



'''simple docstring'''






class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self			:     Optional[Any]		)			->		None:
								UpperCAmelCase :       dict[str, TrieNode] 							=		{}  # Mapping from char to TrieNode
								UpperCAmelCase :       str 							=		False







	def      A	(						self			:     Tuple		,			__snake_case			:     list[str]		)			->		None:
								for word in words:
															self.insert(__snake_case		)







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     str		)			->		None:
								UpperCAmelCase :       Any 							=		self
								for char in word:
															if char not in curr.nodes:
																						UpperCAmelCase :       str 							=		TrieNode()
															UpperCAmelCase :       str 							=		curr.nodes[char]
								UpperCAmelCase :       Optional[Any] 							=		True







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     str		)			->		bool:
								UpperCAmelCase :       str 							=		self
								for char in word:
															if char not in curr.nodes:
																						return False
															UpperCAmelCase :       List[str] 							=		curr.nodes[char]
								return curr.is_leaf







	def      A	(						self			:     List[str]		,			__snake_case			:     str		)			->		None:

								def _delete(__snake_case			:     TrieNode		,			__snake_case			:     str		,			__snake_case			:     int		) -> bool:
															if index == len(__snake_case		):
																						# If word does not exist
																						if not curr.is_leaf:
																													return False
																						UpperCAmelCase :       Union[str, Any] 							=		False
																						return len(curr.nodes		) == 0
															UpperCAmelCase :       Optional[Any] 							=		word[index]
															UpperCAmelCase :       str 							=		curr.nodes.get(__snake_case		)
															# If char not in current trie node
															if not char_node:
																						return False
															# Flag to check if node can be deleted
															UpperCAmelCase :       Union[str, Any] 							=		_delete(__snake_case		,			__snake_case		,			index + 1		)
															if delete_curr:
																						del curr.nodes[char]
																						return len(curr.nodes		) == 0
															return delete_curr

								_delete(self		,			__snake_case		,			0		)


def  snake_case_     (					_lowerCAmelCase  :						TrieNode   ,      _lowerCAmelCase  :						str  )      ->   None:
							if node.is_leaf:
														print(_lowerCAmelCase   ,      end=''' '''  )

							for key, value in node.nodes.items():
														print_words(_lowerCAmelCase   ,      word + key  )


def  snake_case_     (					)      ->   bool:
							UpperCAmelCase :       List[str] 							=		'''banana bananas bandana band apple all beast'''.split()
							UpperCAmelCase :       int 							=		TrieNode()
							root.insert_many(_lowerCAmelCase  )
							# print_words(root, "")
							assert all(root.find(_lowerCAmelCase  ) for word in words  )
							assert root.find('''banana'''  )
							assert not root.find('''bandanas'''  )
							assert not root.find('''apps'''  )
							assert root.find('''apple'''  )
							assert root.find('''all'''  )
							root.delete('''all'''  )
							assert not root.find('''all'''  )
							root.delete('''banana'''  )
							assert not root.find('''banana'''  )
							assert root.find('''bananas'''  )
							return True


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						bool  )      ->   None:
							print(str(_lowerCAmelCase  )   ,      '''works!''' if passes else '''doesn\'t work :('''  )


def  snake_case_     (					)      ->   None:
							assert test_trie()


def  snake_case_     (					)      ->   None:
							print_results('''Testing trie functionality'''   ,      test_trie()  )


if __name__ == "__main__":
						main()










import copy
from typing import Dict, List, Optional

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


A__  :  Optional[Any]								=					{
    '''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__  :  Dict								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''mask2former'''
							_a			      = ['''swin''']
							_a			      = {'''hidden_size''': '''hidden_dim'''}




							def __init__(       self     :       Any				,				A_     :       Optional[Dict] = None				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 1_0_2_4				,				A_     :       str = "relu"				,				A_     :       int = 6				,				A_     :       int = 1_0				,				A_     :       int = 8				,				A_     :       float = 0.0				,				A_     :       int = 2_0_4_8				,				A_     :       bool = False				,				A_     :       bool = False				,				A_     :       int = 4				,				A_     :       int = 2_5_5				,				A_     :       int = 1_0_0				,				A_     :       float = 0.1				,				A_     :       float = 2.0				,				A_     :       float = 5.0				,				A_     :       float = 5.0				,				A_     :       int = 1_2_5_4_4				,				A_     :       float = 3.0				,				A_     :       float = 0.75				,				A_     :       float = 0.02				,				A_     :       float = 1.0				,				A_     :       bool = True				,				A_     :       List[int] = [4, 8, 1_6, 3_2]				,				A_     :       bool = None				,				**A_     :       Dict				,				):
									if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''')
											lowerCAmelCase_			:     int									= 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=A_				,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,				)

									if isinstance(A_				,				A_):
											lowerCAmelCase_			:     List[Any]									= backbone_config.pop('''model_type''')
											lowerCAmelCase_			:     Optional[Any]									= CONFIG_MAPPING[backbone_model_type]
											lowerCAmelCase_			:     List[Any]									= config_class.from_dict(A_)

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

									super().__init__(**A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       List[str]				,				A_     :       PretrainedConfig				,				**A_     :       List[Any]):
									return cls(
									    backbone_config=A_				,				**A_				,				)







							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Dict									= self.backbone_config.to_dict()
									lowerCAmelCase_			:     Optional[int]									= self.__class__.model_type
									return output





