






'''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(  UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						List[str]=[]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=size[0] - overlap_pixels * 2
      SCREAMING_SNAKE_CASE__     :				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
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=np.ones((size_y, size_x),	dtype=np.uinta							) * 2_5_5
      SCREAMING_SNAKE_CASE__     :				List[Any]									=np.pad(UpperCamelCase__,	mode='''linear_ramp''',	pad_width=UpperCamelCase__,	end_values=0							)

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





def  _a(  UpperCamelCase__				:						Dict,	UpperCamelCase__				:						Dict,	UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      return max(UpperCamelCase__,	min(UpperCamelCase__,	UpperCamelCase__							)							)





def  _a(  UpperCamelCase__				:						[int],	UpperCamelCase__				:						[int],	UpperCamelCase__				:						[int]							):




      '''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(  UpperCamelCase__				:						[int],	UpperCamelCase__				:						int,	UpperCamelCase__				:						[int]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Dict									=list(UpperCamelCase__							)
      rect[0] -= overlap
      rect[1] -= overlap
      rect[2] += overlap
      rect[3] += overlap
      SCREAMING_SNAKE_CASE__     :				Tuple									=clamp_rect(UpperCamelCase__,	[0, 0],	[image_size[0], image_size[1]]							)
      return rect





def  _a(  UpperCamelCase__				:						Any,	UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						str,	UpperCamelCase__				:						Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				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(UpperCamelCase__,	(original_slice, 0)							)
      return result





def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Any							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(original_image_slice * 4, 0, tile.size[0], tile.size[1])
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=tile.crop(UpperCamelCase__							)
      return tile





def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Tuple							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=n % d
      return n - divisor






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 def __init__(      self	:							Any					,      __lowercase	:							AutoencoderKL					,      __lowercase	:							CLIPTextModel					,      __lowercase	:							CLIPTokenizer					,      __lowercase	:							UNetaDConditionModel					,      __lowercase	:							DDPMScheduler					,      __lowercase	:							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,      __lowercase	:							int = 3_50					,      )	->						Optional[int]:
       super().__init__(
           vae=__lowercase					,      text_encoder=__lowercase					,      tokenizer=__lowercase					,      unet=__lowercase					,      low_res_scheduler=__lowercase					,      scheduler=__lowercase					,      max_noise_level=__lowercase					,      )




 def      __magic_name__	(      self	:							Optional[Any]					,      __lowercase	:							List[str]					,      __lowercase	:							Optional[int]					,      __lowercase	:							Optional[int]					,      __lowercase	:							str					,      __lowercase	:							Tuple					,      __lowercase	:							Optional[int]					,      __lowercase	:							Any					,      **__lowercase	:							List[Any]      )	->						Dict:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=(
           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      ),
       )
       SCREAMING_SNAKE_CASE__     :				Any									=add_overlap_rect(__lowercase					,      __lowercase					,      image.size      )
       SCREAMING_SNAKE_CASE__     :				Any									=image.crop(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
       SCREAMING_SNAKE_CASE__     :				int									=translated_slice_x - (original_image_slice / 2)
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=max(0					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=squeeze_tile(__lowercase					,      __lowercase					,      __lowercase					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				int									=to_input.size
       SCREAMING_SNAKE_CASE__     :				Tuple									=to_input.resize((tile_size, tile_size)					,      Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__     :				Dict									=super(__lowercase					,      self      ).__call__(image=__lowercase					,      **__lowercase      ).images[0]
       SCREAMING_SNAKE_CASE__     :				List[Any]									=upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)					,      Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__     :				Dict									=unsqueeze_tile(__lowercase					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)					,      Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__     :				str									=[]
       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'''      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=Image.fromarray(
           make_transparency_mask(
               (upscaled_tile.size[0], upscaled_tile.size[1])					,      tile_border * 4					,      remove_borders=__lowercase      )					,      mode='''L'''					,      )
       final_image.paste(
           __lowercase					,      (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)					,      __lowercase      )




 @torch.no_grad()
 def __call__(      self	:							Any					,      __lowercase	:							Union[str, List[str]]					,      __lowercase	:							Union[PIL.Image.Image, List[PIL.Image.Image]]					,      __lowercase	:							int = 75					,      __lowercase	:							float = 9.0					,      __lowercase	:							int = 50					,      __lowercase	:							Optional[Union[str, List[str]]] = None					,      __lowercase	:							Optional[int] = 1					,      __lowercase	:							float = 0.0					,      __lowercase	:							Optional[torch.Generator] = None					,      __lowercase	:							Optional[torch.FloatTensor] = None					,      __lowercase	:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,      __lowercase	:							int = 1					,      __lowercase	:							int = 1_28					,      __lowercase	:							int = 32					,      __lowercase	:							int = 32					,      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Any									=Image.new('''RGB'''					,      (image.size[0] * 4, image.size[1] * 4)      )
       SCREAMING_SNAKE_CASE__     :				str									=math.ceil(image.size[0] / tile_size      )
       SCREAMING_SNAKE_CASE__     :				int									=math.ceil(image.size[1] / tile_size      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=tcx * tcy
       SCREAMING_SNAKE_CASE__     :				List[str]									=0
       for y in range(__lowercase      ):
             for x in range(__lowercase      ):
                   self._process_tile(
                       __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase					,      prompt=__lowercase					,      num_inference_steps=__lowercase					,      guidance_scale=__lowercase					,      noise_level=__lowercase					,      negative_prompt=__lowercase					,      num_images_per_prompt=__lowercase					,      eta=__lowercase					,      generator=__lowercase					,      latents=__lowercase					,      )
                   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'''






      SCREAMING_SNAKE_CASE__     :				Dict									='''stabilityai/stable-diffusion-x4-upscaler'''
      SCREAMING_SNAKE_CASE__     :				Any									=StableDiffusionTiledUpscalePipeline.from_pretrained(UpperCamelCase__,	revision='''fp16''',	torch_dtype=torch.floataa							)
      SCREAMING_SNAKE_CASE__     :				List[str]									=pipe.to('''cuda'''							)
      SCREAMING_SNAKE_CASE__     :				List[str]									=Image.open('''../../docs/source/imgs/diffusers_library.jpg'''							)

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

      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=pipe(image=UpperCamelCase__,	prompt='''Black font, white background, vector''',	noise_level=4_0,	callback=UpperCamelCase__							)
      final_image.save('''diffusers_library.jpg'''							)


if __name__ == "__main__":
  main()







'''simple docstring'''
def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=[[False for _ in range(m + 1							)] for _ in range(n + 1							)]
      SCREAMING_SNAKE_CASE__     :				List[Any]									=True
      for i in range(UpperCamelCase__							):
            for j in range(m + 1							):
                  if dp[i][j]:
                        if j < m and a[i].upper() == b[j]:
                              SCREAMING_SNAKE_CASE__     :				Optional[int]									=True
                        if a[i].islower():
                              SCREAMING_SNAKE_CASE__     :				List[Any]									=True
      return dp[n][m]


if __name__ == "__main__":
  import doctest

  doctest.testmod()