
'''simple docstring'''





import copy
from typing import Dict, Optional

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


a__						:				Optional[int]           =    logging.get_logger(__name__)

a__						:				str           =    {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}


class 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE):
 UpperCAmelCase__      :      Tuple  			=   'oneformer'
 UpperCAmelCase__      :      Union[str, Any]  			=   {'hidden_size': 'hidden_dim'}






 def __init__(     self					:List[str]		,			_A					:Optional[Dict] = None		,			_A					:int = 255		,			_A					:int = 150		,			_A					:int = 0.1		,			_A					:float = 2.0		,			_A					:float = 5.0		,			_A					:float = 5.0		,			_A					:float = 0.5		,			_A					:float = 0.07		,			_A					:int = 12_544		,			_A					:float = 3.0		,			_A					:float = 0.75		,			_A					:float = 0.02		,			_A					:float = 1.0		,			_A					:float = 1E-05		,			_A					:bool = False		,			_A					:bool = True		,			_A					:bool = True		,			_A					:Optional[list] = [4, 8, 16, 32]		,			_A					:int = 77		,			_A					:int = 256		,			_A					:int = 77		,			_A					:int = 6		,			_A					:int = 49_408		,			_A					:int = 2		,			_A					:int = 16		,			_A					:int = 256		,			_A					:int = 256		,			_A					:int = 256		,			_A					:int = 1_024		,			_A					:str = "GN"		,			_A					:int = 6		,			_A					:int = 10		,			_A					:bool = True		,			_A					:int = 8		,			_A					:float = 0.1		,			_A					:int = 2_048		,			_A					:bool = False		,			_A					:bool = False		,			_A					:int = 2		,			_A					:int = 4		,			**_A					:Tuple		,			)			->     Optional[int]:







       '''simple docstring'''



       if backbone_config is None:
             logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'     )
             __A    		=     CONFIG_MAPPING['swin'](
                 image_size=224		,			in_channels=3		,			patch_size=4		,			embed_dim=96		,			depths=[2, 2, 6, 2]		,			num_heads=[3, 6, 12, 24]		,			window_size=7		,			drop_path_rate=0.3		,			use_absolute_embeddings=_A		,			out_features=['stage1', 'stage2', 'stage3', 'stage4']		,			)
       elif isinstance(_A		,			_A     ):
             __A    		=     backbone_config.get('model_type'     )
             __A    		=     CONFIG_MAPPING[backbone_model_type]
             __A    		=     config_class.from_dict(_A     )

       __A    		=     backbone_config

       __A    		=     ignore_value
       __A    		=     num_queries
       __A    		=     no_object_weight
       __A    		=     class_weight
       __A    		=     mask_weight
       __A    		=     dice_weight
       __A    		=     contrastive_weight
       __A    		=     contrastive_temperature
       __A    		=     train_num_points
       __A    		=     oversample_ratio
       __A    		=     importance_sample_ratio
       __A    		=     init_std
       __A    		=     init_xavier_std
       __A    		=     layer_norm_eps
       __A    		=     is_training
       __A    		=     use_auxiliary_loss
       __A    		=     output_auxiliary_logits
       __A    		=     strides
       __A    		=     task_seq_len
       __A    		=     text_encoder_width
       __A    		=     text_encoder_context_length
       __A    		=     text_encoder_num_layers
       __A    		=     text_encoder_vocab_size
       __A    		=     text_encoder_proj_layers
       __A    		=     text_encoder_n_ctx
       __A    		=     conv_dim
       __A    		=     mask_dim
       __A    		=     hidden_dim
       __A    		=     encoder_feedforward_dim
       __A    		=     norm
       __A    		=     encoder_layers
       __A    		=     decoder_layers
       __A    		=     use_task_norm
       __A    		=     num_attention_heads
       __A    		=     dropout
       __A    		=     dim_feedforward
       __A    		=     pre_norm
       __A    		=     enforce_input_proj
       __A    		=     query_dec_layers
       __A    		=     common_stride
       __A    		=     decoder_layers

       super().__init__(**_A     )






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







       '''simple docstring'''



       __A    		=     copy.deepcopy(self.__dict__     )
       __A    		=     self.backbone_config.to_dict()
       __A    		=     self.__class__.model_type
       return output








'''simple docstring'''





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     Union[str, Any]:







      """simple docstring"""
      print('\nVertex Distance'		)
      for i in range(UpperCAmelCase		):
            if dist[i] != float('inf'		):
                  print(UpperCAmelCase ,					'\t' ,					int(dist[i]		) ,					end='\t'		)
            else:
                  print(UpperCAmelCase ,					'\t' ,					'INF' ,					end='\t'		)
            print()





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     List[Any]:







      """simple docstring"""
      __A    		=     float('inf'		)
      __A    		=     -1
      for i in range(UpperCAmelCase		):
            if (not vset[i]) and mdist[i] < min_val:
                  __A    		=     i
                  __A    		=     mdist[i]
      return min_ind





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     Dict:







      """simple docstring"""
      __A    		=     [float('inf'		) for _ in range(UpperCAmelCase		)]
      __A    		=     [False for _ in range(UpperCAmelCase		)]
      __A    		=     0.0

      for _ in range(v - 1		):
            __A    		=     min_dist(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)
            __A    		=     True

            for i in range(UpperCAmelCase		):
                  if (
                      (not vset[i])
                      and graph[u][i] != float('inf'		)
                      and mdist[u] + graph[u][i] < mdist[i]
                  ):
                        __A    		=     mdist[u] + graph[u][i]

      print_dist(UpperCAmelCase ,					UpperCAmelCase		)


if __name__ == "__main__":
 a__						:				Tuple           =    int(input("Enter number of vertices: ").strip())
 a__						:				List[str]           =    int(input("Enter number of edges: ").strip())

 a__						:				int           =    [[float("inf") for i in range(V)] for j in range(V)]

 for i in range(V):
  a__						:				Optional[int]           =    0.0

 for i in range(E):
  print("\nEdge ", i + 1)
  a__						:				Union[str, Any]           =    int(input("Enter source:").strip())
  a__						:				Tuple           =    int(input("Enter destination:").strip())
  a__						:				int           =    float(input("Enter weight:").strip())
  a__						:				Dict           =    weight

 a__						:				int           =    int(input("\nEnter shortest path source:").strip())
 dijkstra(graph, V, gsrc)






