






"""simple docstring"""
from __future__ import annotations

import time

lowerCamelCase_					=      list[tuple[int, int]]

lowerCamelCase_					=      [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCamelCase_					=      [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right







class 			UpperCamelCase_       :





	def __init__(      self					: Optional[int]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Node | None						)						->			Dict:
				UpperCAmelCase_     : Any			     =					pos_x
				UpperCAmelCase_     : str			     =					pos_y
				UpperCAmelCase_     : int			     =					(pos_y, pos_x)
				UpperCAmelCase_     : int			     =					goal_x
				UpperCAmelCase_     : Tuple			     =					goal_y
				UpperCAmelCase_     : Union[str, Any]			     =					parent







class 			UpperCamelCase_       :





	def __init__(      self					: List[Any]	,	lowerCAmelCase_					: tuple[int, int]	,	lowerCAmelCase_					: tuple[int, int]						)						->			Tuple:
				UpperCAmelCase_     : List[str]			     =					Node(start[1]	,	start[0]	,	goal[1]	,	goal[0]	,	lowerCAmelCase_						)
				UpperCAmelCase_     : int			     =					Node(goal[1]	,	goal[0]	,	goal[1]	,	goal[0]	,	lowerCAmelCase_						)

				UpperCAmelCase_     : Union[str, Any]			     =					[self.start]
				UpperCAmelCase_     : int			     =					False





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Path | None:
				while self.node_queue:
							UpperCAmelCase_     : str			     =					self.node_queue.pop(0						)

							if current_node.pos == self.target.pos:
										UpperCAmelCase_     : Optional[Any]			     =					True
										return self.retrace_path(lowerCAmelCase_						)

							UpperCAmelCase_     : Optional[Any]			     =					self.get_successors(lowerCAmelCase_						)

							for node in successors:
										self.node_queue.append(lowerCAmelCase_						)

				if not self.reached:
							return [self.start.pos]
				return None





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: Node						)						->			list[Node]:
				UpperCAmelCase_     : List[str]			     =					[]
				for action in delta:
							UpperCAmelCase_     : List[Any]			     =					parent.pos_x + action[1]
							UpperCAmelCase_     : List[str]			     =					parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(lowerCAmelCase_						) - 1):
										continue

							if grid[pos_y][pos_x] != 0:
										continue

							successors.append(
							    Node(lowerCAmelCase_	,	lowerCAmelCase_	,	self.target.pos_y	,	self.target.pos_x	,	lowerCAmelCase_						)						)
				return successors





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: Node | None						)						->			Path:
				UpperCAmelCase_     : Union[str, Any]			     =					node
				UpperCAmelCase_     : Union[str, Any]			     =					[]
				while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)						)
							UpperCAmelCase_     : Tuple			     =					current_node.parent
				path.reverse()
				return path







class 			UpperCamelCase_       :





	def __init__(      self					: str	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Tuple						)						->			Union[str, Any]:
				UpperCAmelCase_     : Optional[int]			     =					BreadthFirstSearch(lowerCAmelCase_	,	lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					BreadthFirstSearch(lowerCAmelCase_	,	lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[Any]			     =					False





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Path | None:
				while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
							UpperCAmelCase_     : int			     =					self.fwd_bfs.node_queue.pop(0						)
							UpperCAmelCase_     : Dict			     =					self.bwd_bfs.node_queue.pop(0						)

							if current_bwd_node.pos == current_fwd_node.pos:
										UpperCAmelCase_     : str			     =					True
										return self.retrace_bidirectional_path(
										    lowerCAmelCase_	,	lowerCAmelCase_						)

							UpperCAmelCase_     : str			     =					current_bwd_node
							UpperCAmelCase_     : List[str]			     =					current_fwd_node

							UpperCAmelCase_     : Tuple			     =					{
							    self.fwd_bfs: self.fwd_bfs.get_successors(lowerCAmelCase_						),
							    self.bwd_bfs: self.bwd_bfs.get_successors(lowerCAmelCase_						),
							}

							for bfs in [self.fwd_bfs, self.bwd_bfs]:
										for node in successors[bfs]:
													bfs.node_queue.append(lowerCAmelCase_						)

				if not self.reached:
							return [self.fwd_bfs.start.pos]
				return None





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: Node	,	lowerCAmelCase_					: Node						)						->			Path:
				UpperCAmelCase_     : Optional[Any]			     =					self.fwd_bfs.retrace_path(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					self.bwd_bfs.retrace_path(lowerCAmelCase_						)
				bwd_path.pop()
				bwd_path.reverse()
				UpperCAmelCase_     : str			     =					fwd_path + bwd_path
				return path


if __name__ == "__main__":
			# all coordinates are given in format [y,x]
			import doctest

			doctest.testmod()
			lowerCamelCase_					=      (0, 0)
			lowerCamelCase_					=      (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			lowerCamelCase_					=      time.time()
			lowerCamelCase_					=      BreadthFirstSearch(init, goal)
			lowerCamelCase_					=      bfs.search()
			lowerCamelCase_					=      time.time() - start_bfs_time

			print('''Unidirectional BFS computation time : ''', bfs_time)

			lowerCamelCase_					=      time.time()
			lowerCamelCase_					=      BidirectionalBreadthFirstSearch(init, goal)
			lowerCamelCase_					=      bd_bfs.search()
			lowerCamelCase_					=      time.time() - start_bd_bfs_time

			print('''Bidirectional BFS computation time : ''', bd_bfs_time)











