
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  __magic_name__ (   __lowerCAmelCase	:						List[Any]		,  __lowerCAmelCase	:						Any		,  __lowerCAmelCase	:						str=[]							)			->   Any:
				__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(__lowerCAmelCase		,  mode='''linear_ramp'''		,  pad_width=__lowerCAmelCase		,  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  __magic_name__ (   __lowerCAmelCase	:						Any		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						Optional[int]							)			->   int:
				return max(__lowerCAmelCase		,  min(__lowerCAmelCase		,  __lowerCAmelCase							)							)




def  __magic_name__ (   __lowerCAmelCase	:						[int]		,  __lowerCAmelCase	:						[int]		,  __lowerCAmelCase	:						[int]							)			->   Optional[Any]:
				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  __magic_name__ (   __lowerCAmelCase	:						[int]		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						[int]							)			->   List[str]:
				__lowerCamelCase    					=	list(__lowerCAmelCase							)
				rect[0] -= overlap
				rect[1] -= overlap
				rect[2] += overlap
				rect[3] += overlap
				__lowerCamelCase    					=	clamp_rect(__lowerCAmelCase		,  [0, 0]		,  [image_size[0], image_size[1]]							)
				return rect




def  __magic_name__ (   __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						Optional[int]		,  __lowerCAmelCase	:						int							)			->   Any:
				__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(__lowerCAmelCase		,  (original_slice, 0)							)
				return result




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




def  __magic_name__ (   __lowerCAmelCase	:						Optional[int]		,  __lowerCAmelCase	:						str							)			->   Dict:
				__lowerCamelCase    					=	n % d
				return n - divisor

class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							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_50							,			)     ->							Dict:
						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  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			**SCREAMING_SNAKE_CASE__		:							int  )     ->							List[str]:
						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(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			image.size  )
						__lowerCamelCase    					=	image.crop(SCREAMING_SNAKE_CASE__  )
						__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							,			SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	squeeze_tile(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	to_input.size
						__lowerCamelCase    					=	to_input.resize((tile_size, tile_size)							,			Image.BICUBIC  )
						__lowerCamelCase    					=	super(SCREAMING_SNAKE_CASE__							,			self  ).__call__(image=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  ).images[0]
						__lowerCamelCase    					=	upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)							,			Image.BICUBIC  )
						__lowerCamelCase    					=	unsqueeze_tile(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						__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=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		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Union[str, List[str]]							,			SCREAMING_SNAKE_CASE__		:							Union[PIL.Image.Image, List[PIL.Image.Image]]							,			SCREAMING_SNAKE_CASE__		:							int = 75							,			SCREAMING_SNAKE_CASE__		:							float = 9.0							,			SCREAMING_SNAKE_CASE__		:							int = 50							,			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_28							,			SCREAMING_SNAKE_CASE__		:							int = 32							,			SCREAMING_SNAKE_CASE__		:							int = 32							,			)     ->							Union[str, Any]:
						__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(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  __magic_name__ (   )			->   Optional[Any]:
				# Run a demo
				__lowerCamelCase    					=	'''stabilityai/stable-diffusion-x4-upscaler'''
				__lowerCamelCase    					=	StableDiffusionTiledUpscalePipeline.from_pretrained(__lowerCAmelCase		,  revision='''fp16'''		,  torch_dtype=torch.floataa							)
				__lowerCamelCase    					=	pipe.to('''cuda'''							)
				__lowerCamelCase    					=	Image.open('''../../docs/source/imgs/diffusers_library.jpg'''							)

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

				__lowerCamelCase    					=	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 numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class    lowerCAmelCase__     :






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Dict:
						if dst_width < 0 or dst_height < 0:
										raise ValueError('''Destination width/height should be > 0'''  )

						__lowerCamelCase    					=	img
						__lowerCamelCase    					=	img.shape[1]
						__lowerCamelCase    					=	img.shape[0]
						__lowerCamelCase    					=	dst_width
						__lowerCamelCase    					=	dst_height

						__lowerCamelCase    					=	self.src_w / self.dst_w
						__lowerCamelCase    					=	self.src_h / self.dst_h

						__lowerCamelCase    					=	__lowerCamelCase    					=	(
						    np.ones((self.dst_h, self.dst_w, 3)							,			np.uinta  ) * 2_55
						)






		def  __A       (		self		:							List[Any]  )     ->							str:
						for i in range(self.dst_h  ):
										for j in range(self.dst_w  ):
														__lowerCamelCase    					=	self.img[self.get_y(SCREAMING_SNAKE_CASE__  )][self.get_x(SCREAMING_SNAKE_CASE__  )]






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_x * x  )







		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_y * y  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__   :			List[str]												=    800, 600
					SCREAMING_SNAKE_CASE__   :			int												=    imread("image_data/lena.jpg", 1)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    NearestNeighbour(im, dst_w, dst_h)
					n.process()

					imshow(
					    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
					)
					waitKey(0)
					destroyAllWindows()



