"""simple docstring"""






import requests
from bsa import BeautifulSoup


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str = "AAPL"		) ->    str:
      '''simple docstring'''


      lowercase														=	f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
      lowercase														=	BeautifulSoup(requests.get(lowerCAmelCase__		).text					,		"""html.parser"""		)
      lowercase														=	"""My(6px) Pos(r) smartphone_Mt(6px)"""
      return soup.find("""div"""					,		class_=class_		).find("""span"""		).text


if __name__ == "__main__":
       for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
              print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")




"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowerCAmelCase    :       Union[str, Any]					     =0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__lowerCAmelCase    :       Optional[int]					     =[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	WATERMARK_BITS
            lowercase														=	WatermarkEncoder()

            self.encoder.set_watermark("""bits"""		,       self.watermark						)







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if images.shape[-1] < 256:
                  return images

            lowercase														=	(255 * (images / 2 + 0.5)).cpu().permute(0		,       2		,       3		,       1						).float().numpy()

            lowercase														=	[self.encoder.encode(__lowerCAmelCase		,       """dwtDct"""						) for image in images]

            lowercase														=	torch.from_numpy(np.array(__lowerCAmelCase						)						).permute(0		,       3		,       1		,       2						)

            lowercase														=	torch.clamp(2 * (images / 255 - 0.5)		,       min=-1.0		,       max=1.0						)
            return images



