






'''simple docstring'''







import copy
from typing import Dict, List, Optional

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


_A     :      List[str]   					={
    '''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
}

_A     :      Tuple   					=logging.get_logger(__name__)

class _lowercase     (    _lowercase		):
							a    				=	"""mask2former"""
							a    				=	["""swin"""]
							a    				=	{"""hidden_size""": """hidden_dim"""}




							def __init__(							self:    Any      ,							UpperCamelCase__:    Optional[Dict] = None      ,							UpperCamelCase__:    int = 256      ,							UpperCamelCase__:    int = 256      ,							UpperCamelCase__:    int = 256      ,							UpperCamelCase__:    int = 1_024      ,							UpperCamelCase__:    str = "relu"      ,							UpperCamelCase__:    int = 6      ,							UpperCamelCase__:    int = 10      ,							UpperCamelCase__:    int = 8      ,							UpperCamelCase__:    float = 0.0      ,							UpperCamelCase__:    int = 2_048      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    int = 4      ,							UpperCamelCase__:    int = 255      ,							UpperCamelCase__:    int = 100      ,							UpperCamelCase__:    float = 0.1      ,							UpperCamelCase__:    float = 2.0      ,							UpperCamelCase__:    float = 5.0      ,							UpperCamelCase__:    float = 5.0      ,							UpperCamelCase__:    int = 12_544      ,							UpperCamelCase__:    float = 3.0      ,							UpperCamelCase__:    float = 0.75      ,							UpperCamelCase__:    float = 0.02      ,							UpperCamelCase__:    float = 1.0      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    List[int] = [4, 8, 16, 32]      ,							UpperCamelCase__:    bool = None      ,							**UpperCamelCase__:    List[Any]      ,							):
														if backbone_config is None:
																					logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""					)
																					lowerCamelCase__ :     Optional[int]	  =	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__					):
																					lowerCamelCase__ :     Any	  =	backbone_config.pop("""model_type"""					)
																					lowerCamelCase__ :     Any	  =	CONFIG_MAPPING[backbone_model_type]
																					lowerCamelCase__ :     Union[str, Any]	  =	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					)}'''					)

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

														super().__init__(**UpperCamelCase__					)




							@classmethod
							def 		lowerCamelCase_    (							cls:    Optional[Any]      ,							UpperCamelCase__:    PretrainedConfig      ,							**UpperCamelCase__:    List[Any]					):
														return cls(
														    backbone_config=UpperCamelCase__      ,							**UpperCamelCase__      ,							)




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     Optional[int]	  =	copy.deepcopy(self.__dict__					)
														lowerCamelCase__ :     Dict	  =	self.backbone_config.to_dict()
														lowerCamelCase__ :     Union[str, Any]	  =	self.__class__.model_type
														return output









'''simple docstring'''







from __future__ import annotations

from typing import Any

class _lowercase     (    _lowercase		):
							pass



class _lowercase     :




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    Any					):
														lowerCamelCase__ :     Any	  =	data
														lowerCamelCase__ :     Node | None	  =	None




							def __iter__(							self:    List[Any]					):
														lowerCamelCase__ :     Optional[Any]	  =	self
														lowerCamelCase__ :     int	  =	[]
														while node:
																					if node in visited:
																												raise ContainsLoopError
																					visited.append(UpperCamelCase__					)
																					yield node.data
																					lowerCamelCase__ :     List[str]	  =	node.next_node




							@property
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														try:
																					list(self					)
																					return False
														except ContainsLoopError:
																					return True


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

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

			_A     :      Optional[Any]   					=Node(1)
			print(root_node.has_loop)  # False

