
from heapq import heappop, heappush

import numpy as np



def 		UpperCamelCase     (       _UpperCAmelCase  :   np.ndarray ,			_UpperCAmelCase  :   tuple[int, int] ,			_UpperCAmelCase  :   tuple[int, int] ,			_UpperCAmelCase  :   bool ,			)   ->			tuple[float | int, list[tuple[int, int]]]:






						'''simple docstring'''

						_lowercase      ,    _lowercase       :	Tuple           =   grid.shape
						_lowercase       :	Union[str, Any]           =   [-1, 1, 0, 0]
						_lowercase       :	Optional[int]           =   [0, 0, -1, 1]
						if allow_diagonal:
												dx += [-1, -1, 1, 1]
												dy += [-1, 1, -1, 1]

						_lowercase      ,    _lowercase       :	Optional[int]           =   [(0, source)], set()
						_lowercase       :	List[Any]           =   np.full((rows, cols) ,			np.inf  )
						_lowercase       :	Optional[int]           =   0
						_lowercase       :	Any           =   np.empty((rows, cols) ,			dtype=_UpperCAmelCase  )
						_lowercase       :	Optional[int]           =   None

						while queue:
												((_lowercase)      ,    (_lowercase))       :	List[str]           =   heappop(_UpperCAmelCase  )
												if (x, y) in visited:
																		continue
												visited.add((x, y)  )

												if (x, y) == destination:
																		_lowercase       :	int           =   []
																		while (x, y) != source:
																								path.append((x, y)  )
																								_lowercase      ,    _lowercase       :	Union[str, Any]           =   predecessors[x, y]
																		path.append(_UpperCAmelCase  )  # add the source manually
																		path.reverse()
																		return matrix[destination], path

												for i in range(len(_UpperCAmelCase  )  ):
																		_lowercase      ,    _lowercase       :	List[Any]           =   x + dx[i], y + dy[i]
																		if 0 <= nx < rows and 0 <= ny < cols:
																								_lowercase       :	List[Any]           =   grid[nx][ny]
																								if next_node == 1 and matrix[nx, ny] > dist + 1:
																														heappush(_UpperCAmelCase ,			(dist + 1, (nx, ny))  )
																														_lowercase       :	str           =   dist + 1
																														_lowercase       :	Any           =   (x, y)

						return np.inf, []


if __name__ == "__main__":
							import doctest

							doctest.testmod()






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:     Optional[int]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   "timm_backbone"






	def __init__(self				: Any      ,	snake_case				: List[Any]=None      ,	snake_case				: int=3      ,	snake_case				: Dict=True      ,	snake_case				: Union[str, Any]=True      ,	snake_case				: Any=None      ,	**snake_case				: int      ,	) ->			Optional[int]:
							super().__init__(**snake_case    )
							_lowercase       :	Dict           =   backbone
							_lowercase       :	Optional[Any]           =   num_channels
							_lowercase       :	Union[str, Any]           =   features_only
							_lowercase       :	Tuple           =   use_pretrained_backbone
							_lowercase       :	List[str]           =   True
							_lowercase       :	Tuple           =   out_indices if out_indices is not None else (-1,)




