



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase	:		int				=   {
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase	:		Dict				=   ['''ElectraTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase	:		Optional[Any]				=   [
     '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ElectraForCausalLM''',
     '''ElectraForMaskedLM''',
     '''ElectraForMultipleChoice''',
     '''ElectraForPreTraining''',
     '''ElectraForQuestionAnswering''',
     '''ElectraForSequenceClassification''',
     '''ElectraForTokenClassification''',
     '''ElectraModel''',
     '''ElectraPreTrainedModel''',
     '''load_tf_weights_in_electra''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase	:		Tuple				=   [
     '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFElectraForMaskedLM''',
     '''TFElectraForMultipleChoice''',
     '''TFElectraForPreTraining''',
     '''TFElectraForQuestionAnswering''',
     '''TFElectraForSequenceClassification''',
     '''TFElectraForTokenClassification''',
     '''TFElectraModel''',
     '''TFElectraPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase	:		Tuple				=   [
     '''FlaxElectraForCausalLM''',
     '''FlaxElectraForMaskedLM''',
     '''FlaxElectraForMultipleChoice''',
     '''FlaxElectraForPreTraining''',
     '''FlaxElectraForQuestionAnswering''',
     '''FlaxElectraForSequenceClassification''',
     '''FlaxElectraForTokenClassification''',
     '''FlaxElectraModel''',
     '''FlaxElectraPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
 from .tokenization_electra import ElectraTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_electra_fast import ElectraTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_electra import (
      ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      ElectraForCausalLM,
      ElectraForMaskedLM,
      ElectraForMultipleChoice,
      ElectraForPreTraining,
      ElectraForQuestionAnswering,
      ElectraForSequenceClassification,
      ElectraForTokenClassification,
      ElectraModel,
      ElectraPreTrainedModel,
      load_tf_weights_in_electra,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_electra import (
      TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFElectraForMaskedLM,
      TFElectraForMultipleChoice,
      TFElectraForPreTraining,
      TFElectraForQuestionAnswering,
      TFElectraForSequenceClassification,
      TFElectraForTokenClassification,
      TFElectraModel,
      TFElectraPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_electra import (
      FlaxElectraForCausalLM,
      FlaxElectraForMaskedLM,
      FlaxElectraForMultipleChoice,
      FlaxElectraForPreTraining,
      FlaxElectraForQuestionAnswering,
      FlaxElectraForSequenceClassification,
      FlaxElectraForTokenClassification,
      FlaxElectraModel,
      FlaxElectraPreTrainedModel,
  )

else:
 import sys

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




'''simple docstring'''



import requests
from bsa import BeautifulSoup



def   __lowerCAmelCase					(							lowerCamelCase   :						str = "https://www.worldometers.info/coronavirus"					):






	'''simple docstring'''




	__lowerCAmelCase												=  BeautifulSoup(requests.get(lowerCamelCase					).text				,		"html.parser"					)
	__lowerCAmelCase												=  soup.findAll("h1"					)
	__lowerCAmelCase												=  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')