





"""simple docstring"""





from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

_lowerCamelCase  					=     TypeVar('''T''')
_lowerCamelCase  					=     TypeVar('''U''')





class       snake_case		(       Generic[T, U]       ):

      def __init__(      self       :Dict						,							_lowerCamelCase       :T | None						,							_lowerCamelCase       :U | None						):
       __SCREAMING_SNAKE_CASE			:      int 							=	key
       __SCREAMING_SNAKE_CASE			:      Any 							=	val
       __SCREAMING_SNAKE_CASE			:      int 							=	0
       __SCREAMING_SNAKE_CASE			:      DoubleLinkedListNode[T, U] | None 							=	None
       __SCREAMING_SNAKE_CASE			:      DoubleLinkedListNode[T, U] | None 							=	None



      def __repr__(      self       :List[Any]						):
       return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
           self.key						,							self.val						,							self.freq						,							self.next is not None						,							self.prev is not None						)







class       snake_case		(       Generic[T, U]       ):

      def __init__(      self       :Optional[Any]						):
       __SCREAMING_SNAKE_CASE			:      DoubleLinkedListNode[T, U] 							=	DoubleLinkedListNode(_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      DoubleLinkedListNode[T, U] 							=	DoubleLinkedListNode(_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.rear, self.head

      def __repr__(      self       :Any						):
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	['''DoubleLinkedList''']
       __SCREAMING_SNAKE_CASE			:      Any 							=	self.head
       while node.next is not None:
        rep.append(str(_lowerCamelCase						)						)
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	node.next
       rep.append(str(self.rear						)						)
       return ",\n    ".join(_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[Any]						,							_lowerCamelCase       :DoubleLinkedListNode[T, U]						):
       __SCREAMING_SNAKE_CASE			:      int 							=	self.rear.prev

       # All nodes other than self.head are guaranteed to have non-None previous
       assert previous is not None

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	node
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	previous
       __SCREAMING_SNAKE_CASE			:      Any 							=	node
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	self.rear
       node.freq += 1
       self._position_node(_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Dict						,							_lowerCamelCase       :DoubleLinkedListNode[T, U]						):

       while node.prev is not None and node.prev.freq > node.freq:
        # swap node with previous node
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	node.prev

        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	previous_node.prev
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	node.prev
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	previous_node
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	node



      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :DoubleLinkedListNode[T, U]						):

       if node.prev is None or node.next is None:
        return None

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	node.next
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	node.prev
       __SCREAMING_SNAKE_CASE			:      str 							=	None
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	None
       return node







class       snake_case		(       Generic[T, U]       ):
      lowerCamelCase__      =			{}

      def __init__(      self       :Optional[Any]						,							_lowerCamelCase       :int						):
       __SCREAMING_SNAKE_CASE			:      DoubleLinkedList[T, U] 							=	DoubleLinkedList()
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	capacity
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	0
       __SCREAMING_SNAKE_CASE			:      Any 							=	0
       __SCREAMING_SNAKE_CASE			:      int 							=	0
       __SCREAMING_SNAKE_CASE			:      dict[T, DoubleLinkedListNode[T, U]] 							=	{}

      def __repr__(      self       :Union[str, Any]						):
       return (
           f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
           f'''capacity={self.capacity}, current_size={self.num_keys})'''
       )

      def __contains__(      self       :List[Any]						,							_lowerCamelCase       :T						):
       return key in self.cache

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[Any]						,							_lowerCamelCase       :T						):

       if key in self.cache:
        self.hits += 1
        __SCREAMING_SNAKE_CASE			:      DoubleLinkedListNode[T, U] 							=	self.cache[key]
        __SCREAMING_SNAKE_CASE			:      Dict 							=	self.list.remove(self.cache[key]						)
        assert node == value_node

        # node is guaranteed not None because it is in self.cache
        assert node is not None
        self.list.add(_lowerCamelCase						)
        return node.val
       self.miss += 1
       return None

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						,							_lowerCamelCase       :T						,							_lowerCamelCase       :U						):

       if key not in self.cache:
        if self.num_keys >= self.capacity:
         # delete first node when over capacity
         __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.list.head.next

         # guaranteed to have a non-None first node when num_keys > 0
         # explain to type checker via assertions
         assert first_node is not None
         assert first_node.key is not None
         assert self.list.remove(_lowerCamelCase						) is not None
         # first_node guaranteed to be in list

         del self.cache[first_node.key]
         self.num_keys -= 1
        __SCREAMING_SNAKE_CASE			:      Tuple 							=	DoubleLinkedListNode(_lowerCamelCase						,							_lowerCamelCase						)
        self.list.add(self.cache[key]						)
        self.num_keys += 1

       else:
        __SCREAMING_SNAKE_CASE			:      str 							=	self.list.remove(self.cache[key]						)
        assert node is not None  # node guaranteed to be in list
        __SCREAMING_SNAKE_CASE			:      int 							=	value
        self.list.add(_lowerCamelCase						)



