

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__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						str=[]			)					->Dict:
      __A    =size[0] - overlap_pixels * 2
      __A    =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
      __A    =np.ones((size_y, size_x)     ,				dtype=np.uinta			) * 2_55
      __A    =np.pad(__A     ,				mode='''linear_ramp'''     ,				pad_width=__A     ,				end_values=0			)

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





def      A__					(			__A  :						str     ,				__A  :						List[str]     ,				__A  :						int			)					->str:
      return max(__A     ,				min(__A     ,				__A			)			)





def      A__					(			__A  :						[int]     ,				__A  :						[int]     ,				__A  :						[int]			)					->Union[str, 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      A__					(			__A  :						[int]     ,				__A  :						int     ,				__A  :						[int]			)					->Tuple:
      __A    =list(__A			)
      rect[0] -= overlap
      rect[1] -= overlap
      rect[2] += overlap
      rect[3] += overlap
      __A    =clamp_rect(__A     ,				[0, 0]     ,				[image_size[0], image_size[1]]			)
      return rect





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





def      A__					(			__A  :						int     ,				__A  :						int			)					->List[str]:
      __A    =n % d
      return n - divisor





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







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







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             torch.manual_seed(0     )
             __A    =(
                 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     ),
             )
             __A    =add_overlap_rect(lowercase__ ,   lowercase__ ,   image.size     )
             __A    =image.crop(lowercase__     )
             __A    =((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
             __A    =translated_slice_x - (original_image_slice / 2)
             __A    =max(0 ,   lowercase__     )
             __A    =squeeze_tile(lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__     )
             __A    =to_input.size
             __A    =to_input.resize((tile_size, tile_size) ,   Image.BICUBIC     )
             __A    =super(lowercase__ ,   self     ).__call__(image=lowercase__ ,   **lowercase__     ).images[0]
             __A    =upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,   Image.BICUBIC     )
             __A    =unsqueeze_tile(lowercase__ ,   lowercase__     )
             __A    =upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) ,   Image.BICUBIC     )
             __A    =[]
             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'''     )
             __A    =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 ,   lowercase__ ,   lowercase__ ,   lowercase__ = 7_5 ,   lowercase__ = 9.0 ,   lowercase__ = 5_0 ,   lowercase__ = None ,   lowercase__ = 1 ,   lowercase__ = 0.0 ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = 1 ,   lowercase__ = 1_2_8 ,   lowercase__ = 3_2 ,   lowercase__ = 3_2 ,   ):







             '''simple docstring'''



             __A    =Image.new('''RGB''' ,   (image.size[0] * 4, image.size[1] * 4)     )
             __A    =math.ceil(image.size[0] / tile_size     )
             __A    =math.ceil(image.size[1] / tile_size     )
             __A    =tcx * tcy
             __A    =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__					(			)					->int:
      # Run a demo
      __A    ='''stabilityai/stable-diffusion-x4-upscaler'''
      __A    =StableDiffusionTiledUpscalePipeline.from_pretrained(__A     ,				revision='''fp16'''     ,				torch_dtype=torch.floataa			)
      __A    =pipe.to('''cuda'''			)
      __A    =Image.open('''../../docs/source/imgs/diffusers_library.jpg'''			)

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

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


if __name__ == "__main__":
   main()





def      A__					(			__A  :						str     ,				__A  :						str			)					->str:

      if not (isinstance(__A     ,				__A			) and isinstance(__A     ,				__A			)):
            raise ValueError('''longest_common_substring() takes two strings for inputs'''			)

      __A    =len(__A			)
      __A    =len(__A			)

      __A    =[[0] * (texta_length + 1) for _ in range(texta_length + 1			)]
      __A    =0
      __A    =0

      for i in range(1     ,				texta_length + 1			):
            for j in range(1     ,				texta_length + 1			):
                  if texta[i - 1] == texta[j - 1]:
                        __A    =1 + dp[i - 1][j - 1]
                        if dp[i][j] > ans_length:
                              __A    =i
                              __A    =dp[i][j]

      return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


