code: 
  
  
  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
  
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  lowerCAmelCase__   	=				'\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   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       None:
              super().__init__()
  
              self.register_modules(
                  unet=__lowerCamelCase				,		scheduler=__lowerCamelCase				,		)
  
              _A   : int       					=				None
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase=0)    ->       int:
              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   : Optional[Any]       					=				torch.device(F"cuda:{gpu_id}")
  
              if self.device.type != "cpu":
                     self.to("cpu"				,		silence_dtype_warnings=__lowerCamelCase)
                     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(__lowerCamelCase				,		__lowerCamelCase)
  
              if self.safety_checker is not None:
                     cpu_offload(self.safety_checker				,		execution_device=__lowerCamelCase				,		offload_buffers=__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase=0)    ->       List[Any]:
              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   : Union[str, Any]       					=				torch.device(F"cuda:{gpu_id}")
  
              if self.device.type != "cpu":
                     self.to("cpu"				,		silence_dtype_warnings=__lowerCamelCase)
                     torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
              _A   : List[str]       					=				None
              for cpu_offloaded_model in [self.unet]:
                     _A					,    _A   : Union[str, Any]       					=				cpu_offload_with_hook(__lowerCamelCase				,		__lowerCamelCase				,		prev_module_hook=__lowerCamelCase)
  
              if self.safety_checker is not None:
                     _A					,    _A   : Union[str, Any]       					=				cpu_offload_with_hook(self.safety_checker				,		__lowerCamelCase				,		prev_module_hook=__lowerCamelCase)
  
              # We'll offload the last model manually.
              _A   : Optional[int]       					=				hook
  
  
       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              if not hasattr(self.unet				,		"_hf_hook"):
                     return self.device
              for module in self.unet.modules():
                     if (
                         hasattr(__lowerCamelCase				,		"_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				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase=None)    ->       Optional[int]:
              _A   : Any       					=				(batch_size, num_channels, height, width)
              if isinstance(__lowerCamelCase				,		__lowerCamelCase) and len(__lowerCamelCase) != batch_size:
                     raise ValueError(
                         F"You have passed a list of generators of length {len(__lowerCamelCase)}, 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(__lowerCamelCase				,		generator=__lowerCamelCase				,		device=__lowerCamelCase				,		dtype=__lowerCamelCase)
              else:
                     _A   : Any       					=				latents.to(device=__lowerCamelCase				,		dtype=__lowerCamelCase)
  
              # scale the initial noise by the standard deviation required by the scheduler
              _A   : Optional[Any]       					=				latents * self.scheduler.init_noise_sigma
              return latents
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = "pil")    ->       Any:
              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   : Optional[int]       					=				(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   : Optional[Any]       					=				self.numpy_to_pil(__lowerCamelCase)
              return sample
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase=None)    ->       Tuple:
              if self.unet.config.num_class_embeds is not None:
                     if isinstance(__lowerCamelCase				,		__lowerCamelCase):
                            _A   : str       					=				torch.tensor(__lowerCamelCase				,		dtype=torch.int)
                     elif isinstance(__lowerCamelCase				,		__lowerCamelCase):
                            assert batch_size == 1, "Batch size must be 1 if classes is an int"
                            _A   : Any       					=				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   : List[Any]       					=				torch.randint(0				,		self.unet.config.num_class_embeds				,		size=(batch_size,))
                     _A   : str       					=				class_labels.to(__lowerCamelCase)
              else:
                     _A   : Any       					=				None
              return class_labels
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       str:
              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   : List[str]       					=				(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(__lowerCamelCase				,		__lowerCamelCase) or callback_steps <= 0)
              ):
                     raise ValueError(
                         F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                         F" {type(__lowerCamelCase)}.")
  
  
       @torch.no_grad()
       @replace_example_docstring(__lowerCamelCase)
       def __call__(		self				,		__lowerCamelCase = 1				,		__lowerCamelCase = None				,		__lowerCamelCase = 1				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = "pil"				,		__lowerCamelCase = True				,		__lowerCamelCase = None				,		__lowerCamelCase = 1				,		)    ->       Optional[Any]:
              _A   : Any       					=				self.unet.config.sample_size
              _A   : Any       					=				self._execution_device
  
              # 1. Check inputs
              self.check_inputs(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
  
              # 2. Prepare image latents
              # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
              _A   : int       					=				self.prepare_latents(
                  batch_size=__lowerCamelCase				,		num_channels=self.unet.config.in_channels				,		height=__lowerCamelCase				,		width=__lowerCamelCase				,		dtype=self.unet.dtype				,		device=__lowerCamelCase				,		generator=__lowerCamelCase				,		latents=__lowerCamelCase				,		)
  
              # 3. Handle class_labels for class-conditional models
              _A   : List[Any]       					=				self.prepare_class_labels(__lowerCamelCase				,		__lowerCamelCase				,		class_labels=__lowerCamelCase)
  
              # 4. Prepare timesteps
              if timesteps is not None:
                     self.scheduler.set_timesteps(timesteps=__lowerCamelCase				,		device=__lowerCamelCase)
                     _A   : Union[str, Any]       					=				self.scheduler.timesteps
                     _A   : List[Any]       					=				len(__lowerCamelCase)
              else:
                     self.scheduler.set_timesteps(__lowerCamelCase)
                     _A   : Optional[int]       					=				self.scheduler.timesteps
  
              # 5. Denoising loop
              # Multistep sampling: implements Algorithm 1 in the paper
              with self.progress_bar(total=__lowerCamelCase) as progress_bar:
                     for i, t in enumerate(__lowerCamelCase):
                            _A   : Tuple       					=				self.scheduler.scale_model_input(__lowerCamelCase				,		__lowerCamelCase)
                            _A   : str       					=				self.unet(__lowerCamelCase				,		__lowerCamelCase				,		class_labels=__lowerCamelCase				,		return_dict=__lowerCamelCase)[0]
  
                            _A   : int       					=				self.scheduler.step(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		generator=__lowerCamelCase)[0]
  
                            # call the callback, if provided
                            progress_bar.update()
                            if callback is not None and i % callback_steps == 0:
                                   callback(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
  
          # 6. Post-process image sample
              _A   : Tuple       					=				self.postprocess_image(__lowerCamelCase				,		output_type=__lowerCamelCase)
  
              # 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=__lowerCamelCase)
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase__   	=				{
      'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['DetaImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'DetaForObjectDetection',
          'DetaModel',
          'DetaPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .image_processing_deta import DetaImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_deta import (
              DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetaForObjectDetection,
              DetaModel,
              DetaPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  