"""simple docstring"""
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


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''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 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''detr'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}





	def __init__(      self					: Dict	,	lowerCAmelCase_					: str=True	,	lowerCAmelCase_					: Union[str, Any]=None	,	lowerCAmelCase_					: Dict=3	,	lowerCAmelCase_					: List[Any]=100	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: int=8	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: Any=8	,	lowerCAmelCase_					: Dict=0.0	,	lowerCAmelCase_					: Union[str, Any]=0.0	,	lowerCAmelCase_					: Dict=True	,	lowerCAmelCase_					: List[Any]="relu"	,	lowerCAmelCase_					: List[Any]=256	,	lowerCAmelCase_					: Tuple=0.1	,	lowerCAmelCase_					: Optional[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0_2	,	lowerCAmelCase_					: Optional[int]=1.0	,	lowerCAmelCase_					: str=False	,	lowerCAmelCase_					: Tuple="sine"	,	lowerCAmelCase_					: str="resnet50"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: int=False	,	lowerCAmelCase_					: Optional[Any]=1	,	lowerCAmelCase_					: int=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: Dict=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: Union[str, Any]=0.1	,	**lowerCAmelCase_					: Dict	,	)						->			int:
				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."						)
										UpperCAmelCase_     : Tuple			     =					CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
							elif isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
										UpperCAmelCase_     : Dict			     =					backbone_config.get("model_type"						)
										UpperCAmelCase_     : Dict			     =					CONFIG_MAPPING[backbone_model_type]
										UpperCAmelCase_     : Tuple			     =					config_class.from_dict(lowerCAmelCase_						)
							# set timm attributes to None
							UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					None, None, None

				UpperCAmelCase_     : str			     =					use_timm_backbone
				UpperCAmelCase_     : Optional[Any]			     =					backbone_config
				UpperCAmelCase_     : Tuple			     =					num_channels
				UpperCAmelCase_     : Dict			     =					num_queries
				UpperCAmelCase_     : str			     =					d_model
				UpperCAmelCase_     : Any			     =					encoder_ffn_dim
				UpperCAmelCase_     : Union[str, Any]			     =					encoder_layers
				UpperCAmelCase_     : Optional[int]			     =					encoder_attention_heads
				UpperCAmelCase_     : List[str]			     =					decoder_ffn_dim
				UpperCAmelCase_     : Tuple			     =					decoder_layers
				UpperCAmelCase_     : Optional[int]			     =					decoder_attention_heads
				UpperCAmelCase_     : List[Any]			     =					dropout
				UpperCAmelCase_     : Union[str, Any]			     =					attention_dropout
				UpperCAmelCase_     : int			     =					activation_dropout
				UpperCAmelCase_     : List[str]			     =					activation_function
				UpperCAmelCase_     : Optional[int]			     =					init_std
				UpperCAmelCase_     : Union[str, Any]			     =					init_xavier_std
				UpperCAmelCase_     : List[str]			     =					encoder_layerdrop
				UpperCAmelCase_     : Tuple			     =					decoder_layerdrop
				UpperCAmelCase_     : str			     =					encoder_layers
				UpperCAmelCase_     : Any			     =					auxiliary_loss
				UpperCAmelCase_     : Optional[int]			     =					position_embedding_type
				UpperCAmelCase_     : List[str]			     =					backbone
				UpperCAmelCase_     : int			     =					use_pretrained_backbone
				UpperCAmelCase_     : Any			     =					dilation
				# Hungarian matcher
				UpperCAmelCase_     : str			     =					class_cost
				UpperCAmelCase_     : Any			     =					bbox_cost
				UpperCAmelCase_     : int			     =					giou_cost
				# Loss coefficients
				UpperCAmelCase_     : List[str]			     =					mask_loss_coefficient
				UpperCAmelCase_     : Dict			     =					dice_loss_coefficient
				UpperCAmelCase_     : Any			     =					bbox_loss_coefficient
				UpperCAmelCase_     : Union[str, Any]			     =					giou_loss_coefficient
				UpperCAmelCase_     : int			     =					eos_coefficient
				super().__init__(is_encoder_decoder=lowerCAmelCase_	,	**lowerCAmelCase_						)





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





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





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





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







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				version.parse('''1.11'''     )





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





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			float:
				return 1e-5





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			int:
				return 12



