



"""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,
)


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

_lowercase				:				Any   		=		"\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   snake_case__     (	__lowerCamelCase       :					int							,	__lowerCamelCase       :					Optional[Any]							,	__lowerCamelCase       :					Dict=8			):
			"""simple docstring"""
			lowerCamelCase__	:	List[Any]				=height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			lowerCamelCase__	:	List[str]				=width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor






def   snake_case__     (	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					Any=512							,	__lowerCamelCase       :					Optional[int]=512			):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				=pil_image.resize((w, h)							,	resample=Image.BICUBIC							,	reducing_gap=1			)
			lowerCamelCase__	:	List[str]				=np.array(pil_image.convert('''RGB'''			)			)
			lowerCamelCase__	:	int				=arr.astype(np.floataa			) / 1_27.5 - 1
			lowerCamelCase__	:	Optional[Any]				=np.transpose(__lowerCamelCase							,	[2, 0, 1]			)
			lowerCamelCase__	:	List[str]				=torch.from_numpy(__lowerCamelCase			).unsqueeze(0			)
			return image

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def __init__( self							:						List[Any],						lowerCamelCase							:						UNetaDConditionModel,						lowerCamelCase							:						DDPMScheduler,						lowerCamelCase							:						VQModel,						)->      Optional[Any]:
								super().__init__()

								self.register_modules(
								    unet=lowerCamelCase,						scheduler=lowerCamelCase,						movq=lowerCamelCase,						)
								lowerCamelCase__	:	Optional[Any]				=2 ** (len(self.movq.config.block_out_channels  ) - 1)



					def 	snake_case ( self							:						Dict,						lowerCamelCase							:						str,						lowerCamelCase							:						int,						lowerCamelCase							:						str  )->      Dict:
								# get the original timestep using init_timestep
								lowerCamelCase__	:	List[Any]				=min(int(num_inference_steps * strength  ),						lowerCamelCase  )

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

								return timesteps, num_inference_steps - t_start



					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						Dict,						lowerCamelCase							:						int,						lowerCamelCase							:						int,						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Any,						lowerCamelCase							:						Optional[int]=None  )->      Union[str, Any]:
								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  )}'''  )

								lowerCamelCase__	:	List[str]				=image.to(device=lowerCamelCase,						dtype=lowerCamelCase  )

								lowerCamelCase__	:	Dict				=batch_size * num_images_per_prompt

								if image.shape[1] == 4:
											lowerCamelCase__	:	List[str]				=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  ):
														lowerCamelCase__	:	int				=[
														    self.movq.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(lowerCamelCase  )
														]
														lowerCamelCase__	:	List[Any]				=torch.cat(lowerCamelCase,						dim=0  )
											else:
														lowerCamelCase__	:	int				=self.movq.encode(lowerCamelCase  ).latent_dist.sample(lowerCamelCase  )

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

								lowerCamelCase__	:	Optional[int]				=torch.cat([init_latents],						dim=0  )

								lowerCamelCase__	:	Tuple				=init_latents.shape
								lowerCamelCase__	:	Union[str, Any]				=randn_tensor(lowerCamelCase,						generator=lowerCamelCase,						device=lowerCamelCase,						dtype=lowerCamelCase  )

								# get latents
								lowerCamelCase__	:	Any				=self.scheduler.add_noise(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=init_latents

								return latents



					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						int=0  )->      List[Any]:
								if is_accelerate_available():
											from accelerate import cpu_offload
								else:
											raise ImportError('''Please install accelerate via `pip install accelerate`'''  )

								lowerCamelCase__	:	Optional[Any]				=torch.device(F'''cuda:{gpu_id}'''  )

								lowerCamelCase__	:	str				=[
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
														cpu_offload(lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						str=0  )->      List[Any]:
								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__	:	List[str]				=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)

								lowerCamelCase__	:	str				=None
								for cpu_offloaded_model in [self.unet, self.movq]:
											lowerCamelCase__			,     lowerCamelCase__	:	int				=cpu_offload_with_hook(lowerCamelCase,						lowerCamelCase,						prev_module_hook=lowerCamelCase  )

