




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


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


UpperCamelCase								=     '\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     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     UNetaDModel   ,	SCREAMING_SNAKE_CASE__     :     CMStochasticIterativeScheduler   )      ->       None:
         super().__init__()

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

         lowerCAmelCase__		= None


    def    a      (       self     :     str   ,	SCREAMING_SNAKE_CASE__     :     Dict=0   )      ->       List[str]:
         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=SCREAMING_SNAKE_CASE__   )
              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(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )

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


    def    a      (       self     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     Any=0   )      ->       Dict:
         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=SCREAMING_SNAKE_CASE__   )
              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(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	prev_module_hook=SCREAMING_SNAKE_CASE__   )

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

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


    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def    a      (       self     :     Optional[int]   )      ->       str:
         if not hasattr(self.unet   ,	"_hf_hook"   ):
              return self.device
         for module in self.unet.modules():
              if (
                  hasattr(SCREAMING_SNAKE_CASE__   ,	"_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    a      (       self     :     Any   ,	SCREAMING_SNAKE_CASE__     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]=None   )      ->       Union[str, Any]:
         lowerCAmelCase__		= (batch_size, num_channels, height, width)
         if isinstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ) and len(SCREAMING_SNAKE_CASE__   ) != batch_size:
              raise ValueError(
                  f'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__   )}, 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(SCREAMING_SNAKE_CASE__   ,	generator=SCREAMING_SNAKE_CASE__   ,	device=SCREAMING_SNAKE_CASE__   ,	dtype=SCREAMING_SNAKE_CASE__   )
         else:
              lowerCAmelCase__		= latents.to(device=SCREAMING_SNAKE_CASE__   ,	dtype=SCREAMING_SNAKE_CASE__   )

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


    def    a      (       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     torch.FloatTensor   ,	SCREAMING_SNAKE_CASE__     :     str = "pil"   )      ->       Optional[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
         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(SCREAMING_SNAKE_CASE__   )
         return sample


    def    a      (       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]=None   )      ->       Union[str, Any]:
         if self.unet.config.num_class_embeds is not None:
              if isinstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ):
                   lowerCAmelCase__		= torch.tensor(SCREAMING_SNAKE_CASE__   ,	dtype=torch.int   )
              elif isinstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ):
                   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(SCREAMING_SNAKE_CASE__   )
         else:
              lowerCAmelCase__		= None
         return class_labels


    def    a      (       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     str   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   )      ->       int:
         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(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ) or callback_steps <= 0)
         ):
              raise ValueError(
                  f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                  f' {type(SCREAMING_SNAKE_CASE__   )}.'   )


    @torch.no_grad()
    @replace_example_docstring(SCREAMING_SNAKE_CASE__   )
    def __call__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     int = 1   ,	SCREAMING_SNAKE_CASE__     :     Optional[Union[torch.Tensor, List[int], int]] = None   ,	SCREAMING_SNAKE_CASE__     :     int = 1   ,	SCREAMING_SNAKE_CASE__     :     List[int] = None   ,	SCREAMING_SNAKE_CASE__     :     Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,	SCREAMING_SNAKE_CASE__     :     Optional[torch.FloatTensor] = None   ,	SCREAMING_SNAKE_CASE__     :     Optional[str] = "pil"   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	SCREAMING_SNAKE_CASE__     :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,	SCREAMING_SNAKE_CASE__     :     int = 1   ,	)      ->       Any:
         lowerCAmelCase__		= self.unet.config.sample_size
         lowerCAmelCase__		= self._execution_device

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

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

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

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

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

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

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

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

         # 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=SCREAMING_SNAKE_CASE__   )






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Any   )      ->       Union[str, Any]:
         super().__init__()
         self.register_modules(unet=SCREAMING_SNAKE_CASE__   ,	scheduler=SCREAMING_SNAKE_CASE__   )


    @torch.no_grad()
    def __call__(       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     int = 1   ,	SCREAMING_SNAKE_CASE__     :     Optional[torch.Generator] = None   ,	SCREAMING_SNAKE_CASE__     :     int = 50   ,	SCREAMING_SNAKE_CASE__     :     Optional[str] = "pil"   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	**SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	)      ->       Union[ImagePipelineOutput, Tuple]:
         lowerCAmelCase__		= torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,	generator=SCREAMING_SNAKE_CASE__   ,	)
         lowerCAmelCase__		= image.to(self.device   )

         # set step values
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

         for t in self.progress_bar(self.scheduler.timesteps   ):
              # 1. predict noise model_output
              lowerCAmelCase__		= self.unet(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              lowerCAmelCase__		= self.scheduler.step(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).prev_sample

         lowerCAmelCase__		= (image / 2 + 0.5).clamp(0   ,	1   )
         lowerCAmelCase__		= image.cpu().permute(0   ,	2   ,	3   ,	1   ).numpy()
         if output_type == "pil":
              lowerCAmelCase__		= self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

         if not return_dict:
              return (image,), "This is a local test"

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   ), "This is a local test"
