
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__			,		A__			,		A__=[]		)							->							List[str]:
    """simple docstring"""

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

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



def 						lowercase_      (       A__			,		A__			,		A__		)							->							List[str]:
    """simple docstring"""

    return max(A__			,		min(A__			,		A__		)		)



def 						lowercase_      (       A__			,		A__			,		A__		)							->							List[Any]:
    """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 						lowercase_      (       A__			,		A__			,		A__		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case								= list(A__		)
    rect[0] -= overlap
    rect[1] -= overlap
    rect[2] += overlap
    rect[3] += overlap
    snake_case								= clamp_rect(A__			,		[0, 0]			,		[image_size[0], image_size[1]]		)
    return rect



def 						lowercase_      (       A__			,		A__			,		A__			,		A__		)							->							Any:
    """simple docstring"""

    snake_case								= 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__			,		A__		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case								= (original_image_slice * 4, 0, tile.size[0], tile.size[1])
    snake_case								= tile.crop(A__		)
    return tile



def 						lowercase_      (       A__			,		A__		)							->							Any:
    """simple docstring"""

    snake_case								= n % d
    return n - divisor







class 					lowerCamelCase		(  A_  ):





     def __init__(self				:   List[Any]    ,   _A				:   AutoencoderKL    ,   _A				:   CLIPTextModel    ,   _A				:   CLIPTokenizer    ,   _A				:   UNetaDConditionModel    ,   _A				:   DDPMScheduler    ,   _A				:   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]    ,   _A				:   int = 3_5_0    ,   )						->						Optional[Any]:
         super().__init__(
             vae=_A    ,   text_encoder=_A    ,   tokenizer=_A    ,   unet=_A    ,   low_res_scheduler=_A    ,   scheduler=_A    ,   max_noise_level=_A    ,   )





     def 		UpperCAmelCase(self				:   str    ,   _A				:   List[str]    ,   _A				:   int    ,   _A				:   Any    ,   _A				:   str    ,   _A				:   List[Any]    ,   _A				:   List[str]    ,   _A				:   List[Any]    ,   **_A				:   List[str]       )						->						Any:
         torch.manual_seed(0       )
         snake_case								= (
             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								= add_overlap_rect(_A    ,   _A    ,   image.size       )
         snake_case								= image.crop(_A       )
         snake_case								= ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
         snake_case								= translated_slice_x - (original_image_slice / 2)
         snake_case								= max(0    ,   _A       )
         snake_case								= squeeze_tile(_A    ,   _A    ,   _A    ,   _A       )
         snake_case								= to_input.size
         snake_case								= to_input.resize((tile_size, tile_size)    ,   Image.BICUBIC       )
         snake_case								= super(_A    ,   self       ).__call__(image=_A    ,   **_A       ).images[0]
         snake_case								= upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)    ,   Image.BICUBIC       )
         snake_case								= unsqueeze_tile(_A    ,   _A       )
         snake_case								= upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)    ,   Image.BICUBIC       )
         snake_case								= []
         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								= 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				:   Tuple    ,   _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    ,   )						->						Optional[int]:
         snake_case								= Image.new("RGB"    ,   (image.size[0] * 4, image.size[1] * 4)       )
         snake_case								= math.ceil(image.size[0] / tile_size       )
         snake_case								= math.ceil(image.size[1] / tile_size       )
         snake_case								= tcx * tcy
         snake_case								= 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]:
    """simple docstring"""

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

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

    snake_case								= 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 						lowercase_      (       A__ = 1000		)							->							int:
    """simple docstring"""

    return sum(2 * a * ((a - 1) // 2) for a in range(3			,		n + 1		)		)


if __name__ == "__main__":
   print(solution())

