


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


snake_case__			 =						{
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

snake_case__			 =						logging.get_logger(__name__)


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'maskformer'
						lowerCamelCase_         =     {'hidden_size': 'mask_feature_size'}
						lowerCamelCase_         =     ['resnet', 'swin']
						lowerCamelCase_         =     ['detr']




						def __init__(	self						:   int   ,     __A						:   int = 256   ,     __A						:   int = 256   ,     __A						:   float = 0.1   ,     __A						:   bool = False   ,     __A						:   Optional[Dict] = None   ,     __A						:   Optional[Dict] = None   ,     __A						:   float = 0.02   ,     __A						:   float = 1.0   ,     __A						:   float = 1.0   ,     __A						:   float = 1.0   ,     __A						:   float = 20.0   ,     __A						:   Optional[bool] = None   ,     **__A						:   Optional[Any]   ,     )      ->Union[str, Any]:






									"""simple docstring"""






									if backbone_config is None:
												# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
												a__ :List[str]						  =    SwinConfig(
												    image_size=384   ,     in_channels=3   ,     patch_size=4   ,     embed_dim=128   ,     depths=[2, 2, 18, 2]   ,     num_heads=[4, 8, 16, 32]   ,     window_size=12   ,     drop_path_rate=0.3   ,     out_features=["stage1", "stage2", "stage3", "stage4"]   ,     )

									if isinstance(__A   ,     __A						):
												a__ :Any						  =    backbone_config.pop("model_type"						)
												a__ :List[Any]						  =    CONFIG_MAPPING[backbone_model_type]
												a__ :Optional[int]						  =    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 MaskFormer. '''
												    F'''Supported model types: {','.join(self.backbones_supported						)}'''						)

									if decoder_config is None:
												# fall back to https://huggingface.co/facebook/detr-resnet-50
												a__ :Union[str, Any]						  =    DetrConfig()
									else:
												# verify that the decoder is supported
												a__ :List[Any]						  =    (
												    decoder_config.pop("model_type"						) if isinstance(__A   ,     __A						) else decoder_config.model_type
												)
												if decoder_type not in self.decoders_supported:
															raise ValueError(
															    F'''Transformer Decoder {decoder_type} not supported, please use one of'''
															    F''' {','.join(self.decoders_supported						)}'''						)
												if isinstance(__A   ,     __A						):
															a__ :str						  =    CONFIG_MAPPING[decoder_type]
															a__ :List[str]						  =    config_class.from_dict(__A						)

									a__ :List[Any]						  =    backbone_config
									a__ :List[Any]						  =    decoder_config
									# main feature dimension for the model
									a__ :List[Any]						  =    fpn_feature_size
									a__ :str						  =    mask_feature_size
									# initializer
									a__ :List[str]						  =    init_std
									a__ :Any						  =    init_xavier_std
									# Hungarian matcher && loss
									a__ :Dict						  =    cross_entropy_weight
									a__ :Optional[int]						  =    dice_weight
									a__ :List[Any]						  =    mask_weight
									a__ :Optional[int]						  =    use_auxiliary_loss
									a__ :List[str]						  =    no_object_weight
									a__ :List[Any]						  =    output_auxiliary_logits

									a__ :Any						  =    self.decoder_config.encoder_attention_heads
									a__ :Optional[Any]						  =    self.decoder_config.num_hidden_layers
									super().__init__(**__A						)




						@classmethod
						def  _snake_case					(	cls						:   Optional[Any]   ,     __A						:   PretrainedConfig   ,     __A						:   PretrainedConfig   ,     **__A						:   List[Any]						)      ->Any:






									"""simple docstring"""






									return cls(
									    backbone_config=__A   ,     decoder_config=__A   ,     **__A   ,     )






						def  _snake_case					(	self						:   str						)      ->Dict[str, any]:






									"""simple docstring"""






									a__ :str						  =    copy.deepcopy(self.__dict__						)
									a__ :List[str]						  =    self.backbone_config.to_dict()
									a__ :Union[str, Any]						  =    self.decoder_config.to_dict()
									a__ :str						  =    self.__class__.model_type
									return output






class 					lowerCAmelCase_ :




						def __init__(	self						:   Optional[int]						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    {}




						def  _snake_case					(	self						:   Tuple						)      ->None:






									"""simple docstring"""






									print(self.vertex						)
									for i in self.vertex:
												print(__A   ,     " -> "   ,     " -> ".join([str(__A						) for j in self.vertex[i]]						)						)




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   int   ,     __A						:   int						)      ->None:






									"""simple docstring"""






									if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__A						)
									else:
												# else make a new vertex
												a__ :Any						  =    [to_vertex]




						def  _snake_case					(	self						:   Optional[int]						)      ->None:






									"""simple docstring"""






									a__ :List[Any]						  =    [False] * len(self.vertex						)

									# call the recursive helper function
									for i in range(len(self.vertex						)						):
												if not visited[i]:
															self.dfs_recursive(__A   ,     __A						)






						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   int   ,     __A						:   list						)      ->None:






									"""simple docstring"""






									a__ :Dict						  =    True

									print(__A   ,     end=" "						)

									# Recur for all the vertices that are adjacent to this node
									for i in self.vertex:
												if not visited[i]:
															self.dfs_recursive(__A   ,     __A						)


if __name__ == "__main__":
		snake_case__			 =						Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('''DFS:''')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3


