


"""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 _A						(						lowercase       ,    lowercase       ,    lowercase=[]		):







    """simple docstring"""
    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 _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    return max(lowercase       ,    min(lowercase       ,    lowercase		)		)
def _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """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						(						lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    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 _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    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 _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=(original_image_slice * 4, 0, tile.size[0], tile.size[1])
    a 		=tile.crop(lowercase		)
    return tile
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=n % d
    return n - divisor






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 def __init__(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A = 350 ,    )					->     int:
     super().__init__(
         vae=__A ,    text_encoder=__A ,    tokenizer=__A ,    unet=__A ,    low_res_scheduler=__A ,    scheduler=__A ,    max_noise_level=__A ,    )


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    **__A						)					->     Tuple:
     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(__A ,    __A ,    image.size						)
     a 		=image.crop(__A						)
     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 ,    __A						)
     a 		=squeeze_tile(__A ,    __A ,    __A ,    __A						)
     a 		=to_input.size
     a 		=to_input.resize((tile_size, tile_size) ,    Image.BICUBIC						)
     a 		=super(__A ,    self						).__call__(image=__A ,    **__A						).images[0]
     a 		=upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,    Image.BICUBIC						)
     a 		=unsqueeze_tile(__A ,    __A						)
     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=__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 ,    __A ,    __A ,    __A = 75 ,    __A = 9.0 ,    __A = 50 ,    __A = None ,    __A = 1 ,    __A = 0.0 ,    __A = None ,    __A = None ,    __A = None ,    __A = 1 ,    __A = 128 ,    __A = 32 ,    __A = 32 ,    )					->     Optional[int]:
     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(__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 _A						(						):







    """simple docstring"""
    # 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()






import random

class 		UpperCamelCase__ :



     '''simple docstring'''

     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    str       )     ->		tuple[list[int], list[int]]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[ord(lowerCamelCase__       ) for i in text]
           SCREAMING_SNAKE_CASE		=						[]
           SCREAMING_SNAKE_CASE		=						[]
           for i in plain:
                 SCREAMING_SNAKE_CASE		=						random.randint(1						,300       )
                 SCREAMING_SNAKE_CASE		=						(i + k) * k
                 cipher.append(lowerCamelCase__       )
                 key.append(lowerCamelCase__       )
           return cipher, key






     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    list[int]						,lowerCamelCase__						:    list[int]       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]
           for i in range(len(lowerCamelCase__       )       ):
                 SCREAMING_SNAKE_CASE		=						int((cipher[i] - (key[i]) ** 2) / key[i]       )
                 plain.append(chr(lowerCamelCase__       )       )
           return "".join(lowerCamelCase__       )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_   ,						SCREAMING_SNAKE_CASE_						   =       Onepad().encrypt("""Hello""")
      print(c, k)
      print(Onepad().decrypt(c, k))


