code: 
  '''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
  
  
  A           =   logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  A           =   "\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__   (       UpperCamelCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : List[Any]	, lowerCAmelCase_  : UNetaDModel	, lowerCAmelCase_  : CMStochasticIterativeScheduler    )   ->       None:
          super().__init__()
  
          self.register_modules(
              unet=lowerCAmelCase_	, scheduler=lowerCAmelCase_	, )
  
          snake_case_						       =							None
  
    def 		_lowerCamelCase	(self  : Union[str, Any]	, lowerCAmelCase_  : Optional[Any]=0    )   ->       Dict:
          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_						       =							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  : str	, lowerCAmelCase_  : Tuple=0    )   ->       Optional[int]:
          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_						       =							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)
  
          snake_case_						       =							None
          for cpu_offloaded_model in [self.unet]:
                snake_case_       ,	snake_case_						       =							cpu_offload_with_hook(lowerCAmelCase_	, lowerCAmelCase_	, prev_module_hook=lowerCAmelCase_    )
  
          if self.safety_checker is not None:
                snake_case_       ,	snake_case_						       =							cpu_offload_with_hook(self.safety_checker	, lowerCAmelCase_	, prev_module_hook=lowerCAmelCase_    )
  
          # We'll offload the last model manually.
          snake_case_						       =							hook
  
    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Tuple:
          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  : Union[str, Any]	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : Optional[Any]	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : str	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : Any=None    )   ->       str:
          snake_case_						       =							(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:
                snake_case_						       =							randn_tensor(lowerCAmelCase_	, generator=lowerCAmelCase_	, device=lowerCAmelCase_	, dtype=lowerCAmelCase_    )
          else:
                snake_case_						       =							latents.to(device=lowerCAmelCase_	, dtype=lowerCAmelCase_    )
  
          # scale the initial noise by the standard deviation required by the scheduler
          snake_case_						       =							latents * self.scheduler.init_noise_sigma
          return latents
  
    def 		_lowerCamelCase	(self  : Tuple	, lowerCAmelCase_  : torch.FloatTensor	, lowerCAmelCase_  : str = "pil"    )   ->       Optional[int]:
          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_						       =							(sample / 2 + 0.5).clamp(0	, 1    )
          if output_type == "pt":
                return sample
  
          # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
          snake_case_						       =							sample.cpu().permute(0	, 2	, 3	, 1    ).numpy()
          if output_type == "np":
                return sample
  
          # Output_type must be 'pil'
          snake_case_						       =							self.numpy_to_pil(lowerCAmelCase_    )
          return sample
  
    def 		_lowerCamelCase	(self  : Union[str, Any]	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : List[Any]=None    )   ->       Union[str, Any]:
          if self.unet.config.num_class_embeds is not None:
                if isinstance(lowerCAmelCase_	, lowerCAmelCase_    ):
                      snake_case_						       =							torch.tensor(lowerCAmelCase_	, dtype=torch.int    )
                elif isinstance(lowerCAmelCase_	, lowerCAmelCase_    ):
                      assert batch_size == 1, "Batch size must be 1 if classes is an int"
                      snake_case_						       =							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_						       =							torch.randint(0	, self.unet.config.num_class_embeds	, size=(batch_size,)    )
                snake_case_						       =							class_labels.to(lowerCAmelCase_    )
          else:
                snake_case_						       =							None
          return class_labels
  
    def 		_lowerCamelCase	(self  : List[Any]	, lowerCAmelCase_  : Optional[Any]	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : str	, lowerCAmelCase_  : Optional[Any]    )   ->       List[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:
                snake_case_						       =							(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  : Tuple	, lowerCAmelCase_  : int = 1	, lowerCAmelCase_  : Optional[Union[torch.Tensor, List[int], int]] = None	, lowerCAmelCase_  : int = 1	, lowerCAmelCase_  : List[int] = None	, lowerCAmelCase_  : Optional[Union[torch.Generator, List[torch.Generator]]] = None	, lowerCAmelCase_  : Optional[torch.FloatTensor] = None	, lowerCAmelCase_  : Optional[str] = "pil"	, lowerCAmelCase_  : bool = True	, lowerCAmelCase_  : Optional[Callable[[int, int, torch.FloatTensor], None]] = None	, lowerCAmelCase_  : int = 1	, )   ->       Union[str, Any]:
          snake_case_						       =							self.unet.config.sample_size
          snake_case_						       =							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)
          snake_case_						       =							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
          snake_case_						       =							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_    )
                snake_case_						       =							self.scheduler.timesteps
                snake_case_						       =							len(lowerCAmelCase_    )
          else:
                self.scheduler.set_timesteps(lowerCAmelCase_    )
                snake_case_						       =							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_    ):
                      snake_case_						       =							self.scheduler.scale_model_input(lowerCAmelCase_	, lowerCAmelCase_    )
                      snake_case_						       =							self.unet(lowerCAmelCase_	, lowerCAmelCase_	, class_labels=lowerCAmelCase_	, return_dict=lowerCAmelCase_    )[0]
  
                      snake_case_						       =							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
          snake_case_						       =							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: 
  '''simple docstring'''
  
  
  
  
  
  def   snake_case_       (							_a     :							List[str] ,					_a     :							List[str]			):
        snake_case_						       =							1
        while b > 0:
              if b & 1:
                    res *= a
  
              a *= a
              b >>= 1
  
        return res
  def   snake_case_       (							_a     :							Union[str, Any] ,					_a     :							Tuple ,					_a     :							Optional[Any]			):
        snake_case_						       =							1
        while b > 0:
              if b & 1:
                    snake_case_						       =							((res % c) * (a % c)) % c
  
              a *= a
              b >>= 1
  
        return res
  
  