






"""simple docstring"""




from __future__ import annotations

import unittest
from collections.abc import Iterator


class        lowercase__       :

	def __init__(							self						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  label
							lowerCAmelCase__	 =  parent
							lowerCAmelCase__	 =  None
							lowerCAmelCase__	 =  None





class        lowercase__       :

	def __init__(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  None

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  None

	def 						UpperCAmelCase					(							self    )->   bool:







							'''simple docstring'''





							return self.root is None

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._put(self.root						,      lowerCAmelCase__    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase = None    )->   Node:







							'''simple docstring'''





							if node is None:
													lowerCAmelCase__	 =  Node(lowerCAmelCase__						,      lowerCAmelCase__    )
							else:
													if label < node.label:
																			lowerCAmelCase__	 =  self._put(node.left						,      lowerCAmelCase__						,      lowerCAmelCase__    )
													elif label > node.label:
																			lowerCAmelCase__	 =  self._put(node.right						,      lowerCAmelCase__						,      lowerCAmelCase__    )
													else:
																			lowerCAmelCase__	 =  F"Node with label {label} already exists"
																			raise Exception(lowerCAmelCase__    )

							return node

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Node:







							'''simple docstring'''





							return self._search(self.root						,      lowerCAmelCase__    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   Node:







							'''simple docstring'''





							if node is None:
													lowerCAmelCase__	 =  F"Node with label {label} does not exist"
													raise Exception(lowerCAmelCase__    )
							else:
													if label < node.label:
																			lowerCAmelCase__	 =  self._search(node.left						,      lowerCAmelCase__    )
													elif label > node.label:
																			lowerCAmelCase__	 =  self._search(node.right						,      lowerCAmelCase__    )

							return node

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.search(lowerCAmelCase__    )
							if node.right and node.left:
													lowerCAmelCase__	 =  self._get_lowest_node(node.right    )
													lowerCAmelCase__	 =  node.left
													lowerCAmelCase__	 =  node.right
													lowerCAmelCase__	 =  lowest_node
													if node.right:
																			lowerCAmelCase__	 =  lowest_node
													self._reassign_nodes(lowerCAmelCase__						,      lowerCAmelCase__    )
							elif not node.right and node.left:
													self._reassign_nodes(lowerCAmelCase__						,      node.left    )
							elif node.right and not node.left:
													self._reassign_nodes(lowerCAmelCase__						,      node.right    )
							else:
													self._reassign_nodes(lowerCAmelCase__						,      lowerCAmelCase__    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   None:







							'''simple docstring'''





							if new_children:
													lowerCAmelCase__	 =  node.parent

							if node.parent:
													if node.parent.right == node:
																			lowerCAmelCase__	 =  new_children
													else:
																			lowerCAmelCase__	 =  new_children
							else:
													lowerCAmelCase__	 =  new_children

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Node:







							'''simple docstring'''





							if node.left:
													lowerCAmelCase__	 =  self._get_lowest_node(node.left    )
							else:
													lowerCAmelCase__	 =  node
													self._reassign_nodes(lowerCAmelCase__						,      node.right    )

							return lowest_node

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   bool:







							'''simple docstring'''





							try:
													self.search(lowerCAmelCase__    )
													return True
							except Exception:
													return False

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							if self.root is None:
													raise Exception("Binary search tree is empty"    )

							lowerCAmelCase__	 =  self.root
							while node.right is not None:
													lowerCAmelCase__	 =  node.right

							return node.label

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							if self.root is None:
													raise Exception("Binary search tree is empty"    )

							lowerCAmelCase__	 =  self.root
							while node.left is not None:
													lowerCAmelCase__	 =  node.left

							return node.label

	def 						UpperCAmelCase					(							self    )->   Iterator[Node]:







							'''simple docstring'''





							return self._inorder_traversal(self.root    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Iterator[Node]:







