code: 
  import os
  from argparse import ArgumentParser
  from pathlib import Path
  from shutil import copyfile, rmtree
  from typing import Generator
  
  import datasets.config
  from datasets.builder import DatasetBuilder
  from datasets.commands import BaseDatasetsCLICommand
  from datasets.download.download_manager import DownloadMode
  from datasets.load import dataset_module_factory, import_main_class
  from datasets.utils.filelock import logger as fl_logger
  from datasets.utils.info_utils import VerificationMode
  from datasets.utils.logging import ERROR, get_logger
  
  
  lowercase__						=			get_logger(__name__)
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		return TestCommand(
  		    args.dataset   ,					args.name   ,					args.cache_dir   ,					args.data_dir   ,					args.all_configs   ,					args.save_info or args.save_infos   ,					args.ignore_verifications   ,					args.force_redownload   ,					args.clear_cache   ,					)
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	False  # to tell pytest it's not a test class
  
  
  
  		@staticmethod
  		def 			lowerCAmelCase							(			UpperCamelCase__							: Optional[Any]			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[int]     =	parser.add_parser('''test'''     ,  help='''Test dataset implementation.'''			)
  				test_parser.add_argument('''--name'''     ,  type=UpperCamelCase__     ,  default=UpperCamelCase__     ,  help='''Dataset processing name'''			)
  				test_parser.add_argument(
  				    '''--cache_dir'''     ,  type=UpperCamelCase__     ,  default=UpperCamelCase__     ,  help='''Cache directory where the datasets are stored.'''     ,  )
  				test_parser.add_argument(
  				    '''--data_dir'''     ,  type=UpperCamelCase__     ,  default=UpperCamelCase__     ,  help='''Can be used to specify a manual directory to get the files from.'''     ,  )
  				test_parser.add_argument('''--all_configs'''     ,  action='''store_true'''     ,  help='''Test all dataset configurations'''			)
  				test_parser.add_argument(
  				    '''--save_info'''     ,  action='''store_true'''     ,  help='''Save the dataset infos in the dataset card (README.md)'''			)
  				test_parser.add_argument(
  				    '''--ignore_verifications'''     ,  action='''store_true'''     ,  help='''Run the test without checksums and splits checks.'''     ,  )
  				test_parser.add_argument('''--force_redownload'''     ,  action='''store_true'''     ,  help='''Force dataset redownload'''			)
  				test_parser.add_argument(
  				    '''--clear_cache'''     ,  action='''store_true'''     ,  help='''Remove downloaded files and cached datasets after each config test'''     ,  )
  				# aliases
  				test_parser.add_argument('''--save_infos'''     ,  action='''store_true'''     ,  help='''alias to save_info'''			)
  				test_parser.add_argument('''dataset'''     ,  type=UpperCamelCase__     ,  help='''Name of the dataset to download'''			)
  				test_parser.set_defaults(func=_test_command_factory			)
  
  
  
  		def __init__(			self							: Optional[Any]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: Dict     ,  )	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[str]     =	dataset
  				snake_case     :					int     =	name
  				snake_case     :					Dict     =	cache_dir
  				snake_case     :					Tuple     =	data_dir
  				snake_case     :					str     =	all_configs
  				snake_case     :					Optional[int]     =	save_infos
  				snake_case     :					List[str]     =	ignore_verifications
  				snake_case     :					Union[str, Any]     =	force_redownload
  				snake_case     :					str     =	clear_cache
  				if clear_cache and not cache_dir:
  						print(
  						    '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
  						    '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
  						    '''Please provide a --cache_dir that will be used to test the dataset script.'''			)
  						exit(1			)
  				if save_infos:
  						snake_case     :					Any     =	True
  
  
  
  		def 			lowerCAmelCase							(			self							: List[str]			)	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				fl_logger().setLevel(UpperCamelCase__			)
  				if self._name is not None and self._all_configs:
  						print('''Both parameters `config` and `all_configs` can\'t be used at once.'''			)
  						exit(1			)
  				snake_case ,snake_case     :					Any     =	self._dataset, self._name
  				snake_case     :					Optional[int]     =	dataset_module_factory(UpperCamelCase__			)
  				snake_case     :					Optional[int]     =	import_main_class(module.module_path			)
  				snake_case     :					Dict     =	len(builder_cls.BUILDER_CONFIGS			) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1
  
