
import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE__   :			Any												=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    {
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class    lowerCAmelCase__     (	__lowercase			):
		a__       :						List[Any]        =		"""detr"""
		a__       :						int        =		["""past_key_values"""]
		a__       :						Dict        =		{
		    """hidden_size""": """d_model""",
		    """num_attention_heads""": """encoder_attention_heads""",
		}






		def __init__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Dict=True							,			SCREAMING_SNAKE_CASE__		:							int=None							,			SCREAMING_SNAKE_CASE__		:							Dict=3							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=1_00							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=6							,			SCREAMING_SNAKE_CASE__		:							List[Any]=20_48							,			SCREAMING_SNAKE_CASE__		:							Tuple=8							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=6							,			SCREAMING_SNAKE_CASE__		:							str=20_48							,			SCREAMING_SNAKE_CASE__		:							str=8							,			SCREAMING_SNAKE_CASE__		:							Any=0.0							,			SCREAMING_SNAKE_CASE__		:							Tuple=0.0							,			SCREAMING_SNAKE_CASE__		:							Any=True							,			SCREAMING_SNAKE_CASE__		:							str="relu"							,			SCREAMING_SNAKE_CASE__		:							Any=2_56							,			SCREAMING_SNAKE_CASE__		:							Tuple=0.1							,			SCREAMING_SNAKE_CASE__		:							Dict=0.0							,			SCREAMING_SNAKE_CASE__		:							List[str]=0.0							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=0.02							,			SCREAMING_SNAKE_CASE__		:							List[str]=1.0							,			SCREAMING_SNAKE_CASE__		:							List[str]=False							,			SCREAMING_SNAKE_CASE__		:							int="sine"							,			SCREAMING_SNAKE_CASE__		:							Tuple="resnet50"							,			SCREAMING_SNAKE_CASE__		:							Tuple=True							,			SCREAMING_SNAKE_CASE__		:							int=False							,			SCREAMING_SNAKE_CASE__		:							int=1							,			SCREAMING_SNAKE_CASE__		:							Dict=5							,			SCREAMING_SNAKE_CASE__		:							str=2							,			SCREAMING_SNAKE_CASE__		:							str=1							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=1							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=5							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=2							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=0.1							,			**SCREAMING_SNAKE_CASE__		:							int							,			)     ->							Union[str, Any]:
						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.'''  )
														__lowerCamelCase    					=	CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']  )
										elif isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
														__lowerCamelCase    					=	backbone_config.get('''model_type'''  )
														__lowerCamelCase    					=	CONFIG_MAPPING[backbone_model_type]
														__lowerCamelCase    					=	config_class.from_dict(SCREAMING_SNAKE_CASE__  )
										# set timm attributes to None
										__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	None, None, None

						__lowerCamelCase    					=	use_timm_backbone
						__lowerCamelCase    					=	backbone_config
						__lowerCamelCase    					=	num_channels
						__lowerCamelCase    					=	num_queries
						__lowerCamelCase    					=	d_model
						__lowerCamelCase    					=	encoder_ffn_dim
						__lowerCamelCase    					=	encoder_layers
						__lowerCamelCase    					=	encoder_attention_heads
						__lowerCamelCase    					=	decoder_ffn_dim
						__lowerCamelCase    					=	decoder_layers
						__lowerCamelCase    					=	decoder_attention_heads
						__lowerCamelCase    					=	dropout
						__lowerCamelCase    					=	attention_dropout
						__lowerCamelCase    					=	activation_dropout
						__lowerCamelCase    					=	activation_function
						__lowerCamelCase    					=	init_std
						__lowerCamelCase    					=	init_xavier_std
						__lowerCamelCase    					=	encoder_layerdrop
						__lowerCamelCase    					=	decoder_layerdrop
						__lowerCamelCase    					=	encoder_layers
						__lowerCamelCase    					=	auxiliary_loss
						__lowerCamelCase    					=	position_embedding_type
						__lowerCamelCase    					=	backbone
						__lowerCamelCase    					=	use_pretrained_backbone
						__lowerCamelCase    					=	dilation
						# Hungarian matcher
						__lowerCamelCase    					=	class_cost
						__lowerCamelCase    					=	bbox_cost
						__lowerCamelCase    					=	giou_cost
						# Loss coefficients
						__lowerCamelCase    					=	mask_loss_coefficient
						__lowerCamelCase    					=	dice_loss_coefficient
						__lowerCamelCase    					=	bbox_loss_coefficient
						__lowerCamelCase    					=	giou_loss_coefficient
						__lowerCamelCase    					=	eos_coefficient
						super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )






		@property
		def  __A       (		self		:							List[Any]  )     ->							int:
						return self.encoder_attention_heads






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






		@classmethod
		def  __A       (		cls		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							PretrainedConfig							,			**SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Tuple:
						return cls(backbone_config=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )







		def  __A       (		self		:							Any  )     ->							Dict[str, any]:
						__lowerCamelCase    					=	copy.deepcopy(self.__dict__  )
						if output["backbone_config"] is not None:
										__lowerCamelCase    					=	self.backbone_config.to_dict()
						__lowerCamelCase    					=	self.__class__.model_type
						return output





class    lowerCAmelCase__     (	__lowercase			):
		a__       :						List[str]        =		version.parse("""1.11"""			)






		@property
		def  __A       (		self		:							int  )     ->							Mapping[str, Mapping[int, str]]:
						return OrderedDict(
						    [
						        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
						        ('''pixel_mask''', {0: '''batch'''}),
						    ]  )






