


'''simple docstring'''

import copy

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


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}





class 						__SCREAMING_SNAKE_CASE					(      _a ):
					UpperCAmelCase 					=				"""deta"""
					UpperCAmelCase 					=				{
					    """hidden_size""": """d_model""",
					    """num_attention_heads""": """encoder_attention_heads""",
					}






					def __init__(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=900     ,							__UpperCamelCase=2_048     ,							__UpperCamelCase=6     ,							__UpperCamelCase=2_048     ,							__UpperCamelCase=8     ,							__UpperCamelCase=6     ,							__UpperCamelCase=1_024     ,							__UpperCamelCase=8     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=True     ,							__UpperCamelCase="relu"     ,							__UpperCamelCase=256     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1.0     ,							__UpperCamelCase=True     ,							__UpperCamelCase=False     ,							__UpperCamelCase="sine"     ,							__UpperCamelCase=5     ,							__UpperCamelCase=4     ,							__UpperCamelCase=4     ,							__UpperCamelCase=True     ,							__UpperCamelCase=300     ,							__UpperCamelCase=True     ,							__UpperCamelCase=True     ,							__UpperCamelCase=1     ,							__UpperCamelCase=5     ,							__UpperCamelCase=2     ,							__UpperCamelCase=1     ,							__UpperCamelCase=1     ,							__UpperCamelCase=5     ,							__UpperCamelCase=2     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.25     ,							**__UpperCamelCase     ,							)       ->    List[str]:
										if backbone_config is None:
															logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
															_a						=		CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]		)
										else:
															if isinstance(snake_case_     ,							snake_case_		):
																				_a						=		backbone_config.pop("model_type"		)
																				_a						=		CONFIG_MAPPING[backbone_model_type]
																				_a						=		config_class.from_dict(snake_case_		)

										_a						=		backbone_config
										_a						=		num_queries
										_a						=		max_position_embeddings
										_a						=		d_model
										_a						=		encoder_ffn_dim
										_a						=		encoder_layers
										_a						=		encoder_attention_heads
										_a						=		decoder_ffn_dim
										_a						=		decoder_layers
										_a						=		decoder_attention_heads
										_a						=		dropout
										_a						=		attention_dropout
										_a						=		activation_dropout
										_a						=		activation_function
										_a						=		init_std
										_a						=		init_xavier_std
										_a						=		encoder_layerdrop
										_a						=		auxiliary_loss
										_a						=		position_embedding_type
										# deformable attributes
										_a						=		num_feature_levels
										_a						=		encoder_n_points
										_a						=		decoder_n_points
										_a						=		two_stage
										_a						=		two_stage_num_proposals
										_a						=		with_box_refine
										_a						=		assign_first_stage
										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
										_a						=		class_cost
										_a						=		bbox_cost
										_a						=		giou_cost
										# Loss coefficients
										_a						=		mask_loss_coefficient
										_a						=		dice_loss_coefficient
										_a						=		bbox_loss_coefficient
										_a						=		giou_loss_coefficient
										_a						=		eos_coefficient
										_a						=		focal_alpha
										super().__init__(is_encoder_decoder=snake_case_     ,							**snake_case_		)






					@property
					def  a_					(			self		)       ->    int:
										return self.encoder_attention_heads






					@property
					def  a_					(			self		)       ->    int:
										return self.d_model






					def  a_					(			self		)       ->    Any:
										_a						=		copy.deepcopy(self.__dict__		)
										_a						=		self.backbone_config.to_dict()
										_a						=		self.__class__.model_type
										return output





'''simple docstring'''

from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def __UpperCamelCase   (			)      ->     Tuple:







     '''simple docstring'''






     _a			,			_a						=		9, 14  # noqa: F841
     _a						=		[
         [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],
     ]

     _a						=		defaultdict(__lowerCamelCase				)
     for nodea, nodea, cost in edges:
          adjancency[nodea].append([nodea, cost]				)
          adjancency[nodea].append([nodea, cost]				)

     _a						=		mst(__lowerCamelCase				)

     _a						=		[
         [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:
          _a						=		tuple(answer[:2]				)
          _a						=		tuple(edge[::-1]				)
          assert edge in result or reverse in result

