class 			a					:


						def __init__(    self  ,   A_  ,   A_=None  ,   A_=None		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      data
										_UpperCAmelCase    :						Optional[int]							=      previous
										_UpperCAmelCase    :						str							=      next_node


						def __str__(    self		):







										'''simple docstring'''


										return f'{self.data}'


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.data


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.next





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.previous







class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      head


						def __iter__(    self		):







										'''simple docstring'''


										return self





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if not self.current:
														raise StopIteration
										else:
														_UpperCAmelCase    :						List[Any]							=      self.current.get_data()
														_UpperCAmelCase    :						Tuple							=      self.current.get_next()
														return value







class 			a					:


						def __init__(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      None  # First node in list
										_UpperCAmelCase    :						Any							=      None  # Last node in list


						def __str__(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.head
										_UpperCAmelCase    :						Optional[Any]							=      []
										while current is not None:
														nodes.append(current.get_data()		)
														_UpperCAmelCase    :						Tuple							=      current.get_next()
										return " ".join(str(A_		) for node in nodes		)


						def __contains__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.head
										while current:
														if current.get_data() == value:
																		return True
														_UpperCAmelCase    :						Optional[Any]							=      current.get_next()
										return False


						def __iter__(    self		):







										'''simple docstring'''


										return LinkedListIterator(self.head		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if self.head:
														return self.head.get_data()
										return None


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if self.tail:
														return self.tail.get_data()
										return None


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if self.head is None:
														_UpperCAmelCase    :						Optional[Any]							=      node
														_UpperCAmelCase    :						Tuple							=      node
										else:
														self.insert_before_node(self.head  ,   A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if self.head is None:
														self.set_head(A_		)
										else:
														self.insert_after_node(self.tail  ,   A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      Node(A_		)
										if self.head is None:
														self.set_head(A_		)
										else:
														self.set_tail(A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      node
										_UpperCAmelCase    :						Tuple							=      node.previous

										if node.get_previous() is None:
														_UpperCAmelCase    :						Union[str, Any]							=      node_to_insert
										else:
														_UpperCAmelCase    :						Any							=      node_to_insert

										_UpperCAmelCase    :						Optional[int]							=      node_to_insert


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      node
										_UpperCAmelCase    :						List[str]							=      node.next

										if node.get_next() is None:
														_UpperCAmelCase    :						Any							=      node_to_insert
										else:
														_UpperCAmelCase    :						Optional[Any]							=      node_to_insert

										_UpperCAmelCase    :						List[Any]							=      node_to_insert


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      1
										_UpperCAmelCase    :						Any							=      Node(A_		)
										_UpperCAmelCase    :						List[Any]							=      self.head
										while node:
														if current_position == position:
																		self.insert_before_node(A_  ,   A_		)
																		return
														current_position += 1
														_UpperCAmelCase    :						Union[str, Any]							=      node.next
										self.insert_after_node(self.tail  ,   A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.head
										while node:
														if node.get_data() == item:
																		return node
														_UpperCAmelCase    :						Any							=      node.get_next()
										raise Exception("Node not found"		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if (node := self.get_node(A_		)) is not None:
														if node == self.head:
																		_UpperCAmelCase    :						Dict							=      self.head.get_next()

														if node == self.tail:
																		_UpperCAmelCase    :						Dict							=      self.tail.get_previous()

														self.remove_node_pointers(A_		)


						@staticmethod
						def     _UpperCAmelCase			(    A_		):







										'''simple docstring'''


										if node.get_next():
														_UpperCAmelCase    :						Optional[Any]							=      node.previous

										if node.get_previous():
														_UpperCAmelCase    :						List[str]							=      node.next

										_UpperCAmelCase    :						Union[str, Any]							=      None
										_UpperCAmelCase    :						int							=      None





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.head is None







def 							__SCREAMING_SNAKE_CASE							(			)				->    None:
				pass


if __name__ == "__main__":
					import doctest

					doctest.testmod()





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,
)


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

SCREAMING_SNAKE_CASE_            = '\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							(			lowerCAmelCase:					Any					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					int=8	)				->    int:
				_UpperCAmelCase    :						int							=      height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				_UpperCAmelCase    :						List[str]							=      width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor





class 			a					(		UpperCAmelCase			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   ):







										'''simple docstring'''


										super().__init__()

										self.register_modules(
										    unet=A_  ,   scheduler=A_  ,   movq=A_  ,   )
										_UpperCAmelCase    :						Optional[int]							=      2 ** (len(self.movq.config.block_out_channels		) - 1)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										if latents is None:
														_UpperCAmelCase    :						Any							=      randn_tensor(A_  ,   generator=A_  ,   device=A_  ,   dtype=A_		)
										else:
														if latents.shape != shape:
																		raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
														_UpperCAmelCase    :						Optional[int]							=      latents.to(A_		)

