




"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Union[List[PIL.Image.Image], np.ndarray]
				__lowerCAmelCase    :    Optional[List[bool]]


if is_transformers_available() and is_torch_available():
		from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline











"""simple docstring"""

from queue import PriorityQueue
from typing import Any

import numpy as np

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      ):




	"""simple docstring"""






	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A_  :				Tuple	   =		cst_fwd.get(_UpperCAmelCase  ,      np.inf	)
		A_  :				Any	   =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt)	)
			A_  :				Tuple	   =		new_cost_f
			A_  :				Dict	   =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A_  :				Optional[Any]	   =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Tuple	   =		-1

	A_  :				Any	   =		set()
	A_  :				Tuple	   =		set()
	A_  :				Optional[int]	   =		{source: 0}
	A_  :				Dict	   =		{destination: 0}
	A_  :				Any	   =		{source: None}
	A_  :				Optional[Any]	   =		{destination: None}
	A_  :				PriorityQueue[Any]	   =		PriorityQueue()
	A_  :				PriorityQueue[Any]	   =		PriorityQueue()

	A_  :				Union[str, 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_  :				Union[str, Any]	   =		queue_forward.get()
		visited_forward.add(_UpperCAmelCase	)

		A_   , A_  :				Optional[int]	   =		queue_backward.get()
		visited_backward.add(_UpperCAmelCase	)

		A_  :				Any	   =		pass_and_relaxation(
		    _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      )

		A_  :				Dict	   =		pass_and_relaxation(
		    _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      )

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

	if shortest_distance != np.inf:
		A_  :				Optional[Any]	   =		shortest_distance
	return shortest_path_distance


_lowerCamelCase  :			Optional[Any]        =       {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
_lowerCamelCase  :			Any        =       {
    '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()