								# We'll offload the last model manually.
								lowerCamelCase__	:	int				=hook



					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 	snake_case ( self							:						Optional[Any]  )->      List[str]:
								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							:						int = 512,						lowerCamelCase							:						int = 512,						lowerCamelCase							:						int = 100,						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,						)->      List[str]:
								lowerCamelCase__	:	Tuple				=self._execution_device

								lowerCamelCase__	:	str				=guidance_scale > 1.0

								if isinstance(lowerCamelCase,						lowerCamelCase  ):
											lowerCamelCase__	:	List[str]				=torch.cat(lowerCamelCase,						dim=0  )
								lowerCamelCase__	:	Any				=image_embeds.shape[0]
								if isinstance(lowerCamelCase,						lowerCamelCase  ):
											lowerCamelCase__	:	int				=torch.cat(lowerCamelCase,						dim=0  )

								if do_classifier_free_guidance:
											lowerCamelCase__	:	Dict				=image_embeds.repeat_interleave(lowerCamelCase,						dim=0  )
											lowerCamelCase__	:	Optional[int]				=negative_image_embeds.repeat_interleave(lowerCamelCase,						dim=0  )

								lowerCamelCase__	:	Any				=torch.cat([negative_image_embeds, image_embeds],						dim=0  ).to(dtype=self.unet.dtype,						device=lowerCamelCase  )

								if not isinstance(lowerCamelCase,						lowerCamelCase  ):
											lowerCamelCase__	:	Optional[int]				=[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'''  )

								lowerCamelCase__	:	Union[str, Any]				=torch.cat([prepare_image(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  ) for i in image],						dim=0  )
								lowerCamelCase__	:	int				=image.to(dtype=image_embeds.dtype,						device=lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=self.movq.encode(lowerCamelCase  )['''latents''']
								lowerCamelCase__	:	int				=latents.repeat_interleave(lowerCamelCase,						dim=0  )
								self.scheduler.set_timesteps(lowerCamelCase,						device=lowerCamelCase  )
								lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=self.get_timesteps(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )
								lowerCamelCase__	:	Any				=timesteps[:1].repeat(batch_size * num_images_per_prompt  )
								lowerCamelCase__			,     lowerCamelCase__	:	str				=downscale_height_and_width(lowerCamelCase,						lowerCamelCase,						self.movq_scale_factor  )
								lowerCamelCase__	:	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
											lowerCamelCase__	:	Dict				=torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

											lowerCamelCase__	:	Dict				={'''image_embeds''': image_embeds}
											lowerCamelCase__	:	int				=self.unet(
											    sample=lowerCamelCase,						timestep=lowerCamelCase,						encoder_hidden_states=lowerCamelCase,						added_cond_kwargs=lowerCamelCase,						return_dict=lowerCamelCase,						)[0]

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

											# compute the previous noisy sample x_t -> x_t-1
											lowerCamelCase__	:	int				=self.scheduler.step(
											    lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						generator=lowerCamelCase,						)[0]

								# post-processing
								lowerCamelCase__	:	Union[str, Any]				=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"]:
											lowerCamelCase__	:	Dict				=image * 0.5 + 0.5
											lowerCamelCase__	:	str				=image.clamp(0,						1  )
											lowerCamelCase__	:	List[str]				=image.cpu().permute(0,						2,						3,						1  ).float().numpy()

								if output_type == "pil":
											lowerCamelCase__	:	Tuple				=self.numpy_to_pil(lowerCamelCase  )

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=lowerCamelCase  )









"""simple docstring"""






from ..utils import DummyObject, requires_backends

class  __SCREAMING_SNAKE_CASE			(   metaclass=lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							['onnx']



					def __init__( self							:						List[str],						*lowerCamelCase							:						Union[str, Any],						**lowerCamelCase							:						str  )->      Optional[int]:
								requires_backends(self,						['''onnx''']  )



					@classmethod
					def 	snake_case ( cls							:						List[str],						*lowerCamelCase							:						Any,						**lowerCamelCase							:						Union[str, Any]  )->      Optional[int]:
								requires_backends(cls,						['''onnx''']  )




					@classmethod
					def 	snake_case ( cls							:						Union[str, Any],						*lowerCamelCase							:						Tuple,						**lowerCamelCase							:						Tuple  )->      Optional[int]:
								requires_backends(cls,						['''onnx''']  )