		@property
		def  __A       (		self		:							List[Any]  )     ->							float:
						return 1e-5







		@property
		def  __A       (		self		:							Dict  )     ->							int:
						return 12





from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__   :			List[Any]												=    TypeVar("T")




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (position - 1) // 2




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (2 * position) + 1




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (2 * position) + 2

class    lowerCAmelCase__     (	Generic[T]			):






		def __init__(		self		:							List[str]  )     ->							None:
						__lowerCamelCase    					=	[]
						__lowerCamelCase    					=	{}
						__lowerCamelCase    					=	0






		def __len__(		self		:							Optional[int]  )     ->							int:
						return self.elements






		def __repr__(		self		:							Optional[int]  )     ->							str:
						return str(self.heap  )






		def  __A       (		self		:							Union[str, Any]  )     ->							bool:
						# Check if the priority queue is empty
						return self.elements == 0






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Add an element with given priority to the queue
						self.heap.append((elem, weight)  )
						__lowerCamelCase    					=	self.elements
						self.elements += 1
						self._bubble_up(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Any  )     ->							T:
						# Remove and return the element with lowest weight (highest priority)
						if self.elements > 1:
										self._swap_nodes(0							,			self.elements - 1  )
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap.pop()
						del self.position_map[elem]
						self.elements -= 1
						if self.elements > 0:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[0]
										self._bubble_down(SCREAMING_SNAKE_CASE__  )
						return elem






		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Update the weight of the given key
						__lowerCamelCase    					=	self.position_map[elem]
						__lowerCamelCase    					=	(elem, weight)
						if position > 0:
										__lowerCamelCase    					=	get_parent_position(SCREAMING_SNAKE_CASE__  )
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[parent_position]
										if parent_weight > weight:
														self._bubble_up(SCREAMING_SNAKE_CASE__  )
										else:
														self._bubble_down(SCREAMING_SNAKE_CASE__  )
						else:
										self._bubble_down(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Place a node at the proper position (upward movement) [to be used internally
						# only]
						__lowerCamelCase    					=	self.position_map[elem]
						if curr_pos == 0:
										return None
						__lowerCamelCase    					=	get_parent_position(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[curr_pos]
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[parent_position]
						if parent_weight > weight:
										self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										return self._bubble_up(SCREAMING_SNAKE_CASE__  )
						return None






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Place a node at the proper position (downward movement) [to be used
						# internally only]
						__lowerCamelCase    					=	self.position_map[elem]
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[curr_pos]
						__lowerCamelCase    					=	get_child_left_position(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	get_child_right_position(SCREAMING_SNAKE_CASE__  )
						if child_left_position < self.elements and child_right_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_left_position]
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_right_position]
										if child_right_weight < child_left_weight and child_right_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						if child_left_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_left_position]
										if child_left_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						else:
										return None
						if child_right_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_right_position]
										if child_right_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						return None







		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Swap the nodes at the given positions
						__lowerCamelCase    					=	self.heap[nodea_pos][0]
						__lowerCamelCase    					=	self.heap[nodea_pos][0]
						__lowerCamelCase					,    __lowerCamelCase    					=	(
						    self.heap[nodea_pos],
						    self.heap[nodea_pos],
						)
						__lowerCamelCase    					=	nodea_pos
						__lowerCamelCase    					=	nodea_pos





class    lowerCAmelCase__     (	Generic[T]			):






		def __init__(		self		:							Tuple  )     ->							None:
						__lowerCamelCase    					=	{}
						__lowerCamelCase    					=	0






		def __repr__(		self		:							Optional[int]  )     ->							str:
						return str(self.connections  )






		def __len__(		self		:							List[str]  )     ->							int:
						return self.nodes






		def  __A       (		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Add a node in the graph if it is not in the graph
						if node not in self.connections:
										__lowerCamelCase    					=	{}
										self.nodes += 1







		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Add an edge between 2 nodes in the graph
						self.add_node(SCREAMING_SNAKE_CASE__  )
						self.add_node(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	weight
						__lowerCamelCase    					=	weight





def  __magic_name__ (   __lowerCAmelCase	:						GraphUndirectedWeighted[T]		,  )			->   tuple[dict[T, int], dict[T, T | None]]:
				__lowerCamelCase    					=	{node: maxsize for node in graph.connections}
				__lowerCamelCase    					=	{node: None for node in graph.connections}

				__lowerCamelCase    					=	MinPriorityQueue()
				for node, weight in dist.items():
								priority_queue.push(__lowerCAmelCase		,  __lowerCAmelCase							)

				if priority_queue.is_empty():
								return dist, parent

				# initialization
				__lowerCamelCase    					=	priority_queue.extract_min()
				__lowerCamelCase    					=	0
				for neighbour in graph.connections[node]:
								if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
												__lowerCamelCase    					=	dist[node] + graph.connections[node][neighbour]
												priority_queue.update_key(__lowerCAmelCase		,  dist[neighbour]							)
												__lowerCamelCase    					=	node

    # running prim's algorithm
				while not priority_queue.is_empty():
								__lowerCamelCase    					=	priority_queue.extract_min()
								for neighbour in graph.connections[node]:
												if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																__lowerCamelCase    					=	dist[node] + graph.connections[node][neighbour]
																priority_queue.update_key(__lowerCAmelCase		,  dist[neighbour]							)
																__lowerCamelCase    					=	node
				return dist, parent



