


from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_   :				Optional[Any]						      = logging.get_logger(__name__)
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Optional[int]       =		['''pixel_values''']
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : Dict[str, int] = None  ,							SCREAMING_SNAKE_CASE__       : PILImageResampling = PILImageResampling.BICUBIC  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : Dict[str, int] = None  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : Union[int, float] = 1 / 2_5_5  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD  ,							**SCREAMING_SNAKE_CASE__       : Tuple  ,							)	->    None:
             super().__init__(**SCREAMING_SNAKE_CASE__  )
             a_      :	Tuple							=						size if size is not None else {'shortest_edge': 2_2_4}
             a_      :	List[str]							=						get_size_dict(SCREAMING_SNAKE_CASE__  ,							default_to_square=SCREAMING_SNAKE_CASE__  )
             a_      :	List[str]							=						crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
             a_      :	Dict							=						get_size_dict(SCREAMING_SNAKE_CASE__  ,							param_name='crop_size'  )

             a_      :	str							=						do_resize
             a_      :	Optional[int]							=						size
             a_      :	Tuple							=						resample
             a_      :	str							=						do_center_crop
             a_      :	int							=						crop_size
             a_      :	Any							=						do_rescale
             a_      :	Tuple							=						rescale_factor
             a_      :	List[Any]							=						do_normalize
             a_      :	Tuple							=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
             a_      :	Optional[Any]							=						image_std if image_std is not None else IMAGENET_DEFAULT_STD
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							SCREAMING_SNAKE_CASE__       : np.ndarray  ,							SCREAMING_SNAKE_CASE__       : Dict[str, int]  ,							SCREAMING_SNAKE_CASE__       : PILImageResampling = PILImageResampling.BICUBIC  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[str, ChannelDimension]] = None  ,							**SCREAMING_SNAKE_CASE__       : Tuple  ,							)	->    np.ndarray:
             a_      :	int							=						get_size_dict(SCREAMING_SNAKE_CASE__  ,							default_to_square=SCREAMING_SNAKE_CASE__  )
             # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
             if "shortest_edge" in size:
                    a_      :	List[str]							=						int((2_5_6 / 2_2_4) * size['shortest_edge']  )
                    a_      :	Union[str, Any]							=						get_resize_output_image_size(SCREAMING_SNAKE_CASE__  ,							size=SCREAMING_SNAKE_CASE__  ,							default_to_square=SCREAMING_SNAKE_CASE__  )
                    a_      :	List[Any]							=						{'height': output_size[0], 'width': output_size[1]}
             if "height" not in size_dict or "width" not in size_dict:
                    raise ValueError(
                        F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""  )
             return resize(
                 SCREAMING_SNAKE_CASE__  ,							size=(size_dict['height'], size_dict['width'])  ,							resample=SCREAMING_SNAKE_CASE__  ,							data_format=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : np.ndarray  ,							SCREAMING_SNAKE_CASE__       : Dict[str, int]  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[str, ChannelDimension]] = None  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  ,							)	->    np.ndarray:
             a_      :	Tuple							=						get_size_dict(SCREAMING_SNAKE_CASE__  )
             if "height" not in size or "width" not in size:
                    raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""  )
             return center_crop(SCREAMING_SNAKE_CASE__  ,							size=(size['height'], size['width'])  ,							data_format=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : np.ndarray  ,							SCREAMING_SNAKE_CASE__       : Union[int, float]  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[str, ChannelDimension]] = None  ,							**SCREAMING_SNAKE_CASE__       : Dict  ,							)	->    np.ndarray:
             return rescale(SCREAMING_SNAKE_CASE__  ,							scale=SCREAMING_SNAKE_CASE__  ,							data_format=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : np.ndarray  ,							SCREAMING_SNAKE_CASE__       : Union[float, List[float]]  ,							SCREAMING_SNAKE_CASE__       : Union[float, List[float]]  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[str, ChannelDimension]] = None  ,							**SCREAMING_SNAKE_CASE__       : Optional[int]  ,							)	->    np.ndarray:
             return normalize(SCREAMING_SNAKE_CASE__  ,							mean=SCREAMING_SNAKE_CASE__  ,							std=SCREAMING_SNAKE_CASE__  ,							data_format=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : ImageInput  ,							SCREAMING_SNAKE_CASE__       : Optional[bool] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[Dict[str, int]] = None  ,							SCREAMING_SNAKE_CASE__       : PILImageResampling = None  ,							SCREAMING_SNAKE_CASE__       : Optional[bool] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[Dict[str, int]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[bool] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[float] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[bool] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[float, Iterable[float]]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[float, Iterable[float]]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[TensorType] = None  ,							SCREAMING_SNAKE_CASE__       : ChannelDimension = ChannelDimension.FIRST  ,							**SCREAMING_SNAKE_CASE__       : List[str]  ,							)	->    BatchFeature:
             a_      :	int							=						do_resize if do_resize is not None else self.do_resize
             a_      :	Dict							=						resample if resample is not None else self.resample
             a_      :	str							=						do_center_crop if do_center_crop is not None else self.do_center_crop
             a_      :	int							=						do_rescale if do_rescale is not None else self.do_rescale
             a_      :	int							=						rescale_factor if rescale_factor is not None else self.rescale_factor
             a_      :	str							=						do_normalize if do_normalize is not None else self.do_normalize
             a_      :	Dict							=						image_mean if image_mean is not None else self.image_mean
             a_      :	Dict							=						image_std if image_std is not None else self.image_std

