__            =     self.__class__.model_type
										return output











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:int = 2_0_0_0_0_0_0   ):

						'''simple docstring'''






						__magic_name__            =     [0 for i in range(n + 1   )]
						__magic_name__            =     1
						__magic_name__            =     1

						for i in range(2 ,						int(n**0.5   ) + 1   ):
												if primality_list[i] == 0:
																		for j in range(i * i ,						n + 1 ,						__lowerCamelCase   ):
																								__magic_name__            =     1
						__magic_name__            =     0
						for i in range(__lowerCamelCase   ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


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



