

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      a      (			A__	:   Optional[Any]      ,   A__	:   Optional[Any]      ,   A__	:   Any=[]     )    ->      Optional[int]:





      """simple docstring"""



      _lowercase	       =size[0] - overlap_pixels * 2
      _lowercase	       =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
      _lowercase	       =np.ones((size_y, size_x)      ,   dtype=np.uinta     ) * 255
      _lowercase	       =np.pad(A__      ,   mode='linear_ramp'      ,   pad_width=A__      ,   end_values=0     )

      if "l" in remove_borders:
            _lowercase	       =mask[:, overlap_pixels : mask.shape[1]]
      if "r" in remove_borders:
            _lowercase	       =mask[:, 0 : mask.shape[1] - overlap_pixels]
      if "t" in remove_borders:
            _lowercase	       =mask[overlap_pixels : mask.shape[0], :]
      if "b" in remove_borders:
            _lowercase	       =mask[0 : mask.shape[0] - overlap_pixels, :]
      return mask
def      a      (			A__	:   Optional[Any]      ,   A__	:   Optional[int]      ,   A__	:   Union[str, Any]     )    ->      Any:





      """simple docstring"""



      return max(A__      ,   min(A__      ,   A__     )     )
def      a      (			A__	:   [int]      ,   A__	:   [int]      ,   A__	:   [int]     )    ->      int:





      """simple docstring"""



      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      a      (			A__	:   [int]      ,   A__	:   int      ,   A__	:   [int]     )    ->      Optional[int]:





      """simple docstring"""



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





      """simple docstring"""



      _lowercase	       =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      a      (			A__	:   Union[str, Any]      ,   A__	:   List[Any]     )    ->      Dict:





      """simple docstring"""



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





      """simple docstring"""



      _lowercase	       =n % d
      return n - divisor





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 350							,   )  -> int:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   **lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






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




  @torch.no_grad()
  def __call__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 75							,   lowerCAmelCase = 9.0							,   lowerCAmelCase = 50							,   lowerCAmelCase = None							,   lowerCAmelCase = 1							,   lowerCAmelCase = 0.0							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = 1							,   lowerCAmelCase = 128							,   lowerCAmelCase = 32							,   lowerCAmelCase = 32							,   )  -> Optional[int]:
        '''simple docstring'''






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





      """simple docstring"""



      _lowercase	       ='stabilityai/stable-diffusion-x4-upscaler'
      _lowercase	       =StableDiffusionTiledUpscalePipeline.from_pretrained(A__      ,   revision='fp16'      ,   torch_dtype=torch.floataa     )
      _lowercase	       =pipe.to('cuda'     )
      _lowercase	       =Image.open('../../docs/source/imgs/diffusers_library.jpg'     )

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

      _lowercase	       =pipe(image=A__      ,   prompt='Black font, white background, vector'      ,   noise_level=40      ,   callback=A__     )
      final_image.save('diffusers_library.jpg'     )


if __name__ == "__main__":
  main()










def 						_a    (      a :float	,							a :float )							->			float:
 return price * (1 + tax_rate)


if __name__ == "__main__":
     print(f"""{price_plus_tax(100, 0.25) = }""")
     print(f"""{price_plus_tax(125.50, 0.05) = }""")


