





"""simple docstring"""





from heapq import heappop, heappush

import numpy as np


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	):




		"""simple docstring"""


		UpperCamelCase   ,				UpperCamelCase   =	grid.shape
		UpperCamelCase   =	[-1, 1, 0, 0]
		UpperCamelCase   =	[0, 0, -1, 1]
		if allow_diagonal:
				dx += [-1, -1, 1, 1]
				dy += [-1, 1, -1, 1]

		UpperCamelCase   ,				UpperCamelCase   =	[(0, source)], set()
		UpperCamelCase   =	np.full((rows, cols)  ,	np.inf    )
		UpperCamelCase   =	0
		UpperCamelCase   =	np.empty((rows, cols)  ,	dtype=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	None

		while queue:
				((UpperCamelCase)   ,				(UpperCamelCase))   =	heappop(_SCREAMING_SNAKE_CASE    )
				if (x, y) in visited:
						continue
				visited.add((x, y)    )

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

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

		return np.inf, []


if __name__ == "__main__":
						import doctest

						doctest.testmod()













"""simple docstring"""





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


lowerCAmelCase__     			=			logging.get_logger(__name__)

lowerCAmelCase__     			=			{
    '''microsoft/swinv2-tiny-patch4-window8-256''': (
        '''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
    ),
}





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"swinv2"

						UpperCAmelCase_ =			{
						    "num_attention_heads": "num_heads",
						    "num_hidden_layers": "num_layers",
						}


						def __init__(self   ,							__a=2_24   ,							__a=4   ,							__a=3   ,							__a=96   ,							__a=[2, 2, 6, 2]   ,							__a=[3, 6, 12, 24]   ,							__a=7   ,							__a=4.0   ,							__a=True   ,							__a=0.0   ,							__a=0.0   ,							__a=0.1   ,							__a="gelu"   ,							__a=False   ,							__a=0.02   ,							__a=1e-5   ,							__a=32   ,							**__a   ,							) ->   Optional[Any]:
								super().__init__(**__a		)

								UpperCamelCase   =	image_size
								UpperCamelCase   =	patch_size
								UpperCamelCase   =	num_channels
								UpperCamelCase   =	embed_dim
								UpperCamelCase   =	depths
								UpperCamelCase   =	len(__a		)
								UpperCamelCase   =	num_heads
								UpperCamelCase   =	window_size
								UpperCamelCase   =	mlp_ratio
								UpperCamelCase   =	qkv_bias
								UpperCamelCase   =	hidden_dropout_prob
								UpperCamelCase   =	attention_probs_dropout_prob
								UpperCamelCase   =	drop_path_rate
								UpperCamelCase   =	hidden_act
								UpperCamelCase   =	use_absolute_embeddings
								UpperCamelCase   =	layer_norm_eps
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	encoder_stride
								# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								UpperCamelCase   =	int(embed_dim * 2 ** (len(__a		) - 1)		)
								UpperCamelCase   =	(0, 0, 0, 0)