      @classmethod
      def 						SCREAMING_SNAKE_CASE_      (      cls       :type[LFUCache[T, U]]						,							_lowerCamelCase       :int = 1_2_8						):

       def cache_decorator_inner(_lowerCamelCase       :Callable[[T], U]						) -> Callable[..., U]:
        def cache_decorator_wrapper(*_lowerCamelCase       :T						) -> U:
         if func not in cls.decorator_function_to_instance_map:
          __SCREAMING_SNAKE_CASE			:      Dict 							=	LFUCache(_lowerCamelCase						)

         __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	cls.decorator_function_to_instance_map[func].get(args[0]						)
         if result is None:
          __SCREAMING_SNAKE_CASE			:      str 							=	func(*_lowerCamelCase						)
          cls.decorator_function_to_instance_map[func].put(args[0]						,							_lowerCamelCase						)
         return result

        def cache_info() -> LFUCache[T, U]:
         return cls.decorator_function_to_instance_map[func]

        setattr(_lowerCamelCase						,							'''cache_info'''						,							_lowerCamelCase						)  # noqa: B010

        return cache_decorator_wrapper

       return cache_decorator_inner


if __name__ == "__main__":
    import doctest

    doctest.testmod()








"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


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

_lowerCamelCase  					=     '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> 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"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''




def 		lowerCAmelCase_     (  lowercase_       :							Optional[int]			,				lowercase_       :							Optional[Any]			,				lowercase_       :							Optional[int]=8		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Tuple 							=	height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor




def 		lowerCAmelCase_     (  lowercase_       :							str			,				lowercase_       :							Tuple=512			,				lowercase_       :							Any=512		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	pil_image.resize((w, h)			,				resample=Image.BICUBIC			,				reducing_gap=1		)
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	np.array(pil_image.convert('''RGB'''		)		)
 __SCREAMING_SNAKE_CASE			:      Dict 							=	arr.astype(np.floataa		) / 127.5 - 1
 __SCREAMING_SNAKE_CASE			:      Dict 							=	np.transpose(lowercase_			,				[2, 0, 1]		)
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	torch.from_numpy(lowercase_		).unsqueeze(0		)
 return image






class       snake_case		(       __UpperCAmelCase       ):

      def __init__(      self       :Dict						,							_lowerCamelCase       :UNetaDConditionModel						,							_lowerCamelCase       :DDPMScheduler						,							_lowerCamelCase       :VQModel						,							):
       super().__init__()

       self.register_modules(
           unet=_lowerCamelCase						,							scheduler=_lowerCamelCase						,							movq=_lowerCamelCase						,							)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	2 ** (len(self.movq.config.block_out_channels						) - 1)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :List[Any]						,							_lowerCamelCase       :List[Any]						):
       # get the original timestep using init_timestep
       __SCREAMING_SNAKE_CASE			:      str 							=	min(int(num_inference_steps * strength						)						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	max(num_inference_steps - init_timestep						,							0						)
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start

      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						,							_lowerCamelCase       :str						,							_lowerCamelCase       :int						,							_lowerCamelCase       :str						,							_lowerCamelCase       :Optional[int]						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :Dict=None						):
       if not isinstance(_lowerCamelCase						,							(torch.Tensor, PIL.Image.Image, list)						):
        raise ValueError(
            f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCamelCase						)}'''						)

       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	image.to(device=_lowerCamelCase						,							dtype=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	batch_size * num_images_per_prompt

       if image.shape[1] == 4:
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	image

       else:
        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.'''						)

        elif isinstance(_lowerCamelCase						,							_lowerCamelCase						):
         __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	[
             self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(_lowerCamelCase						)
         ]
         __SCREAMING_SNAKE_CASE			:      List[Any] 							=	torch.cat(_lowerCamelCase						,							dim=0						)
        else:
         __SCREAMING_SNAKE_CASE			:      Dict 							=	self.movq.encode(_lowerCamelCase						).latent_dist.sample(_lowerCamelCase						)

        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.movq.config.scaling_factor * init_latents

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	torch.cat([init_latents]						,							dim=0						)

       __SCREAMING_SNAKE_CASE			:      int 							=	init_latents.shape
       __SCREAMING_SNAKE_CASE			:      int 							=	randn_tensor(_lowerCamelCase						,							generator=_lowerCamelCase						,							device=_lowerCamelCase						,							dtype=_lowerCamelCase						)

       # get latents
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.scheduler.add_noise(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      List[str] 							=	init_latents

       return latents

      def 						SCREAMING_SNAKE_CASE_      (      self       :str						,							_lowerCamelCase       :List[Any]=0						):
       if is_accelerate_available():
        from accelerate import cpu_offload
       else:
        raise ImportError('''Please install accelerate via `pip install accelerate`'''						)

