


'''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 os







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    A       :   List[Any]					=						os.path.join(os.path.dirname(snake_case__						)		,				'''num.txt'''						)
    with open(snake_case__						) as file_hand:
        return str(sum(int(snake_case__						) for line in file_hand						)						)[:10]


if __name__ == "__main__":
      print(solution())