							'''simple docstring'''





							if node is not None:
													yield from self._inorder_traversal(node.left    )
													yield node
													yield from self._inorder_traversal(node.right    )

	def 						UpperCAmelCase					(							self    )->   Iterator[Node]:







							'''simple docstring'''





							return self._preorder_traversal(self.root    )


	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Iterator[Node]:







							'''simple docstring'''





							if node is not None:
													yield node
													yield from self._preorder_traversal(node.left    )
													yield from self._preorder_traversal(node.right    )





class        lowercase__       (     unittest.TestCase		):

	@staticmethod
	def 						UpperCAmelCase					(							)->   BinarySearchTree:







							'''simple docstring'''





							lowerCAmelCase__	 =  BinarySearchTree()
							t.put(8    )
							t.put(3    )
							t.put(6    )
							t.put(1    )
							t.put(10    )
							t.put(14    )
							t.put(13    )
							t.put(4    )
							t.put(7    )
							t.put(5    )

							return t

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  BinarySearchTree()
							assert t.is_empty()

							t.put(8    )
							assert t.root is not None
							assert t.root.parent is None
							assert t.root.label == 8

							t.put(10    )
							assert t.root.right is not None
							assert t.root.right.parent == t.root
							assert t.root.right.label == 10

							t.put(3    )
							assert t.root.left is not None
							assert t.root.left.parent == t.root
							assert t.root.left.label == 3

							t.put(6    )
							assert t.root.left.right is not None
							assert t.root.left.right.parent == t.root.left
							assert t.root.left.right.label == 6

							t.put(1    )
							assert t.root.left.left is not None
							assert t.root.left.left.parent == t.root.left
							assert t.root.left.left.label == 1

							with self.assertRaises(lowerCAmelCase__    ):  # noqa: B017
													t.put(1    )

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_binary_search_tree()

							lowerCAmelCase__	 =  t.search(6    )
							assert node.label == 6

							lowerCAmelCase__	 =  t.search(13    )
							assert node.label == 13

							with self.assertRaises(lowerCAmelCase__    ):  # noqa: B017
													t.search(2    )

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_binary_search_tree()

							t.remove(13    )
							assert t.root is not None
							assert t.root.right is not None
							assert t.root.right.right is not None
							assert t.root.right.right.right is None
							assert t.root.right.right.left is None

							t.remove(7    )
							assert t.root.left is not None
							assert t.root.left.right is not None
							assert t.root.left.right.left is not None
							assert t.root.left.right.right is None
							assert t.root.left.right.left.label == 4

							t.remove(6    )
							assert t.root.left.left is not None
							assert t.root.left.right.right is not None
							assert t.root.left.left.label == 1
							assert t.root.left.right.label == 4
							assert t.root.left.right.right.label == 5
							assert t.root.left.right.left is None
							assert t.root.left.left.parent == t.root.left
							assert t.root.left.right.parent == t.root.left

							t.remove(3    )
							assert t.root is not None
							assert t.root.left.label == 4
							assert t.root.left.right.label == 5
							assert t.root.left.left.label == 1
							assert t.root.left.parent == t.root
							assert t.root.left.left.parent == t.root.left
							assert t.root.left.right.parent == t.root.left

							t.remove(4    )
							assert t.root.left is not None
							assert t.root.left.left is not None
							assert t.root.left.label == 5
							assert t.root.left.right is None
							assert t.root.left.left.label == 1
							assert t.root.left.parent == t.root
							assert t.root.left.left.parent == t.root.left

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_binary_search_tree()

							t.remove(3    )
							assert t.root is not None
							assert t.root.left is not None
							assert t.root.left.left is not None
							assert t.root.left.right is not None
							assert t.root.left.right.left is not None
							assert t.root.left.right.right is not None
							assert t.root.left.label == 4
							assert t.root.left.right.label == 6
							assert t.root.left.left.label == 1
							assert t.root.left.right.right.label == 7
							assert t.root.left.right.left.label == 5
							assert t.root.left.parent == t.root
							assert t.root.left.right.parent == t.root.left
							assert t.root.left.left.parent == t.root.left
							assert t.root.left.right.left.parent == t.root.left.right

