






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       42
	__magic_name__			   =       42
	__magic_name__			   =       None







class     UpperCamelCase_ (__A       ,		__A  ):
	__magic_name__			   =       2





	@register_to_config
	def __init__(   self      :				Any			,			lowerCAmelCase_      :				float = 0.0_2			,			lowerCAmelCase_      :				float = 100			,			lowerCAmelCase_      :				float = 1.0_0_7			,			lowerCAmelCase_      :				float = 80			,			lowerCAmelCase_      :				float = 0.0_5			,			lowerCAmelCase_      :				float = 50			,			)		->    Union[str, Any]:
				# standard deviation of the initial noise distribution
				UpperCAmelCase_  :				Any      =      sigma_max

				# setable values
				UpperCAmelCase_  :				int      =      None
				UpperCAmelCase_  :				np.IntTensor      =      None
				UpperCAmelCase_  :				torch.FloatTensor      =      None  # sigma(t_i)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				torch.FloatTensor			,			lowerCAmelCase_      :				Optional[int] = None		)		->    torch.FloatTensor:
				return sample





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Union[str, torch.device] = None		)		->    int:
				UpperCAmelCase_  :				Union[str, Any]      =      num_inference_steps
				UpperCAmelCase_  :				Optional[Any]      =      np.arange(0			,			self.num_inference_steps		)[::-1].copy()
				UpperCAmelCase_  :				List[str]      =      torch.from_numpy(lowerCAmelCase_		).to(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      [
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in self.timesteps
				]
				UpperCAmelCase_  :				Optional[Any]      =      torch.tensor(lowerCAmelCase_			,			dtype=torch.floataa			,			device=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				torch.FloatTensor			,			lowerCAmelCase_      :				float			,			lowerCAmelCase_      :				Optional[torch.Generator] = None		)		->    Tuple[torch.FloatTensor, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_  :				List[str]      =      min(self.config.s_churn / self.num_inference_steps			,			2**0.5 - 1		)
				else:
							UpperCAmelCase_  :				Optional[int]      =      0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_  :				Tuple      =      self.config.s_noise * randn_tensor(sample.shape			,			generator=lowerCAmelCase_		).to(sample.device		)
				UpperCAmelCase_  :				Optional[Any]      =      sigma + gamma * sigma
				UpperCAmelCase_  :				List[str]      =      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				torch.FloatTensor			,			lowerCAmelCase_      :				float			,			lowerCAmelCase_      :				float			,			lowerCAmelCase_      :				torch.FloatTensor			,			lowerCAmelCase_      :				bool = True			,			)		->    Union[KarrasVeOutput, Tuple]:
				UpperCAmelCase_  :				int      =      sample_hat + sigma_hat * model_output
				UpperCAmelCase_  :				Dict      =      (sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_  :				Union[str, Any]      =      sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative)

				return KarrasVeOutput(
				    prev_sample=lowerCAmelCase_			,			derivative=lowerCAmelCase_			,			pred_original_sample=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				torch.FloatTensor			,			lowerCAmelCase_      :				float			,			lowerCAmelCase_      :				float			,			lowerCAmelCase_      :				torch.FloatTensor			,			lowerCAmelCase_      :				torch.FloatTensor			,			lowerCAmelCase_      :				torch.FloatTensor			,			lowerCAmelCase_      :				bool = True			,			)		->    Union[KarrasVeOutput, Tuple]:
				UpperCAmelCase_  :				Optional[Any]      =      sample_prev + sigma_prev * model_output
				UpperCAmelCase_  :				Dict      =      (sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_  :				List[str]      =      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative)

				return KarrasVeOutput(
				    prev_sample=lowerCAmelCase_			,			derivative=lowerCAmelCase_			,			pred_original_sample=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				List[str]		)		->    Tuple:
				raise NotImplementedError()











"""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()



