





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


UpperCAmelCase						:   List[str]			  =     logging.get_logger(__name__)

UpperCAmelCase						:   List[Any]			  =     {
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  int					    =	'''levit'''



				def __init__(			self     ,					_A=224     ,					_A=3     ,					_A=3     ,					_A=2     ,					_A=1     ,					_A=16     ,					_A=[128, 256, 384]     ,					_A=[4, 8, 12]     ,					_A=[4, 4, 4]     ,					_A=[16, 16, 16]     ,					_A=0     ,					_A=[2, 2, 2]     ,					_A=[2, 2, 2]     ,					_A=0.0_2     ,					**_A     ,					):
									super().__init__(**_A     )
									__A	:						Tuple       =				image_size
									__A	:						int       =				num_channels
									__A	:						List[str]       =				kernel_size
									__A	:						Union[str, Any]       =				stride
									__A	:						Optional[int]       =				padding
									__A	:						Tuple       =				hidden_sizes
									__A	:						Optional[int]       =				num_attention_heads
									__A	:						List[str]       =				depths
									__A	:						Tuple       =				key_dim
									__A	:						str       =				drop_path_rate
									__A	:						str       =				patch_size
									__A	:						Dict       =				attention_ratio
									__A	:						Union[str, Any]       =				mlp_ratio
									__A	:						List[str]       =				initializer_range
									__A	:						Dict       =				[
									    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
									    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
									]





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  int					    =	version.parse('''1.11'''   )



				@property
				def 					UpperCAmelCase_		(			self     ):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]     )





				@property
				def 					UpperCAmelCase_		(			self     ):
									return 1e-4











from heapq import heappop, heappush

import numpy as np







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a					,						)      ->      tuple[float | int, list[tuple[int, int]]]:
					__A    ,				__A	:						int       =				grid.shape
					__A	:						Any       =				[-1, 1, 0, 0]
					__A	:						Optional[Any]       =				[0, 0, -1, 1]
					if allow_diagonal:
										dx += [-1, -1, 1, 1]
										dy += [-1, 1, -1, 1]

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

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

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

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

					return np.inf, []


if __name__ == "__main__":
				import doctest

				doctest.testmod()




