






"""simple docstring"""







from __future__ import annotations

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

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


@dataclass
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : int
						_SCREAMING_SNAKE_CASE  : Node | None


class      UpperCamelCase		:






						def __init__(		self  :Optional[Any]			, __magic_name__  :Iterable[int]				)      ->None:
										lowercase    :      Node | None       = None
										for i in sorted(__magic_name__			, reverse=__magic_name__				):
														lowercase    :      Optional[Any]       = Node(__magic_name__			, self.head				)






						def __iter__(		self  :int				)      ->Iterator[int]:
										lowercase    :      Union[str, Any]       = self.head
										while node:
														yield node.data
														lowercase    :      Tuple       = node.next_node






						def __len__(		self  :Any				)      ->int:
										return sum(1 for _ in self				)






						def __str__(		self  :Optional[int]				)      ->str:
										return " -> ".join([str(__magic_name__				) for node in self]				)


def      UpperCamelCase       ( _A  ,				_A )     ->       SortedLinkedList:
				return SortedLinkedList(list(_A ) + list(_A ) )


if __name__ == "__main__":
				import doctest

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








"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      UpperCamelCase		(__snake_case     ):






						def __init__(		self  :Any			, __magic_name__  :Union[str, Any]			, __magic_name__  :Optional[Any]				)      ->Dict:
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase    :      Optional[Any]       = DDIMScheduler.from_config(scheduler.config				)

										self.register_modules(unet=__magic_name__			, scheduler=__magic_name__				)






						@torch.no_grad()
						def __call__(		self  :Optional[int]			, __magic_name__  :int = 1			, __magic_name__  :Optional[Union[torch.Generator, List[torch.Generator]]] = None			, __magic_name__  :float = 0.0			, __magic_name__  :int = 50			, __magic_name__  :Optional[bool] = None			, __magic_name__  :Optional[str] = "pil"			, __magic_name__  :bool = True			, )      ->Union[ImagePipelineOutput, Tuple]:

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size			, __magic_name__				):
														lowercase    :      int       = (
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
										else:
														lowercase    :      Tuple       = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(__magic_name__			, __magic_name__				) and len(__magic_name__				) != batch_size:
														raise ValueError(
														    f"""You have passed a list of generators of length {len(__magic_name__				)}, but requested an effective batch"""
														    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

										lowercase    :      Dict       = randn_tensor(__magic_name__			, generator=__magic_name__			, device=self.device			, dtype=self.unet.dtype				)

										# set step values
										self.scheduler.set_timesteps(__magic_name__				)

										for t in self.progress_bar(self.scheduler.timesteps				):
														# 1. predict noise model_output
														lowercase    :      Union[str, Any]       = self.unet(__magic_name__			, __magic_name__				).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														lowercase    :      int       = self.scheduler.step(
														    __magic_name__			, __magic_name__			, __magic_name__			, eta=__magic_name__			, use_clipped_model_output=__magic_name__			, generator=__magic_name__				).prev_sample

										lowercase    :      Optional[Any]       = (image / 2 + 0.5).clamp(0			, 1				)
										lowercase    :      Tuple       = image.cpu().permute(0			, 2			, 3			, 1				).numpy()
										if output_type == "pil":
														lowercase    :      Any       = self.numpy_to_pil(__magic_name__				)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__magic_name__				)
