


"""simple docstring"""






import requests
from bsa import BeautifulSoup


def __lowercase     (      lowerCamelCase_  :      str = "https://www.worldometers.info/coronavirus"  ):
				SCREAMING_SNAKE_CASE__     							=				BeautifulSoup(requests.get(lowerCamelCase_  ).text						,					"html.parser"  )
				SCREAMING_SNAKE_CASE__     							=				soup.findAll("h1"  )
				SCREAMING_SNAKE_CASE__     							=				soup.findAll("div"						,					{"class": "maincounter-number"}  )
				keys += soup.findAll("span"						,					{"class": "panel-title"}  )
				values += soup.findAll("div"						,					{"class": "number-table-main"}  )
				return {key.text.strip(): value.text.strip() for key, value in zip(lowerCamelCase_						,					lowerCamelCase_  )}


if __name__ == "__main__":
	print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
	for key, value in world_covidaa_stats().items():
		print(f"""{key}\n{value}\n""")




"""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     =  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     =  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				WATERMARK_BITS
								SCREAMING_SNAKE_CASE__     							=				WatermarkEncoder()

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






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# can't encode images that are smaller than 256
								if images.shape[-1] < 256:
												return images

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

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

								SCREAMING_SNAKE_CASE__     							=				torch.from_numpy(np.array(UpperCAmelCase__     )     ).permute(0       ,		3       ,		1       ,		2     )

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