



'''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 lowerCAmelCase						(__A						,			__A						,			__A=[]):



     """simple docstring"""



     _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) * 255
     _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 lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     return max(__A						,			min(__A						,			__A))




def lowerCAmelCase						(__A						,			__A						,			__A):



     """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						(__A						,			__A						,			__A):



     """simple docstring"""



     _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 lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     _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 lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



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




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						n % d
     return n - divisor






class __A    (						A		):



 '''simple docstring'''







 def __init__(self				,     A				,     A				,     A				,     A				,     A				,     A				,     A = 350				,     )					->  List[Any]:


      """simple docstring"""



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



 def       a__						(self				,     A				,     A				,     A				,     A				,     A				,     A				,     A				,     **A )					->  Union[str, Any]:


      """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(A				,     A				,     image.size )
      _a											=						image.crop(A )
      _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				,     A )
      _a											=						squeeze_tile(A				,     A				,     A				,     A )
      _a											=						to_input.size
      _a											=						to_input.resize((tile_size, tile_size)				,     Image.BICUBIC )
      _a											=						super(A				,     self ).__call__(image=A				,     **A ).images[0]
      _a											=						upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)				,     Image.BICUBIC )
      _a											=						unsqueeze_tile(A				,     A )
      _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=A )				,     mode='''L'''				,     )
      final_image.paste(
          A				,     (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)				,     A )



 @torch.no_grad()
 def __call__(self				,     A				,     A				,     A = 75				,     A = 9.0				,     A = 50				,     A = None				,     A = 1				,     A = 0.0				,     A = None				,     A = None				,     A = None				,     A = 1				,     A = 128				,     A = 32				,     A = 32				,     )					->  int:


      """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(A ):
           for x in range(A ):
                self._process_tile(
                    A				,     A				,     A				,     A				,     A				,     A				,     A				,     prompt=A				,     num_inference_steps=A				,     guidance_scale=A				,     noise_level=A				,     negative_prompt=A				,     num_images_per_prompt=A				,     eta=A				,     generator=A				,     latents=A				,     )
                current_count += 1
                if callback is not None:
                     callback({'''progress''': current_count / total_tile_count, '''image''': final_image} )
      return final_image





def lowerCAmelCase						():



     """simple docstring"""



     _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):
          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()








'''simple docstring'''





lowercase_	 =						256
# Modulus to hash a string
lowercase_	 =						1_000_003




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						len(__A)
     _a											=						len(__A)
     if p_len > t_len:
          return False

     _a											=						0
     _a											=						0
     _a											=						1

     # Calculating the hash of pattern and substring of text
     for i in range(__A):
          _a											=						(ord(pattern[i]) + p_hash * alphabet_size) % modulus
          _a											=						(ord(text[i]) + text_hash * alphabet_size) % modulus
          if i == p_len - 1:
               continue
          _a											=						(modulus_power * alphabet_size) % modulus

     for i in range(0						,			t_len - p_len + 1):
          if text_hash == p_hash and text[i : i + p_len] == pattern:
               return True
          if i == t_len - p_len:
               continue
          # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
          _a											=						(
              (text_hash - ord(text[i]) * modulus_power) * alphabet_size
              + ord(text[i + p_len])
          ) % modulus
     return False




def lowerCAmelCase						():



     """simple docstring"""



     _a											=						'''abc1abc12'''
     _a											=						'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
     _a											=						'''alskfjaldsk23adsfabcabc'''
     assert rabin_karp(__A						,			__A) and not rabin_karp(__A						,			__A)

     # Test 2)
     _a											=						'''ABABX'''
     _a											=						'''ABABZABABYABABX'''
     assert rabin_karp(__A						,			__A)

     # Test 3)
     _a											=						'''AAAB'''
     _a											=						'''ABAAAAAB'''
     assert rabin_karp(__A						,			__A)

     # Test 4)
     _a											=						'''abcdabcy'''
     _a											=						'''abcxabcdabxabcdabcdabcy'''
     assert rabin_karp(__A						,			__A)

     # Test 5)
     _a											=						'''Lü'''
     _a											=						'''Lüsai'''
     assert rabin_karp(__A						,			__A)
     _a											=						'''Lue'''
     assert not rabin_karp(__A						,			__A)
     print('''Success.''')


if __name__ == "__main__":
  test_rabin_karp()



