






from __future__ import annotations

from typing import Any




class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a = 6   )			->       None:
								snake_case_           =				None
								snake_case_           =				None
								self.create_linked_list(a   )




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								snake_case_           =				Node()
								snake_case_           =				current_node
								snake_case_           =				current_node
								snake_case_           =				current_node
								for _ in range(1			,     a   ):
															snake_case_           =				Node()
															snake_case_           =				current_node
															snake_case_           =				previous_node
															snake_case_           =				current_node
								snake_case_           =				self.front
								snake_case_           =				previous_node




	def 				_UpperCamelCase     (			self   )			->       bool:
								return (
								    self.front == self.rear
								    and self.front is not None
								    and self.front.data is None
								)




	def 				_UpperCamelCase     (			self   )			->       Any | None:
								self.check_can_perform_operation()
								return self.front.data if self.front else None




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								if self.rear is None:
															return

								self.check_is_full()
								if not self.is_empty():
															snake_case_           =				self.rear.next
								if self.rear:
															snake_case_           =				data




	def 				_UpperCamelCase     (			self   )			->       Any:
								self.check_can_perform_operation()
								if self.rear is None or self.front is None:
															return None
								if self.front == self.rear:
															snake_case_           =				self.front.data
															snake_case_           =				None
															return data

								snake_case_           =				self.front
								snake_case_           =				old_front.next
								snake_case_           =				old_front.data
								snake_case_           =				None
								return data




	def 				_UpperCamelCase     (			self   )			->       None:
								if self.is_empty():
															raise Exception('Empty Queue'   )






	def 				_UpperCamelCase     (			self   )			->       None:
								if self.rear and self.rear.next == self.front:
															raise Exception('Full Queue'   )






class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self   )			->       None:
								snake_case_           =				None
								snake_case_           =				None
								snake_case_           =				None


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 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

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

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






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

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

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

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

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

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

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

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





