


"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       Any									=    ['''image_processor''', '''tokenizer''']
   _a   :       Optional[int]									=    '''ViTImageProcessor'''
   _a   :       Optional[int]									=    ('''CLIPTokenizer''', '''CLIPTokenizerFast''')


   def __init__(						self ,     lowerCamelCase__=None ,     lowerCamelCase__=None ,     **lowerCamelCase__						)      ->							Dict:
    lowercase__       :       Union[str, Any]				=						None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead.""" ,     lowerCamelCase__ ,     )
     lowercase__       :       Dict				=						kwargs.pop("""feature_extractor"""						)

    lowercase__       :       Optional[Any]				=						image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`."""						)
    if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`."""						)

    super().__init__(lowerCamelCase__ ,     lowerCamelCase__						)


   def __call__(						self ,     lowerCamelCase__=None ,     lowerCamelCase__=None ,     lowerCamelCase__=None ,     lowerCamelCase__=None ,     **lowerCamelCase__						)      ->							Optional[int]:
    if text is None and visual_prompt is None and images is None:
     raise ValueError("""You have to specify either text, visual prompt or images."""						)

    if text is not None and visual_prompt is not None:
     raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""						)

    if text is not None:
     lowercase__       :       str				=						self.tokenizer(lowerCamelCase__ ,     return_tensors=lowerCamelCase__ ,     **lowerCamelCase__						)

    if visual_prompt is not None:
     lowercase__       :       List[Any]				=						self.image_processor(lowerCamelCase__ ,     return_tensors=lowerCamelCase__ ,     **lowerCamelCase__						)

    if images is not None:
     lowercase__       :       Union[str, Any]				=						self.image_processor(lowerCamelCase__ ,     return_tensors=lowerCamelCase__ ,     **lowerCamelCase__						)

    if visual_prompt is not None and images is not None:
     lowercase__       :       Any				=						{
         """pixel_values""": image_features.pixel_values,
         """conditional_pixel_values""": prompt_features.pixel_values,
     }
     return encoding
    elif text is not None and images is not None:
     lowercase__       :       int				=						image_features.pixel_values
     return encoding
    elif text is not None:
     return encoding
    elif visual_prompt is not None:
     lowercase__       :       Any				=						{
         """conditional_pixel_values""": prompt_features.pixel_values,
     }
     return encoding
    else:
     return BatchEncoding(data=dict(**lowerCamelCase__						) ,     tensor_type=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     *lowerCamelCase__ ,     **lowerCamelCase__						)      ->							Dict:
    return self.tokenizer.batch_decode(*lowerCamelCase__ ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     *lowerCamelCase__ ,     **lowerCamelCase__						)      ->							List[Any]:
    return self.tokenizer.decode(*lowerCamelCase__ ,     **lowerCamelCase__						)


   @property
   def 					UpperCAmelCase__(						self						)      ->							str:
    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,     lowerCamelCase__ ,     )
    return self.image_processor_class







   @property
   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,     lowerCamelCase__ ,     )
    return self.image_processor



"""simple docstring"""


import requests
from bsa import BeautifulSoup


def    _lowerCamelCase   (     lowerCamelCase__	:							str = "https://www.worldometers.info/coronavirus"  ):
 lowercase__       :       Optional[Any]				=						BeautifulSoup(requests.get(lowerCamelCase__  ).text       ,		"""html.parser"""  )
 lowercase__       :       Union[str, Any]				=						soup.findAll("""h1"""  )
 lowercase__       :       List[str]				=						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")