


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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					str=[]				):
     __UpperCamelCase    							=size[0] - overlap_pixels * 2
     __UpperCamelCase    							=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
     __UpperCamelCase    							=np.ones((size_y, size_x)			,     dtype=np.uinta				) * 2_55
     __UpperCamelCase    							=np.pad(SCREAMING_SNAKE_CASE__			,     mode='linear_ramp'			,     pad_width=SCREAMING_SNAKE_CASE__			,     end_values=0				)

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					List[str]				):
     return max(SCREAMING_SNAKE_CASE__			,     min(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					[int]			,     SCREAMING_SNAKE_CASE__			:					[int]			,     SCREAMING_SNAKE_CASE__			:					[int]				):
     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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					[int]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					[int]				):
     __UpperCamelCase    							=list(SCREAMING_SNAKE_CASE__				)
     rect[0] -= overlap
     rect[1] -= overlap
     rect[2] += overlap
     rect[3] += overlap
     __UpperCamelCase    							=clamp_rect(SCREAMING_SNAKE_CASE__			,     [0, 0]			,     [image_size[0], image_size[1]]				)
     return rect

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					Optional[int]				):
     __UpperCamelCase    							=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(SCREAMING_SNAKE_CASE__			,     (original_slice, 0)				)
     return result

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					Dict				):
     __UpperCamelCase    							=(original_image_slice * 4, 0, tile.size[0], tile.size[1])
     __UpperCamelCase    							=tile.crop(SCREAMING_SNAKE_CASE__				)
     return tile

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]				):
     __UpperCamelCase    							=n % d
     return n - divisor






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def __init__(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_ = 350					,   )  ->       Union[str, Any]:
         super().__init__(
             vae=A_					,   text_encoder=A_					,   tokenizer=A_					,   unet=A_					,   low_res_scheduler=A_					,   scheduler=A_					,   max_noise_level=A_					,   )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   **A_    )  ->       Union[str, Any]:
         torch.manual_seed(0    )
         __UpperCamelCase    							=(
             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    ),
         )
         __UpperCamelCase    							=add_overlap_rect(A_					,   A_					,   image.size    )
         __UpperCamelCase    							=image.crop(A_    )
         __UpperCamelCase    							=((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
         __UpperCamelCase    							=translated_slice_x - (original_image_slice / 2)
         __UpperCamelCase    							=max(0					,   A_    )
         __UpperCamelCase    							=squeeze_tile(A_					,   A_					,   A_					,   A_    )
         __UpperCamelCase    							=to_input.size
         __UpperCamelCase    							=to_input.resize((tile_size, tile_size)					,   Image.BICUBIC    )
         __UpperCamelCase    							=super(A_					,   self    ).__call__(image=A_					,   **A_    ).images[0]
         __UpperCamelCase    							=upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)					,   Image.BICUBIC    )
         __UpperCamelCase    							=unsqueeze_tile(A_					,   A_    )
         __UpperCamelCase    							=upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)					,   Image.BICUBIC    )
         __UpperCamelCase    							=[]
         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'    )
         __UpperCamelCase    							=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					,   )  ->       Tuple:
         __UpperCamelCase    							=Image.new('RGB'					,   (image.size[0] * 4, image.size[1] * 4)    )
         __UpperCamelCase    							=math.ceil(image.size[0] / tile_size    )
         __UpperCamelCase    							=math.ceil(image.size[1] / tile_size    )
         __UpperCamelCase    							=tcx * tcy
         __UpperCamelCase    							=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 						_UpperCAmelCase				(			):
     # Run a demo
     __UpperCamelCase    							='stabilityai/stable-diffusion-x4-upscaler'
     __UpperCamelCase    							=StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE__			,     revision='fp16'			,     torch_dtype=torch.floataa				)
     __UpperCamelCase    							=pipe.to('cuda'				)
     __UpperCamelCase    							=Image.open('../../docs/source/imgs/diffusers_library.jpg'				)

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

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


if __name__ == "__main__":
   main()









def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int = 10**12				):
     __UpperCamelCase    							=1
     __UpperCamelCase    							=0

     __UpperCamelCase    							=1
     __UpperCamelCase    							=1

     while numerator <= 2 * min_total - 1:
          prev_numerator += 2 * numerator
          numerator += 2 * prev_numerator

          prev_denominator += 2 * denominator
          denominator += 2 * prev_denominator

     return (denominator + 1) // 2


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





