

"""simple docstring"""





from __future__ import annotations

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

_a       =							(3, 9, -11, 0, 7, 5, 1, -1)
_a       =							(4, 6, 2, 0, 8, 10, 3, -2)




@dataclass
class 					_lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase	:					int
 __UpperCAmelCase	:					Node | None







class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			Optional[int],					UpperCAmelCase__							:			Iterable[int]	):
    __lowercase								=						None
    for i in sorted(UpperCAmelCase__,					reverse=UpperCAmelCase__	):
       __lowercase								=						Node(UpperCAmelCase__,					self.head	)


 def __iter__(			self							:			str	):
    __lowercase								=						self.head
    while node:
       yield node.data
       __lowercase								=						node.next_node


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



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







def   _A  (  UpperCamelCase_     :       SortedLinkedList,			UpperCamelCase_     :       SortedLinkedList)	->						SortedLinkedList:
   '''simple docstring'''


   return SortedLinkedList(list(UpperCamelCase_) + list(UpperCamelCase_))


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _a       =							SortedLinkedList
   print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      A__							(     __magic_name__	):
				lowercase			    =  42
				lowercase			    =  42






				def __init__(						self					:      Any    ,     a					:      UNetaDModel    ,     a					:      ScoreSdeVeScheduler   ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a    ,     scheduler=a   )






				@torch.no_grad()
				def __call__(						self					:      List[str]    ,     a					:      int = 1    ,     a					:      int = 2_000    ,     a					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     a					:      Optional[str] = "pil"    ,     a					:      bool = True    ,     **a					:      int    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.unet.config.sample_size
							lowerCAmelCase__      :      Union[str, Any]         =		(batch_size, 3, img_size, img_size)

							lowerCAmelCase__      :      Tuple         =		self.unet

							lowerCAmelCase__      :      Optional[Any]         =		randn_tensor(a    ,     generator=a   ) * self.scheduler.init_noise_sigma
							lowerCAmelCase__      :      int         =		sample.to(self.device   )

							self.scheduler.set_timesteps(a   )
							self.scheduler.set_sigmas(a   )

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
										lowerCAmelCase__      :      Dict         =		self.scheduler.sigmas[i] * torch.ones(shape[0]    ,     device=self.device   )

										# correction step
										for _ in range(self.scheduler.config.correct_steps   ):
													lowerCAmelCase__      :      Optional[Any]         =		self.unet(a    ,     a   ).sample
													lowerCAmelCase__      :      Dict         =		self.scheduler.step_correct(a    ,     a    ,     generator=a   ).prev_sample

										# prediction step
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     a   ).sample
										lowerCAmelCase__      :      Optional[Any]         =		self.scheduler.step_pred(a    ,     a    ,     a    ,     generator=a   )

										lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		output.prev_sample, output.prev_sample_mean

							lowerCAmelCase__      :      Any         =		sample_mean.clamp(0    ,     1   )
							lowerCAmelCase__      :      List[str]         =		sample.cpu().permute(0    ,     2    ,     3    ,     1   ).numpy()
							if output_type == "pil":
										lowerCAmelCase__      :      int         =		self.numpy_to_pil(a   )

							if not return_dict:
										return (sample,)

							return ImagePipelineOutput(images=a   )