code: 
  
  
  '''simple docstring'''
  
  
  
  from copy import deepcopy
  from unittest.case import TestCase
  
  import pytest
  
  from datasets.arrow_dataset import Dataset
  from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
  from datasets.info import DatasetInfo
  from datasets.tasks import (
      AudioClassification,
      AutomaticSpeechRecognition,
      ImageClassification,
      LanguageModeling,
      QuestionAnsweringExtractive,
      Summarization,
      TextClassification,
      task_template_from_dict,
  )
  from datasets.utils.py_utils import asdict
  
  
  __lowercase			:		List[Any] =			{
      'id': '5733be284776f41900661182',
      'title': 'University_of_Notre_Dame',
      'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
      'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
      'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [5_15]},
  }
  
  
  
  
  
  
  @pytest.mark.parametrize(
      'task_cls'		,   [
          AudioClassification,
          AutomaticSpeechRecognition,
          ImageClassification,
          LanguageModeling,
          QuestionAnsweringExtractive,
          Summarization,
          TextClassification,
      ]		,   )
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int     ):
  					__a    : int      					=						task_cls()
  					__a    : Dict      					=						asdict(_SCREAMING_SNAKE_CASE     )
  					__a    : int      					=						task_template_from_dict(_SCREAMING_SNAKE_CASE     )
  					assert task == reloaded
  
  
  
  
  
  class 							__UpperCamelCase       :
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						LanguageModeling(text_column='input_text'							)
  						assert {"input_text": "text"} == task.column_mapping
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						Features({'text': Value('string'							)}							)
  						__a    : List[Any]      					=						{'text_column': 'input_text'}
  						__a    : Dict      					=						LanguageModeling.from_dict(__a							)
  						assert "language-modeling" == task.task
  						assert input_schema == task.input_schema
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						sorted(['pos', 'neg']							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						TextClassification(text_column='input_text'		,       label_column='input_label'							)
  						self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}		,       task.column_mapping							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[int]      					=						Features({'text': Value('string'							)}							)
  						# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
  						__a    : Any      					=						Features({'labels': ClassLabel}							)
  						__a    : List[str]      					=						{'text_column': 'input_text', 'label_column': 'input_labels'}
  						__a    : str      					=						TextClassification.from_dict(__a							)
  						self.assertEqual('text-classification'		,       task.task							)
  						self.assertEqual(__a		,       task.input_schema							)
  						self.assertEqual(__a		,       task.label_schema							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						TextClassification(text_column='input_text'		,       label_column='input_label'							)
  						self.assertEqual(task.label_schema['labels']		,       __a							)
  						__a    : int      					=						task.align_with_features(Features({'input_label': ClassLabel(names=self.labels							)}							)							)
  						self.assertEqual(task.label_schema['labels']		,       ClassLabel(names=self.labels							)							)
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						QuestionAnsweringExtractive(
  						    context_column='input_context'		,       question_column='input_question'		,       answers_column='input_answers'							)
  						self.assertDictEqual(
  						    {'input_context': 'context', 'input_question': 'question', 'input_answers': 'answers'}		,       task.column_mapping							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						Features({'question': Value('string'							), 'context': Value('string'							)}							)
  						__a    : Optional[int]      					=						Features(
  						    {
  						        'answers': Sequence(
  						            {
  						                'text': Value('string'							),
  						                'answer_start': Value('int32'							),
  						            }							)
  						    }							)
  						__a    : Optional[int]      					=						{
  						    'context_column': 'input_input_context',
  						    'question_column': 'input_question',
  						    'answers_column': 'input_answers',
  						}
  						__a    : int      					=						QuestionAnsweringExtractive.from_dict(__a							)
  						self.assertEqual('question-answering-extractive'		,       task.task							)
  						self.assertEqual(__a		,       task.input_schema							)
  						self.assertEqual(__a		,       task.label_schema							)
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						Summarization(text_column='input_text'		,       summary_column='input_summary'							)
  						self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'}		,       task.column_mapping							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[int]      					=						Features({'text': Value('string'							)}							)
  						__a    : Any      					=						Features({'summary': Value('string'							)}							)
  						__a    : Tuple      					=						{'text_column': 'input_text', 'summary_column': 'input_summary'}
  						__a    : Tuple      					=						Summarization.from_dict(__a							)
  						self.assertEqual('summarization'		,       task.task							)
  						self.assertEqual(__a		,       task.input_schema							)
  						self.assertEqual(__a		,       task.label_schema							)
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						AutomaticSpeechRecognition(audio_column='input_audio'		,       transcription_column='input_transcription'							)
  						self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'}		,       task.column_mapping							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[int]      					=						Features({'audio': Audio()}							)
  						__a    : Tuple      					=						Features({'transcription': Value('string'							)}							)
  						__a    : List[str]      					=						{
  						    'audio_column': 'input_audio',
  						    'transcription_column': 'input_transcription',
  						}
  						__a    : Tuple      					=						AutomaticSpeechRecognition.from_dict(__a							)
  						self.assertEqual('automatic-speech-recognition'		,       task.task							)
  						self.assertEqual(__a		,       task.input_schema							)
  						self.assertEqual(__a		,       task.label_schema							)
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Tuple      					=						sorted(['pos', 'neg']							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[Any]      					=						AudioClassification(audio_column='input_audio'		,       label_column='input_label'							)
  						self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'}		,       task.column_mapping							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						Features({'audio': Audio()}							)
  						__a    : Dict      					=						Features({'labels': ClassLabel}							)
  						__a    : Union[str, Any]      					=						{
  						    'audio_column': 'input_image',
  						    'label_column': 'input_label',
  						}
  						__a    : Optional[int]      					=						AudioClassification.from_dict(__a							)
  						self.assertEqual('audio-classification'		,       task.task							)
  						self.assertEqual(__a		,       task.input_schema							)
  						self.assertEqual(__a		,       task.label_schema							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						AudioClassification(audio_column='input_audio'		,       label_column='input_label'							)
  						self.assertEqual(task.label_schema['labels']		,       __a							)
  						__a    : int      					=						task.align_with_features(Features({'input_label': ClassLabel(names=self.labels							)}							)							)
  						self.assertEqual(task.label_schema['labels']		,       ClassLabel(names=self.labels							)							)
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						sorted(['pos', 'neg']							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[Any]      					=						ImageClassification(image_column='input_image'		,       label_column='input_label'							)
  						self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'}		,       task.column_mapping							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[int]      					=						Features({'image': Image()}							)
  						__a    : List[str]      					=						Features({'labels': ClassLabel}							)
  						__a    : Union[str, Any]      					=						{
  						    'image_column': 'input_image',
  						    'label_column': 'input_label',
  						}
  						__a    : Optional[int]      					=						ImageClassification.from_dict(__a							)
  						self.assertEqual('image-classification'		,       task.task							)
  						self.assertEqual(__a		,       task.input_schema							)
  						self.assertEqual(__a		,       task.label_schema							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						ImageClassification(image_column='input_image'		,       label_column='input_label'							)
  						self.assertEqual(task.label_schema['labels']		,       __a							)
  						__a    : Optional[Any]      					=						task.align_with_features(Features({'input_label': ClassLabel(names=self.labels							)}							)							)
  						self.assertEqual(task.label_schema['labels']		,       ClassLabel(names=self.labels							)							)
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Union[str, Any]      					=						DatasetInfo(task_templates=QuestionAnsweringExtractive()							)
  						__a    : Optional[int]      					=						Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}		,       info=__a							)
  						assert isinstance(dataset.info.task_templates		,       __a							)
  						assert len(dataset.info.task_templates							) == 1
  
