





import copy
from typing import Dict, List, Optional

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


snake_case 				=       {
    """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 				=       logging.get_logger(__name__)





class 				A_				(      __lowerCamelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Dict      =  '''mask2former'''
       SCREAMING_SNAKE_CASE_      : Dict      =  ['''swin''']
       SCREAMING_SNAKE_CASE_      : Tuple      =  {'''hidden_size''': '''hidden_dim'''}







       def __init__(    self	:	str						,__A	:	Optional[Dict] = None						,__A	:	int = 256						,__A	:	int = 256						,__A	:	int = 256						,__A	:	int = 1024						,__A	:	str = "relu"						,__A	:	int = 6						,__A	:	int = 10						,__A	:	int = 8						,__A	:	float = 0.0						,__A	:	int = 2048						,__A	:	bool = False						,__A	:	bool = False						,__A	:	int = 4						,__A	:	int = 255						,__A	:	int = 100						,__A	:	float = 0.1						,__A	:	float = 2.0						,__A	:	float = 5.0						,__A	:	float = 5.0						,__A	:	int = 1_2544						,__A	:	float = 3.0						,__A	:	float = 0.75						,__A	:	float = 0.02						,__A	:	float = 1.0						,__A	:	bool = True						,__A	:	List[int] = [4, 8, 16, 32]						,__A	:	bool = None						,**__A	:	Optional[int]						,)						->					int:
         if backbone_config is None:
           logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'  )
           _lowercase							= 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=UpperCAmelCase_						,out_features=['stage1', 'stage2', 'stage3', 'stage4']						,)

         if isinstance(UpperCAmelCase_						,UpperCAmelCase_  ):
           _lowercase							= backbone_config.pop('model_type'  )
           _lowercase							= CONFIG_MAPPING[backbone_model_type]
           _lowercase							= config_class.from_dict(UpperCAmelCase_  )

         # 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  )}"""  )

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

         super().__init__(**UpperCAmelCase_  )







       @classmethod
       def    __UpperCAmelCase (    cls	:	Optional[int]						,__A	:	PretrainedConfig						,**__A	:	Any  )						->					Union[str, Any]:
         return cls(
             backbone_config=UpperCAmelCase_						,**UpperCAmelCase_						,)







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Optional[int]:
         _lowercase							= copy.deepcopy(self.__dict__  )
         _lowercase							= self.backbone_config.to_dict()
         _lowercase							= self.__class__.model_type
         return output












from __future__ import annotations

from collections.abc import Generator

def 							SCREAMING_SNAKE_CASE__       (  )      ->						Generator[int, None, None]:
  _lowercase							= {}
  _lowercase							= 2
  while True:
    _lowercase							= factor_map.pop(snake_case__			,							snake_case__     )
    if factor:
      _lowercase							= factor + prime
      while x in factor_map:
        x += factor
      _lowercase							= factor
    else:
      _lowercase							= prime
      yield prime
    prime += 1

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :float = 1E10     )      ->						int:
  _lowercase							= sieve()

  _lowercase							= 1
  while True:
    _lowercase							= next(snake_case__     )
    if (2 * prime * n) > limit:
      return n
    # Ignore the next prime as the reminder will be 2.
    next(snake_case__     )
    n += 2


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