       __SCREAMING_SNAKE_CASE			:      List[str] 							=	torch.device(f'''cuda:{gpu_id}'''						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	[
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
         cpu_offload(_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :Optional[Any]=0						):
       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.'''						)

       __SCREAMING_SNAKE_CASE			:      Dict 							=	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)

       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	None
       for cpu_offloaded_model in [self.unet, self.movq]:
        __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	cpu_offload_with_hook(_lowerCamelCase						,							_lowerCamelCase						,							prev_module_hook=_lowerCamelCase						)

       # We'll offload the last model manually.
       __SCREAMING_SNAKE_CASE			:      Any 							=	hook

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 						SCREAMING_SNAKE_CASE_      (      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



      @torch.no_grad()
      @replace_example_docstring(_lowerCamelCase						)
      def __call__(      self       :List[str]						,							_lowerCamelCase       :Union[torch.FloatTensor, List[torch.FloatTensor]]						,							_lowerCamelCase       :Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,							_lowerCamelCase       :Union[torch.FloatTensor, List[torch.FloatTensor]]						,							_lowerCamelCase       :torch.FloatTensor						,							_lowerCamelCase       :int = 5_1_2						,							_lowerCamelCase       :int = 5_1_2						,							_lowerCamelCase       :int = 1_0_0						,							_lowerCamelCase       :float = 4.0						,							_lowerCamelCase       :float = 0.3						,							_lowerCamelCase       :int = 1						,							_lowerCamelCase       :Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							_lowerCamelCase       :Optional[str] = "pil"						,							_lowerCamelCase       :bool = True						,							):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self._execution_device

       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	guidance_scale > 1.0

       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	torch.cat(_lowerCamelCase						,							dim=0						)
       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	torch.cat(_lowerCamelCase						,							dim=0						)
       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.cat(_lowerCamelCase						,							dim=0						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_embeds.shape[0]

       if do_classifier_free_guidance:
        __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_embeds.repeat_interleave(_lowerCamelCase						,							dim=0						)
        __SCREAMING_SNAKE_CASE			:      Any 							=	negative_image_embeds.repeat_interleave(_lowerCamelCase						,							dim=0						)
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	hint.repeat_interleave(_lowerCamelCase						,							dim=0						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	torch.cat([negative_image_embeds, image_embeds]						,							dim=0						).to(dtype=self.unet.dtype						,							device=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	torch.cat([hint, hint]						,							dim=0						).to(dtype=self.unet.dtype						,							device=_lowerCamelCase						)

       if not isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[image]
       if not all(isinstance(_lowerCamelCase						,							(PIL.Image.Image, torch.Tensor)						) for i in image						):
        raise ValueError(
            f'''Input is in incorrect format: {[type(_lowerCamelCase						) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''						)

       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	torch.cat([prepare_image(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						) for i in image]						,							dim=0						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	image.to(dtype=image_embeds.dtype						,							device=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.movq.encode(_lowerCamelCase						)['''latents''']
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	latents.repeat_interleave(_lowerCamelCase						,							dim=0						)
       self.scheduler.set_timesteps(_lowerCamelCase						,							device=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.get_timesteps(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	timesteps[:1].repeat(batch_size * num_images_per_prompt						)
       __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Dict 							=	downscale_height_and_width(_lowerCamelCase						,							_lowerCamelCase						,							self.movq_scale_factor						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.prepare_latents(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							image_embeds.dtype						,							_lowerCamelCase						,							_lowerCamelCase						)
       for i, t in enumerate(self.progress_bar(_lowerCamelCase						)						):
        # expand the latents if we are doing classifier free guidance
        __SCREAMING_SNAKE_CASE			:      int 							=	torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

        __SCREAMING_SNAKE_CASE			:      Any 							=	{'''image_embeds''': image_embeds, '''hint''': hint}
        __SCREAMING_SNAKE_CASE			:      int 							=	self.unet(
            sample=_lowerCamelCase						,							timestep=_lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							added_cond_kwargs=_lowerCamelCase						,							return_dict=_lowerCamelCase						,							)[0]

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

        # compute the previous noisy sample x_t -> x_t-1

        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.scheduler.step(
            _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							generator=_lowerCamelCase						,							)[0]

       # post-processing
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.movq.decode(_lowerCamelCase						,							force_not_quantize=_lowerCamelCase						)['''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"]:
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	image * 0.5 + 0.5
        __SCREAMING_SNAKE_CASE			:      str 							=	image.clamp(0						,							1						)
        __SCREAMING_SNAKE_CASE			:      int 							=	image.cpu().permute(0						,							2						,							3						,							1						).float().numpy()

       if output_type == "pil":
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.numpy_to_pil(_lowerCamelCase						)

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=_lowerCamelCase						)

