
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    Dict:






							'''simple docstring'''





							__UpperCamelCase	:   Dict			   =					[0] * len(_lowerCamelCase)
							__UpperCamelCase	:   List[str]			   =					[]
							__UpperCamelCase	:   Dict			   =					[]
							__UpperCamelCase	:   Optional[Any]			   =					0

							for values in graph.values():
														for i in values:
																					indegree[i] += 1

							for i in range(len(_lowerCamelCase)):
														if indegree[i] == 0:
																					queue.append(_lowerCamelCase)

							while queue:
														__UpperCamelCase	:   Optional[int]			   =					queue.pop(0)
														cnt += 1
														topo.append(_lowerCamelCase)
														for x in graph[vertex]:
																					indegree[x] -= 1
																					if indegree[x] == 0:
																												queue.append(_lowerCamelCase)

							if cnt != len(_lowerCamelCase):
														print("Cycle exists")
							else:
														print(_lowerCamelCase)


# Adjacency List of Graph
lowercase  :					Any													=       {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}




topological_sort(graph)

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'):
		lowercase  :					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:
		lowercase  :					Any													=       {
		    'linear': PIL.Image.LINEAR,
		    'bilinear': PIL.Image.BILINEAR,
		    'bicubic': PIL.Image.BICUBIC,
		    'lanczos': PIL.Image.LANCZOS,
		    'nearest': PIL.Image.NEAREST,
		}




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					(images / 2 + 0.5).clamp(0						,  1)
							__UpperCamelCase	:   int			   =					images.cpu().permute(0						,  2						,  3						,  1).float().numpy()
							__UpperCamelCase	:   Dict			   =					numpy_to_pil(_lowerCamelCase)
							return images




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any])					->    Optional[Any]:






							'''simple docstring'''





							if images.ndim == 3:
														__UpperCamelCase	:   Optional[int]			   =					images[None, ...]
							__UpperCamelCase	:   Dict			   =					(images * 255).round().astype("uint8")
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														__UpperCamelCase	:   int			   =					[Image.fromarray(image.squeeze()						,  mode="L") for image in images]
							else:
														__UpperCamelCase	:   Union[str, Any]			   =					[Image.fromarray(_lowerCamelCase) for image in images]

							return pil_images