






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














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''')






