



import math

import tensorflow as tf
from packaging import version



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
   lowercase__   					=			tf.convert_to_tensor(SCREAMING_SNAKE_CASE_					)
   lowercase__   					=			0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0					)  ,					x.dtype					)					))

   return x * cdf



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
   lowercase__   					=			tf.convert_to_tensor(SCREAMING_SNAKE_CASE_					)
   lowercase__   					=			tf.cast(math.pi  ,					x.dtype					)
   lowercase__   					=			tf.cast(0.04_4715  ,					x.dtype					)
   lowercase__   					=			0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi					) * (x + coeff * tf.pow(SCREAMING_SNAKE_CASE_  ,					3					))					))

   return x * cdf



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
   lowercase__   					=			tf.convert_to_tensor(SCREAMING_SNAKE_CASE_					)

   return x * tf.tanh(tf.math.softplus(SCREAMING_SNAKE_CASE_					)					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
   lowercase__   					=			tf.convert_to_tensor(SCREAMING_SNAKE_CASE_					)
   lowercase__   					=			tf.cast(0.04_4715  ,					x.dtype					)
   lowercase__   					=			tf.cast(0.79_7884_5608  ,					x.dtype					)

   return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)					))



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
   lowercase__   					=			tf.convert_to_tensor(SCREAMING_SNAKE_CASE_					)
   lowercase__   					=			tf.cast(1.702  ,					x.dtype					)
   return x * tf.math.sigmoid(coeff * x					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
   return tf.clip_by_value(_gelu(SCREAMING_SNAKE_CASE_					)  ,					-10  ,					10					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=-1					):
   lowercase__					,    lowercase__   					=			tf.split(SCREAMING_SNAKE_CASE_  ,					2  ,					axis=SCREAMING_SNAKE_CASE_					)
   return a * tf.math.sigmoid(SCREAMING_SNAKE_CASE_					)


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):



       def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
          return tf.keras.activations.gelu(SCREAMING_SNAKE_CASE_  ,					approximate=SCREAMING_SNAKE_CASE_					)

       lowercase_						=       tf.keras.activations.gelu




       lowercase_						=       approximate_gelu_wrap
else:
       lowercase_						=       _gelu
       lowercase_						=       _gelu_new


lowercase_						=       {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}




def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
   if activation_string in ACTaFN:
      return ACTaFN[activation_string]
   else:
      raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()					)}'''					)









from collections import namedtuple

import requests
from lxml import html  # type: ignore

lowercase_						=       namedtuple("""covid_data""", """cases deaths recovered""")



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_ = "https://www.worldometers.info/coronavirus/"					):
			lowercase__   					=			"//div[@class = \"maincounter-number\"]/span/text()"
			return covid_data(*html.fromstring(requests.get(SCREAMING_SNAKE_CASE_					).content					).xpath(SCREAMING_SNAKE_CASE_					)					)


lowercase_						=       """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""




print(fmt.format(*covid_stats()))