  						def keep_task(__a							):
  											return x
  
  						def dont_keep_task(__a							):
  											__a    : Any      					=						deepcopy(__a							)
  											__a    : Tuple      					=						0
  											return out
  
  						__a    : Tuple      					=						dataset.map(__a							)
  						assert mapped_dataset.info.task_templates == dataset.info.task_templates
  						# reload from cache
  						__a    : Dict      					=						dataset.map(__a							)
  						assert mapped_dataset.info.task_templates == dataset.info.task_templates
  
  						__a    : Any      					=						dataset.map(__a							)
  						assert mapped_dataset.info.task_templates == []
  						# reload from cache
  						__a    : Union[str, Any]      					=						dataset.map(__a							)
  						assert mapped_dataset.info.task_templates == []
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						Features({'text': Value('string'							), 'label': ClassLabel(names=('pos', 'neg')							)}							)
  						__a    : List[Any]      					=						TextClassification(text_column='text'		,       label_column='label'							)
  						__a    : Dict      					=						DatasetInfo(features=__a		,       task_templates=__a							)
  						__a    : Optional[int]      					=						Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']}		,       info=__a							)
  
  						def process(__a							):
  											return example
  
  						__a    : Dict      					=						dataset.remove_columns('label'							)
  						__a    : Optional[int]      					=						modified_dataset.map(__a							)
  						assert mapped_dataset.info.task_templates == []
  
  
  
  
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			:		List[str] =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  __lowercase			:		Optional[int] =			'\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 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def __init__(     self		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().__init__()
  
  						self.register_modules(
  						    unet=__a		,       scheduler=__a		,       )
  
  						__a    : List[str]      					=						None
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a=0							):
  
  						'''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'							)
  
  						__a    : str      					=						torch.device(f"""cuda:{gpu_id}"""							)
  
  						if self.device.type != "cpu":
  											self.to('cpu'		,       silence_dtype_warnings=__a							)
  											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(__a		,       __a							)
  
  						if self.safety_checker is not None:
  											cpu_offload(self.safety_checker		,       execution_device=__a		,       offload_buffers=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a=0							):
  
  						'''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.'							)
  
