




"""simple docstring"""





from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
A   =       0

A   =       [
    [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],
]

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

A   =       tuple[int, int]



class      a__				:


			def __init__(      self      :    Tuple      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Node | None      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					pos_x
						__UpperCAmelCase	: Any		=					pos_y
						__UpperCAmelCase	: Optional[int]		=					(pos_y, pos_x)
						__UpperCAmelCase	: Optional[Any]		=					goal_x
						__UpperCAmelCase	: Dict		=					goal_y
						__UpperCAmelCase	: List[str]		=					g_cost
						__UpperCAmelCase	: Dict		=					parent
						__UpperCAmelCase	: Any		=					self.calculate_heuristic()
						__UpperCAmelCase	: Optional[Any]		=					self.g_cost + self.h_cost


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					self.pos_x - self.goal_x
						__UpperCAmelCase	: Tuple		=					self.pos_y - self.goal_y
						if HEURISTIC == 1:
									return abs(UpperCamelCase_) + abs(UpperCamelCase_)
						else:
									return sqrt(dy**2 + dx**2)


			def __lt__(      self      :    Any      ,  UpperCamelCase_      :    Node):


						"""simple docstring"""




						return self.f_cost < other.f_cost






class      a__				:


			def __init__(      self      :    Optional[Any]      ,  UpperCamelCase_      :    TPosition      ,  UpperCamelCase_      :    TPosition):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					Node(start[1]      ,  start[0]      ,  goal[1]      ,  goal[0]      ,  0      ,  UpperCamelCase_)
						__UpperCAmelCase	: str		=					Node(goal[1]      ,  goal[0]      ,  goal[1]      ,  goal[0]      ,  99999      ,  UpperCamelCase_)

						__UpperCAmelCase	: Any		=					[self.start]
						__UpperCAmelCase	: list[Node]		=					[]

						__UpperCAmelCase	: Dict		=					False


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						while self.open_nodes:
									# Open Nodes are sorted using __lt__
									self.open_nodes.sort()
									__UpperCAmelCase	: Optional[int]		=					self.open_nodes.pop(0)

									if current_node.pos == self.target.pos:
												return self.retrace_path(UpperCamelCase_)

									self.closed_nodes.append(UpperCamelCase_)
									__UpperCAmelCase	: Optional[Any]		=					self.get_successors(UpperCamelCase_)

									for child_node in successors:
												if child_node in self.closed_nodes:
															continue

												if child_node not in self.open_nodes:
															self.open_nodes.append(UpperCamelCase_)
												else:
															# retrieve the best current path
															__UpperCAmelCase	: Any		=					self.open_nodes.pop(self.open_nodes.index(UpperCamelCase_))

															if child_node.g_cost < better_node.g_cost:
																		self.open_nodes.append(UpperCamelCase_)
															else:
																		self.open_nodes.append(UpperCamelCase_)

						return [self.start.pos]


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    Node):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					[]
						for action in delta:
									__UpperCAmelCase	: Dict		=					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(UpperCamelCase_) - 1):
												continue

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

									successors.append(
									    Node(
									        UpperCamelCase_      ,  UpperCamelCase_      ,  self.target.pos_y      ,  self.target.pos_x      ,  parent.g_cost + 1      ,  UpperCamelCase_      ,  ))
						return successors


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    Node | None):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					node
						__UpperCAmelCase	: Tuple		=					[]
						while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x))
									__UpperCAmelCase	: Optional[Any]		=					current_node.parent
						path.reverse()
						return path






class      a__				:


			def __init__(      self      :    str      ,  UpperCamelCase_      :    TPosition      ,  UpperCamelCase_      :    TPosition):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					AStar(UpperCamelCase_      ,  UpperCamelCase_)
						__UpperCAmelCase	: str		=					AStar(UpperCamelCase_      ,  UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					False


			def       a_     (      self      :    int):


						"""simple docstring"""




						while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
									self.fwd_astar.open_nodes.sort()
									self.bwd_astar.open_nodes.sort()
									__UpperCAmelCase	: List[Any]		=					self.fwd_astar.open_nodes.pop(0)
									__UpperCAmelCase	: Optional[int]		=					self.bwd_astar.open_nodes.pop(0)

									if current_bwd_node.pos == current_fwd_node.pos:
												return self.retrace_bidirectional_path(
												    UpperCamelCase_      ,  UpperCamelCase_)

									self.fwd_astar.closed_nodes.append(UpperCamelCase_)
									self.bwd_astar.closed_nodes.append(UpperCamelCase_)

