





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						):






  @property
  def 				UpperCAmelCase__						(							self			:					Any      )->int:






   '''simple docstring'''

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






  @property
  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       ort.SessionOptions()
   __lowerCAmelCase					:	int      =       False
   return options






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""      )
   __lowerCAmelCase					:	List[Any]      =       load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""      )
   __lowerCAmelCase					:	Optional[Any]      =       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
   __lowerCAmelCase					:	int      =       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      )

   __lowerCAmelCase					:	Tuple      =       """A red cat sitting on a park bench"""

   __lowerCAmelCase					:	Dict      =       np.random.RandomState(0      )
   __lowerCAmelCase					:	Any      =       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"""       ,  )

   __lowerCAmelCase					:	Tuple      =       output.images[0]

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






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

 # invalid input
 if index >= len(SCREAMING_SNAKE_CASE   ) or index < 0:
  return None

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # index is the pivot
 if m <= index < m + count:
  return pivot
 # must be in smaller
 elif m > index:
  return quick_select(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 # must be in larger
 else:
  return quick_select(SCREAMING_SNAKE_CASE   ,   index - (m + count)   )