






"""simple docstring"""
from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCamelCase_       			=   transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def        snake_case   (     A__		):
			if isinstance(A__			,torch.Tensor		):
						return image
			elif isinstance(A__			,PIL.Image.Image		):
						UpperCAmelCase_  :				Optional[int]      =      [image]

			UpperCAmelCase_  :				List[str]      =      [trans(img.convert("RGB"		)		) for img in image]
			UpperCAmelCase_  :				Union[str, Any]      =      torch.stack(A__		)
			return image







class     UpperCamelCase_ (__A  ):





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Union[str, Any]		)		->    List[str]:
				super().__init__()

				# make sure scheduler can always be converted to DDIM
				UpperCAmelCase_  :				Any      =      DDIMScheduler.from_config(scheduler.config		)

				self.register_modules(unet=lowerCAmelCase_			,			scheduler=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				Optional[int]		)		->    Union[str, Any]:
				if strength < 0 or strength > 1:
							raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}"""		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				int		)		->    Tuple:
				# get the original timestep using init_timestep
				UpperCAmelCase_  :				Any      =      min(int(num_inference_steps * strength		)			,			lowerCAmelCase_		)

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

				return timesteps, num_inference_steps - t_start





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				Union[str, Any]=None		)		->    Optional[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_		)}"""		)

				UpperCAmelCase_  :				int      =      image.to(device=lowerCAmelCase_			,			dtype=lowerCAmelCase_		)

				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."""		)

				UpperCAmelCase_  :				Optional[Any]      =      init_latents.shape
				UpperCAmelCase_  :				Tuple      =      randn_tensor(lowerCAmelCase_			,			generator=lowerCAmelCase_			,			device=lowerCAmelCase_			,			dtype=lowerCAmelCase_		)

				# get latents
				print("add noise to latents at timestep"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      self.scheduler.add_noise(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Dict      =      init_latents

				return latents





	@torch.no_grad()
	def __call__(   self      :				Any			,			lowerCAmelCase_      :				Union[torch.FloatTensor, PIL.Image.Image] = None			,			lowerCAmelCase_      :				float = 0.8			,			lowerCAmelCase_      :				int = 1			,			lowerCAmelCase_      :				Optional[Union[torch.Generator, List[torch.Generator]]] = None			,			lowerCAmelCase_      :				float = 0.0			,			lowerCAmelCase_      :				int = 50			,			lowerCAmelCase_      :				Optional[bool] = None			,			lowerCAmelCase_      :				Optional[str] = "pil"			,			lowerCAmelCase_      :				bool = True			,			)		->    Union[ImagePipelineOutput, Tuple]:
				self.check_inputs(lowerCAmelCase_		)

				# 2. Preprocess image
				UpperCAmelCase_  :				Dict      =      preprocess(lowerCAmelCase_		)

				# 3. set timesteps
				self.scheduler.set_timesteps(lowerCAmelCase_			,			device=self.device		)
				UpperCAmelCase_  ,			UpperCAmelCase_  :				Any      =      self.get_timesteps(lowerCAmelCase_			,			lowerCAmelCase_			,			self.device		)
				UpperCAmelCase_  :				Dict      =      timesteps[:1].repeat(lowerCAmelCase_		)

				# 4. Prepare latent variables
				UpperCAmelCase_  :				Tuple      =      self.prepare_latents(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			self.unet.dtype			,			self.device			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      latents

				# 5. Denoising loop
				for t in self.progress_bar(lowerCAmelCase_		):
							# 1. predict noise model_output
							UpperCAmelCase_  :				Dict      =      self.unet(lowerCAmelCase_			,			lowerCAmelCase_		).sample

							# 2. predict previous mean of image x_t-1 and add variance depending on eta
							# eta corresponds to η in paper and should be between [0, 1]
							# do x_t -> x_t-1
							UpperCAmelCase_  :				List[Any]      =      self.scheduler.step(
							    lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			eta=lowerCAmelCase_			,			use_clipped_model_output=lowerCAmelCase_			,			generator=lowerCAmelCase_			,			).prev_sample

				UpperCAmelCase_  :				Union[str, Any]      =      (image / 2 + 0.5).clamp(0			,			1		)
				UpperCAmelCase_  :				Any      =      image.cpu().permute(0			,			2			,			3			,			1		).numpy()
				if output_type == "pil":
							UpperCAmelCase_  :				Dict      =      self.numpy_to_pil(lowerCAmelCase_		)

				if not return_dict:
							return (image, latent_timestep.item())

				return ImagePipelineOutput(images=lowerCAmelCase_		)