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_binary_search_tree()
							t.empty()
							assert t.root is None

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_binary_search_tree()
							assert not t.is_empty()

							t.empty()
							assert t.is_empty()

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_binary_search_tree()

							assert t.exists(6    )
							assert not t.exists(-1    )

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_binary_search_tree()

							assert t.get_max_label() == 14

							t.empty()
							with self.assertRaises(lowerCAmelCase__    ):  # noqa: B017
													t.get_max_label()

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_binary_search_tree()

							assert t.get_min_label() == 1

							t.empty()
							with self.assertRaises(lowerCAmelCase__    ):  # noqa: B017
													t.get_min_label()

	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_binary_search_tree()

							lowerCAmelCase__	 =  [i.label for i in t.inorder_traversal()]
							assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]


	def 						UpperCAmelCase					(							self    )->   None:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_binary_search_tree()

							lowerCAmelCase__	 =  [i.label for i in t.preorder_traversal()]
							assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]





def     _a					(       )	->				None:
						"""simple docstring"""







						lowerCAmelCase__	 =  BinarySearchTree()
						t.put(8				)
						t.put(3				)
						t.put(6				)
						t.put(1				)
						t.put(10				)
						t.put(14				)
						t.put(13				)
						t.put(4				)
						t.put(7				)
						t.put(5				)

						print(
						    "\n            8\n           / \\\n          3   10\n         / \\    \\\n        1   6    14\n           / \\   /\n          4   7 13\n           \\\n            5\n        "				)

						print("Label 6 exists:"      ,				t.exists(6				)				)
						print("Label 13 exists:"      ,				t.exists(13				)				)
						print("Label -1 exists:"      ,				t.exists(-1				)				)
						print("Label 12 exists:"      ,				t.exists(12				)				)

						# Prints all the elements of the list in inorder traversal
						lowerCAmelCase__	 =  [i.label for i in t.inorder_traversal()]
						print("Inorder traversal:"      ,				lowercase_				)

						# Prints all the elements of the list in preorder traversal
						lowerCAmelCase__	 =  [i.label for i in t.preorder_traversal()]
						print("Preorder traversal:"      ,				lowercase_				)

						print("Max. label:"      ,				t.get_max_label()				)
						print("Min. label:"      ,				t.get_min_label()				)

						# Delete elements
						print("\nDeleting elements 13, 10, 8, 3, 6, 14"				)
						print(
						    "\n          4\n         / \\\n        1   7\n             \\\n              5\n        "				)
						t.remove(13				)
						t.remove(10				)
						t.remove(8				)
						t.remove(3				)
						t.remove(6				)
						t.remove(14				)

						# Prints all the elements of the list in inorder traversal after delete
						lowerCAmelCase__	 =  [i.label for i in t.inorder_traversal()]
						print("Inorder traversal after delete:"      ,				lowercase_				)

						# Prints all the elements of the list in preorder traversal after delete
						lowerCAmelCase__	 =  [i.label for i in t.preorder_traversal()]
						print("Preorder traversal after delete:"      ,				lowercase_				)

						print("Max. label:"      ,				t.get_max_label()				)
						print("Min. label:"      ,				t.get_min_label()				)


if __name__ == "__main__":
	binary_search_tree_example()














from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


a_							=      logging.get_logger(__name__)  # pylint: disable=invalid-name


a_							=      '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''


class        lowercase__       (     _UpperCAmelCase		):

	def __init__(							self						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   None:







							'''simple docstring'''





							super().__init__()

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

							lowerCAmelCase__	 =  None

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=0    )->   Optional[Any]:







