





from queue import PriorityQueue
from typing import Any

import numpy as np






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, ):
			for nxt, d in graph[v]:
						if nxt in visited_forward:
									continue
						a__	:						Dict      		=  cst_fwd.get(lowerCamelCase							, np.inf   )
						a__	:						Union[str, Any]      		=  cst_fwd[v] + d
						if new_cost_f < old_cost_f:
									queue.put((new_cost_f, nxt)   )
									a__	:						Union[str, Any]      		=  new_cost_f
									a__	:						Tuple      		=  v
						if nxt in visited_backward:
									if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
												a__	:						int      		=  cst_fwd[v] + d + cst_bwd[nxt]
			return shortest_distance






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
			a__	:						str      		=  -1

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

			a__	:						Optional[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__	:						List[str]      		=  queue_forward.get()
						visited_forward.add(lowerCamelCase   )

						a__		,     a__	:						Optional[Any]      		=  queue_backward.get()
						visited_backward.add(lowerCamelCase   )

						a__	:						Optional[int]      		=  pass_and_relaxation(
						    lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, )

						a__	:						Optional[Any]      		=  pass_and_relaxation(
						    lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, )

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

			if shortest_distance != np.inf:
						a__	:						Tuple      		=  shortest_distance
			return shortest_path_distance


SCREAMING_SNAKE_CASE__       :			int			      =						{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
SCREAMING_SNAKE_CASE__       :			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()








import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  SCREAMING_SNAKE_CASE__       :			int			      =						{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  SCREAMING_SNAKE_CASE__       :			Dict			      =						{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }






def       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  (images / 2 + 0.5).clamp(0							, 1   )
   a__	:						List[Any]      		=  images.cpu().permute(0							, 2							, 3							, 1   ).float().numpy()
   a__	:						int      		=  numpy_to_pil(lowerCamelCase   )
   return images






def       _A  (      lowerCamelCase   ):
   if images.ndim == 3:
      a__	:						Tuple      		=  images[None, ...]
   a__	:						Dict      		=  (images * 255).round().astype("uint8"   )
   if images.shape[-1] == 1:
      # special case for grayscale (single channel) images
      a__	:						str      		=  [Image.fromarray(image.squeeze()							, mode="L"   ) for image in images]
   else:
      a__	:						List[Any]      		=  [Image.fromarray(lowerCamelCase   ) for image in images]

   return pil_images

