
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,
)


lowercase  :					List[Any]													=       logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase  :					Union[str, Any]													=       '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[Any]=8)					->    Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase	:   Tuple			   =					height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							__UpperCamelCase	:   List[str]			   =					width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:List[str]     ,   a		:UNetaDConditionModel     ,   a		:DDPMScheduler     ,   a		:VQModel     ,   )		-> List[str]:
								super().__init__()

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







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Optional[int]     ,   a		:Tuple     ,   a		:Tuple     ,   a		:Optional[int]     ,   a		:Optional[Any]     ,   a		:Any						)		-> str:
								if latents is None:
															__UpperCamelCase	:   Dict			   =					randn_tensor(a     ,   generator=a     ,   device=a     ,   dtype=a						)
								else:
															if latents.shape != shape:
																						raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'						)
															__UpperCamelCase	:   Tuple			   =					latents.to(a						)

								__UpperCamelCase	:   Optional[Any]			   =					latents * scheduler.init_noise_sigma
								return latents







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:Tuple=0						)		-> Optional[Any]:
								if is_accelerate_available():
															from accelerate import cpu_offload
								else:
															raise ImportError("Please install accelerate via `pip install accelerate`"						)

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

								__UpperCamelCase	:   Any			   =					[
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																						cpu_offload(a     ,   a						)







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:Any=0						)		-> int:
								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."						)

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

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

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

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







	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def    _lowerCamelCase				(					self		:int						)		-> str:
								if not hasattr(self.unet     ,   "_hf_hook"						):
															return self.device
								for module in self.unet.modules():
															if (
															    hasattr(a     ,   "_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(a						)
	def __call__(					self		:Optional[int]     ,   a		:Union[torch.FloatTensor, List[torch.FloatTensor]]     ,   a		:Union[torch.FloatTensor, List[torch.FloatTensor]]     ,   a		:int = 5_1_2     ,   a		:int = 5_1_2     ,   a		:int = 1_0_0     ,   a		:float = 4.0     ,   a		:int = 1     ,   a		:Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,   a		:Optional[torch.FloatTensor] = None     ,   a		:Optional[str] = "pil"     ,   a		:bool = True     ,   )		-> str:
								__UpperCamelCase	:   int			   =					self._execution_device

								__UpperCamelCase	:   Tuple			   =					guidance_scale > 1.0

								if isinstance(a     ,   a						):
															__UpperCamelCase	:   Tuple			   =					torch.cat(a     ,   dim=0						)
								__UpperCamelCase	:   List[str]			   =					image_embeds.shape[0] * num_images_per_prompt
								if isinstance(a     ,   a						):
															__UpperCamelCase	:   List[str]			   =					torch.cat(a     ,   dim=0						)

								if do_classifier_free_guidance:
															__UpperCamelCase	:   Any			   =					image_embeds.repeat_interleave(a     ,   dim=0						)
															__UpperCamelCase	:   Union[str, Any]			   =					negative_image_embeds.repeat_interleave(a     ,   dim=0						)

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

								self.scheduler.set_timesteps(a     ,   device=a						)
								__UpperCamelCase	:   Optional[Any]			   =					self.scheduler.timesteps

								__UpperCamelCase	:   List[str]			   =					self.unet.config.in_channels

								__UpperCamelCase   ,						__UpperCamelCase	:   Any			   =					downscale_height_and_width(a     ,   a     ,   self.movq_scale_factor						)

								# create initial latent
								__UpperCamelCase	:   List[str]			   =					self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)     ,   image_embeds.dtype     ,   a     ,   a     ,   a     ,   self.scheduler     ,   )

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

															__UpperCamelCase	:   Tuple			   =					{"image_embeds": image_embeds}
															__UpperCamelCase	:   Optional[Any]			   =					self.unet(
															    sample=a     ,   timestep=a     ,   encoder_hidden_states=a     ,   added_cond_kwargs=a     ,   return_dict=a     ,   )[0]

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

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

								if output_type == "pil":
															__UpperCamelCase	:   str			   =					self.numpy_to_pil(a						)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a						)

from PIL import Image




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Image						,  _lowerCamelCase :			int)					->    Image:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					(259 * (level + 255)) / (255 * (259 - level))

							def contrast(_lowerCamelCase :			int) -> int:
														return int(128 + factor * (c - 128))

							return img.point(_lowerCamelCase)


if __name__ == "__main__":
		# Load image
		with Image.open('image_data/lena.jpg') as img:
				# Change contrast to 170
				lowercase  :					Tuple													=       change_contrast(img, 170)
				cont_img.save('image_data/lena_high_contrast.png', format='png')