  				def get_builders() -> Generator[DatasetBuilder, None, None]:
  						if self._all_configs and builder_cls.BUILDER_CONFIGS:
  								for i, config in enumerate(builder_cls.BUILDER_CONFIGS			):
  										if "config_name" in module.builder_kwargs:
  												yield builder_cls(
  												    cache_dir=self._cache_dir     ,  data_dir=self._data_dir     ,  **module.builder_kwargs     ,  )
  										else:
  												yield builder_cls(
  												    config_name=config.name     ,  cache_dir=self._cache_dir     ,  data_dir=self._data_dir     ,  **module.builder_kwargs     ,  )
  						else:
  								if "config_name" in module.builder_kwargs:
  										yield builder_cls(cache_dir=self._cache_dir     ,  data_dir=self._data_dir     ,  **module.builder_kwargs			)
  								else:
  										yield builder_cls(
  										    config_name=UpperCamelCase__     ,  cache_dir=self._cache_dir     ,  data_dir=self._data_dir     ,  **module.builder_kwargs     ,  )
  
  				for j, builder in enumerate(get_builders()			):
  						print(f'Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'			)
  						snake_case     :					Optional[Any]     =	os.path.exists(
  						    os.path.join(builder.get_imported_module_dir()     ,  datasets.config.DATASETDICT_INFOS_FILENAME			)			)  # record checksums only if we need to update a (deprecated) dataset_infos.json
  						builder.download_and_prepare(
  						    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
  						    if not self._force_redownload
  						    else DownloadMode.FORCE_REDOWNLOAD     ,  verification_mode=VerificationMode.NO_CHECKS
  						    if self._ignore_verifications
  						    else VerificationMode.ALL_CHECKS     ,  try_from_hf_gcs=UpperCamelCase__     ,  )
  						builder.as_dataset()
  						if self._save_infos:
  								builder._save_infos()
  
  						# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
  						# The dataset_infos are saved in the YAML part of the README.md
  
  						# Let's move it to the original directory of the dataset script, to allow the user to
  						# upload them on S3 at the same time afterwards.
  						if self._save_infos:
  								snake_case     :					List[Any]     =	os.path.join(builder_cls.get_imported_module_dir()     ,  '''README.md'''			)
  								snake_case     :					Union[str, Any]     =	Path(UpperCamelCase__			).name + '''.py'''
  								snake_case     :					Union[str, Any]     =	os.path.join(UpperCamelCase__     ,  UpperCamelCase__			)
  								if os.path.isfile(UpperCamelCase__			):
  										snake_case     :					Dict     =	os.path.dirname(UpperCamelCase__			)
  								elif os.path.isfile(UpperCamelCase__			):
  										snake_case     :					str     =	path
  								elif os.path.isdir(UpperCamelCase__			):  # for local directories containing only data files
  										snake_case     :					Union[str, Any]     =	path
  								else:  # in case of a remote dataset
  										snake_case     :					Tuple     =	None
  										print(f'Dataset card saved at {dataset_readme_path}'			)
  
  								# Move dataset_info back to the user
  								if dataset_dir is not None:
  										snake_case     :					Tuple     =	os.path.join(UpperCamelCase__     ,  '''README.md'''			)
  										copyfile(UpperCamelCase__     ,  UpperCamelCase__			)
  										print(f'Dataset card saved at {user_dataset_readme_path}'			)
  
  										   # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
  						if self._clear_cache:
  								if os.path.isdir(builder._cache_dir			):
  										logger.warning(f'Clearing cache at {builder._cache_dir}'			)
  										rmtree(builder._cache_dir			)
  								snake_case     :					Dict     =	os.path.join(self._cache_dir     ,  datasets.config.DOWNLOADED_DATASETS_DIR			)
  								if os.path.isdir(UpperCamelCase__			):
  										logger.warning(f'Clearing cache at {download_dir}'			)
  										rmtree(UpperCamelCase__			)
  
  				print('''Test successful.'''			)
  
  
  
style_context: 
  '''simple docstring'''
  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
  
  
  lowercase__						=			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  lowercase__						=			"\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: List[Any]     ,  UpperCamelCase__							: UNetaDModel     ,  UpperCamelCase__							: CMStochasticIterativeScheduler			)	-> None:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				super().__init__()
  
  				self.register_modules(
  				    unet=UpperCamelCase__     ,  scheduler=UpperCamelCase__     ,  )
  
  				snake_case     :					str     =	None
  
  
  
  		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: str=0			)	-> int:
  
  
  
  
  
  
  				"""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'''			)
  
  				snake_case     :					Optional[Any]     =	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 			lowerCAmelCase							(			self							: Optional[int]     ,  UpperCamelCase__							: int=0			)	-> Dict:
  
