






import math
import random







def 		_UpperCamelCase				(      UpperCamelCase_    :   float      ,    UpperCamelCase_    :   bool = False						)					->				float:

						"""simple docstring"""



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


# Initial Value
__snake_case      :      Any             =       0.02







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   int						)					->				float:

						"""simple docstring"""



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

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

						return layer_a * 100


if __name__ == "__main__":
					import doctest

					doctest.testmod()

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













from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