             a_      :	Optional[int]							=						size if size is not None else self.size
             a_      :	int							=						get_size_dict(SCREAMING_SNAKE_CASE__  ,							default_to_square=SCREAMING_SNAKE_CASE__  )
             a_      :	List[str]							=						crop_size if crop_size is not None else self.crop_size
             a_      :	Any							=						get_size_dict(SCREAMING_SNAKE_CASE__  ,							param_name='crop_size'  )

             a_      :	Dict							=						make_list_of_images(SCREAMING_SNAKE_CASE__  )

             if not valid_images(SCREAMING_SNAKE_CASE__  ):
                    raise ValueError(
                        'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                        'torch.Tensor, tf.Tensor or jax.ndarray.'  )

             if do_resize and size is None:
                    raise ValueError('Size must be specified if do_resize is True.'  )

             if do_center_crop and crop_size is None:
                    raise ValueError('Crop size must be specified if do_center_crop is True.'  )

             if do_rescale and rescale_factor is None:
                    raise ValueError('Rescale factor must be specified if do_rescale is True.'  )

             if do_normalize and (image_mean is None or image_std is None):
                    raise ValueError('Image mean and std must be specified if do_normalize is True.'  )

             # All transformations expect numpy arrays.
             a_      :	int							=						[to_numpy_array(SCREAMING_SNAKE_CASE__  ) for image in images]

             if do_resize:
                    a_      :	Any							=						[self.resize(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ) for image in images]

             if do_center_crop:
                    a_      :	Optional[int]							=						[self.center_crop(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ) for image in images]

             if do_rescale:
                    a_      :	Optional[Any]							=						[self.rescale(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ) for image in images]

             if do_normalize:
                    a_      :	Optional[int]							=						[self.normalize(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ) for image in images]

             a_      :	Tuple							=						[to_channel_dimension_format(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ) for image in images]

             a_      :	Any							=						{'pixel_values': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__  ,							tensor_type=SCREAMING_SNAKE_CASE__  )






from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

UpperCAmelCase_   :				str						      = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Tuple						,					__A				:    Tuple=8       )			->	Dict:

       """simple docstring"""

       a_      :	Optional[int]							=						height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       a_      :	int							=						width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : UNetaDConditionModel  ,							SCREAMING_SNAKE_CASE__       : DDPMScheduler  ,							SCREAMING_SNAKE_CASE__       : VQModel  ,							)	->    Any:
             super().__init__()

             self.register_modules(
                 unet=SCREAMING_SNAKE_CASE__  ,							scheduler=SCREAMING_SNAKE_CASE__  ,							movq=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	List[str]							=						2 ** (len(self.movq.config.block_out_channels  ) - 1)
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    str:
             if latents is None:
                    a_      :	List[str]							=						randn_tensor(SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							device=SCREAMING_SNAKE_CASE__  ,							dtype=SCREAMING_SNAKE_CASE__  )
             else:
                    if latents.shape != shape:
                           raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
                    a_      :	Any							=						latents.to(SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						latents * scheduler.init_noise_sigma
             return latents
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0  )	->    Dict:
             if is_accelerate_available():
                    from accelerate import cpu_offload
             else:
                    raise ImportError('Please install accelerate via `pip install accelerate`'  )

             a_      :	Union[str, Any]							=						torch.device(F"""cuda:{gpu_id}"""  )

             a_      :	Optional[Any]							=						[
                 self.unet,
                 self.movq,
             ]
             for cpu_offloaded_model in models:
                    if cpu_offloaded_model is not None:
                           cpu_offload(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=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.'  )

             a_      :	Tuple							=						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)

             a_      :	Dict							=						None
             for cpu_offloaded_model in [self.unet, self.movq]:
                    a_							,	a_      :	Optional[Any]							=						cpu_offload_with_hook(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							prev_module_hook=SCREAMING_SNAKE_CASE__  )