										_UpperCAmelCase    :						Tuple							=      latents * scheduler.init_noise_sigma
										return latents


						def     _UpperCAmelCase			(    self  ,   A_=0		):







										'''simple docstring'''


										if is_accelerate_available():
														from accelerate import cpu_offload
										else:
														raise ImportError("Please install accelerate via `pip install accelerate`"		)

										_UpperCAmelCase    :						Union[str, Any]							=      torch.device(f'cuda:{gpu_id}'		)

										_UpperCAmelCase    :						Any							=      [
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																		cpu_offload(A_  ,   A_		)


						def     _UpperCAmelCase			(    self  ,   A_=0		):







										'''simple docstring'''


										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."		)

										_UpperCAmelCase    :						str							=      torch.device(f'cuda:{gpu_id}'		)

										if self.device.type != "cpu":
														self.to("cpu"  ,   silence_dtype_warnings=A_		)
														torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										_UpperCAmelCase    :						Optional[int]							=      None
										for cpu_offloaded_model in [self.unet, self.movq]:
														_UpperCAmelCase						,							_UpperCAmelCase    :						Dict							=      cpu_offload_with_hook(A_  ,   A_  ,   prev_module_hook=A_		)

										# We'll offload the last model manually.
										_UpperCAmelCase    :						Optional[Any]							=      hook


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if not hasattr(self.unet  ,   "_hf_hook"		):
														return self.device
										for module in self.unet.modules():
														if (
														    hasattr(A_  ,   "_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(A_		)
						def __call__(    self  ,   A_  ,   A_  ,   A_  ,   A_ = 512  ,   A_ = 512  ,   A_ = 100  ,   A_ = 4.0  ,   A_ = 1  ,   A_ = None  ,   A_ = None  ,   A_ = "pil"  ,   A_ = True  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self._execution_device

										_UpperCAmelCase    :						Tuple							=      guidance_scale > 1.0

										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Union[str, Any]							=      torch.cat(A_  ,   dim=0		)
										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Dict							=      torch.cat(A_  ,   dim=0		)
										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Any							=      torch.cat(A_  ,   dim=0		)

										_UpperCAmelCase    :						Optional[Any]							=      image_embeds.shape[0] * num_images_per_prompt

										if do_classifier_free_guidance:
														_UpperCAmelCase    :						Optional[int]							=      image_embeds.repeat_interleave(A_  ,   dim=0		)
														_UpperCAmelCase    :						Union[str, Any]							=      negative_image_embeds.repeat_interleave(A_  ,   dim=0		)
														_UpperCAmelCase    :						Tuple							=      hint.repeat_interleave(A_  ,   dim=0		)

										_UpperCAmelCase    :						List[Any]							=      torch.cat([negative_image_embeds, image_embeds]  ,   dim=0		).to(dtype=self.unet.dtype  ,   device=A_		)
										_UpperCAmelCase    :						List[Any]							=      torch.cat([hint, hint]  ,   dim=0		).to(dtype=self.unet.dtype  ,   device=A_		)
										self.scheduler.set_timesteps(A_  ,   device=A_		)
										_UpperCAmelCase    :						Dict							=      self.scheduler.timesteps

										_UpperCAmelCase    :						Union[str, Any]							=      self.movq.config.latent_channels

										_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      downscale_height_and_width(A_  ,   A_  ,   self.movq_scale_factor		)

										# create initial latent
										_UpperCAmelCase    :						Tuple							=      self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)  ,   image_embeds.dtype  ,   A_  ,   A_  ,   A_  ,   self.scheduler  ,   )

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

														_UpperCAmelCase    :						Optional[Any]							=      {"image_embeds": image_embeds, "hint": hint}
														_UpperCAmelCase    :						Optional[int]							=      self.unet(
														    sample=A_  ,   timestep=A_  ,   encoder_hidden_states=A_  ,   added_cond_kwargs=A_  ,   return_dict=A_  ,   )[0]

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

														# compute the previous noisy sample x_t -> x_t-1
														_UpperCAmelCase    :						int							=      self.scheduler.step(
														    A_  ,   A_  ,   A_  ,   generator=A_  ,   )[0]
										# post-processing
										_UpperCAmelCase    :						Optional[Any]							=      self.movq.decode(A_  ,   force_not_quantize=A_		)["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"]:
														_UpperCAmelCase    :						Union[str, Any]							=      image * 0.5 + 0.5
														_UpperCAmelCase    :						Dict							=      image.clamp(0  ,   1		)
														_UpperCAmelCase    :						List[str]							=      image.cpu().permute(0  ,   2  ,   3  ,   1		).float().numpy()

										if output_type == "pil":
														_UpperCAmelCase    :						str							=      self.numpy_to_pil(A_		)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=A_		)


