



from heapq import heappop, heappush

import numpy as np

def        a						(      _UpperCAmelCase							:					np.ndarray ,   _UpperCAmelCase							:					tuple[int, int] ,   _UpperCAmelCase							:					tuple[int, int] ,   _UpperCAmelCase							:					bool ,   ):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	grid.shape
		__UpperCAmelCase    :     str													=	[-1, 1, 0, 0]
		__UpperCAmelCase    :     Union[str, Any]													=	[0, 0, -1, 1]
		if allow_diagonal:
				dx += [-1, -1, 1, 1]
				dy += [-1, 1, -1, 1]

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

		while queue:
				((__UpperCAmelCase)							,					(__UpperCAmelCase))    :     Any													=	heappop(_UpperCAmelCase						)
				if (x, y) in visited:
						continue
				visited.add((x, y)						)

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

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

		return np.inf, []


if __name__ == "__main__":
				import doctest

				doctest.testmod()










from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
				from .pipeline_kandinsky import KandinskyPipeline
				from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
				from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
				from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
				from .text_encoder import MultilingualCLIP





