




'''simple docstring'''



import copy

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


UpperCamelCase_    	=				logging.get_logger(__name__)

UpperCamelCase_    	=				{
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Dict        =						'''deformable_detr'''
       A						:				List[str]        =						{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
       }







       def __init__(      self,							A=True,							A=None,							A=3,							A=300,							A=1_024,							A=6,							A=1_024,							A=8,							A=6,							A=1_024,							A=8,							A=0.0,							A=True,							A="relu",							A=256,							A=0.1,							A=0.0,							A=0.0,							A=0.02,							A=1.0,							A=True,							A=False,							A="sine",							A="resnet50",							A=True,							A=False,							A=4,							A=4,							A=4,							A=False,							A=300,							A=False,							A=1,							A=5,							A=2,							A=1,							A=1,							A=5,							A=2,							A=0.1,							A=0.25,							A=False,							**A,							):







             '''simple docstring'''







             if backbone_config is not None and use_timm_backbone:
                   raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'  )

             if not use_timm_backbone:
                   if backbone_config is None:
                         logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'  )
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						CONFIG_MAPPING['resnet'](out_features=['stage4']  )
                   elif isinstance(A,							A  ):
                         SCREAMING_SNAKE_CASE   :      Any    					=						backbone_config.get('model_type'  )
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						CONFIG_MAPPING[backbone_model_type]
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						config_class.from_dict(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						use_timm_backbone
             SCREAMING_SNAKE_CASE   :      Dict    					=						backbone_config
             SCREAMING_SNAKE_CASE   :      List[str]    					=						num_channels
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						num_queries
             SCREAMING_SNAKE_CASE   :      List[str]    					=						max_position_embeddings
             SCREAMING_SNAKE_CASE   :      int    					=						d_model
             SCREAMING_SNAKE_CASE   :      List[str]    					=						encoder_ffn_dim
             SCREAMING_SNAKE_CASE   :      List[str]    					=						encoder_layers
             SCREAMING_SNAKE_CASE   :      Tuple    					=						encoder_attention_heads
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						decoder_ffn_dim
             SCREAMING_SNAKE_CASE   :      Tuple    					=						decoder_layers
             SCREAMING_SNAKE_CASE   :      Dict    					=						decoder_attention_heads
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						dropout
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						attention_dropout
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						activation_dropout
             SCREAMING_SNAKE_CASE   :      int    					=						activation_function
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						init_std
             SCREAMING_SNAKE_CASE   :      List[str]    					=						init_xavier_std
             SCREAMING_SNAKE_CASE   :      str    					=						encoder_layerdrop
             SCREAMING_SNAKE_CASE   :      Dict    					=						auxiliary_loss
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						position_embedding_type
             SCREAMING_SNAKE_CASE   :      List[str]    					=						backbone
             SCREAMING_SNAKE_CASE   :      Any    					=						use_pretrained_backbone
             SCREAMING_SNAKE_CASE   :      Dict    					=						dilation
             # deformable attributes
             SCREAMING_SNAKE_CASE   :      List[str]    					=						num_feature_levels
             SCREAMING_SNAKE_CASE   :      Dict    					=						encoder_n_points
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						decoder_n_points
             SCREAMING_SNAKE_CASE   :      List[str]    					=						two_stage
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						two_stage_num_proposals
             SCREAMING_SNAKE_CASE   :      Any    					=						with_box_refine
             if two_stage is True and with_box_refine is False:
                   raise ValueError('If two_stage is True, with_box_refine must be True.'  )
             # Hungarian matcher
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						class_cost
             SCREAMING_SNAKE_CASE   :      Any    					=						bbox_cost
             SCREAMING_SNAKE_CASE   :      Tuple    					=						giou_cost
             # Loss coefficients
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						mask_loss_coefficient
             SCREAMING_SNAKE_CASE   :      List[str]    					=						dice_loss_coefficient
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						bbox_loss_coefficient
             SCREAMING_SNAKE_CASE   :      Tuple    					=						giou_loss_coefficient
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						eos_coefficient
             SCREAMING_SNAKE_CASE   :      Tuple    					=						focal_alpha
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						disable_custom_kernels
             super().__init__(is_encoder_decoder=A,							**A  )







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self.encoder_attention_heads







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self.d_model







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						copy.deepcopy(self.__dict__  )
             if self.backbone_config is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.backbone_config.to_dict()
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.__class__.model_type
             return output








'''simple docstring'''



from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      str    					=						9, 14  # noqa: F841
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[
          [0, 1, 4],
          [0, 7, 8],
          [1, 2, 8],
          [7, 8, 7],
          [7, 6, 1],
          [2, 8, 2],
          [8, 6, 6],
          [2, 3, 7],
          [2, 5, 4],
          [6, 5, 2],
          [3, 5, 14],
          [3, 4, 9],
          [5, 4, 10],
          [1, 7, 11],
      ]

      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						defaultdict(__UpperCamelCase    )
      for nodea, nodea, cost in edges:
            adjancency[nodea].append([nodea, cost]    )
            adjancency[nodea].append([nodea, cost]    )

      SCREAMING_SNAKE_CASE   :      Dict    					=						mst(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[
          [7, 6, 1],
          [2, 8, 2],
          [6, 5, 2],
          [0, 1, 4],
          [2, 5, 4],
          [2, 3, 7],
          [0, 7, 8],
          [3, 4, 9],
      ]

      for answer in expected:
            SCREAMING_SNAKE_CASE   :      Any    					=						tuple(answer[:2]    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						tuple(edge[::-1]    )
            assert edge in result or reverse in result


