

'''simple docstring'''
def  a__       (    _SCREAMING_SNAKE_CASE   :       Dict	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]	,				_SCREAMING_SNAKE_CASE   :       int	,				_SCREAMING_SNAKE_CASE   :       List[Any]				)     ->					Tuple:


	"""simple docstring"""





	UpperCAmelCase_     :      Dict						=  [False] * len(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      int						=  []
	queue.append(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Any						=  True

	while queue:
		UpperCAmelCase_     :      Tuple						=  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				)
				UpperCAmelCase_     :      int						=  True
				UpperCAmelCase_     :      Optional[Any]						=  u

	return visited[t]


def  a__       (    _SCREAMING_SNAKE_CASE   :       Dict	,				_SCREAMING_SNAKE_CASE   :       str	,				_SCREAMING_SNAKE_CASE   :       int				)     ->					Dict:


	"""simple docstring"""





	UpperCAmelCase_     :      str						=  [-1] * (len(_SCREAMING_SNAKE_CASE				))
	UpperCAmelCase_     :      Any						=  0
	while bfs(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				):
		UpperCAmelCase_     :      List[Any]						=  float("Inf"				)
		UpperCAmelCase_     :      List[Any]						=  sink

		while s != source:
			# Find the minimum value in select path
			UpperCAmelCase_     :      List[str]						=  min(_SCREAMING_SNAKE_CASE	,				graph[parent[s]][s]				)
			UpperCAmelCase_     :      str						=  parent[s]

		max_flow += path_flow
		UpperCAmelCase_     :      Dict						=  sink

		while v != source:
			UpperCAmelCase_     :      Dict						=  parent[v]
			graph[u][v] -= path_flow
			graph[v][u] += path_flow
			UpperCAmelCase_     :      Optional[Any]						=  parent[v]
	return max_flow


_lowerCamelCase      					=     [
    [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],
]

_lowerCamelCase					,      _lowerCamelCase      					=     0, 5





print(ford_fulkerson(graph, source, sink))





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a  :      List[str]            =    False

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    pass





@nightly
@require_torch_gpu
class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def      _a    (    self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							List[str]       =	torch.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCamelCase__ )
          lowerCAmelCase_:							int       =	VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__ )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	generator.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	VersatileDiffusionTextToImagePipeline.from_pretrained(
           "shi-labs/versatile-diffusion"       ,						torch_dtype=torch.floataa )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							str       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							int       =	torch.manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=50       ,						output_type="numpy" ).images

       lowerCAmelCase_:							List[Any]       =	image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCAmelCase_:							Optional[int]       =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

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