


'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase	:						Optional[Any]       =	logging.get_logger(__name__)

__lowercase	:						Tuple       =	{
    'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
    'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json',
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "markuplm"




	def __init__(   self						,				__a=3_0522						,				__a=768						,				__a=12						,				__a=12						,				__a=3072						,				__a="gelu"						,				__a=0.1						,				__a=0.1						,				__a=512						,				__a=2						,				__a=0.02						,				__a=1E-1_2						,				__a=0						,				__a=0						,				__a=2						,				__a=256						,				__a=1024						,				__a=216						,				__a=1001						,				__a=32						,				__a=50						,				__a="absolute"						,				__a=True						,				__a=None						,				**__a						,				):

						'''simple docstring'''





						super().__init__(
						    pad_token_id=__a						,				bos_token_id=__a						,				eos_token_id=__a						,				**__a						,				)
						__a			:       List[str]					    =  vocab_size
						__a			:       Optional[Any]					    =  hidden_size
						__a			:       Optional[int]					    =  num_hidden_layers
						__a			:       Optional[Any]					    =  num_attention_heads
						__a			:       Optional[int]					    =  hidden_act
						__a			:       Union[str, Any]					    =  intermediate_size
						__a			:       str					    =  hidden_dropout_prob
						__a			:       List[Any]					    =  attention_probs_dropout_prob
						__a			:       Dict					    =  max_position_embeddings
						__a			:       Optional[Any]					    =  type_vocab_size
						__a			:       Union[str, Any]					    =  initializer_range
						__a			:       Optional[Any]					    =  layer_norm_eps
						__a			:       Optional[Any]					    =  position_embedding_type
						__a			:       Optional[int]					    =  use_cache
						__a			:       Optional[Any]					    =  classifier_dropout
						# additional properties
						__a			:       int					    =  max_depth
						__a			:       Union[str, Any]					    =  max_xpath_tag_unit_embeddings
						__a			:       str					    =  max_xpath_subs_unit_embeddings
						__a			:       Optional[Any]					    =  tag_pad_id
						__a			:       Dict					    =  subs_pad_id
						__a			:       List[str]					    =  xpath_unit_hidden_size







'''simple docstring'''



import requests
from bsa import BeautifulSoup






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str = "https://www.worldometers.info/coronavirus"   ):
					__a			:       List[Any]					    =  BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE   ).text						,      'html.parser'   )
					__a			:       Union[str, Any]					    =  soup.findAll('h1'   )
					__a			:       int					    =  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(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )}


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''')



