

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

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   List[str]       				=  ["image_processor", "tokenizer"]
	_lowercase  :   Dict       				=  "AutoImageProcessor"
	_lowercase  :   List[Any]       				=  "AutoTokenizer"







	def __init__(   self: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





						super().__init__(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    self.image_processor







	def __call__(   self: Optional[Any]  , __UpperCamelCase: Optional[Any]=None  , __UpperCamelCase: int=None  , __UpperCamelCase: Optional[int]=None  , **__UpperCamelCase: Tuple      ):

						'''simple docstring'''





						if text is None and images is None:
											raise ValueError('You have to specify either text or images. Both cannot be none.'      )

						if text is not None:
											__magic_name__							=    self.tokenizer(__UpperCamelCase  , return_tensors=__UpperCamelCase  , **__UpperCamelCase      )

						if images is not None:
											__magic_name__							=    self.image_processor(__UpperCamelCase  , return_tensors=__UpperCamelCase  , **__UpperCamelCase      )

						if text is not None and images is not None:
											__magic_name__							=    image_features.pixel_values
											return encoding
						elif text is not None:
											return encoding
						else:
											return BatchEncoding(data=dict(**__UpperCamelCase      )  , tensor_type=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , *__UpperCamelCase: Tuple  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						return self.tokenizer.batch_decode(*__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , *__UpperCamelCase: Optional[Any]  , **__UpperCamelCase: str      ):

						'''simple docstring'''





						return self.tokenizer.decode(*__UpperCamelCase  , **__UpperCamelCase      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						return ["input_ids", "attention_mask", "pixel_values"]







import requests
from bsa import BeautifulSoup




def 					_lowercase     (				a_				:					str = "https://www.worldometers.info/coronavirus"				)				->	dict:




					'''simple docstring'''







					__magic_name__							=    BeautifulSoup(requests.get(a_				).text					,'html.parser'				)
					__magic_name__							=    soup.findAll('h1'				)
					__magic_name__							=    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''')




