


import copy
from typing import Dict, List, Optional

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


snake_case							:       Dict										=	{
    '''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							:       str										=	logging.get_logger(__name__)







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'mask2former'
 SCREAMING_SNAKE_CASE__ =			['swin']
 SCREAMING_SNAKE_CASE__ =			{'hidden_size': 'hidden_dim'}




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

       if isinstance(_lowerCamelCase   ,    _lowerCamelCase       ):
             a		:List[str]									=			backbone_config.pop('''model_type'''       )
             a		:int									=			CONFIG_MAPPING[backbone_model_type]
             a		: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       )}'''       )

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

       super().__init__(**_lowerCamelCase       )




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						cls   ,    _lowerCamelCase   ,    **_lowerCamelCase       ):
       return cls(
           backbone_config=_lowerCamelCase   ,    **_lowerCamelCase   ,    )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			copy.deepcopy(self.__dict__       )
       a		:int									=			self.backbone_config.to_dict()
       a		:Optional[Any]									=			self.__class__.model_type
       return output









from __future__ import annotations

from typing import Any







class 						_snake_case    ( _snake_case				):
 pass







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Any									=			data
       a		:Node | None									=			None




 def __iter__(						self       ):
       a		:Dict									=			self
       a		:Optional[int]									=			[]
       while node:
             if node in visited:
                   raise ContainsLoopError
             visited.append(_lowerCamelCase       )
             yield node.data
             a		:str									=			node.next_node




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       try:
             list(self       )
             return False
       except ContainsLoopError:
             return True


if __name__ == "__main__":
     snake_case							:       int										=	Node(1)
     snake_case							:       int										=	Node(2)
     snake_case							:       Optional[int]										=	Node(3)
     snake_case							:       List[str]										=	Node(4)
     print(root_node.has_loop)  # False
     snake_case							:       Optional[Any]										=	root_node.next_node
     print(root_node.has_loop)  # True

     snake_case							:       Dict										=	Node(5)
     snake_case							:       List[Any]										=	Node(6)
     snake_case							:       Optional[int]										=	Node(5)
     snake_case							:       Union[str, Any]										=	Node(6)
     print(root_node.has_loop)  # False

     snake_case							:       Tuple										=	Node(1)
     print(root_node.has_loop)  # False





