



"""simple docstring"""





from __future__ import annotations

from random import random
class 				lowercase__       :







       '''simple docstring'''


       def __init__(							self      :  int		,    _UpperCAmelCase      :  Tuple = None   )			->						List[str]:


             '''simple docstring'''




             UpperCAmelCase_      			=							value
             UpperCAmelCase_      			=							random()
             UpperCAmelCase_      			=							None
             UpperCAmelCase_      			=							None



       def __repr__(							self      :  Optional[int]   )			->						Optional[int]:


             '''simple docstring'''




             from pprint import pformat

             if self.left is None and self.right is None:
                   return F"""'{self.value}: {self.prior:.5}'"""
             else:
                   return pformat(
                       {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}		,    indent=1   )







       def __str__(							self      :  Optional[int]   )			->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							str(self.value   ) + " "
             UpperCAmelCase_      			=							str(self.left or ""   )
             UpperCAmelCase_      			=							str(self.right or ""   )
             return value + left + right



def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      if root is None:  # None tree is split into 2 Nones
            return None, None
      elif root.value is None:
            return None, None
      else:
            if value < root.value:
                  UpperCAmelCase_			,   UpperCAmelCase_      			=							split(root.left     ,      lowercase_				)
                  return left, root
            else:
                  UpperCAmelCase_			,   UpperCAmelCase_      			=							split(root.right     ,      lowercase_				)
                  return root, right
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      if (not left) or (not right):  # If one node is None, return the other
            return left or right
      elif left.prior < right.prior:
            UpperCAmelCase_      			=							merge(left.right     ,      lowercase_				)
            return left
      else:
            UpperCAmelCase_      			=							merge(lowercase_     ,      right.left				)
            return right
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							Node(lowercase_				)
      UpperCAmelCase_			,   UpperCAmelCase_      			=							split(lowercase_     ,      lowercase_				)
      return merge(merge(lowercase_     ,      lowercase_				)     ,      lowercase_				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_			,   UpperCAmelCase_      			=							split(lowercase_     ,      value - 1				)
      UpperCAmelCase_			,   UpperCAmelCase_      			=							split(lowercase_     ,      lowercase_				)
      return merge(lowercase_     ,      lowercase_				)
def        a__     (				lowerCAmelCase__				):
      if not root:  # None
            return
      else:
            inorder(root.left				)
            print(root.value     ,      end=","				)
            inorder(root.right				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      for arg in args.split():
            if arg[0] == "+":
                  UpperCAmelCase_      			=							insert(lowercase_     ,      int(arg[1:]				)				)

            elif arg[0] == "-":
                  UpperCAmelCase_      			=							erase(lowercase_     ,      int(arg[1:]				)				)

            else:
                  print("Unknown command"				)

      return root
def        a__     (				):
      UpperCAmelCase_      			=							None
      print(
          "enter numbers to create a tree, + value to add value into treap, "
          "- value to erase all nodes with value. \'q\' to quit. "				)

      UpperCAmelCase_      			=							input()
      while args != "q":
            UpperCAmelCase_      			=							interact_treap(lowercase_     ,      lowercase_				)
            print(lowercase_				)
            UpperCAmelCase_      			=							input()

      print("good by!"				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()









"""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




