




"""simple docstring"""


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


A_						=				logging.get_logger(__name__)

A_						=				{
    '''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 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"markuplm"


 def __init__(				self:		Optional[Any],      a_:		List[str]=30_522,      a_:		List[str]=768,      a_:		Optional[int]=12,      a_:		List[str]=12,      a_:		Optional[int]=3_072,      a_:		str="gelu",      a_:		str=0.1,      a_:		int=0.1,      a_:		Dict=512,      a_:		List[Any]=2,      a_:		List[Any]=0.02,      a_:		Optional[Any]=1E-12,      a_:		Any=0,      a_:		Union[str, Any]=0,      a_:		int=2,      a_:		Union[str, Any]=256,      a_:		Tuple=1_024,      a_:		str=216,      a_:		str=1_001,      a_:		str=32,      a_:		Optional[Any]=50,      a_:		List[str]="absolute",      a_:		Dict=True,      a_:		int=None,      **a_:		Any,      ):




   '''simple docstring'''



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









import numpy as np
def      _A       (		SCREAMING_SNAKE_CASE     :   np.array						):





  """simple docstring"""



  return 1 / (1 + np.exp(-vector						))


if __name__ == "__main__":
     import doctest

     doctest.testmod()






