




SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Optional[int]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[False] * len(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						[s]
			SCREAMING_SNAKE_CASE_								=						True

			while queue:
						SCREAMING_SNAKE_CASE_								=						queue.pop(0 )
						for ind in range(len(graph[u] ) ):
									if visited[ind] is False and graph[u][ind] > 0:
												queue.append(SCREAMING_SNAKE_CASE )
												SCREAMING_SNAKE_CASE_								=						True
												SCREAMING_SNAKE_CASE_								=						u

			return visited[t]

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Tuple:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[-1] * (len(SCREAMING_SNAKE_CASE ))
			SCREAMING_SNAKE_CASE_								=						0
			SCREAMING_SNAKE_CASE_								=						[]
			SCREAMING_SNAKE_CASE_								=						[i[:] for i in graph]  # Record original cut, copy.
			while bfs(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ):
						SCREAMING_SNAKE_CASE_								=						float('Inf' )
						SCREAMING_SNAKE_CASE_								=						sink

						while s != source:
									# Find the minimum value in select path
									SCREAMING_SNAKE_CASE_								=						min(SCREAMING_SNAKE_CASE       ,    graph[parent[s]][s] )
									SCREAMING_SNAKE_CASE_								=						parent[s]

						max_flow += path_flow
						SCREAMING_SNAKE_CASE_								=						sink

						while v != source:
									SCREAMING_SNAKE_CASE_								=						parent[v]
									graph[u][v] -= path_flow
									graph[v][u] += path_flow
									SCREAMING_SNAKE_CASE_								=						parent[v]

			for i in range(len(SCREAMING_SNAKE_CASE ) ):
						for j in range(len(graph[0] ) ):
									if graph[i][j] == 0 and temp[i][j] > 0:
												res.append((i, j) )

			return res


if __name__ == "__main__":
				print(mincut(test_graph, source=0, sink=5))






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
				import jax



@require_flax
class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= FlaxAutoencoderKL





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						4
				SCREAMING_SNAKE_CASE_								=						3
				SCREAMING_SNAKE_CASE_								=						(32, 32)

				SCREAMING_SNAKE_CASE_								=						jax.random.PRNGKey(0      )
				SCREAMING_SNAKE_CASE_								=						jax.random.uniform(SCREAMING_SNAKE_CASE      ,     ((batch_size, num_channels) + sizes)      )

				return {"sample": image, "prng_key": prng_key}





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{
				    'block_out_channels': [32, 64],
				    'in_channels': 3,
				    'out_channels': 3,
				    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
				    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
				    'latent_channels': 4,
				}
				SCREAMING_SNAKE_CASE_								=						self.dummy_input
				return init_dict, inputs_dict