							'''simple docstring'''





							if is_accelerate_available() and is_accelerate_version(">="						,      "0.14.0"    ):
													from accelerate import cpu_offload
							else:
													raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"    )

							lowerCAmelCase__	 =  torch.device(F"cuda:{gpu_id}"    )

							if self.device.type != "cpu":
													self.to("cpu"						,      silence_dtype_warnings=__UpperCAmelCase    )
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							for cpu_offloaded_model in [self.unet]:
													cpu_offload(__UpperCAmelCase						,      __UpperCAmelCase    )

							if self.safety_checker is not None:
													cpu_offload(self.safety_checker						,      execution_device=__UpperCAmelCase						,      offload_buffers=__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=0    )->   Any:







							'''simple docstring'''





							if is_accelerate_available() and is_accelerate_version(">="						,      "0.17.0.dev0"    ):
													from accelerate import cpu_offload_with_hook
							else:
													raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."    )

							lowerCAmelCase__	 =  torch.device(F"cuda:{gpu_id}"    )

							if self.device.type != "cpu":
													self.to("cpu"						,      silence_dtype_warnings=__UpperCAmelCase    )
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							lowerCAmelCase__	 =  None
							for cpu_offloaded_model in [self.unet]:
													lowerCAmelCase__ ,							lowerCAmelCase__	 =  cpu_offload_with_hook(__UpperCAmelCase						,      __UpperCAmelCase						,      prev_module_hook=__UpperCAmelCase    )

							if self.safety_checker is not None:
													lowerCAmelCase__ ,							lowerCAmelCase__	 =  cpu_offload_with_hook(self.safety_checker						,      __UpperCAmelCase						,      prev_module_hook=__UpperCAmelCase    )

							# We'll offload the last model manually.
							lowerCAmelCase__	 =  hook

	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							if not hasattr(self.unet						,      "_hf_hook"    ):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(__UpperCAmelCase						,      "_hf_hook"    )
													    and hasattr(module._hf_hook						,      "execution_device"    )
													    and module._hf_hook.execution_device is not None
													):
																			return torch.device(module._hf_hook.execution_device    )
							return self.device

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase=None    )->   Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  (batch_size, num_channels, height, width)
							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."    )

							if latents is None:
													lowerCAmelCase__	 =  randn_tensor(__UpperCAmelCase						,      generator=__UpperCAmelCase						,      device=__UpperCAmelCase						,      dtype=__UpperCAmelCase    )
							else:
													lowerCAmelCase__	 =  latents.to(device=__UpperCAmelCase						,      dtype=__UpperCAmelCase    )

							# scale the initial noise by the standard deviation required by the scheduler
							lowerCAmelCase__	 =  latents * self.scheduler.init_noise_sigma
							return latents

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase = "pil"    )->   Tuple:







							'''simple docstring'''





							if output_type not in ["pt", "np", "pil"]:
													raise ValueError(
													    F"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"    )

							# Equivalent to diffusers.VaeImageProcessor.denormalize
							lowerCAmelCase__	 =  (sample / 2 + 0.5).clamp(0						,      1    )
							if output_type == "pt":
													return sample

							# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
							lowerCAmelCase__	 =  sample.cpu().permute(0						,      2						,      3						,      1    ).numpy()
							if output_type == "np":
													return sample

							# Output_type must be 'pil'
							lowerCAmelCase__	 =  self.numpy_to_pil(__UpperCAmelCase    )
							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase=None    )->   List[Any]:







							'''simple docstring'''





							if self.unet.config.num_class_embeds is not None:
													if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			lowerCAmelCase__	 =  torch.tensor(__UpperCAmelCase						,      dtype=torch.int    )
													elif isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			assert batch_size == 1, "Batch size must be 1 if classes is an int"
																			lowerCAmelCase__	 =  torch.tensor([class_labels]						,      dtype=torch.int    )
													elif class_labels is None:
																			# Randomly generate batch_size class labels
																			# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																			lowerCAmelCase__	 =  torch.randint(0						,      self.unet.config.num_class_embeds						,      size=(batch_size,)    )
													lowerCAmelCase__	 =  class_labels.to(__UpperCAmelCase    )
							else:
													lowerCAmelCase__	 =  None
							return class_labels

