




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,
)


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

_lowerCamelCase   :	Any       =    "\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 				_UpperCAmelCase	(UpperCamelCase_			:			Tuple   ,     UpperCamelCase_			:			Any   ,     UpperCamelCase_			:			Dict=8							):
				'''simple docstring'''
				_lowerCAmelCase     :     Dict              =   height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				_lowerCAmelCase     :     Tuple              =   width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor





class 							__snake_case	(_UpperCamelCase		):



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





								'''simple docstring'''




								super().__init__()

								self.register_modules(
								    unet=lowercase__		,						scheduler=lowercase__		,						movq=lowercase__		,						)
								_lowerCAmelCase     :     Any              =   2 ** (len(self.movq.config.block_out_channels   ) - 1)



				def    SCREAMING_SNAKE_CASE							(   self   :		Dict		,						_UpperCAmelCase   :		Tuple		,						_UpperCAmelCase   :		List[Any]		,						_UpperCAmelCase   :		Union[str, Any]		,						_UpperCAmelCase   :		List[str]		,						_UpperCAmelCase   :		Dict		,						_UpperCAmelCase   :		Union[str, Any]   )	->     Any:





								'''simple docstring'''




								if latents is None:
												_lowerCAmelCase     :     Dict              =   randn_tensor(lowercase__		,						generator=lowercase__		,						device=lowercase__		,						dtype=lowercase__   )
								else:
												if latents.shape != shape:
																raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
												_lowerCAmelCase     :     Any              =   latents.to(lowercase__   )

								_lowerCAmelCase     :     int              =   latents * scheduler.init_noise_sigma
								return latents



				def    SCREAMING_SNAKE_CASE							(   self   :		Any		,						_UpperCAmelCase   :		Optional[Any]=0   )	->     Optional[int]:





								'''simple docstring'''




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

								_lowerCAmelCase     :     Optional[int]              =   torch.device(f"cuda:{gpu_id}"   )

								_lowerCAmelCase     :     Union[str, Any]              =   [
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																cpu_offload(lowercase__		,						lowercase__   )



				def    SCREAMING_SNAKE_CASE							(   self   :		Any		,						_UpperCAmelCase   :		Any=0   )	->     Optional[int]:





								'''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."""   )

								_lowerCAmelCase     :     Tuple              =   torch.device(f"cuda:{gpu_id}"   )

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

								_lowerCAmelCase     :     List[Any]              =   None
								for cpu_offloaded_model in [self.unet, self.movq]:
												_lowerCAmelCase     :     Optional[int]              =   cpu_offload_with_hook(lowercase__		,						lowercase__		,						prev_module_hook=lowercase__   )

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



				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def    SCREAMING_SNAKE_CASE							(   self   :		List[Any]   )	->     Optional[int]:





								'''simple docstring'''




								if not hasattr(self.unet		,						"""_hf_hook"""   ):
												return self.device
								for module in self.unet.modules():
												if (
												    hasattr(lowercase__		,						"""_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(lowercase__   )
				def __call__(   self   :		Tuple		,						_UpperCAmelCase   :		int		,						_UpperCAmelCase   :		Optional[int]		,						_UpperCAmelCase   :		str		,						_UpperCAmelCase   :		Optional[Any] = 512		,						_UpperCAmelCase   :		Union[str, Any] = 512		,						_UpperCAmelCase   :		List[Any] = 100		,						_UpperCAmelCase   :		Union[str, Any] = 4.0		,						_UpperCAmelCase   :		Tuple = 1		,						_UpperCAmelCase   :		Dict = None		,						_UpperCAmelCase   :		Any = None		,						_UpperCAmelCase   :		Dict = "pil"		,						_UpperCAmelCase   :		Optional[Any] = True		,						)	->     int:





								'''simple docstring'''




								_lowerCAmelCase     :     Union[str, Any]              =   self._execution_device

								_lowerCAmelCase     :     Optional[Any]              =   guidance_scale > 1.0

								if isinstance(lowercase__		,						lowercase__   ):
												_lowerCAmelCase     :     Tuple              =   torch.cat(lowercase__		,						dim=0   )
								if isinstance(lowercase__		,						lowercase__   ):
												_lowerCAmelCase     :     Tuple              =   torch.cat(lowercase__		,						dim=0   )
								if isinstance(lowercase__		,						lowercase__   ):
												_lowerCAmelCase     :     Optional[int]              =   torch.cat(lowercase__		,						dim=0   )

