

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			):




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




				@property
				def     a							(					self       ):
											snake_case_							=    ort.SessionOptions()
											snake_case_							=    False
											return options




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

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

											snake_case_							=    np.random.RandomState(0       )
											snake_case_							=    pipe(
											    prompt=snake_case      ,   image=snake_case      ,   mask_image=snake_case      ,   strength=0.75      ,   guidance_scale=7.5      ,   num_inference_steps=15      ,   generator=snake_case      ,   output_type='np'      ,   )

											snake_case_							=    output.images[0]

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







from __future__ import annotations

import numpy as np





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_				,    snake_case_							=    np.shape(UpperCamelCase__      )
							if rows != columns:
														snake_case_							=    (
														    '\'table\' has to be of square shaped array but got a '
														    F'''{rows}x{columns} array:\n{table}'''
														)
														raise ValueError(UpperCamelCase__      )

							snake_case_							=    np.zeros((rows, columns)      )
							snake_case_							=    np.zeros((rows, columns)      )
							for i in range(UpperCamelCase__      ):
														for j in range(UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					if upper[j][j] == 0:
																												raise ArithmeticError('No LU decomposition exists'      )
																					snake_case_							=    (table[i][j] - total) / upper[j][j]
														snake_case_							=    1
														for j in range(UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					snake_case_							=    table[i][j] - total
							return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()




