



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   snake_case_ (unittest.TestCase  ):




       """simple docstring"""






       @property
       def  A_   (     self):

             """simple docstring"""





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



       @property
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		ort.SessionOptions()
             UpperCAmelCase_  :	List[str]				      =		False
             return options



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo.png")
             UpperCAmelCase_  :	Union[str, Any]				      =		load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo_mask.png")
             UpperCAmelCase_  :	Dict				      =		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_  :	int				      =		OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
                 "CompVis/stable-diffusion-v1-4"     ,revision="onnx"     ,safety_checker=lowercase     ,feature_extractor=lowercase     ,provider=self.gpu_provider     ,sess_options=self.gpu_options     ,)
             pipe.set_progress_bar_config(disable=lowercase)

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

             UpperCAmelCase_  :	Dict				      =		np.random.RandomState(0)
             UpperCAmelCase_  :	List[Any]				      =		pipe(
                 prompt=lowercase     ,image=lowercase     ,mask_image=lowercase     ,strength=0.75     ,guidance_scale=7.5     ,num_inference_steps=15     ,generator=lowercase     ,output_type="np"     ,)

             UpperCAmelCase_  :	Any				      =		output.images[0]

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





import operator







def    _snake_case (       __snake_case					,  __snake_case = False					,  __snake_case = None    )     ->     list:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		operator.lt if reverse else operator.gt
      UpperCAmelCase_  :	int				      =		solution or []

      if not arr:
            return solution

      UpperCAmelCase_  :	Union[str, Any]				      =		[arr.pop(0    )]
      for i, item in enumerate(__snake_case    ):
            if _operator(__snake_case					,  sublist[-1]    ):
                  sublist.append(__snake_case    )
                  arr.pop(__snake_case    )

    #  merging sublist into solution list
      if not solution:
            solution.extend(__snake_case    )
      else:
            while sublist:
                  UpperCAmelCase_  :	Optional[int]				      =		sublist.pop(0    )
                  for i, xx in enumerate(__snake_case    ):
                        if not _operator(__snake_case					,  __snake_case    ):
                              solution.insert(__snake_case					,  __snake_case    )
                              break
                  else:
                        solution.append(__snake_case    )

      strand_sort(__snake_case					,  __snake_case					,  __snake_case    )
      return solution


if __name__ == "__main__":
   assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
   assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