             # We'll offload the last model manually.
             a_      :	str							=						hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[Any]:
             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

      @torch.no_grad()
      @replace_example_docstring(SCREAMING_SNAKE_CASE__  )
      def __call__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							SCREAMING_SNAKE_CASE__       : Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							SCREAMING_SNAKE_CASE__       : torch.FloatTensor  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 1_0_0  ,							SCREAMING_SNAKE_CASE__       : float = 4.0  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							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  ,							)	->    int:
             a_      :	Dict							=						self._execution_device

             a_      :	int							=						guidance_scale > 1.0

             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Union[str, Any]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )
             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Optional[Any]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )
             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Optional[int]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )

             a_      :	Dict							=						image_embeds.shape[0] * num_images_per_prompt

             if do_classifier_free_guidance:
                    a_      :	Optional[Any]							=						image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )
                    a_      :	Optional[Any]							=						negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )
                    a_      :	int							=						hint.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )

             a_      :	int							=						torch.cat([negative_image_embeds, image_embeds]  ,							dim=0  ).to(dtype=self.unet.dtype  ,							device=SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						torch.cat([hint, hint]  ,							dim=0  ).to(dtype=self.unet.dtype  ,							device=SCREAMING_SNAKE_CASE__  )
             self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  ,							device=SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						self.scheduler.timesteps

             a_      :	Optional[int]							=						self.movq.config.latent_channels

             a_							,	a_      :	Dict							=						downscale_height_and_width(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							self.movq_scale_factor  )

             # create initial latent
             a_      :	int							=						self.prepare_latents(
                 (batch_size, num_channels_latents, height, width)  ,							image_embeds.dtype  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							self.scheduler  ,							)

             for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__  )  ):
                    # expand the latents if we are doing classifier free guidance
                    a_      :	int							=						torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

                    a_      :	int							=						{'image_embeds': image_embeds, 'hint': hint}
                    a_      :	str							=						self.unet(
                        sample=SCREAMING_SNAKE_CASE__  ,							timestep=SCREAMING_SNAKE_CASE__  ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__  ,							added_cond_kwargs=SCREAMING_SNAKE_CASE__  ,							return_dict=SCREAMING_SNAKE_CASE__  ,							)[0]

                    if do_classifier_free_guidance:
                           a_							,	a_      :	Optional[int]							=						noise_pred.split(latents.shape[1]  ,							dim=1  )
                           a_							,	a_      :	str							=						noise_pred.chunk(2  )
                           a_							,	a_      :	Union[str, Any]							=						variance_pred.chunk(2  )
                           a_      :	Optional[int]							=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                           a_      :	str							=						torch.cat([noise_pred, variance_pred_text]  ,							dim=1  )

                    if not (
                        hasattr(self.scheduler.config  ,							'variance_type'  )
                        and self.scheduler.config.variance_type in ["learned", "learned_range"]
                    ):
                           a_							,	a_      :	Optional[int]							=						noise_pred.split(latents.shape[1]  ,							dim=1  )

                    # compute the previous noisy sample x_t -> x_t-1
                    a_      :	str							=						self.scheduler.step(
                        SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							)[0]
             # post-processing
             a_      :	str							=						self.movq.decode(SCREAMING_SNAKE_CASE__  ,							force_not_quantize=SCREAMING_SNAKE_CASE__  )['sample']

             if output_type not in ["pt", "np", "pil"]:
                    raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""  )

             if output_type in ["np", "pil"]:
                    a_      :	str							=						image * 0.5 + 0.5
                    a_      :	Union[str, Any]							=						image.clamp(0  ,							1  )
                    a_      :	Optional[int]							=						image.cpu().permute(0  ,							2  ,							3  ,							1  ).float().numpy()

             if output_type == "pil":
                    a_      :	List[str]							=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__  )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__  )


