
"""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_  (__a					, __a					, __a=[]					):

					'''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					) * 255
					A_								=    np.pad(lowerCAmelCase__					, mode="linear_ramp"					, pad_width=lowerCAmelCase__					, 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_  (__a					, __a					, __a					):

					'''simple docstring'''





					return max(lowerCAmelCase__					, min(lowerCAmelCase__					, lowerCAmelCase__					)					)
def 				A_  (__a					, __a					, __a					):

					'''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_  (__a					, __a					, __a					):

					'''simple docstring'''





					A_								=    list(lowerCAmelCase__					)
					rect[0] -= overlap
					rect[1] -= overlap
					rect[2] += overlap
					rect[3] += overlap
					A_								=    clamp_rect(lowerCAmelCase__					, [0, 0]					, [image_size[0], image_size[1]]					)
					return rect
def 				A_  (__a					, __a					, __a					, __a					):

					'''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(lowerCAmelCase__					, (original_slice, 0)					)
					return result
def 				A_  (__a					, __a					):

					'''simple docstring'''





					A_								=    (original_image_slice * 4, 0, tile.size[0], tile.size[1])
					A_								=    tile.crop(lowerCAmelCase__					)
					return tile
def 				A_  (__a					, __a					):

					'''simple docstring'''





					A_								=    n % d
					return n - divisor



class __lowerCAmelCase					(       snake_case__   ):




			"""simple docstring"""







			def __init__(					self    :       Union[str, Any]							, _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							, )   ->			Tuple:







								"""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    :       int							, _snake_case    :       Any							, _snake_case    :       Optional[int]							, _snake_case    :       Any							, _snake_case    :       Dict							, _snake_case    :       Optional[Any]							, _snake_case    :       int							, _snake_case    :       Tuple							, **_snake_case    :       Optional[int]						)   ->			Any:







								"""simple docstring"""
								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    :       Optional[int]							, _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							, )   ->			Tuple:







								"""simple docstring"""
								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 				A_  ():

					'''simple docstring'''





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

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

					A_								=    pipe(image=lowerCAmelCase__					, prompt="Black font, white background, vector"					, noise_level=40					, callback=lowerCAmelCase__					)
					final_image.save("diffusers_library.jpg"					)


if __name__ == "__main__":
		main()











def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     int 						=      str(bin(lowerCAmelCase__						)						)
		binary_number += "0" * shift_amount
		return binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     Union[str, Any] 						=      str(bin(lowerCAmelCase__						)						)[2:]
		if shift_amount >= len(lowerCAmelCase__						):
				return "0b0"
		lowerCamelCase_	:     List[str] 						=      binary_number[: len(lowerCAmelCase__						) - shift_amount]
		return "0b" + shifted_binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number >= 0:  # Get binary representation of positive number
				lowerCamelCase_	:     List[Any] 						=      '0' + str(bin(lowerCAmelCase__						)						).strip('-'						)[2:]
		else:  # Get binary (2's complement) representation of negative number
				lowerCamelCase_	:     Any 						=      len(bin(lowerCAmelCase__						)[3:]						)  # Find 2's complement of number
				lowerCamelCase_	:     List[str] 						=      bin(abs(lowerCAmelCase__						) - (1 << binary_number_length)						)[3:]
				lowerCamelCase_	:     List[str] 						=      (
				    '1' + '0' * (binary_number_length - len(lowerCAmelCase__						)) + binary_number
				)

		if shift_amount >= len(lowerCAmelCase__						):
				return "0b" + binary_number[0] * len(lowerCAmelCase__						)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(lowerCAmelCase__						) - shift_amount]
		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
