


"""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 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    @property
    def 	__snake_case							(					self				:    Tuple):
      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )






    @property
    def 	__snake_case							(					self				:    List[str]):
      a						:		Optional[int]      							=			ort.SessionOptions()
      a						:		str      							=			False
      return options






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Tuple      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo.png")
      a						:		Optional[int]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo_mask.png")
      a						:		List[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
      a						:		List[Any]      							=			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)

      a						:		int      							=			"A red cat sitting on a park bench"

      a						:		Optional[Any]      							=			np.random.RandomState(0)
      a						:		Optional[Any]      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, strength=0.75				, guidance_scale=7.5				, num_inference_steps=15				, generator=__UpperCAmelCase				, output_type="np"				, )

      a						:		str      							=			output.images[0]

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







'''simple docstring'''

from __future__ import annotations

from dataclasses import dataclass







@dataclass
class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       float
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None







def      UpperCamelCase_						(					snake_case_       :       TreeNode | None      )					->     bool:






 '''simple docstring'''

 def is_valid_tree(snake_case_       :       TreeNode | None      ) -> bool:
  if node is None:
   return True

  if not isinstance(snake_case_						,		snake_case_      ):
   return False

  try:
   float(node.data      )
  except (TypeError, ValueError):
   return False

  return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

 if not is_valid_tree(snake_case_      ):
  raise ValueError(
      """Each node should be type of TreeNode and data should be float."""      )

 def is_binary_search_tree_recursive_check(
     snake_case_       :       TreeNode | None						,		snake_case_       :       float						,		snake_case_       :       float      ) -> bool:

  if node is None:
   return True

  return (
      left_bound < node.data < right_bound
      and is_binary_search_tree_recursive_check(node.left						,		snake_case_						,		node.data      )
      and is_binary_search_tree_recursive_check(
          node.right						,		node.data						,		snake_case_      )
  )

 return is_binary_search_tree_recursive_check(snake_case_						,		-float("""inf"""      )						,		float("""inf"""      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