  
  
  
  
  
  				"""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.'''			)
  
  				snake_case     :					Union[str, Any]     =	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)
  
  				snake_case     :					List[Any]     =	None
  				for cpu_offloaded_model in [self.unet]:
  						snake_case ,snake_case     :					Union[str, Any]     =	cpu_offload_with_hook(UpperCamelCase__     ,  UpperCamelCase__     ,  prev_module_hook=UpperCamelCase__			)
  
  				if self.safety_checker is not None:
  						snake_case ,snake_case     :					Optional[Any]     =	cpu_offload_with_hook(self.safety_checker     ,  UpperCamelCase__     ,  prev_module_hook=UpperCamelCase__			)
  
  				# We'll offload the last model manually.
  				snake_case     :					Optional[Any]     =	hook
  
  
  
  		@property
  		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  		def 			lowerCAmelCase							(			self							: int			)	-> List[str]:
  
  
  
  
  
  
  				"""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 			lowerCAmelCase							(			self							: int     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Optional[int]=None			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	(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:
  						snake_case     :					List[Any]     =	randn_tensor(UpperCamelCase__     ,  generator=UpperCamelCase__     ,  device=UpperCamelCase__     ,  dtype=UpperCamelCase__			)
  				else:
  						snake_case     :					Tuple     =	latents.to(device=UpperCamelCase__     ,  dtype=UpperCamelCase__			)
  
  				# scale the initial noise by the standard deviation required by the scheduler
  				snake_case     :					str     =	latents * self.scheduler.init_noise_sigma
  				return latents
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: torch.FloatTensor     ,  UpperCamelCase__							: str = "pil"			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""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
  				snake_case     :					Any     =	(sample / 2 + 0.5).clamp(0     ,  1			)
  				if output_type == "pt":
  						return sample
  
  				# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
  				snake_case     :					Optional[int]     =	sample.cpu().permute(0     ,  2     ,  3     ,  1			).numpy()
  				if output_type == "np":
  						return sample
  
  				# Output_type must be 'pil'
  				snake_case     :					Tuple     =	self.numpy_to_pil(UpperCamelCase__			)
  				return sample
  
  
  
  		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: List[str]=None			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if self.unet.config.num_class_embeds is not None:
  						if isinstance(UpperCamelCase__     ,  UpperCamelCase__			):
  								snake_case     :					str     =	torch.tensor(UpperCamelCase__     ,  dtype=torch.int			)
  						elif isinstance(UpperCamelCase__     ,  UpperCamelCase__			):
  								assert batch_size == 1, "Batch size must be 1 if classes is an int"
  								snake_case     :					Dict     =	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
  								snake_case     :					List[Any]     =	torch.randint(0     ,  self.unet.config.num_class_embeds     ,  size=(batch_size,)			)
  						snake_case     :					Dict     =	class_labels.to(UpperCamelCase__			)
  				else:
  						snake_case     :					int     =	None
  				return class_labels
  
  
  
  		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Union[str, Any]			)	-> Union[str, 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:
  						snake_case     :					List[Any]     =	(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							: Optional[int]     ,  UpperCamelCase__							: int = 1     ,  UpperCamelCase__							: Optional[Union[torch.Tensor, List[int], int]] = None     ,  UpperCamelCase__							: int = 1     ,  UpperCamelCase__							: List[int] = None     ,  UpperCamelCase__							: Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,  UpperCamelCase__							: Optional[torch.FloatTensor] = None     ,  UpperCamelCase__							: Optional[str] = "pil"     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,  UpperCamelCase__							: int = 1     ,  )	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[int]     =	self.unet.config.sample_size
  				snake_case     :					List[Any]     =	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)
  				snake_case     :					List[str]     =	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
  				snake_case     :					List[str]     =	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__			)
  						snake_case     :					Union[str, Any]     =	self.scheduler.timesteps
  						snake_case     :					Optional[int]     =	len(UpperCamelCase__			)
  				else:
  						self.scheduler.set_timesteps(UpperCamelCase__			)
  						snake_case     :					List[str]     =	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__			):
  								snake_case     :					Any     =	self.scheduler.scale_model_input(UpperCamelCase__     ,  UpperCamelCase__			)
  								snake_case     :					int     =	self.unet(UpperCamelCase__     ,  UpperCamelCase__     ,  class_labels=UpperCamelCase__     ,  return_dict=UpperCamelCase__			)[0]
  
  								snake_case     :					Dict     =	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
  				snake_case     :					Any     =	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__			)
  
  
  