									__UpperCAmelCase	: Optional[Any]		=					current_bwd_node
									__UpperCAmelCase	: Any		=					current_fwd_node

									__UpperCAmelCase	: int		=					{
									    self.fwd_astar: self.fwd_astar.get_successors(UpperCamelCase_),
									    self.bwd_astar: self.bwd_astar.get_successors(UpperCamelCase_),
									}

									for astar in [self.fwd_astar, self.bwd_astar]:
												for child_node in successors[astar]:
															if child_node in astar.closed_nodes:
																		continue

															if child_node not in astar.open_nodes:
																		astar.open_nodes.append(UpperCamelCase_)
															else:
																		# retrieve the best current path
																		__UpperCAmelCase	: str		=					astar.open_nodes.pop(
																		    astar.open_nodes.index(UpperCamelCase_))

																		if child_node.g_cost < better_node.g_cost:
																					astar.open_nodes.append(UpperCamelCase_)
																		else:
																					astar.open_nodes.append(UpperCamelCase_)

						return [self.fwd_astar.start.pos]


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    Node      ,  UpperCamelCase_      :    Node):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					self.fwd_astar.retrace_path(UpperCamelCase_)
						__UpperCAmelCase	: str		=					self.bwd_astar.retrace_path(UpperCamelCase_)
						bwd_path.pop()
						bwd_path.reverse()
						__UpperCAmelCase	: Any		=					fwd_path + bwd_path
						return path


if __name__ == "__main__":
					# all coordinates are given in format [y,x]
					A   =       (0, 0)
					A   =       (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					A   =       time.time()
					A   =       AStar(init, goal)
					A   =       a_star.search()
					A   =       time.time() - start_time
					print(f'''AStar execution time = {end_time:f} seconds''')

					A   =       time.time()
					A   =       BidirectionalAStar(init, goal)
					A   =       time.time() - bd_start_time
					print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


A   =       logging.get_logger(__name__)

A   =       {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}



class      a__				(	__magic_name__   ):
			lowercase_    =	"table-transformer"
			lowercase_    =	["past_key_values"]
			lowercase_    =	{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}


			def __init__(      self      :    List[Any]      ,  UpperCamelCase_      :    int=True      ,  UpperCamelCase_      :    Tuple=None      ,  UpperCamelCase_      :    Optional[int]=3      ,  UpperCamelCase_      :    Any=100      ,  UpperCamelCase_      :    Dict=6      ,  UpperCamelCase_      :    List[str]=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=6      ,  UpperCamelCase_      :    Dict=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=0.0      ,  UpperCamelCase_      :    str=0.0      ,  UpperCamelCase_      :    Union[str, Any]=True      ,  UpperCamelCase_      :    Optional[int]="relu"      ,  UpperCamelCase_      :    str=256      ,  UpperCamelCase_      :    str=0.1      ,  UpperCamelCase_      :    Tuple=0.0      ,  UpperCamelCase_      :    Dict=0.0      ,  UpperCamelCase_      :    Dict=0.02      ,  UpperCamelCase_      :    Optional[Any]=1.0      ,  UpperCamelCase_      :    Optional[int]=False      ,  UpperCamelCase_      :    int="sine"      ,  UpperCamelCase_      :    Optional[Any]="resnet50"      ,  UpperCamelCase_      :    List[str]=True      ,  UpperCamelCase_      :    int=False      ,  UpperCamelCase_      :    Tuple=1      ,  UpperCamelCase_      :    Any=5      ,  UpperCamelCase_      :    Optional[Any]=2      ,  UpperCamelCase_      :    int=1      ,  UpperCamelCase_      :    List[str]=1      ,  UpperCamelCase_      :    List[str]=5      ,  UpperCamelCase_      :    Union[str, Any]=2      ,  UpperCamelCase_      :    Optional[int]=0.1      ,  **UpperCamelCase_      :    int      ,  ):


						"""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.")
												__UpperCAmelCase	: int		=					CONFIG_MAPPING["resnet"](out_features=["stage4"])
									elif isinstance(UpperCamelCase_      ,  UpperCamelCase_):
												__UpperCAmelCase	: List[Any]		=					backbone_config.get("model_type")
												__UpperCAmelCase	: List[Any]		=					CONFIG_MAPPING[backbone_model_type]
												__UpperCAmelCase	: Optional[int]		=					config_class.from_dict(UpperCamelCase_)
									# set timm attributes to None
									__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: List[Any]		=					None, None, None

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


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.encoder_attention_heads


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.d_model






class      a__				(	__magic_name__   ):
			lowercase_    =	version.parse("1.11"   )


			@property
			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						        ("pixel_mask", {0: "batch"}),
						    ])


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return 1e-5


			@property
			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						return 12


