

"""simple docstring"""




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


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

A_	:			List[str]  	=      {
    '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						(_lowerCamelCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Tuple  							=	'''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__							:   Any					       =		image_size
						A__							:   str					       =		num_channels
						A__							:   Union[str, Any]					       =		kernel_size
						A__							:   List[str]					       =		stride
						A__							:   List[Any]					       =		padding
						A__							:   Optional[Any]					       =		hidden_sizes
						A__							:   str					       =		num_attention_heads
						A__							:   Any					       =		depths
						A__							:   str					       =		key_dim
						A__							:   Any					       =		drop_path_rate
						A__							:   Optional[Any]					       =		patch_size
						A__							:   List[Any]					       =		attention_ratio
						A__							:   Any					       =		mlp_ratio
						A__							:   Union[str, Any]					       =		initializer_range
						A__							:   Optional[Any]					       =		[
						    ["""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						(_lowerCamelCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	version.parse('''1.11'''    )






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






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






from queue import PriorityQueue
from typing import Any

import numpy as np

def 						UpperCamelCase (lowercase_:			dict   ,				lowercase_:			str   ,				lowercase_:			set   ,				lowercase_:			set   ,				lowercase_:			dict   ,				lowercase_:			dict   ,				lowercase_:			PriorityQueue   ,				lowercase_:			dict   ,				lowercase_:			float | int   ,				)					->				float | int:
	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A__							:   Any					       =		cst_fwd.get(lowercase_   ,				np.inf )
		A__							:   List[Any]					       =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt) )
			A__							:   Tuple					       =		new_cost_f
			A__							:   Any					       =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A__							:   Optional[int]					       =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			dict   ,				lowercase_:			dict )					->				int:
	A__							:   Dict					       =		-1

	A__							:   List[Any]					       =		set()
	A__							:   Union[str, Any]					       =		set()
	A__							:   Optional[Any]					       =		{source: 0}
	A__							:   int					       =		{destination: 0}
	A__							:   Optional[Any]					       =		{source: None}
	A__							:   Union[str, Any]					       =		{destination: None}
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()

	A__							:   List[Any]					       =		np.inf

	queue_forward.put((0, source) )
	queue_backward.put((0, destination) )

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		A__       ,       A__							:   Tuple					       =		queue_forward.get()
		visited_forward.add(lowercase_ )

		A__       ,       A__							:   Optional[Any]					       =		queue_backward.get()
		visited_backward.add(lowercase_ )

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		A__							:   int					       =		shortest_distance
	return shortest_path_distance


A_	:			List[Any]  	=      {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A_	:			Optional[int]  	=      {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
	import doctest

	doctest.testmod()



