





"""simple docstring"""


class    lowercase_ :

       def __init__(	self   ):






        '''simple docstring'''

        snake_case__										= [0, 1]

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        if (difference := index - (len(self.sequence   ) - 2)) >= 1:
         for _ in range(__SCREAMING_SNAKE_CASE   ):
          self.sequence.append(self.sequence[-1] + self.sequence[-2]   )
        return self.sequence[:index]





def 							a__	(     )			->     None:

 '''simple docstring'''






 print(
     "Fibonacci Series Using Dynamic Programming\n"		,   "Enter the index of the Fibonacci number you want to calculate "		,   "in the prompt below. (To exit enter exit or Ctrl-C)\n"		,   sep=""		,   )
 snake_case__										= Fibonacci()

 while True:
  snake_case__										= input(">> "		)
  if prompt in {"exit", "quit"}:
   break

  try:
   snake_case__										= int(_UpperCAmelCase		)
  except ValueError:
   print("Enter a number or 'exit'"		)
   continue

  print(fibonacci.get(_UpperCAmelCase		)		)


if __name__ == "__main__":
   main()








"""simple docstring"""


from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION



class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):

       def __init__(	self   ):






        '''simple docstring'''

        super().__init__()

        self.register_buffer("watermark_image"      ,    torch.zeros((62, 62, 4)   )   )
        snake_case__										= None

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= images[0].height
        snake_case__										= images[0].width

        snake_case__										= sample_size or h

        snake_case__										= min(h / sample_size      ,    w / sample_size   )
        snake_case__							,      snake_case__										= (int(h / coef   ), int(w / coef   )) if coef < 1 else (h, w)

        snake_case__							,      snake_case__										= 1024**2, img_w * img_h
        snake_case__										= (Sa / Sa) ** 0.5
        snake_case__							,      snake_case__							,      snake_case__										= int(K * 62   ), img_w - int(14 * K   ), img_h - int(14 * K   )

        if self.watermark_image_as_pil is None:
         snake_case__										= self.watermark_image.to(torch.uinta   ).cpu().numpy()
         snake_case__										= Image.fromarray(__SCREAMING_SNAKE_CASE      ,    mode="RGBA"   )
         snake_case__										= watermark_image

        snake_case__										= self.watermark_image_as_pil.resize(
            (wm_size, wm_size)      ,    PIL_INTERPOLATION["bicubic"]      ,    reducing_gap=__SCREAMING_SNAKE_CASE   )

        for pil_img in images:
         pil_img.paste(__SCREAMING_SNAKE_CASE      ,    box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)      ,    mask=wm_img.split()[-1]   )

        return images

