


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__				:							Tuple						=     logging.get_logger(__name__)

a__				:							Optional[int]						=     {
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  Optional[int]    					=			"xlm-roberta-xl"




	def __init__( self			:				Union[str, Any]			,			UpperCAmelCase__			:				int=2_5_0_8_8_0			,			UpperCAmelCase__			:				Optional[Any]=2_5_6_0			,			UpperCAmelCase__			:				Optional[Any]=3_6			,			UpperCAmelCase__			:				str=3_2			,			UpperCAmelCase__			:				Optional[Any]=1_0_2_4_0			,			UpperCAmelCase__			:				List[Any]="gelu"			,			UpperCAmelCase__			:				Union[str, Any]=0.1			,			UpperCAmelCase__			:				List[str]=0.1			,			UpperCAmelCase__			:				Dict=5_1_4			,			UpperCAmelCase__			:				Optional[int]=1			,			UpperCAmelCase__			:				Dict=0.02			,			UpperCAmelCase__			:				Tuple=1E-05			,			UpperCAmelCase__			:				Any=1			,			UpperCAmelCase__			:				str=0			,			UpperCAmelCase__			:				Tuple=2			,			UpperCAmelCase__			:				Tuple="absolute"			,			UpperCAmelCase__			:				Optional[int]=True			,			UpperCAmelCase__			:				Any=None			,			**UpperCAmelCase__			:				Tuple			,			)       ->	Optional[int]:
						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




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	@property
	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
											__SCREAMING_SNAKE_CASE						   =				{0: "batch", 1: "choice", 2: "sequence"}
						else:
											__SCREAMING_SNAKE_CASE						   =				{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]   )




"""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))
