



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			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase=[]   )     ->     Tuple:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				size[0] - overlap_pixels * 2
					SCREAMING_SNAKE_CASE							:     Dict						=				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
					SCREAMING_SNAKE_CASE							:     Tuple						=				np.ones((size_y, size_x)      ,	dtype=np.uinta   ) * 255
					SCREAMING_SNAKE_CASE							:     Any						=				np.pad(__lowerCAmelCase      ,	mode='linear_ramp'      ,	pad_width=__lowerCAmelCase      ,	end_values=0   )

					if "l" in remove_borders:
										SCREAMING_SNAKE_CASE							:     str						=				mask[:, overlap_pixels : mask.shape[1]]
					if "r" in remove_borders:
										SCREAMING_SNAKE_CASE							:     Any						=				mask[:, 0 : mask.shape[1] - overlap_pixels]
					if "t" in remove_borders:
										SCREAMING_SNAKE_CASE							:     Dict						=				mask[overlap_pixels : mask.shape[0], :]
					if "b" in remove_borders:
										SCREAMING_SNAKE_CASE							:     Optional[int]						=				mask[0 : mask.shape[0] - overlap_pixels, :]
					return mask

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     str:
					return max(__lowerCAmelCase      ,	min(__lowerCAmelCase      ,	__lowerCAmelCase   )   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     List[str]:
					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			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Dict:
					SCREAMING_SNAKE_CASE							:     Tuple						=				list(__lowerCAmelCase   )
					rect[0] -= overlap
					rect[1] -= overlap
					rect[2] += overlap
					rect[3] += overlap
					SCREAMING_SNAKE_CASE							:     int						=				clamp_rect(__lowerCAmelCase      ,	[0, 0]      ,	[image_size[0], image_size[1]]   )
					return rect

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Dict:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				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(__lowerCAmelCase      ,	(original_slice, 0)   )
					return result

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				(original_image_slice * 4, 0, tile.size[0], tile.size[1])
					SCREAMING_SNAKE_CASE							:     str						=				tile.crop(__lowerCAmelCase   )
					return tile

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				n % d
					return n - divisor


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						def __init__(  self    :				str       ,	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       ,	):



											'''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       lowerCamelCase_				(  self    :				str       ,	snake_case    :				List[str]       ,	snake_case    :				List[str]       ,	snake_case    :				Tuple       ,	snake_case    :				Tuple       ,	snake_case    :				List[Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				Union[str, Any]       ,	**snake_case    :				Optional[Any]	):



											'''simple docstring'''


											torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				(
											    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	),
											)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				add_overlap_rect(snake_case       ,	snake_case       ,	image.size	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				image.crop(snake_case	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				translated_slice_x - (original_image_slice / 2)
											SCREAMING_SNAKE_CASE							:     Any						=				max(0       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				squeeze_tile(snake_case       ,	snake_case       ,	snake_case       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				to_input.size
											SCREAMING_SNAKE_CASE							:     str						=				to_input.resize((tile_size, tile_size)       ,	Image.BICUBIC	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				super(snake_case       ,	self	).__call__(image=snake_case       ,	**snake_case	).images[0]
											SCREAMING_SNAKE_CASE							:     List[Any]						=				upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)       ,	Image.BICUBIC	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				unsqueeze_tile(snake_case       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)       ,	Image.BICUBIC	)
											SCREAMING_SNAKE_CASE							:     Dict						=				[]
											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'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				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       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				Image.new('RGB'       ,	(image.size[0] * 4, image.size[1] * 4)	)
											SCREAMING_SNAKE_CASE							:     Dict						=				math.ceil(image.size[0] / tile_size	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				math.ceil(image.size[1] / tile_size	)
											SCREAMING_SNAKE_CASE							:     str						=				tcx * tcy
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				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        __a			(   )     ->     Optional[int]:
					# Run a demo
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				'stabilityai/stable-diffusion-x4-upscaler'
					SCREAMING_SNAKE_CASE							:     int						=				StableDiffusionTiledUpscalePipeline.from_pretrained(__lowerCAmelCase      ,	revision='fp16'      ,	torch_dtype=torch.floataa   )
					SCREAMING_SNAKE_CASE							:     Any						=				pipe.to('cuda'   )
					SCREAMING_SNAKE_CASE							:     str						=				Image.open('../../docs/source/imgs/diffusers_library.jpg'   )

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

					SCREAMING_SNAKE_CASE							:     Any						=				pipe(image=__lowerCAmelCase      ,	prompt='Black font, white background, vector'      ,	noise_level=40      ,	callback=__lowerCAmelCase   )
					final_image.save('diffusers_library.jpg'   )


if __name__ == "__main__":
			main()




from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase    :		Dict		      =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase    :		Any		      =    typing.Union[np.floataa, int, float]  # noqa: UP007

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return np.sqrt(np.sum((np.asarray(__lowerCAmelCase   ) - np.asarray(__lowerCAmelCase   )) ** 2   )   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return sum((va - va) ** 2 for va, va in zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ) ** (1 / 2)


if __name__ == "__main__":

			def        __a			(   )     ->     None:
								from timeit import timeit

								print('Without Numpy'   )
								print(
								    timeit(
								        'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )
								print('With Numpy'   )
								print(
								    timeit(
								        'euclidean_distance([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )



			benchmark()