								_lowerCAmelCase     :     int              =   image_embeds.shape[0] * num_images_per_prompt

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

								_lowerCAmelCase     :     Optional[int]              =   torch.cat([negative_image_embeds, image_embeds]		,						dim=0   ).to(dtype=self.unet.dtype		,						device=lowercase__   )
								_lowerCAmelCase     :     Union[str, Any]              =   torch.cat([hint, hint]		,						dim=0   ).to(dtype=self.unet.dtype		,						device=lowercase__   )
								self.scheduler.set_timesteps(lowercase__		,						device=lowercase__   )
								_lowerCAmelCase     :     Union[str, Any]              =   self.scheduler.timesteps

								_lowerCAmelCase     :     Dict              =   self.movq.config.latent_channels

								_lowerCAmelCase     :     Any              =   downscale_height_and_width(lowercase__		,						lowercase__		,						self.movq_scale_factor   )

								# create initial latent
								_lowerCAmelCase     :     Dict              =   self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)		,						image_embeds.dtype		,						lowercase__		,						lowercase__		,						lowercase__		,						self.scheduler		,						)

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

												_lowerCAmelCase     :     Optional[Any]              =   {'''image_embeds''': image_embeds, '''hint''': hint}
												_lowerCAmelCase     :     List[str]              =   self.unet(
												    sample=lowercase__		,						timestep=lowercase__		,						encoder_hidden_states=lowercase__		,						added_cond_kwargs=lowercase__		,						return_dict=lowercase__		,						)[0]

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

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

								if output_type == "pil":
												_lowerCAmelCase     :     Optional[int]              =   self.numpy_to_pil(lowercase__   )

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=lowercase__   )





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		Optional[torch.FloatTensor]										=						None






class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						2


	@register_to_config
	def __init__(    self				,     lowercase__ = 0.0_2				,     lowercase__ = 1_0_0				,     lowercase__ = 1.0_0_7				,     lowercase__ = 8_0				,     lowercase__ = 0.0_5				,     lowercase__ = 5_0				,     ):
							# standard deviation of the initial noise distribution
							__UpperCAmelCase			:							Union[str, Any]	    =	sigma_max

							# setable values
							__UpperCAmelCase			:							int	    =	None
							__UpperCAmelCase			:							np.IntTensor	    =	None
							__UpperCAmelCase			:							torch.FloatTensor	    =	None  # sigma(t_i)


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							return sample


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							int	    =	num_inference_steps
							__UpperCAmelCase			:							List[str]	    =	np.arange(0				,     self.num_inference_steps)[::-1].copy()
							__UpperCAmelCase			:							Any	    =	torch.from_numpy(lowercase__).to(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							__UpperCAmelCase			:							Tuple	    =	torch.tensor(lowercase__				,     dtype=torch.floataa				,     device=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None):
							if self.config.s_min <= sigma <= self.config.s_max:
													__UpperCAmelCase			:							int	    =	min(self.config.s_churn / self.num_inference_steps				,     2**0.5 - 1)
							else:
													__UpperCAmelCase			:							int	    =	0

							# sample eps ~ N(0, S_noise^2 * I)
							__UpperCAmelCase			:							List[str]	    =	self.config.s_noise * randn_tensor(sample.shape				,     generator=lowercase__).to(sample.device)
							__UpperCAmelCase			:							Optional[int]	    =	sigma + gamma * sigma
							__UpperCAmelCase			:							List[Any]	    =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							str	    =	sample_hat + sigma_hat * model_output
							__UpperCAmelCase			:							Tuple	    =	(sample_hat - pred_original_sample) / sigma_hat
							__UpperCAmelCase			:							str	    =	sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							Any	    =	sample_prev + sigma_prev * model_output
							__UpperCAmelCase			:							List[str]	    =	(sample_prev - pred_original_sample) / sigma_prev
							__UpperCAmelCase			:							Union[str, Any]	    =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							raise NotImplementedError()

