


from heapq import heappop, heappush

import numpy as np




def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    )	-> tuple[float | int, list[tuple[int, int]]]:




      '''simple docstring'''

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

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

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

            if (x, y) == destination:
                  SCREAMING_SNAKE_CASE__       :  Any						     =		[]
                  while (x, y) != source:
                        path.append((x, y)							)
                        SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Any						     =		predecessors[x, y]
                  path.append(A__							)  # add the source manually
                  path.reverse()
                  return matrix[destination], path

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

      return np.inf, []


if __name__ == "__main__":
    import doctest

    doctest.testmod()










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCAmelCase:						Optional[Any]   				=       {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						List[str]   				=       [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	_lowerCAmelCase:						List[Any]   				=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

