"""simple docstring"""




class     UpperCamelCase__:  # Public class to implement a graph





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				None:
													A__	     =							row
													A__	     =							col
													A__	     =							graph





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				bool:
													return (
													    0 <= i < self.ROW
													    and 0 <= j < self.COL
													    and not visited[i][j]
													    and self.graph[i][j]
													)





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				None:
													# Checking all 8 elements surrounding nth element
													A__	     =							[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
													A__	     =							[-1, 0, 1, -1, 1, -1, 0, 1]
													A__	     =							True  # Make those cells visited
													for k in range(8     ):
																				if self.is_safe(i + row_nbr[k]		,j + col_nbr[k]		,__UpperCAmelCase     ):
																											self.diffs(i + row_nbr[k]		,j + col_nbr[k]		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				int:  # And finally, count all islands.
													A__	     =							[[False for j in range(self.COL     )] for i in range(self.ROW     )]
													A__	     =							0
													for i in range(self.ROW     ):
																				for j in range(self.COL     ):
																											if visited[i][j] is False and self.graph[i][j] == 1:
																																		self.diffs(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )
																																		count += 1
													return count






"""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     UpperCamelCase__(			unittest.TestCase ):





						@property
						def 				snake_case__	(       self     )					->				int:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)





						@property
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							ort.SessionOptions()
													A__	     =							False
													return options





						def 				snake_case__	(       self     )					->				str:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo.png'     )
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'     )
													A__	     =							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
													A__	     =							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'		,revision='onnx'		,safety_checker=__UpperCAmelCase		,feature_extractor=__UpperCAmelCase		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							'A red cat sitting on a park bench'

													A__	     =							np.random.RandomState(0     )
													A__	     =							pipe(
													    prompt=__UpperCAmelCase		,image=__UpperCAmelCase		,mask_image=__UpperCAmelCase		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=15		,generator=__UpperCAmelCase		,output_type='np'		,)

													A__	     =							output.images[0]

													assert image.shape == (5_12, 5_12, 3)
													assert np.abs(expected_image - image     ).max() < 1e-2





