



"""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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=[]	)							->      List[Any]:
		lowercase__   :    Any				=			size[0] - overlap_pixels * 2
		lowercase__   :    Union[str, Any]				=			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
		lowercase__   :    List[str]				=			np.ones((size_y, size_x)						,     dtype=np.uinta	) * 2_55
		lowercase__   :    str				=			np.pad(a__						,     mode='''linear_ramp'''						,     pad_width=a__						,     end_values=0	)

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



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      int:
		return max(a__						,     min(a__						,     a__	)	)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    int				=			list(a__	)
		rect[0] -= overlap
		rect[1] -= overlap
		rect[2] += overlap
		rect[3] += overlap
		lowercase__   :    Optional[int]				=			clamp_rect(a__						,     [0, 0]						,     [image_size[0], image_size[1]]	)
		return rect



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      int:
		lowercase__   :    Optional[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(a__						,     (original_slice, 0)	)
		return result



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      List[str]:
		lowercase__   :    Optional[int]				=			(original_image_slice * 4, 0, tile.size[0], tile.size[1])
		lowercase__   :    Dict				=			tile.crop(a__	)
		return tile



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      List[str]:
		lowercase__   :    Optional[Any]				=			n % d
		return n - divisor
class        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  List[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			,)					->		int:






				"""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 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  str			,_snake_case						:  Tuple			,_snake_case						:  Optional[int]			,_snake_case						:  Optional[Any]			,_snake_case						:  Tuple			,_snake_case						:  List[Any]			,_snake_case						:  int			,**_snake_case						:  str						)					->		List[Any]:






				"""simple docstring"""



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






				"""simple docstring"""



				lowercase__   :    Dict				=			Image.new('''RGB'''			,(image.size[0] * 4, image.size[1] * 4)						)
				lowercase__   :    Any				=			math.ceil(image.size[0] / tile_size						)
				lowercase__   :    str				=			math.ceil(image.size[1] / tile_size						)
				lowercase__   :    Tuple				=			tcx * tcy
				lowercase__   :    List[Any]				=			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      __UpperCAmelCase      (    )							->      Optional[Any]:
		lowercase__   :    Union[str, Any]				=			'''stabilityai/stable-diffusion-x4-upscaler'''
		lowercase__   :    Union[str, Any]				=			StableDiffusionTiledUpscalePipeline.from_pretrained(a__						,     revision='''fp16'''						,     torch_dtype=torch.floataa	)
		lowercase__   :    int				=			pipe.to('''cuda'''	)
		lowercase__   :    List[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'''	)

		lowercase__   :    Dict				=			pipe(image=a__						,     prompt='''Black font, white background, vector'''						,     noise_level=40						,     callback=a__	)
		final_image.save('''diffusers_library.jpg'''	)


if __name__ == "__main__":
							main()



'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      n
      __SCREAMING_SNAKE_CASE     =      [None] * self.n
      __SCREAMING_SNAKE_CASE     =      0  # index of the first element
      __SCREAMING_SNAKE_CASE     =      0
      __SCREAMING_SNAKE_CASE     =      0
 def __len__(		self					:       Tuple			)  -> int:


      """simple docstring"""




      return self.size
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> bool:


      """simple docstring"""




      return self.size == 0
 def       UpperCAmelCase__		(		self					:       Any			)  -> int:


      """simple docstring"""




      return False if self.is_empty() else self.array[self.front]
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> Dict:


      """simple docstring"""




      if self.size >= self.n:
           raise Exception("""QUEUE IS FULL"""			)

      __SCREAMING_SNAKE_CASE     =      data
      __SCREAMING_SNAKE_CASE     =      (self.rear + 1) % self.n
      self.size += 1
      return self


 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[Any]:


      """simple docstring"""




      if self.size == 0:
           raise Exception("""UNDERFLOW"""			)

      __SCREAMING_SNAKE_CASE     =      self.array[self.front]
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      (self.front + 1) % self.n
      self.size -= 1
      return temp






