



class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Tuple=None						,  __UpperCAmelCase     :						Optional[Any]=None					)      ->			int:
							UpperCAmelCase_=       data
							UpperCAmelCase_=       previous
							UpperCAmelCase_=       next_node




	def __str__(     self     :						Tuple					)      ->			str:
							return F"""{self.data}"""




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			int:
							return self.data




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[Any]:
							return self.next




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Union[str, Any]:
							return self.previous






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Dict					)      ->			Optional[Any]:
							UpperCAmelCase_=       head




	def __iter__(     self     :						Optional[Any]					)      ->			Optional[int]:
							return self




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Union[str, Any]:
							if not self.current:
													raise StopIteration
							else:
													UpperCAmelCase_=       self.current.get_data()
													UpperCAmelCase_=       self.current.get_next()
													return value






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						str					)      ->			int:
							UpperCAmelCase_=       None  # First node in list
							UpperCAmelCase_=       None  # Last node in list




	def __str__(     self     :						Any					)      ->			Tuple:
							UpperCAmelCase_=       self.head
							UpperCAmelCase_=       []
							while current is not None:
													nodes.append(current.get_data()					)
													UpperCAmelCase_=       current.get_next()
							return " ".join(str(__UpperCAmelCase					) for node in nodes					)




	def __contains__(     self     :						Optional[int]						,  __UpperCAmelCase     :						int					)      ->			int:
							UpperCAmelCase_=       self.head
							while current:
													if current.get_data() == value:
																			return True
													UpperCAmelCase_=       current.get_next()
							return False




	def __iter__(     self     :						Any					)      ->			Dict:
							return LinkedListIterator(self.head					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			str:
							if self.head:
													return self.head.get_data()
							return None




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Dict:
							if self.tail:
													return self.tail.get_data()
							return None




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict						,  __UpperCAmelCase     :						Node					)      ->			None:
							if self.head is None:
													UpperCAmelCase_=       node
													UpperCAmelCase_=       node
							else:
													self.insert_before_node(self.head						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Node					)      ->			None:
							if self.head is None:
													self.set_head(__UpperCAmelCase					)
							else:
													self.insert_after_node(self.tail						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						int					)      ->			None:
							UpperCAmelCase_=       Node(__UpperCAmelCase					)
							if self.head is None:
													self.set_head(__UpperCAmelCase					)
							else:
													self.set_tail(__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						Node						,  __UpperCAmelCase     :						Node					)      ->			None:
							UpperCAmelCase_=       node
							UpperCAmelCase_=       node.previous

							if node.get_previous() is None:
													UpperCAmelCase_=       node_to_insert
							else:
													UpperCAmelCase_=       node_to_insert

							UpperCAmelCase_=       node_to_insert




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Node						,  __UpperCAmelCase     :						Node					)      ->			None:
							UpperCAmelCase_=       node
							UpperCAmelCase_=       node.next

							if node.get_next() is None:
													UpperCAmelCase_=       node_to_insert
							else:
													UpperCAmelCase_=       node_to_insert

							UpperCAmelCase_=       node_to_insert




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						int					)      ->			None:
							UpperCAmelCase_=       1
							UpperCAmelCase_=       Node(__UpperCAmelCase					)
							UpperCAmelCase_=       self.head
							while node:
													if current_position == position:
																			self.insert_before_node(__UpperCAmelCase						,  __UpperCAmelCase					)
																			return
													current_position += 1
													UpperCAmelCase_=       node.next
							self.insert_after_node(self.tail						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						int					)      ->			Node:
							UpperCAmelCase_=       self.head
							while node:
													if node.get_data() == item:
																			return node
													UpperCAmelCase_=       node.get_next()
							raise Exception("""Node not found"""					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						Optional[Any]					)      ->			Optional[Any]:
							if (node := self.get_node(__UpperCAmelCase					)) is not None:
													if node == self.head:
																			UpperCAmelCase_=       self.head.get_next()

													if node == self.tail:
																			UpperCAmelCase_=       self.tail.get_previous()

													self.remove_node_pointers(__UpperCAmelCase					)




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						Node					)      ->			None:
							if node.get_next():
													UpperCAmelCase_=       node.previous

							if node.get_previous():
													UpperCAmelCase_=       node.next

							UpperCAmelCase_=       None
							UpperCAmelCase_=       None




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			List[Any]:
							return self.head is None



def 	__a					(   )				->			None:






						'''simple docstring'''





if __name__ == "__main__":
	import doctest

	doctest.testmod()









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 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							super().__init__()

							# make sure scheduler can always be converted to DDIM
							UpperCAmelCase_=       DDIMScheduler.from_config(scheduler.config					)

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




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

							# Sample gaussian noise to begin loop
							if isinstance(self.unet.config.sample_size						,  __UpperCAmelCase					):
													UpperCAmelCase_=       (
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
							else:
													UpperCAmelCase_=       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

							UpperCAmelCase_=       randn_tensor(__UpperCAmelCase						,  generator=__UpperCAmelCase						,  device=self.device						,  dtype=self.unet.dtype					)

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

							for t in self.progress_bar(self.scheduler.timesteps					):
													# 1. predict noise model_output
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					).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
													UpperCAmelCase_=       self.scheduler.step(
													    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  eta=__UpperCAmelCase						,  use_clipped_model_output=__UpperCAmelCase						,  generator=__UpperCAmelCase					).prev_sample

							UpperCAmelCase_=       (image / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					).numpy()
							if output_type == "pil":
													UpperCAmelCase_=       self.numpy_to_pil(__UpperCAmelCase					)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase					)