"""simple docstring"""
import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler

def        snake_case   (     A__			,A__			,A__=[]		):
			UpperCAmelCase_  :				int      =      size[0] - overlap_pixels * 2
			UpperCAmelCase_  :				Dict      =      size[1] - overlap_pixels * 2
			for letter in ["l", "r"]:
						if letter in remove_borders:
									size_x += overlap_pixels
			for letter in ["t", "b"]:
						if letter in remove_borders:
									size_y += overlap_pixels
			UpperCAmelCase_  :				Union[str, Any]      =      np.ones((size_y, size_x)			,dtype=np.uinta		) * 2_55
			UpperCAmelCase_  :				Optional[int]      =      np.pad(A__			,mode="linear_ramp"			,pad_width=A__			,end_values=0		)

			if "l" in remove_borders:
						UpperCAmelCase_  :				List[str]      =      mask[:, overlap_pixels : mask.shape[1]]
			if "r" in remove_borders:
						UpperCAmelCase_  :				Any      =      mask[:, 0 : mask.shape[1] - overlap_pixels]
			if "t" in remove_borders:
						UpperCAmelCase_  :				Optional[int]      =      mask[overlap_pixels : mask.shape[0], :]
			if "b" in remove_borders:
						UpperCAmelCase_  :				Any      =      mask[0 : mask.shape[0] - overlap_pixels, :]
			return mask

def        snake_case   (     A__			,A__			,A__		):
			return max(A__			,min(A__			,A__		)		)

def        snake_case   (     A__			,A__			,A__		):
			return (
			    clamp(rect[0]			,min[0]			,max[0]		),
			    clamp(rect[1]			,min[1]			,max[1]		),
			    clamp(rect[2]			,min[0]			,max[0]		),
			    clamp(rect[3]			,min[1]			,max[1]		),
			)

def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      list(A__		)
			rect[0] -= overlap
			rect[1] -= overlap
			rect[2] += overlap
			rect[3] += overlap
			UpperCAmelCase_  :				Optional[Any]      =      clamp_rect(A__			,[0, 0]			,[image_size[0], image_size[1]]		)
			return rect

def        snake_case   (     A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				Dict      =      Image.new("RGB"			,(tile.size[0] + original_slice, tile.size[1])		)
			result.paste(
			    original_image.resize((tile.size[0], tile.size[1])			,Image.BICUBIC		).crop(
			        (slice_x, 0, slice_x + original_slice, tile.size[1])		)			,(0, 0)			,)
			result.paste(A__			,(original_slice, 0)		)
			return result

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				Optional[int]      =      (original_image_slice * 4, 0, tile.size[0], tile.size[1])
			UpperCAmelCase_  :				Optional[int]      =      tile.crop(A__		)
			return tile

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      n % d
			return n - divisor







class     UpperCamelCase_ (__A  ):





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				AutoencoderKL			,			lowerCAmelCase_      :				CLIPTextModel			,			lowerCAmelCase_      :				CLIPTokenizer			,			lowerCAmelCase_      :				UNetaDConditionModel			,			lowerCAmelCase_      :				DDPMScheduler			,			lowerCAmelCase_      :				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,			lowerCAmelCase_      :				int = 350			,			)		->    Optional[Any]:
				super().__init__(
				    vae=lowerCAmelCase_			,			text_encoder=lowerCAmelCase_			,			tokenizer=lowerCAmelCase_			,			unet=lowerCAmelCase_			,			low_res_scheduler=lowerCAmelCase_			,			scheduler=lowerCAmelCase_			,			max_noise_level=lowerCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Any			,			**lowerCAmelCase_      :				List[str]		)		->    Dict:
				torch.manual_seed(0		)
				UpperCAmelCase_  :				Optional[Any]      =      (
				    min(image.size[0] - (tile_size + original_image_slice)			,			x * tile_size		),
				    min(image.size[1] - (tile_size + original_image_slice)			,			y * tile_size		),
				    min(image.size[0]			,			(x + 1) * tile_size		),
				    min(image.size[1]			,			(y + 1) * tile_size		),
				)
				UpperCAmelCase_  :				Dict      =      add_overlap_rect(lowerCAmelCase_			,			lowerCAmelCase_			,			image.size		)
				UpperCAmelCase_  :				Tuple      =      image.crop(lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
				UpperCAmelCase_  :				int      =      translated_slice_x - (original_image_slice / 2)
				UpperCAmelCase_  :				Any      =      max(0			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      squeeze_tile(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      to_input.size
				UpperCAmelCase_  :				Optional[int]      =      to_input.resize((tile_size, tile_size)			,			Image.BICUBIC		)
				UpperCAmelCase_  :				Optional[Any]      =      super(lowerCAmelCase_			,			self		).__call__(image=lowerCAmelCase_			,			**lowerCAmelCase_		).images[0]
				UpperCAmelCase_  :				Union[str, Any]      =      upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)			,			Image.BICUBIC		)
				UpperCAmelCase_  :				List[Any]      =      unsqueeze_tile(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)			,			Image.BICUBIC		)
				UpperCAmelCase_  :				List[Any]      =      []
				if x == 0:
							remove_borders.append("l"		)
				elif crop_rect[2] == image.size[0]:
							remove_borders.append("r"		)
				if y == 0:
							remove_borders.append("t"		)
				elif crop_rect[3] == image.size[1]:
							remove_borders.append("b"		)
				UpperCAmelCase_  :				str      =      Image.fromarray(
				    make_transparency_mask(
				        (upscaled_tile.size[0], upscaled_tile.size[1])			,			tile_border * 4			,			remove_borders=lowerCAmelCase_		)			,			mode="L"			,			)
				final_image.paste(
				    lowerCAmelCase_			,			(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)			,			lowerCAmelCase_		)





