





"""simple docstring"""




import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort


class        __UpperCamelCase				(						a__		,       unittest.TestCase						):
							# FIXME: add fast tests
							pass


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






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






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






							def     __a   (   self      )	-> Dict:
								a      :		List[str]	=				load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
								a      :		List[Any]	=				load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
								a      :		int	=				OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"      ,					revision="onnx"      ,					safety_checker=lowerCAmelCase__      ,					feature_extractor=lowerCAmelCase__      ,					provider=self.gpu_provider      ,					sess_options=self.gpu_options      ,					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase__      )

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

								a      :		Dict	=				np.random.RandomState(0      )
								a      :		str	=				pipe(
								    prompt=lowerCAmelCase__      ,					image=lowerCAmelCase__      ,					mask_image=lowerCAmelCase__      ,					guidance_scale=7.5      ,					num_inference_steps=10      ,					generator=lowerCAmelCase__      ,					output_type="np"      ,					)
								a      :		Union[str, Any]	=				output.images
								a      :		str	=				images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								a      :		int	=				np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3






							def     __a   (   self      )	-> Tuple:
								a      :		Union[str, Any]	=				load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
								a      :		Union[str, Any]	=				load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
								a      :		Dict	=				LMSDiscreteScheduler.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"      ,					subfolder="scheduler"      ,					revision="onnx"      )
								a      :		Union[str, Any]	=				OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"      ,					revision="onnx"      ,					scheduler=lowerCAmelCase__      ,					safety_checker=lowerCAmelCase__      ,					feature_extractor=lowerCAmelCase__      ,					provider=self.gpu_provider      ,					sess_options=self.gpu_options      ,					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase__      )

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

								a      :		List[Any]	=				np.random.RandomState(0      )
								a      :		List[Any]	=				pipe(
								    prompt=lowerCAmelCase__      ,					image=lowerCAmelCase__      ,					mask_image=lowerCAmelCase__      ,					guidance_scale=7.5      ,					num_inference_steps=20      ,					generator=lowerCAmelCase__      ,					output_type="np"      ,					)
								a      :		Any	=				output.images
								a      :		List[Any]	=				images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								a      :		Any	=				np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3






'''simple docstring'''

class   __lowerCAmelCase						:  # Public class to implement a graph





      '''simple docstring'''






      def __init__(self    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







             lowercase__      			= row
             lowercase__      			= col
             lowercase__      			= graph



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







             return (
                 0 <= i < self.ROW
                 and 0 <= j < self.COL
                 and not visited[i][j]
                 and self.graph[i][j]
             )



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







             lowercase__      			= [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
             lowercase__      			= [-1, 0, 1, -1, 1, -1, 0, 1]
             lowercase__      			= 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       UpperCamelCase__		(self    :    Dict ):  # And finally, count all islands.

             '''simple docstring'''







             lowercase__      			= [[False for j in range(self.COL )] for i in range(self.ROW )]
             lowercase__      			= 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