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   List[Any]:







							'''simple docstring'''





							if num_inference_steps is None and timesteps is None:
													raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."    )

							if num_inference_steps is not None and timesteps is not None:
													logger.warning(
													    F"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"
													    " `timesteps` will be used over `num_inference_steps`."    )

							if latents is not None:
													lowerCAmelCase__	 =  (batch_size, 3, img_size, img_size)
													if latents.shape != expected_shape:
																			raise ValueError(F"The shape of latents is {latents.shape} but is expected to be {expected_shape}."    )

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ) or callback_steps <= 0)
							):
													raise ValueError(
													    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
													    F" {type(__UpperCAmelCase    )}."    )


	@torch.no_grad()
	@replace_example_docstring(__UpperCAmelCase    )
	def __call__(							self						,      __UpperCAmelCase = 1						,      __UpperCAmelCase = None						,      __UpperCAmelCase = 1						,      __UpperCAmelCase = None						,      __UpperCAmelCase = None						,      __UpperCAmelCase = None						,      __UpperCAmelCase = "pil"						,      __UpperCAmelCase = True						,      __UpperCAmelCase = None						,      __UpperCAmelCase = 1						,      )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.unet.config.sample_size
							lowerCAmelCase__	 =  self._execution_device

							# 1. Check inputs
							self.check_inputs(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )

							# 2. Prepare image latents
							# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
							lowerCAmelCase__	 =  self.prepare_latents(
							    batch_size=__UpperCAmelCase						,      num_channels=self.unet.config.in_channels						,      height=__UpperCAmelCase						,      width=__UpperCAmelCase						,      dtype=self.unet.dtype						,      device=__UpperCAmelCase						,      generator=__UpperCAmelCase						,      latents=__UpperCAmelCase						,      )

							# 3. Handle class_labels for class-conditional models
							lowerCAmelCase__	 =  self.prepare_class_labels(__UpperCAmelCase						,      __UpperCAmelCase						,      class_labels=__UpperCAmelCase    )

							# 4. Prepare timesteps
							if timesteps is not None:
													self.scheduler.set_timesteps(timesteps=__UpperCAmelCase						,      device=__UpperCAmelCase    )
													lowerCAmelCase__	 =  self.scheduler.timesteps
													lowerCAmelCase__	 =  len(__UpperCAmelCase    )
							else:
													self.scheduler.set_timesteps(__UpperCAmelCase    )
													lowerCAmelCase__	 =  self.scheduler.timesteps

							# 5. Denoising loop
							# Multistep sampling: implements Algorithm 1 in the paper
							with self.progress_bar(total=__UpperCAmelCase    ) as progress_bar:
													for i, t in enumerate(__UpperCAmelCase    ):
																			lowerCAmelCase__	 =  self.scheduler.scale_model_input(__UpperCAmelCase						,      __UpperCAmelCase    )
																			lowerCAmelCase__	 =  self.unet(__UpperCAmelCase						,      __UpperCAmelCase						,      class_labels=__UpperCAmelCase						,      return_dict=__UpperCAmelCase    )[0]

																			lowerCAmelCase__	 =  self.scheduler.step(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      generator=__UpperCAmelCase    )[0]

																			# call the callback, if provided
																			progress_bar.update()
																			if callback is not None and i % callback_steps == 0:
																									callback(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )

        # 6. Post-process image sample
							lowerCAmelCase__	 =  self.postprocess_image(__UpperCAmelCase						,      output_type=__UpperCAmelCase    )

							# Offload last model to CPU
							if hasattr(self						,      "final_offload_hook"    ) and self.final_offload_hook is not None:
													self.final_offload_hook.offload()

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase    )






