



def        __a			(   __lowerCAmelCase   )     ->     int:
					SCREAMING_SNAKE_CASE							:     int						=				[0] * len(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				[]
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				[]
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				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:
										SCREAMING_SNAKE_CASE							:     List[str]						=				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
_lowerCamelCase    :		Dict		      =    {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}



topological_sort(graph)




from datetime import datetime

import matplotlib.pyplot as plt
import torch

def        __a			(   __lowerCAmelCase   )     ->     int:
					for param in module.parameters():
										SCREAMING_SNAKE_CASE							:     List[Any]						=				False

def        __a			(   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										SCREAMING_SNAKE_CASE							:     List[str]						=				'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'   )
					return device

def        __a			(   __lowerCAmelCase   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				plt.imshow(__lowerCAmelCase   )
					fig.axes.get_xaxis().set_visible(__lowerCAmelCase   )
					fig.axes.get_yaxis().set_visible(__lowerCAmelCase   )
					plt.show()



def        __a			(   )     ->     Optional[Any]:
					SCREAMING_SNAKE_CASE							:     str						=				datetime.now()
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				current_time.strftime('%H:%M:%S'   )
					return timestamp