


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   ( __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				Optional[Any]=[]   ):
							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_5_5
							a__             =     np.pad(SCREAMING_SNAKE_CASE__					,    mode='linear_ramp'					,    pad_width=SCREAMING_SNAKE_CASE__					,    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 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				Optional[Any]   ):
							return max(SCREAMING_SNAKE_CASE__					,    min(SCREAMING_SNAKE_CASE__					,    SCREAMING_SNAKE_CASE__   )   )






def 							__lowercase   ( __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				Optional[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 							__lowercase   ( __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				Optional[Any]   ):
							a__             =     list(SCREAMING_SNAKE_CASE__   )
							rect[0] -= overlap
							rect[1] -= overlap
							rect[2] += overlap
							rect[3] += overlap
							a__             =     clamp_rect(SCREAMING_SNAKE_CASE__					,    [0, 0]					,    [image_size[0], image_size[1]]   )
							return rect






def 							__lowercase   ( __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				List[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(SCREAMING_SNAKE_CASE__					,    (original_slice, 0)   )
							return result






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				Optional[int]   ):
							a__             =     (original_image_slice * 4, 0, tile.size[0], tile.size[1])
							a__             =     tile.crop(SCREAMING_SNAKE_CASE__   )
							return tile






def 							__lowercase   ( __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				int   ):
							a__             =     n % d
							return n - divisor







class        snake_case_   (_snake_case      ):
					def __init__(      self   :int			,__snake_case   :Optional[int]			,__snake_case   :Optional[Any]			,__snake_case   :List[Any]			,__snake_case   :Union[str, Any]			,__snake_case   :int			,__snake_case   :Tuple			,__snake_case   :List[Any] = 3_50			,)					->    Optional[int]:
												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  lowerCamelCase__(      self   :Optional[int]			,__snake_case   :Tuple			,__snake_case   :Optional[Any]			,__snake_case   :List[Any]			,__snake_case   :List[str]			,__snake_case   :Optional[Any]			,__snake_case   :List[Any]			,__snake_case   :Tuple			,**__snake_case   :Optional[int]      )					->    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(__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[Any]			,__snake_case   :Optional[Any]			,__snake_case   :Dict			,__snake_case   :Any = 75			,__snake_case   :List[str] = 9.0			,__snake_case   :List[str] = 50			,__snake_case   :Any = None			,__snake_case   :Dict = 1			,__snake_case   :Tuple = 0.0			,__snake_case   :Any = None			,__snake_case   :Dict = None			,__snake_case   :Optional[Any] = None			,__snake_case   :Union[str, Any] = 1			,__snake_case   :Tuple = 1_28			,__snake_case   :int = 32			,__snake_case   :Union[str, Any] = 32			,)					->    List[str]:
												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 							__lowercase   ( ):
							a__             =     """stabilityai/stable-diffusion-x4-upscaler"""
							a__             =     StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE__					,    revision='fp16'					,    torch_dtype=torch.floataa   )
							a__             =     pipe.to('cuda'   )
							a__             =     Image.open('../../docs/source/imgs/diffusers_library.jpg'   )

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

							a__             =     pipe(image=SCREAMING_SNAKE_CASE__					,    prompt='Black font, white background, vector'					,    noise_level=4_0					,    callback=SCREAMING_SNAKE_CASE__   )
							final_image.save('diffusers_library.jpg'   )


if __name__ == "__main__":
						main()








'''simple docstring'''


from collections import defaultdict
from math import ceil, sqrt







def 			snake_case_   (  SCREAMING_SNAKE_CASE__ = 100_0000							,      SCREAMING_SNAKE_CASE__ = 10     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       defaultdict     		=      defaultdict(SCREAMING_SNAKE_CASE__     )

				for outer_width in range(3							,      (t_limit // 4) + 2     ):
								if outer_width * outer_width > t_limit:
												_SCREAMING_SNAKE_CASE :       int     		=      max(
												    ceil(sqrt(outer_width * outer_width - t_limit     )     )							,      1     )
								else:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      1

								hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

								for hole_width in range(SCREAMING_SNAKE_CASE__							,      outer_width - 1							,      2     ):
												count[outer_width * outer_width - hole_width * hole_width] += 1

				return sum(1 for n in count.values() if 1 <= n <= 10     )


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




