


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,
)


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

A				:       int      =  '\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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase=8			)    ->						Optional[int]:


						'''simple docstring'''






						__snake_case				=		height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						__snake_case				=		width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor






class      UpperCamelCase(	_a ):


					def __init__(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		UNetaDConditionModel  ,     SCREAMING_SNAKE_CASE						:		DDPMScheduler  ,     SCREAMING_SNAKE_CASE						:		VQModel  ,     )      ->						int:




											'''simple docstring'''

											super().__init__()

											self.register_modules(
											    unet=SCREAMING_SNAKE_CASE  ,     scheduler=SCREAMING_SNAKE_CASE  ,     movq=SCREAMING_SNAKE_CASE  ,     )
											__snake_case				=		2 ** (len(self.movq.config.block_out_channels    ) - 1)


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						Optional[int]:




											'''simple docstring'''

											if latents is None:
																	__snake_case				=		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}'''    )
																	__snake_case				=		latents.to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		latents * scheduler.init_noise_sigma
											return latents


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		List[str]=0    )      ->						Optional[Any]:




											'''simple docstring'''

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

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

											__snake_case				=		[
											    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						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=0    )      ->						Union[str, Any]:




											'''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."    )

											__snake_case				=		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)

											__snake_case				=		None
											for cpu_offloaded_model in [self.unet, self.movq]:
																	__snake_case		,       __snake_case				=		cpu_offload_with_hook(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     prev_module_hook=SCREAMING_SNAKE_CASE    )

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


					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						List[str]:




											'''simple docstring'''

											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						:		Union[str, Any]  ,     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  ,     )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self._execution_device

											__snake_case				=		guidance_scale > 1.0

											if isinstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		torch.cat(SCREAMING_SNAKE_CASE  ,     dim=0    )
											if isinstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		torch.cat(SCREAMING_SNAKE_CASE  ,     dim=0    )
											if isinstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		torch.cat(SCREAMING_SNAKE_CASE  ,     dim=0    )

											__snake_case				=		image_embeds.shape[0] * num_images_per_prompt

											if do_classifier_free_guidance:
																	__snake_case				=		image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE  ,     dim=0    )
																	__snake_case				=		negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE  ,     dim=0    )
																	__snake_case				=		hint.repeat_interleave(SCREAMING_SNAKE_CASE  ,     dim=0    )

											__snake_case				=		torch.cat([negative_image_embeds, image_embeds]  ,     dim=0    ).to(dtype=self.unet.dtype  ,     device=SCREAMING_SNAKE_CASE    )
											__snake_case				=		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    )
											__snake_case				=		self.scheduler.timesteps

											__snake_case				=		self.movq.config.latent_channels

											__snake_case		,       __snake_case				=		downscale_height_and_width(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     self.movq_scale_factor    )

											# create initial latent
											__snake_case				=		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
																	__snake_case				=		torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

																	__snake_case				=		{"image_embeds": image_embeds, "hint": hint}
																	__snake_case				=		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:
																							__snake_case		,       __snake_case				=		noise_pred.split(latents.shape[1]  ,     dim=1    )
																							__snake_case		,       __snake_case				=		noise_pred.chunk(2    )
																							__snake_case		,       __snake_case				=		variance_pred.chunk(2    )
																							__snake_case				=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																							__snake_case				=		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"]
																	):
																							__snake_case		,       __snake_case				=		noise_pred.split(latents.shape[1]  ,     dim=1    )

																	# compute the previous noisy sample x_t -> x_t-1
																	__snake_case				=		self.scheduler.step(
																	    SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     generator=SCREAMING_SNAKE_CASE  ,     )[0]
											# post-processing
											__snake_case				=		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"]:
																	__snake_case				=		image * 0.5 + 0.5
																	__snake_case				=		image.clamp(0  ,     1    )
																	__snake_case				=		image.cpu().permute(0  ,     2  ,     3  ,     1    ).float().numpy()

											if output_type == "pil":
																	__snake_case				=		self.numpy_to_pil(SCREAMING_SNAKE_CASE    )

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE    )









A				:       Any      =  '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A				:       Optional[int]      =  [{'type': 'code', 'content': INSTALL_CONTENT}]
A				:       Optional[int]      =  {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}





