code: 
  
  
  
  
  
  
  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 							UpperCAmelCase_     (		__lowerCamelCase       ,			__lowerCamelCase		):
  
  
  
  
       def __init__(							self		):
            super().__init__()
  
            self.register_buffer("""watermark_image"""  ,	torch.zeros((62, 62, 4)		)		)
            UpperCAmelCase__			:     List[Any]										= None
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=None		):
            # copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287
  
            UpperCAmelCase__			:     Union[str, Any]										= images[0].height
            UpperCAmelCase__			:     Optional[Any]										= images[0].width
  
            UpperCAmelCase__			:     int										= sample_size or h
  
            UpperCAmelCase__			:     str										= min(h / sample_size  ,	w / sample_size		)
            UpperCAmelCase__			,		UpperCAmelCase__			:     Union[str, Any]										= (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)
  
            UpperCAmelCase__			,		UpperCAmelCase__			:     Union[str, Any]										= 1024**2, img_w * img_h
            UpperCAmelCase__			:     int										= (Sa / Sa) ** 0.5
            UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[Any]										= int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)
  
            if self.watermark_image_as_pil is None:
                 UpperCAmelCase__			:     Optional[Any]										= self.watermark_image.to(torch.uinta		).cpu().numpy()
                 UpperCAmelCase__			:     str										= Image.fromarray(_lowerCAmelCase  ,	mode="""RGBA"""		)
                 UpperCAmelCase__			:     List[Any]										= watermark_image
  
            UpperCAmelCase__			:     Tuple										= self.watermark_image_as_pil.resize(
                (wm_size, wm_size)  ,	PIL_INTERPOLATION["""bicubic"""]  ,	reducing_gap=_lowerCAmelCase		)
  
            for pil_img in images:
                 pil_img.paste(_lowerCAmelCase  ,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)  ,	mask=wm_img.split()[-1]		)
  
            return images
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from .stack import Stack
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> bool:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Stack[str]										= Stack()
       UpperCAmelCase__			:     str										= {"""(""": """)""", """[""": """]""", """{""": """}"""}
       for bracket in parentheses:
            if bracket in bracket_pairs:
                 stack.push(__lowerCamelCase					)
            elif bracket in (")", "]", "}"):
                 if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                      return False
       return stack.is_empty()
  
  
  if __name__ == "__main__":
        from doctest import testmod
  
        testmod()
  
        SCREAMING_SNAKE_CASE__		:       Any   							=							["""((()))""", """((())""", """(()))"""]
        print("""Balanced parentheses demonstration:\n""")
        for example in examples:
              SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""""" if balanced_parentheses(example) else """not """
              print(f'''{example} is {not_str}balanced''')
  
  
  
  
  
  
  