

import requests
from bsa import BeautifulSoup

def 						UpperCamelCase (lowercase_:			str = "https://www.worldometers.info/coronavirus" )					->				dict:
	A__							:   Any					       =		BeautifulSoup(requests.get(lowercase_ ).text   ,				"""html.parser""" )
	A__							:   Any					       =		soup.findAll("""h1""" )
	A__							:   Dict					       =		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(lowercase_   ,				lowercase_ )}


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''')






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_	:			Optional[int]  	=      {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['FNetTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Tuple  	=      ['FNetTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			List[Any]  	=      [
	    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FNetForMaskedLM',
	    'FNetForMultipleChoice',
	    'FNetForNextSentencePrediction',
	    'FNetForPreTraining',
	    'FNetForQuestionAnswering',
	    'FNetForSequenceClassification',
	    'FNetForTokenClassification',
	    'FNetLayer',
	    'FNetModel',
	    'FNetPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet import FNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet_fast import FNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fnet import (
		    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FNetForMaskedLM,
		    FNetForMultipleChoice,
		    FNetForNextSentencePrediction,
		    FNetForPreTraining,
		    FNetForQuestionAnswering,
		    FNetForSequenceClassification,
		    FNetForTokenClassification,
		    FNetLayer,
		    FNetModel,
		    FNetPreTrainedModel,
		)


else:
	import sys

	A_	:			Optional[Any]  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