  						__a    : Tuple      					=						torch.device(f"""cuda:{gpu_id}"""							)
  
  						if self.device.type != "cpu":
  											self.to('cpu'		,       silence_dtype_warnings=__a							)
  											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  						__a    : List[Any]      					=						None
  						for cpu_offloaded_model in [self.unet]:
  											__a					,       __a    : List[str]      					=						cpu_offload_with_hook(__a		,       __a		,       prev_module_hook=__a							)
  
  						if self.safety_checker is not None:
  											__a					,       __a    : str      					=						cpu_offload_with_hook(self.safety_checker		,       __a		,       prev_module_hook=__a							)
  
  						# We'll offload the last model manually.
  						__a    : List[Any]      					=						hook
  
  
  
  
  	@property
  	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if not hasattr(self.unet		,       '_hf_hook'							):
  											return self.device
  						for module in self.unet.modules():
  											if (
  											    hasattr(__a		,       '_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		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a=None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						(batch_size, num_channels, height, width)
  						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."""							)
  
  						if latents is None:
  											__a    : Optional[Any]      					=						randn_tensor(__a		,       generator=__a		,       device=__a		,       dtype=__a							)
  						else:
  											__a    : Dict      					=						latents.to(device=__a		,       dtype=__a							)
  
  						# scale the initial noise by the standard deviation required by the scheduler
  						__a    : Union[str, Any]      					=						latents * self.scheduler.init_noise_sigma
  						return latents
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a = "pil"							):
  
  						'''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
  						__a    : List[Any]      					=						(sample / 2 + 0.5).clamp(0		,       1							)
  						if output_type == "pt":
  											return sample
  
  						# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
  						__a    : str      					=						sample.cpu().permute(0		,       2		,       3		,       1							).numpy()
  						if output_type == "np":
  											return sample
  
  						# Output_type must be 'pil'
  						__a    : Union[str, Any]      					=						self.numpy_to_pil(__a							)
  						return sample
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a=None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if self.unet.config.num_class_embeds is not None:
  											if isinstance(__a		,       __a							):
  																__a    : Tuple      					=						torch.tensor(__a		,       dtype=torch.int							)
  											elif isinstance(__a		,       __a							):
  																assert batch_size == 1, "Batch size must be 1 if classes is an int"
  																__a    : int      					=						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
  																__a    : str      					=						torch.randint(0		,       self.unet.config.num_class_embeds		,       size=(batch_size,)							)
  											__a    : str      					=						class_labels.to(__a							)
  						else:
  											__a    : Optional[int]      					=						None
  						return class_labels
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a							):
  
  						'''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:
  											__a    : Optional[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(__a		,       __a							) or callback_steps <= 0)
  						):
  											raise ValueError(
  											    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
  											    f""" {type(__a							)}."""							)
  
  
  
  
  
  	@torch.no_grad()
  	@replace_example_docstring(__a							)
  	def __call__(     self		,       __a = 1		,       __a = None		,       __a = 1		,       __a = None		,       __a = None		,       __a = None		,       __a = "pil"		,       __a = True		,       __a = None		,       __a = 1		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						self.unet.config.sample_size
  						__a    : Union[str, Any]      					=						self._execution_device
  
  						# 1. Check inputs
  						self.check_inputs(__a		,       __a		,       __a		,       __a		,       __a		,       __a							)
  
  						# 2. Prepare image latents
  						# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
  						__a    : Union[str, Any]      					=						self.prepare_latents(
  						    batch_size=__a		,       num_channels=self.unet.config.in_channels		,       height=__a		,       width=__a		,       dtype=self.unet.dtype		,       device=__a		,       generator=__a		,       latents=__a		,       )
  
  						# 3. Handle class_labels for class-conditional models
  						__a    : Optional[Any]      					=						self.prepare_class_labels(__a		,       __a		,       class_labels=__a							)
  
  						# 4. Prepare timesteps
  						if timesteps is not None:
  											self.scheduler.set_timesteps(timesteps=__a		,       device=__a							)
  											__a    : Union[str, Any]      					=						self.scheduler.timesteps
  											__a    : Tuple      					=						len(__a							)
  						else:
  											self.scheduler.set_timesteps(__a							)
  											__a    : Tuple      					=						self.scheduler.timesteps
  
  						# 5. Denoising loop
  						# Multistep sampling: implements Algorithm 1 in the paper
  						with self.progress_bar(total=__a							) as progress_bar:
  											for i, t in enumerate(__a							):
  																__a    : Optional[int]      					=						self.scheduler.scale_model_input(__a		,       __a							)
  																__a    : List[str]      					=						self.unet(__a		,       __a		,       class_labels=__a		,       return_dict=__a							)[0]
  
  																__a    : int      					=						self.scheduler.step(__a		,       __a		,       __a		,       generator=__a							)[0]
  
  																# call the callback, if provided
  																progress_bar.update()
  																if callback is not None and i % callback_steps == 0:
  																					callback(__a		,       __a		,       __a							)
  
          # 6. Post-process image sample
  						__a    : Tuple      					=						self.postprocess_image(__a		,       output_type=__a							)
  
  						# 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=__a							)
  
  
  
  