


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      lowerCamelCase	(  SCREAMING_SNAKE_CASE,			SCREAMING_SNAKE_CASE     ):

       def __init__(			self					):






        '''simple docstring'''






        super().__init__()

        self.register_buffer('''watermark_image'''	,						torch.zeros((62, 62, 4)					)					)
        snake_case   : Tuple	  =      None



       def 				UpperCAmelCase				(			self	,						a__	,						a__=None					):






        '''simple docstring'''






        snake_case   : Dict	  =      images[0].height
        snake_case   : Any	  =      images[0].width

        snake_case   : List[Any]	  =      sample_size or h

        snake_case   : Union[str, Any]	  =      min(h / sample_size	,						w / sample_size					)
        snake_case   : int	  =      (int(h / coef					), int(w / coef					)) if coef < 1 else (h, w)

        snake_case   : List[Any]	  =      10_24**2, img_w * img_h
        snake_case   : Dict	  =      (Sa / Sa) ** 0.5
        snake_case   : Optional[int]	  =      int(K * 62					), img_w - int(14 * K					), img_h - int(14 * K					)

        if self.watermark_image_as_pil is None:
         snake_case   : Any	  =      self.watermark_image.to(torch.uinta					).cpu().numpy()
         snake_case   : int	  =      Image.fromarray(a__	,						mode='''RGBA'''					)
         snake_case   : Tuple	  =      watermark_image

        snake_case   : str	  =      self.watermark_image_as_pil.resize(
            (wm_size, wm_size)	,						PIL_INTERPOLATION['''bicubic''']	,						reducing_gap=a__					)

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

        return images










from typing import Callable






def    _UpperCAmelCase	(   __lowerCamelCase       )		->Tuple:


 '''simple docstring'''

 def wrapper(__lowerCamelCase       ):
  snake_case   : str	  =      function_with_docstring.__doc__
  return target_function

 return wrapper






