






"""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     __lowercase					(    a :				List[str]       ,		a :				List[str]       ,		a :				Any=[] )					->					Optional[int]:
      __snake_case      :    Tuple  			=size[0] - overlap_pixels * 2
      __snake_case      :    Optional[int]  			=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
      __snake_case      :    Tuple  			=np.ones((size_y, size_x)       ,		dtype=np.uinta ) * 255
      __snake_case      :    Optional[Any]  			=np.pad(a       ,		mode='''linear_ramp'''       ,		pad_width=a       ,		end_values=0 )

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



def     __lowercase					(    a :				int       ,		a :				Optional[int]       ,		a :				Tuple )					->					Dict:
      return max(a       ,		min(a       ,		a ) )



def     __lowercase					(    a :				[int]       ,		a :				[int]       ,		a :				[int] )					->					Optional[Any]:
      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     __lowercase					(    a :				[int]       ,		a :				int       ,		a :				[int] )					->					List[Any]:
      __snake_case      :    str  			=list(a )
      rect[0] -= overlap
      rect[1] -= overlap
      rect[2] += overlap
      rect[3] += overlap
      __snake_case      :    Optional[Any]  			=clamp_rect(a       ,		[0, 0]       ,		[image_size[0], image_size[1]] )
      return rect



def     __lowercase					(    a :				Optional[int]       ,		a :				Optional[Any]       ,		a :				Union[str, Any]       ,		a :				Union[str, Any] )					->					Dict:
      __snake_case      :    Optional[Any]  			=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     __lowercase					(    a :				List[str]       ,		a :				Dict )					->					Optional[int]:
      __snake_case      :    Optional[Any]  			=(original_image_slice * 4, 0, tile.size[0], tile.size[1])
      __snake_case      :    str  			=tile.crop(a )
      return tile



def     __lowercase					(    a :				int       ,		a :				Tuple )					->					int:
      __snake_case      :    int  			=n % d
      return n - divisor


class      _lowercase (     lowerCAmelCase						):


      def __init__(    self				:	Any							,   a				:	AutoencoderKL							,   a				:	CLIPTextModel							,   a				:	CLIPTokenizer							,   a				:	UNetaDConditionModel							,   a				:	DDPMScheduler							,   a				:	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,   a				:	int = 3_5_0							,   ):





            """simple docstring"""







            super().__init__(
                vae=a							,   text_encoder=a							,   tokenizer=a							,   unet=a							,   low_res_scheduler=a							,   scheduler=a							,   max_noise_level=a							,   )


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   a				:	List[str]							,   a				:	int							,   a				:	str							,   a				:	List[Any]							,   a				:	str							,   a				:	Dict							,   a				:	int							,   **a				:	Union[str, Any]						):





            """simple docstring"""







            torch.manual_seed(0						)
            __snake_case      :    List[str]  			=(
                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						),
            )
            __snake_case      :    Optional[Any]  			=add_overlap_rect(a							,   a							,   image.size						)
            __snake_case      :    Union[str, Any]  			=image.crop(a						)
            __snake_case      :    List[str]  			=((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
            __snake_case      :    List[str]  			=translated_slice_x - (original_image_slice / 2)
            __snake_case      :    Any  			=max(0							,   a						)
            __snake_case      :    Dict  			=squeeze_tile(a							,   a							,   a							,   a						)
            __snake_case      :    int  			=to_input.size
            __snake_case      :    Optional[int]  			=to_input.resize((tile_size, tile_size)							,   Image.BICUBIC						)
            __snake_case      :    int  			=super(a							,   self						).__call__(image=a							,   **a						).images[0]
            __snake_case      :    Optional[Any]  			=upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)							,   Image.BICUBIC						)
            __snake_case      :    Union[str, Any]  			=unsqueeze_tile(a							,   a						)
            __snake_case      :    Optional[int]  			=upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)							,   Image.BICUBIC						)
            __snake_case      :    Optional[int]  			=[]
            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'''						)
            __snake_case      :    List[Any]  			=Image.fromarray(
                make_transparency_mask(
                    (upscaled_tile.size[0], upscaled_tile.size[1])							,   tile_border * 4							,   remove_borders=a						)							,   mode='''L'''							,   )
            final_image.paste(
                a							,   (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)							,   a						)







      @torch.no_grad()
      def __call__(    self				:	Dict							,   a				:	Union[str, List[str]]							,   a				:	Union[PIL.Image.Image, List[PIL.Image.Image]]							,   a				:	int = 7_5							,   a				:	float = 9.0							,   a				:	int = 5_0							,   a				:	Optional[Union[str, List[str]]] = None							,   a				:	Optional[int] = 1							,   a				:	float = 0.0							,   a				:	Optional[torch.Generator] = None							,   a				:	Optional[torch.FloatTensor] = None							,   a				:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,   a				:	int = 1							,   a				:	int = 1_2_8							,   a				:	int = 3_2							,   a				:	int = 3_2							,   ):





            """simple docstring"""







            __snake_case      :    Tuple  			=Image.new('''RGB'''							,   (image.size[0] * 4, image.size[1] * 4)						)
            __snake_case      :    List[str]  			=math.ceil(image.size[0] / tile_size						)
            __snake_case      :    Any  			=math.ceil(image.size[1] / tile_size						)
            __snake_case      :    List[str]  			=tcx * tcy
            __snake_case      :    List[Any]  			=0
            for y in range(a						):
                  for x in range(a						):
                        self._process_tile(
                            a							,   a							,   a							,   a							,   a							,   a							,   a							,   prompt=a							,   num_inference_steps=a							,   guidance_scale=a							,   noise_level=a							,   negative_prompt=a							,   num_images_per_prompt=a							,   eta=a							,   generator=a							,   latents=a							,   )
                        current_count += 1
                        if callback is not None:
                              callback({'''progress''': current_count / total_tile_count, '''image''': final_image}						)
            return final_image



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

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

      __snake_case      :    Optional[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()










"""simple docstring"""





def     __lowercase					(    a :				int       ,		a :				int )					->					int:
      return x if y == 0 else greatest_common_divisor(a       ,		x % y )



def     __lowercase					(    a :				int       ,		a :				int )					->					int:
      return (x * y) // greatest_common_divisor(a       ,		a )



def     __lowercase					(    a :				int = 20 )					->					int:
      __snake_case      :    Optional[Any]  			=1
      for i in range(1       ,		n + 1 ):
            __snake_case      :    Optional[int]  			=lcm(a       ,		a )
      return g


if __name__ == "__main__":
    print(F'''{solution() = }''')


