





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__(__snake_case ,__snake_case ,__snake_case=[]							)       ->   List[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	size[0] - overlap_pixels * 2
	lowerCamelCase__              =	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
	lowerCamelCase__              =	np.ones((size_y, size_x) ,dtype=np.uinta							) * 255
	lowerCamelCase__              =	np.pad(__snake_case ,mode='''linear_ramp''' ,pad_width=__snake_case ,end_values=0							)

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





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   Any:







	'''simple docstring'''







	return max(__snake_case ,min(__snake_case ,__snake_case							)							)





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   Tuple:







	'''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__(__snake_case ,__snake_case ,__snake_case							)       ->   str:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	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(__snake_case ,(original_slice, 0)							)
	return result





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   str:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	n % d
	return n - divisor






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = 3_5_0 ,						):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__              =	(
				    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							),
				)
				lowerCamelCase__              =	add_overlap_rect(__lowerCAmelCase ,						__lowerCAmelCase ,						image.size							)
				lowerCamelCase__              =	image.crop(__lowerCAmelCase							)
				lowerCamelCase__              =	((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
				lowerCamelCase__              =	translated_slice_x - (original_image_slice / 2)
				lowerCamelCase__              =	max(0 ,						__lowerCAmelCase							)
				lowerCamelCase__              =	squeeze_tile(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				lowerCamelCase__              =	to_input.size
				lowerCamelCase__              =	to_input.resize((tile_size, tile_size) ,						Image.BICUBIC							)
				lowerCamelCase__              =	super(__lowerCAmelCase ,						self							).__call__(image=__lowerCAmelCase ,						**__lowerCAmelCase							).images[0]
				lowerCamelCase__              =	upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,						Image.BICUBIC							)
				lowerCamelCase__              =	unsqueeze_tile(__lowerCAmelCase ,						__lowerCAmelCase							)
				lowerCamelCase__              =	upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) ,						Image.BICUBIC							)
				lowerCamelCase__              =	[]
				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'''							)
				lowerCamelCase__              =	Image.fromarray(
				    make_transparency_mask(
				        (upscaled_tile.size[0], upscaled_tile.size[1]) ,						tile_border * 4 ,						remove_borders=__lowerCAmelCase							) ,						mode='''L''' ,						)
				final_image.paste(
				    __lowerCAmelCase ,						(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) ,						__lowerCAmelCase							)





			@torch.no_grad()
			def __call__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = 7_5 ,						__lowerCAmelCase = 9.0 ,						__lowerCAmelCase = 5_0 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = 1 ,						__lowerCAmelCase = 0.0 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = 1 ,						__lowerCAmelCase = 1_2_8 ,						__lowerCAmelCase = 3_2 ,						__lowerCAmelCase = 3_2 ,						):



				'''simple docstring'''







				lowerCamelCase__              =	Image.new('''RGB''' ,						(image.size[0] * 4, image.size[1] * 4)							)
				lowerCamelCase__              =	math.ceil(image.size[0] / tile_size							)
				lowerCamelCase__              =	math.ceil(image.size[1] / tile_size							)
				lowerCamelCase__              =	tcx * tcy
				lowerCamelCase__              =	0
				for y in range(__lowerCAmelCase							):
					for x in range(__lowerCAmelCase							):
						self._process_tile(
						    __lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						prompt=__lowerCAmelCase ,						num_inference_steps=__lowerCAmelCase ,						guidance_scale=__lowerCAmelCase ,						noise_level=__lowerCAmelCase ,						negative_prompt=__lowerCAmelCase ,						num_images_per_prompt=__lowerCAmelCase ,						eta=__lowerCAmelCase ,						generator=__lowerCAmelCase ,						latents=__lowerCAmelCase ,						)
						current_count += 1
						if callback is not None:
							callback({'''progress''': current_count / total_tile_count, '''image''': final_image}							)
				return final_image





def lowerCAmelCase__()       ->   List[Any]:







	'''simple docstring'''







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

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

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


if __name__ == "__main__":
			main()










import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_a						=   logging.get_logger(__name__)






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			def __init__(					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				warnings.warn(
				    '''The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use OwlViTImageProcessor instead.''' ,						__lowerCAmelCase ,						)
				super().__init__(*__lowerCAmelCase ,						**__lowerCAmelCase							)



