


from typing import TYPE_CHECKING

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


snake_case__				:	Tuple					       =					{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case__				:	List[str]					       =					['AlbertTokenizer']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case__				:	Any					       =					['AlbertTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case__				:	Tuple					       =					[
          'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'AlbertForMaskedLM',
          'AlbertForMultipleChoice',
          'AlbertForPreTraining',
          'AlbertForQuestionAnswering',
          'AlbertForSequenceClassification',
          'AlbertForTokenClassification',
          'AlbertModel',
          'AlbertPreTrainedModel',
          'load_tf_weights_in_albert',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case__				:	Union[str, Any]					       =					[
          'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFAlbertForMaskedLM',
          'TFAlbertForMultipleChoice',
          'TFAlbertForPreTraining',
          'TFAlbertForQuestionAnswering',
          'TFAlbertForSequenceClassification',
          'TFAlbertForTokenClassification',
          'TFAlbertMainLayer',
          'TFAlbertModel',
          'TFAlbertPreTrainedModel',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case__				:	Dict					       =					[
          'FlaxAlbertForMaskedLM',
          'FlaxAlbertForMultipleChoice',
          'FlaxAlbertForPreTraining',
          'FlaxAlbertForQuestionAnswering',
          'FlaxAlbertForSequenceClassification',
          'FlaxAlbertForTokenClassification',
          'FlaxAlbertModel',
          'FlaxAlbertPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_albert import AlbertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_albert_fast import AlbertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_albert import (
                ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                AlbertForMaskedLM,
                AlbertForMultipleChoice,
                AlbertForPreTraining,
                AlbertForQuestionAnswering,
                AlbertForSequenceClassification,
                AlbertForTokenClassification,
                AlbertModel,
                AlbertPreTrainedModel,
                load_tf_weights_in_albert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_albert import (
                TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFAlbertForMaskedLM,
                TFAlbertForMultipleChoice,
                TFAlbertForPreTraining,
                TFAlbertForQuestionAnswering,
                TFAlbertForSequenceClassification,
                TFAlbertForTokenClassification,
                TFAlbertMainLayer,
                TFAlbertModel,
                TFAlbertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_albert import (
                FlaxAlbertForMaskedLM,
                FlaxAlbertForMultipleChoice,
                FlaxAlbertForPreTraining,
                FlaxAlbertForQuestionAnswering,
                FlaxAlbertForSequenceClassification,
                FlaxAlbertForTokenClassification,
                FlaxAlbertModel,
                FlaxAlbertPreTrainedModel,
            )
else:
      import sys

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









import requests
from bsa import BeautifulSoup


def    __lowerCamelCase   (      A__						: str = "https://www.worldometers.info/coronavirus"					)     ->    dict:
   lowerCamelCase_						:   List[str]		= BeautifulSoup(requests.get(A__					).text						,						"""html.parser"""					)
   lowerCamelCase_						:   Tuple		= soup.findAll("""h1"""					)
   lowerCamelCase_						:   Tuple		= 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(A__						,						A__					)}


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')





