






'''simple docstring'''





from __future__ import annotations

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

__UpperCamelCase      :      List[str]     						=						(3, 9, -11, 0, 7, 5, 1, -1)
__UpperCamelCase      :      int     						=						(4, 6, 2, 0, 8, 10, 3, -2)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:int
	__snake_case	:Node | None


class        __UpperCamelCase    :







	def __init__(  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							Iterable[int]			)		-> None:
		"""simple docstring"""







		__lowercase             =  None
		for i in sorted(_lowerCAmelCase       ,				reverse=_lowerCAmelCase			):
			__lowercase             =  Node(_lowerCAmelCase       ,				self.head			)







	def __iter__(  self      :							int			)		-> Iterator[int]:
		"""simple docstring"""







		__lowercase             =  self.head
		while node:
			yield node.data
			__lowercase             =  node.next_node







	def __len__(  self      :							Dict			)		-> int:
		"""simple docstring"""







		return sum(1 for _ in self			)







	def __str__(  self      :							int			)		-> str:
		"""simple docstring"""







		return " -> ".join([str(_lowerCAmelCase			) for node in self]			)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return SortedLinkedList(list(lowerCamelCase					) + list(lowerCamelCase					)					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__UpperCamelCase      :      List[str]     						=						SortedLinkedList
						print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))









import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Any   =       IFInpaintingPipeline
	__snake_case	:str   =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
	__snake_case	:Optional[int]   =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__snake_case	:str   =       PipelineTesterMixin.required_optional_params - {'latents'}







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		return self._get_dummy_components()







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Dict=0			)		-> Any:
		"""simple docstring"""







		if str(_lowerCAmelCase			).startswith("""mps"""			):
			__lowercase             =  torch.manual_seed(_lowerCAmelCase			)
		else:
			__lowercase             =  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)

		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)
		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)

		__lowercase             =  {
		    """prompt""": """A painting of a squirrel eating a burger""",
		    """image""": image,
		    """mask_image""": mask_image,
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """output_type""": """numpy""",
		}

		return inputs







	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()       ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,				)
	def  _a   (  self      :							Tuple			)		-> Dict:
		"""simple docstring"""







		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		self._test_save_load_optional_components()







	@unittest.skipIf(torch_device != """cuda"""       ,				reason="""float16 requires CUDA"""			)
	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		super().test_save_load_floataa(expected_max_diff=1e-1			)







	def  _a   (  self      :							Tuple			)		-> Optional[Any]:
		"""simple docstring"""







		self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		self._test_save_load_local()







	def  _a   (  self      :							List[str]			)		-> List[Any]:
		"""simple docstring"""







		self._test_inference_batch_single_identical(
		    expected_max_diff=1e-2       ,				)

