


"""simple docstring"""







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


a__				:							str						=     logging.get_logger(__name__)

a__				:							Dict						=     {
    '''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_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  List[str]    					=			"markuplm"




	def __init__( self			:				int			,			UpperCAmelCase__			:				Tuple=3_0_5_2_2			,			UpperCAmelCase__			:				List[Any]=7_6_8			,			UpperCAmelCase__			:				Optional[Any]=1_2			,			UpperCAmelCase__			:				int=1_2			,			UpperCAmelCase__			:				List[str]=3_0_7_2			,			UpperCAmelCase__			:				Dict="gelu"			,			UpperCAmelCase__			:				Union[str, Any]=0.1			,			UpperCAmelCase__			:				int=0.1			,			UpperCAmelCase__			:				List[str]=5_1_2			,			UpperCAmelCase__			:				Any=2			,			UpperCAmelCase__			:				List[str]=0.02			,			UpperCAmelCase__			:				Optional[Any]=1E-12			,			UpperCAmelCase__			:				str=0			,			UpperCAmelCase__			:				Optional[int]=0			,			UpperCAmelCase__			:				int=2			,			UpperCAmelCase__			:				int=2_5_6			,			UpperCAmelCase__			:				str=1_0_2_4			,			UpperCAmelCase__			:				str=2_1_6			,			UpperCAmelCase__			:				List[Any]=1_0_0_1			,			UpperCAmelCase__			:				List[Any]=3_2			,			UpperCAmelCase__			:				Optional[Any]=5_0			,			UpperCAmelCase__			:				Union[str, Any]="absolute"			,			UpperCAmelCase__			:				Dict=True			,			UpperCAmelCase__			:				int=None			,			**UpperCAmelCase__			:				List[str]			,			)       ->	Union[str, Any]:
						super().__init__(
						    pad_token_id=UpperCAmelCase__			,			bos_token_id=UpperCAmelCase__			,			eos_token_id=UpperCAmelCase__			,			**UpperCAmelCase__			,			)
						__SCREAMING_SNAKE_CASE						   =				vocab_size
						__SCREAMING_SNAKE_CASE						   =				hidden_size
						__SCREAMING_SNAKE_CASE						   =				num_hidden_layers
						__SCREAMING_SNAKE_CASE						   =				num_attention_heads
						__SCREAMING_SNAKE_CASE						   =				hidden_act
						__SCREAMING_SNAKE_CASE						   =				intermediate_size
						__SCREAMING_SNAKE_CASE						   =				hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						   =				attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE						   =				max_position_embeddings
						__SCREAMING_SNAKE_CASE						   =				type_vocab_size
						__SCREAMING_SNAKE_CASE						   =				initializer_range
						__SCREAMING_SNAKE_CASE						   =				layer_norm_eps
						__SCREAMING_SNAKE_CASE						   =				position_embedding_type
						__SCREAMING_SNAKE_CASE						   =				use_cache
						__SCREAMING_SNAKE_CASE						   =				classifier_dropout
						# additional properties
						__SCREAMING_SNAKE_CASE						   =				max_depth
						__SCREAMING_SNAKE_CASE						   =				max_xpath_tag_unit_embeddings
						__SCREAMING_SNAKE_CASE						   =				max_xpath_subs_unit_embeddings
						__SCREAMING_SNAKE_CASE						   =				tag_pad_id
						__SCREAMING_SNAKE_CASE						   =				subs_pad_id
						__SCREAMING_SNAKE_CASE						   =				xpath_unit_hidden_size




"""simple docstring"""







import math
import random




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = False   ):




					'''simple docstring'''


					if deriv:
										return value * (1 - value)
					return 1 / (1 + math.exp(-value   ))


# Initial Value
a__				:							Tuple						=     0.02




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				float(2 * (random.randint(1  ,						100   )) - 1   )

					for _ in range(lowerCAmelCase_   ):
										# Forward propagation
										__SCREAMING_SNAKE_CASE						   =				sigmoid_function(INITIAL_VALUE * weight   )
										# How much did we miss?
										__SCREAMING_SNAKE_CASE						   =				(expected / 100) - layer_a
										# Error delta
										__SCREAMING_SNAKE_CASE						   =				layer_1_error * sigmoid_function(lowerCAmelCase_  ,						lowerCAmelCase_   )
										# Update weight
										weight += INITIAL_VALUE * layer_1_delta

					return layer_a * 100


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a__				:							List[str]						=     int(input('''Expected value: '''))
		a__				:							str						=     int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
