



"""simple docstring"""
__a      =				[
    [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    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :int						=			[False] * len(_lowercase	)
						snake_case_ :int						=			[s]
						snake_case_ :Tuple						=			True

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

						return visited[t]
def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			[-1] * (len(_lowercase	))
						snake_case_ :Optional[int]						=			0
						snake_case_ :Any						=			[]
						snake_case_ :List[str]						=			[i[:] for i in graph]  # Record original cut, copy.
						while bfs(_lowercase,					_lowercase,					_lowercase,					_lowercase	):
												snake_case_ :Optional[Any]						=			float("""Inf"""	)
												snake_case_ :Tuple						=			sink

												while s != source:
																		# Find the minimum value in select path
																		snake_case_ :Optional[int]						=			min(_lowercase,					graph[parent[s]][s]	)
																		snake_case_ :Dict						=			parent[s]

												max_flow += path_flow
												snake_case_ :List[str]						=			sink

												while v != source:
																		snake_case_ :Optional[int]						=			parent[v]
																		graph[u][v] -= path_flow
																		graph[v][u] += path_flow
																		snake_case_ :str						=			parent[v]

						for i in range(len(_lowercase	)	):
												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))






"""simple docstring"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort






@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 @property
 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )


 @property
 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Any								=  ort.SessionOptions()
   _snake_case							:		Union[str, Any]								=  False
   return options


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
   _snake_case							:		Union[str, Any]								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
   _snake_case							:		Union[str, Any]								=  load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""		)

   # using the PNDM scheduler by default
   _snake_case							:		Optional[Any]								=  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
       """CompVis/stable-diffusion-v1-4""",      revision="""onnx""",      safety_checker=a_,      feature_extractor=a_,      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		Optional[Any]								=  """A red cat sitting on a park bench"""

   _snake_case							:		Optional[int]								=  np.random.RandomState(0		)
   _snake_case							:		Any								=  pipe(
       prompt=a_,      image=a_,      mask_image=a_,      strength=0.75,      guidance_scale=7.5,      num_inference_steps=15,      generator=a_,      output_type="""np""",      )

   _snake_case							:		Dict								=  output.images[0]

   assert image.shape == (512, 512, 3)
   assert np.abs(expected_image - image		).max() < 1E-2


