




"""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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_=[]						)						->		str:
 A__	=    size[0] - overlap_pixels * 2
 A__	=    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
 A__	=    np.ones((size_y, size_x)      ,			dtype=np.uinta						) * 2_55
 A__	=    np.pad(lowercase_      ,			mode="linear_ramp"      ,			pad_width=lowercase_      ,			end_values=0						)

 if "l" in remove_borders:
  A__	=    mask[:, overlap_pixels : mask.shape[1]]
 if "r" in remove_borders:
  A__	=    mask[:, 0 : mask.shape[1] - overlap_pixels]
 if "t" in remove_borders:
  A__	=    mask[overlap_pixels : mask.shape[0], :]
 if "b" in remove_borders:
  A__	=    mask[0 : mask.shape[0] - overlap_pixels, :]
 return mask







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		List[Any]:
 return max(lowercase_      ,			min(lowercase_      ,			lowercase_						)						)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Union[str, 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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		str:
 A__	=    list(lowercase_						)
 rect[0] -= overlap
 rect[1] -= overlap
 rect[2] += overlap
 rect[3] += overlap
 A__	=    clamp_rect(lowercase_      ,			[0, 0]      ,			[image_size[0], image_size[1]]						)
 return rect







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_						)						->		Optional[Any]:
 A__	=    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(lowercase_      ,			(original_slice, 0)						)
 return result







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Optional[int]:
 A__	=    (original_image_slice * 4, 0, tile.size[0], tile.size[1])
 A__	=    tile.crop(lowercase_						)
 return tile







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		int:
 A__	=    n % d
 return n - divisor




class      UpperCAmelCase_  (						A_							):




    def __init__(   self						:	Optional[int]     ,			snake_case_						:	AutoencoderKL     ,			snake_case_						:	CLIPTextModel     ,			snake_case_						:	CLIPTokenizer     ,			snake_case_						:	UNetaDConditionModel     ,			snake_case_						:	DDPMScheduler     ,			snake_case_						:	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,			snake_case_						:	int = 350     ,			)				->							str:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	Any     ,			**snake_case_						:	str				)				->							List[Any]:




     '''simple docstring'''

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




    @torch.no_grad()
    def __call__(   self						:	List[str]     ,			snake_case_						:	Union[str, List[str]]     ,			snake_case_						:	Union[PIL.Image.Image, List[PIL.Image.Image]]     ,			snake_case_						:	int = 75     ,			snake_case_						:	float = 9.0     ,			snake_case_						:	int = 50     ,			snake_case_						:	Optional[Union[str, List[str]]] = None     ,			snake_case_						:	Optional[int] = 1     ,			snake_case_						:	float = 0.0     ,			snake_case_						:	Optional[torch.Generator] = None     ,			snake_case_						:	Optional[torch.FloatTensor] = None     ,			snake_case_						:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,			snake_case_						:	int = 1     ,			snake_case_						:	int = 128     ,			snake_case_						:	int = 32     ,			snake_case_						:	int = 32     ,			)				->							List[str]:




     '''simple docstring'''

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







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

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

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


if __name__ == "__main__":
      main()











"""simple docstring"""



import baseaa







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		bytes:
 return baseaa.aaaencode(string.encode("utf-8"						)						)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		str:
 return baseaa.aaadecode(lowercase_						).decode("utf-8"						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