	@torch.no_grad()
	def __call__(   self      :				int			,			lowerCAmelCase_      :				Union[str, List[str]]			,			lowerCAmelCase_      :				Union[PIL.Image.Image, List[PIL.Image.Image]]			,			lowerCAmelCase_      :				int = 75			,			lowerCAmelCase_      :				float = 9.0			,			lowerCAmelCase_      :				int = 50			,			lowerCAmelCase_      :				Optional[Union[str, List[str]]] = None			,			lowerCAmelCase_      :				Optional[int] = 1			,			lowerCAmelCase_      :				float = 0.0			,			lowerCAmelCase_      :				Optional[torch.Generator] = None			,			lowerCAmelCase_      :				Optional[torch.FloatTensor] = None			,			lowerCAmelCase_      :				Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,			lowerCAmelCase_      :				int = 1			,			lowerCAmelCase_      :				int = 128			,			lowerCAmelCase_      :				int = 32			,			lowerCAmelCase_      :				int = 32			,			)		->    int:
				UpperCAmelCase_  :				List[Any]      =      Image.new("RGB"			,			(image.size[0] * 4, image.size[1] * 4)		)
				UpperCAmelCase_  :				Any      =      math.ceil(image.size[0] / tile_size		)
				UpperCAmelCase_  :				str      =      math.ceil(image.size[1] / tile_size		)
				UpperCAmelCase_  :				Any      =      tcx * tcy
				UpperCAmelCase_  :				List[Any]      =      0
				for y in range(lowerCAmelCase_		):
							for x in range(lowerCAmelCase_		):
										self._process_tile(
										    lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			prompt=lowerCAmelCase_			,			num_inference_steps=lowerCAmelCase_			,			guidance_scale=lowerCAmelCase_			,			noise_level=lowerCAmelCase_			,			negative_prompt=lowerCAmelCase_			,			num_images_per_prompt=lowerCAmelCase_			,			eta=lowerCAmelCase_			,			generator=lowerCAmelCase_			,			latents=lowerCAmelCase_			,			)
										current_count += 1
										if callback is not None:
													callback({"progress": current_count / total_tile_count, "image": final_image}		)
				return final_image





def        snake_case   (     ):
			# Run a demo
			UpperCAmelCase_  :				List[str]      =      "stabilityai/stable-diffusion-x4-upscaler"
			UpperCAmelCase_  :				Optional[Any]      =      StableDiffusionTiledUpscalePipeline.from_pretrained(A__			,revision="fp16"			,torch_dtype=torch.floataa		)
			UpperCAmelCase_  :				Optional[int]      =      pipe.to("cuda"		)
			UpperCAmelCase_  :				Tuple      =      Image.open("../../docs/source/imgs/diffusers_library.jpg"		)

			def callback(A__		):
						print(F"""progress: {obj['progress']:.4f}"""		)
						obj["image"].save("diffusers_library_progress.jpg"		)

			UpperCAmelCase_  :				Union[str, Any]      =      pipe(image=A__			,prompt="Black font, white background, vector"			,noise_level=40			,callback=A__		)
			final_image.save("diffusers_library.jpg"		)


if __name__ == "__main__":
			main()



