



'''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,
)


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

snake_case_			=     """\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"""






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Optional[int]=8       ):
				SCREAMING_SNAKE_CASE  :						Any      					= height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				SCREAMING_SNAKE_CASE  :						List[Any]      					= width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :str=5_12    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]=5_12       ):
				SCREAMING_SNAKE_CASE  :						str      					= pil_image.resize((w, h)    ,	resample=Image.BICUBIC    ,	reducing_gap=1       )
				SCREAMING_SNAKE_CASE  :						int      					= np.array(pil_image.convert('''RGB'''       )       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= arr.astype(np.floataa       ) / 127.5 - 1
				SCREAMING_SNAKE_CASE  :						Dict      					= np.transpose(lowercase__    ,	[2, 0, 1]       )
				SCREAMING_SNAKE_CASE  :						str      					= torch.from_numpy(lowercase__       ).unsqueeze(0       )
				return image




class 	a__     (				__snake_case	):


	def __init__(self : Tuple,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : Any,					)     ->			List[Any]:

					"""simple docstring"""

					super().__init__()

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


	def 				lowercase__  (self : int,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : int,					__UpperCAmelCase : List[Any]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= min(int(num_inference_steps * strength      ),					__UpperCamelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= max(num_inference_steps - init_timestep,					0      )
					SCREAMING_SNAKE_CASE  :						str      					= self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start


	def 				lowercase__  (self : int,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : List[str]=None      )     ->			Optional[int]:

					"""simple docstring"""

					if not isinstance(__UpperCamelCase,					(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(__UpperCamelCase      )}'''      )

					SCREAMING_SNAKE_CASE  :						Dict      					= image.to(device=__UpperCamelCase,					dtype=__UpperCamelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= batch_size * num_images_per_prompt

					if image.shape[1] == 4:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= image

					else:
									if isinstance(__UpperCamelCase,					__UpperCamelCase      ) and len(__UpperCamelCase      ) != batch_size:
													raise ValueError(
													    F'''You have passed a list of generators of length {len(__UpperCamelCase      )}, but requested an effective batch'''
													    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

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

									SCREAMING_SNAKE_CASE  :						int      					= self.movq.config.scaling_factor * init_latents

					SCREAMING_SNAKE_CASE  :						str      					= torch.cat([init_latents],					dim=0      )

					SCREAMING_SNAKE_CASE  :						Dict      					= init_latents.shape
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= randn_tensor(__UpperCamelCase,					generator=__UpperCamelCase,					device=__UpperCamelCase,					dtype=__UpperCamelCase      )

					# get latents
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.scheduler.add_noise(__UpperCamelCase,					__UpperCamelCase,					__UpperCamelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= init_latents

					return latents


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : Union[str, Any]=0      )     ->			List[str]:

					"""simple docstring"""

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

					SCREAMING_SNAKE_CASE  :						List[Any]      					= 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(__UpperCamelCase,					__UpperCamelCase      )


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : Any=0      )     ->			str:

					"""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.'''      )

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

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

					SCREAMING_SNAKE_CASE  :						List[str]      					= None
					for cpu_offloaded_model in [self.unet, self.movq]:
									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= cpu_offload_with_hook(__UpperCamelCase,					__UpperCamelCase,					prev_module_hook=__UpperCamelCase      )

					# We'll offload the last model manually.
					SCREAMING_SNAKE_CASE  :						Dict      					= hook


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

					"""simple docstring"""

					if not hasattr(self.unet,					'''_hf_hook'''      ):
									return self.device
					for module in self.unet.modules():
									if (
									    hasattr(__UpperCamelCase,					'''_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(__UpperCamelCase      )
	def __call__(self : Dict,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any,					__UpperCAmelCase : Any = 512,					__UpperCAmelCase : Optional[int] = 512,					__UpperCAmelCase : Union[str, Any] = 100,					__UpperCAmelCase : Optional[int] = 4.0,					__UpperCAmelCase : List[Any] = 0.3,					__UpperCAmelCase : Union[str, Any] = 1,					__UpperCAmelCase : Any = None,					__UpperCAmelCase : Dict = "pil",					__UpperCAmelCase : int = True,					)     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self._execution_device

					SCREAMING_SNAKE_CASE  :						List[Any]      					= guidance_scale > 1.0

					if isinstance(__UpperCamelCase,					__UpperCamelCase      ):
									SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.cat(__UpperCamelCase,					dim=0      )
					SCREAMING_SNAKE_CASE  :						Dict      					= image_embeds.shape[0]
					if isinstance(__UpperCamelCase,					__UpperCamelCase      ):
									SCREAMING_SNAKE_CASE  :						Tuple      					= torch.cat(__UpperCamelCase,					dim=0      )

					if do_classifier_free_guidance:
									SCREAMING_SNAKE_CASE  :						Any      					= image_embeds.repeat_interleave(__UpperCamelCase,					dim=0      )
									SCREAMING_SNAKE_CASE  :						Any      					= negative_image_embeds.repeat_interleave(__UpperCamelCase,					dim=0      )

					SCREAMING_SNAKE_CASE  :						int      					= torch.cat([negative_image_embeds, image_embeds],					dim=0      ).to(dtype=self.unet.dtype,					device=__UpperCamelCase      )

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

					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.cat([prepare_image(__UpperCamelCase,					__UpperCamelCase,					__UpperCamelCase      ) for i in image],					dim=0      )
					SCREAMING_SNAKE_CASE  :						Any      					= image.to(dtype=image_embeds.dtype,					device=__UpperCamelCase      )

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

									SCREAMING_SNAKE_CASE  :						Optional[int]      					= {'''image_embeds''': image_embeds}
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.unet(
									    sample=__UpperCamelCase,					timestep=__UpperCamelCase,					encoder_hidden_states=__UpperCamelCase,					added_cond_kwargs=__UpperCamelCase,					return_dict=__UpperCamelCase,					)[0]

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

									# compute the previous noisy sample x_t -> x_t-1
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.scheduler.step(
									    __UpperCamelCase,					__UpperCamelCase,					__UpperCamelCase,					generator=__UpperCamelCase,					)[0]

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

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

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=__UpperCamelCase      )









'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__     (				_lowercase	):
	__magic_name__		:    UNetaDModel
	__magic_name__		:    ScoreSdeVeScheduler


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDModel,					__UpperCAmelCase : ScoreSdeVeScheduler      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=__UpperCAmelCase,					scheduler=__UpperCAmelCase      )






	@torch.no_grad()
	def __call__(self : int,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : int = 2000,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					**__UpperCAmelCase : List[str],					)     ->			Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet.config.sample_size
					SCREAMING_SNAKE_CASE  :						Dict      					= (batch_size, 3, img_size, img_size)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.unet

					SCREAMING_SNAKE_CASE  :						str      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase      ) * self.scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						Any      					= sample.to(self.device      )

					self.scheduler.set_timesteps(__UpperCAmelCase      )
					self.scheduler.set_sigmas(__UpperCAmelCase      )

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
									SCREAMING_SNAKE_CASE  :						int      					= self.scheduler.sigmas[i] * torch.ones(shape[0],					device=self.device      )

									# correction step
									for _ in range(self.scheduler.config.correct_steps      ):
													SCREAMING_SNAKE_CASE  :						Any      					= self.unet(__UpperCAmelCase,					__UpperCAmelCase      ).sample
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step_correct(__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# prediction step
									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      ).sample
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step_pred(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.prev_sample, output.prev_sample_mean

					SCREAMING_SNAKE_CASE  :						Dict      					= sample_mean.clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						str      					= sample.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						int      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )




