






import copy

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


lowerCamelCase__							=     logging.get_logger(__name__)

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




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				List[Any] 				= "deta"
       lowerCamelCase							:				Optional[Any] 				= {
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }



       def __init__(							self			,						a__=None			,						a__=9_00			,						a__=20_48			,						a__=6			,						a__=20_48			,						a__=8			,						a__=6			,						a__=10_24			,						a__=8			,						a__=0.0			,						a__=True			,						a__="relu"			,						a__=2_56			,						a__=0.1			,						a__=0.0			,						a__=0.0			,						a__=0.02			,						a__=1.0			,						a__=True			,						a__=False			,						a__="sine"			,						a__=5			,						a__=4			,						a__=4			,						a__=True			,						a__=3_00			,						a__=True			,						a__=True			,						a__=1			,						a__=5			,						a__=2			,						a__=1			,						a__=1			,						a__=5			,						a__=2			,						a__=0.1			,						a__=0.25			,						**a__			,						) ->      Any:






            '''simple docstring'''





            if backbone_config is None:
                 logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""    )
                 __snake_case			:List[Any]          =						CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]    )
            else:
                 if isinstance(a__			,						a__    ):
                      __snake_case			:List[Any]          =						backbone_config.pop("""model_type"""    )
                      __snake_case			:List[str]          =						CONFIG_MAPPING[backbone_model_type]
                      __snake_case			:Union[str, Any]          =						config_class.from_dict(a__    )

            __snake_case			:Optional[int]          =						backbone_config
            __snake_case			:Union[str, Any]          =						num_queries
            __snake_case			:Optional[Any]          =						max_position_embeddings
            __snake_case			:List[Any]          =						d_model
            __snake_case			:Dict          =						encoder_ffn_dim
            __snake_case			:Any          =						encoder_layers
            __snake_case			:str          =						encoder_attention_heads
            __snake_case			:Union[str, Any]          =						decoder_ffn_dim
            __snake_case			:Any          =						decoder_layers
            __snake_case			:Optional[int]          =						decoder_attention_heads
            __snake_case			:str          =						dropout
            __snake_case			:Any          =						attention_dropout
            __snake_case			:Any          =						activation_dropout
            __snake_case			:Dict          =						activation_function
            __snake_case			:Tuple          =						init_std
            __snake_case			:Tuple          =						init_xavier_std
            __snake_case			:Optional[Any]          =						encoder_layerdrop
            __snake_case			:List[str]          =						auxiliary_loss
            __snake_case			:str          =						position_embedding_type
            # deformable attributes
            __snake_case			:Union[str, Any]          =						num_feature_levels
            __snake_case			:List[str]          =						encoder_n_points
            __snake_case			:Dict          =						decoder_n_points
            __snake_case			:List[Any]          =						two_stage
            __snake_case			:Optional[Any]          =						two_stage_num_proposals
            __snake_case			:str          =						with_box_refine
            __snake_case			:List[str]          =						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
            __snake_case			:Optional[int]          =						class_cost
            __snake_case			:Optional[int]          =						bbox_cost
            __snake_case			:Tuple          =						giou_cost
            # Loss coefficients
            __snake_case			:int          =						mask_loss_coefficient
            __snake_case			:List[Any]          =						dice_loss_coefficient
            __snake_case			:str          =						bbox_loss_coefficient
            __snake_case			:Optional[int]          =						giou_loss_coefficient
            __snake_case			:int          =						eos_coefficient
            __snake_case			:Any          =						focal_alpha
            super().__init__(is_encoder_decoder=a__			,						**a__    )



       @property
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            return self.encoder_attention_heads



       @property
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            return self.d_model



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





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










from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass




@dataclass
class 			snake_case__   :

       '''simple docstring'''
       lowerCamelCase							:				int
       lowerCamelCase							:				int




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__    ) ->      Any:






            '''simple docstring'''





            __snake_case			:list[list[Edge]]          =						[[] for _ in range(a__    )]
            __snake_case			:List[str]          =						size



       def __getitem__(							self			,						a__    ) ->      Iterator[Edge]:






            '''simple docstring'''





            return iter(self._graph[vertex]    )



       @property
       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            return self._size



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      List[Any]:






            '''simple docstring'''





            if weight not in (0, 1):
                 raise ValueError("""Edge weight must be either 0 or 1."""    )

            if to_vertex < 0 or to_vertex >= self.size:
                 raise ValueError("""Vertex indexes must be in [0; size)."""    )

            self._graph[from_vertex].append(Edge(a__			,						a__    )    )



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      int | None:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						deque([start_vertex]    )
            __snake_case			:list[int | None]          =						[None] * self.size
            __snake_case			:Tuple          =						0

            while queue:
                 __snake_case			:List[Any]          =						queue.popleft()
                 __snake_case			:Optional[Any]          =						distances[current_vertex]
                 if current_distance is None:
                      continue

                 for edge in self[current_vertex]:
                      __snake_case			:Optional[Any]          =						current_distance + edge.weight
                      __snake_case			:Dict          =						distances[edge.destination_vertex]
                      if (
                          isinstance(a__			,						a__    )
                          and new_distance >= dest_vertex_distance
                      ):
                           continue
                      __snake_case			:Dict          =						new_distance
                      if edge.weight == 0:
                           queue.appendleft(edge.destination_vertex    )
                      else:
                           queue.append(edge.destination_vertex    )

            if distances[finish_vertex] is None:
                 raise ValueError("""No path from start_vertex to finish_vertex."""    )

            return distances[finish_vertex]


if __name__ == "__main__":
     import doctest

     doctest.testmod()


