



"""simple docstring"""





from __future__ import annotations

import numpy as np
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_			,   UpperCAmelCase_      			=							np.shape(lowerCAmelCase__				)
      if rows != columns:
            UpperCAmelCase_      			=							(
                "'table' has to be of square shaped array but got a "
                f"""{rows}x{columns} array:\n{table}"""
            )
            raise ValueError(lowerCAmelCase__				)

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()









"""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 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




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



       @property
       def 					lowercase__    (							self      :  List[str]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							ort.SessionOptions()
             UpperCAmelCase_      			=							False
             return options







       def 					lowercase__    (							self      :  Dict   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo.png"   )
             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"   )
             UpperCAmelCase_      			=							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
             UpperCAmelCase_      			=							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   )

             UpperCAmelCase_      			=							"A red cat sitting on a park bench"

             UpperCAmelCase_      			=							np.random.RandomState(0   )
             UpperCAmelCase_      			=							pipe(
                 prompt=_UpperCAmelCase		,    image=_UpperCAmelCase		,    mask_image=_UpperCAmelCase		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=15		,    generator=_UpperCAmelCase		,    output_type="np"		,    )

             UpperCAmelCase_      			=							output.images[0]

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




