




from __future__ import annotations

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[int]							)					-> list[int]:  # This function is recursive
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)
      # If the array contains only one element, we return it (it's the stop condition of
      # recursion)
      if array_length <= 1:
            return array
            # Else
      SCREAMING_SNAKE_CASE_							=			array[0]
      SCREAMING_SNAKE_CASE_							=			False
      SCREAMING_SNAKE_CASE_							=			1
      SCREAMING_SNAKE_CASE_							=			[]
      while not is_found and i < array_length:
            if array[i] < pivot:
                  SCREAMING_SNAKE_CASE_							=			True
                  SCREAMING_SNAKE_CASE_							=			[element for element in array[i:] if element >= array[i]]
                  SCREAMING_SNAKE_CASE_							=			longest_subsequence(__UpperCAmelCase							)
                  if len(__UpperCAmelCase							) > len(__UpperCAmelCase							):
                        SCREAMING_SNAKE_CASE_							=			temp_array
            else:
                  i += 1

      SCREAMING_SNAKE_CASE_							=			[element for element in array[1:] if element >= pivot]
      SCREAMING_SNAKE_CASE_							=			[pivot, *longest_subsequence(__UpperCAmelCase							)]
      if len(__UpperCAmelCase							) > len(__UpperCAmelCase							):
            return temp_array
      else:
            return longest_subseq


if __name__ == "__main__":
   import doctest

   doctest.testmod()





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        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			ort.SessionOptions()
       SCREAMING_SNAKE_CASE_							=			False
       return options




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

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

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

       SCREAMING_SNAKE_CASE_							=			output.images[0]

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