


from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

__lowerCAmelCase	: Dict  				=(3, 9, -1_1, 0, 7, 5, 1, -1)
__lowerCAmelCase	: int  				=(4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class        _lowercase      :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			int
				SCREAMING_SNAKE_CASE__				:			Node | None




class        _lowercase      :






				'''simple docstring'''



				def __init__(       self			:str			,			lowerCAmelCase__			:Iterable[int]	)		->			None:
										__SCREAMING_SNAKE_CASE						:     Node | None					=		None
										for i in sorted(lowerCAmelCase__			,			reverse=lowerCAmelCase__	):
																__SCREAMING_SNAKE_CASE						:     int					=		Node(lowerCAmelCase__			,			self.head	)


				def __iter__(       self			:Optional[Any]	)		->			Iterator[int]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.head
										while node:
																yield node.data
																__SCREAMING_SNAKE_CASE						:     int					=		node.next_node


				def __len__(       self			:Optional[int]	)		->			int:
										return sum(1 for _ in self	)



				def __str__(       self			:List[Any]	)		->			str:
										return " -> ".join([str(lowerCAmelCase__	) for node in self]	)




def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return SortedLinkedList(list(lowercase__      ) + list(lowercase__      )      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase	: Union[str, Any]  				=SortedLinkedList
						print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))






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