





'''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,
)


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

A   : Optional[int]             =			'\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_      (		lowercase__				,  lowercase__				,  lowercase__=8						)   ->int:
						_snake_case:   Union[str, Any]  					=		height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						_snake_case:   str  					=		width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor





def       lowercase_      (		lowercase__				,  lowercase__=512				,  lowercase__=512						)   ->Optional[int]:
						_snake_case:   Dict  					=		pil_image.resize((w, h)				,  resample=Image.BICUBIC				,  reducing_gap=1						)
						_snake_case:   Tuple  					=		np.array(pil_image.convert('RGB'						)						)
						_snake_case:   List[Any]  					=		arr.astype(np.floataa						) / 1_2_7.5 - 1
						_snake_case:   Optional[int]  					=		np.transpose(lowercase__				,  [2, 0, 1]						)
						_snake_case:   Dict  					=		torch.from_numpy(lowercase__						).unsqueeze(0						)
						return image







class 			lowerCamelCase					(						__UpperCAmelCase  ):


					def __init__(					self	:  Dict       ,     __snake_case	:  UNetaDConditionModel       ,     __snake_case	:  DDPMScheduler       ,     __snake_case	:  VQModel       ,     ):





											'''simple docstring'''





											super().__init__()

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   Any  					=		min(int(num_inference_steps * strength       )       ,     __snake_case       )

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

											return timesteps, num_inference_steps - t_start


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  Any       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  List[str]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Union[str, Any]=None       ):





											'''simple docstring'''





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

											_snake_case:   int  					=		image.to(device=__snake_case       ,     dtype=__snake_case       )

											_snake_case:   Optional[int]  					=		batch_size * num_images_per_prompt

											if image.shape[1] == 4:
																	_snake_case:   Any  					=		image

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

																	elif isinstance(__snake_case       ,     __snake_case       ):
																							_snake_case:   Union[str, Any]  					=		[
																							    self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(__snake_case       )
																							]
																							_snake_case:   int  					=		torch.cat(__snake_case       ,     dim=0       )
																	else:
																							_snake_case:   Dict  					=		self.movq.encode(__snake_case       ).latent_dist.sample(__snake_case       )

																	_snake_case:   Any  					=		self.movq.config.scaling_factor * init_latents

											_snake_case:   Union[str, Any]  					=		torch.cat([init_latents]       ,     dim=0       )

											_snake_case:   str  					=		init_latents.shape
											_snake_case:   List[Any]  					=		randn_tensor(__snake_case       ,     generator=__snake_case       ,     device=__snake_case       ,     dtype=__snake_case       )

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

											_snake_case:   List[str]  					=		init_latents

											return latents


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  Tuple=0       ):





											'''simple docstring'''





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

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

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  Union[str, Any]=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.'       )

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

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

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

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


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





											'''simple docstring'''





											if not hasattr(self.unet       ,     '_hf_hook'       ):
																	return self.device
											for module in self.unet.modules():
																	if (
																	    hasattr(__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(__snake_case       )
					def __call__(					self	:  Tuple       ,     __snake_case	:  Union[torch.FloatTensor, List[torch.FloatTensor]]       ,     __snake_case	:  Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]       ,     __snake_case	:  Union[torch.FloatTensor, List[torch.FloatTensor]]       ,     __snake_case	:  int = 5_12       ,     __snake_case	:  int = 5_12       ,     __snake_case	:  int = 1_00       ,     __snake_case	:  float = 4.0       ,     __snake_case	:  float = 0.3       ,     __snake_case	:  int = 1       ,     __snake_case	:  Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,     __snake_case	:  Optional[str] = "pil"       ,     __snake_case	:  bool = True       ,     ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self._execution_device

											_snake_case:   str  					=		guidance_scale > 1.0

											if isinstance(__snake_case       ,     __snake_case       ):
																	_snake_case:   Any  					=		torch.cat(__snake_case       ,     dim=0       )
											_snake_case:   Optional[int]  					=		image_embeds.shape[0]
											if isinstance(__snake_case       ,     __snake_case       ):
																	_snake_case:   List[Any]  					=		torch.cat(__snake_case       ,     dim=0       )

											if do_classifier_free_guidance:
																	_snake_case:   Dict  					=		image_embeds.repeat_interleave(__snake_case       ,     dim=0       )
																	_snake_case:   int  					=		negative_image_embeds.repeat_interleave(__snake_case       ,     dim=0       )

											_snake_case:   str  					=		torch.cat([negative_image_embeds, image_embeds]       ,     dim=0       ).to(dtype=self.unet.dtype       ,     device=__snake_case       )

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

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

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

																	_snake_case:   List[Any]  					=		{'image_embeds': image_embeds}
																	_snake_case:   Optional[Any]  					=		self.unet(
																	    sample=__snake_case       ,     timestep=__snake_case       ,     encoder_hidden_states=__snake_case       ,     added_cond_kwargs=__snake_case       ,     return_dict=__snake_case       ,     )[0]

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

																	# compute the previous noisy sample x_t -> x_t-1
																	_snake_case:   Union[str, Any]  					=		self.scheduler.step(
																	    __snake_case       ,     __snake_case       ,     __snake_case       ,     generator=__snake_case       ,     )[0]

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

											if output_type == "pil":
																	_snake_case:   List[Any]  					=		self.numpy_to_pil(__snake_case       )

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=__snake_case       )












'''simple docstring'''






import torch





def       lowercase_      (		)   ->Dict:
						if torch.cuda.is_available():
												_snake_case:   Any  					=		torch.cuda.device_count()
						else:
												_snake_case:   Optional[int]  					=		0
						print(F'''Successfully ran on {num_gpus} GPUs'''						)


if __name__ == "__main__":
		main()





