


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 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=[] )						->	Tuple:


						'''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(_lowerCAmelCase      ,	mode="linear_ramp"      ,	pad_width=_lowerCAmelCase      ,	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 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






						return max(_lowerCAmelCase      ,	min(_lowerCAmelCase      ,	_lowerCAmelCase ) )





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	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 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	int:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	int:


						'''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(_lowerCAmelCase      ,	(original_slice, 0) )
						return result





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Optional[Any]:


						'''simple docstring'''






						__snake_case				=		n % d
						return n - divisor






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		AutoencoderKL     ,    SCREAMING_SNAKE_CASE	:		CLIPTextModel     ,    SCREAMING_SNAKE_CASE	:		CLIPTokenizer     ,    SCREAMING_SNAKE_CASE	:		UNetaDConditionModel     ,    SCREAMING_SNAKE_CASE	:		DDPMScheduler     ,    SCREAMING_SNAKE_CASE	:		Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,    SCREAMING_SNAKE_CASE	:		int = 3_5_0     ,    )			->	List[Any]:




											'''simple docstring'''

											super().__init__(
											    vae=SCREAMING_SNAKE_CASE     ,    text_encoder=SCREAMING_SNAKE_CASE     ,    tokenizer=SCREAMING_SNAKE_CASE     ,    unet=SCREAMING_SNAKE_CASE     ,    low_res_scheduler=SCREAMING_SNAKE_CASE     ,    scheduler=SCREAMING_SNAKE_CASE     ,    max_noise_level=SCREAMING_SNAKE_CASE     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		int     ,    **SCREAMING_SNAKE_CASE	:		List[str]						)			->	Dict:




											'''simple docstring'''

											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(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    image.size						)
											__snake_case				=		image.crop(SCREAMING_SNAKE_CASE						)
											__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     ,    SCREAMING_SNAKE_CASE						)
											__snake_case				=		squeeze_tile(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											__snake_case				=		to_input.size
											__snake_case				=		to_input.resize((tile_size, tile_size)     ,    Image.BICUBIC						)
											__snake_case				=		super(SCREAMING_SNAKE_CASE     ,    self						).__call__(image=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						).images[0]
											__snake_case				=		upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)     ,    Image.BICUBIC						)
											__snake_case				=		unsqueeze_tile(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											__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=SCREAMING_SNAKE_CASE						)     ,    mode="L"     ,    )
											final_image.paste(
											    SCREAMING_SNAKE_CASE     ,    (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)     ,    SCREAMING_SNAKE_CASE						)






					@torch.no_grad()
					def __call__(						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[str, List[str]]     ,    SCREAMING_SNAKE_CASE	:		Union[PIL.Image.Image, List[PIL.Image.Image]]     ,    SCREAMING_SNAKE_CASE	:		int = 7_5     ,    SCREAMING_SNAKE_CASE	:		float = 9.0     ,    SCREAMING_SNAKE_CASE	:		int = 5_0     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, List[str]]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = 1     ,    SCREAMING_SNAKE_CASE	:		float = 0.0     ,    SCREAMING_SNAKE_CASE	:		Optional[torch.Generator] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[torch.FloatTensor] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,    SCREAMING_SNAKE_CASE	:		int = 1     ,    SCREAMING_SNAKE_CASE	:		int = 1_2_8     ,    SCREAMING_SNAKE_CASE	:		int = 3_2     ,    SCREAMING_SNAKE_CASE	:		int = 3_2     ,    )			->	int:




											'''simple docstring'''

											__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(SCREAMING_SNAKE_CASE						):
																	for x in range(SCREAMING_SNAKE_CASE						):
																							self._process_tile(
																							    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    prompt=SCREAMING_SNAKE_CASE     ,    num_inference_steps=SCREAMING_SNAKE_CASE     ,    guidance_scale=SCREAMING_SNAKE_CASE     ,    noise_level=SCREAMING_SNAKE_CASE     ,    negative_prompt=SCREAMING_SNAKE_CASE     ,    num_images_per_prompt=SCREAMING_SNAKE_CASE     ,    eta=SCREAMING_SNAKE_CASE     ,    generator=SCREAMING_SNAKE_CASE     ,    latents=SCREAMING_SNAKE_CASE     ,    )
																							current_count += 1
																							if callback is not None:
																													callback({"progress": current_count / total_tile_count, "image": final_image}						)
											return final_image





def 	_lowerCAmelCase       (    )						->	int:


						'''simple docstring'''






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

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

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


if __name__ == "__main__":
	main()









import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


A							:					Any     						=     logging.get_logger(__name__)






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    *SCREAMING_SNAKE_CASE	:		Tuple     ,    **SCREAMING_SNAKE_CASE	:		List[Any]						)			->	None:




											'''simple docstring'''

											warnings.warn(
											    "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
											    " use BeitImageProcessor instead."     ,    SCREAMING_SNAKE_CASE     ,    )
											super().__init__(*SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)





