


'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     Any		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					[False] * len(lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					[]
	queue.append(lowerCAmelCase		)
	__magic_name__	:   str					     =					True

	while queue:
		__magic_name__	:   Optional[Any]					     =					queue.pop(0		)
		for ind in range(len(graph[u]		)		):
			if visited[ind] is False and graph[u][ind] > 0:
				queue.append(lowerCAmelCase		)
				__magic_name__	:   List[str]					     =					True
				__magic_name__	:   Tuple					     =					u

	return visited[t]





def lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   str					     =					[-1] * (len(lowerCAmelCase		))
	__magic_name__	:   Optional[int]					     =					0
	while bfs(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   Dict					     =					float('Inf'		)
		__magic_name__	:   Tuple					     =					sink

		while s != source:
			# Find the minimum value in select path
			__magic_name__	:   Union[str, Any]					     =					min(lowerCAmelCase		,		graph[parent[s]][s]		)
			__magic_name__	:   int					     =					parent[s]

		max_flow += path_flow
		__magic_name__	:   Optional[Any]					     =					sink

		while v != source:
			__magic_name__	:   List[Any]					     =					parent[v]
			graph[u][v] -= path_flow
			graph[v][u] += path_flow
			__magic_name__	:   Tuple					     =					parent[v]
	return max_flow


lowerCAmelCase				:Optional[int]        =      [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

lowerCAmelCase     ,   lowerCAmelCase				:Union[str, Any]        =      0, 5





print(ford_fulkerson(graph, source, sink))



'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		@property
		def        __lowerCAmelCase		( self		:						Dict      )  ->			List[str]:
			torch.manual_seed(0      )
			__magic_name__	:   Dict					     =					UNetaDModel(
			    block_out_channels=(32, 64)  ,					layers_per_block=2  ,					sample_size=32  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,					)
			return model







		def        __lowerCAmelCase		( self		:						str      )  ->			Any:
			__magic_name__	:   Union[str, Any]					     =					self.dummy_uncond_unet
			__magic_name__	:   str					     =					KarrasVeScheduler()

			__magic_name__	:   List[Any]					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Dict					     =					torch.manual_seed(0      )
			__magic_name__	:   int					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   str					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'  ,					return_dict=_A      )[0]

			__magic_name__	:   int					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   str					     =					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__magic_name__	:   List[Any]					     =					np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2







@slow
@require_torch
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			str:
			__magic_name__	:   Optional[int]					     =					'google/ncsnpp-celebahq-256'
			__magic_name__	:   List[str]					     =					UNetaDModel.from_pretrained(_A      )
			__magic_name__	:   int					     =					KarrasVeScheduler()

			__magic_name__	:   str					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   Union[str, Any]					     =					pipe(num_inference_steps=20  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   List[str]					     =					image[0, -3:, -3:, -1]
			assert image.shape == (1, 256, 256, 3)
			__magic_name__	:   int					     =					np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2