


"""simple docstring"""







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				HfArgumentParser(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				parser.parse_args_into_dataclasses()[0]
					__SCREAMING_SNAKE_CASE						   =				TensorFlowBenchmark(args=lowerCAmelCase_   )
					try:
										__SCREAMING_SNAKE_CASE						   =				parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										__SCREAMING_SNAKE_CASE						   =				"Arg --no_{0} is no longer used, please use --no-{0} instead."
										__SCREAMING_SNAKE_CASE						   =				" ".join(str(lowerCAmelCase_   ).split(" "   )[:-1]   )
										__SCREAMING_SNAKE_CASE						   =				""
										__SCREAMING_SNAKE_CASE						   =				eval(str(lowerCAmelCase_   ).split(" "   )[-1]   )
										__SCREAMING_SNAKE_CASE						   =				[]
										for arg in depreciated_args:
															# arg[2:] removes '--'
															if arg[2:] in TensorFlowBenchmark.deprecated_args:
																				# arg[5:] removes '--no_'
																				full_error_msg += arg_error_msg.format(arg[5:]   )
															else:
																				wrong_args.append(lowerCAmelCase_   )
										if len(lowerCAmelCase_   ) > 0:
															__SCREAMING_SNAKE_CASE						   =				full_error_msg + begin_error_msg + str(lowerCAmelCase_   )
										raise ValueError(lowerCAmelCase_   )
					benchmark.run()


if __name__ == "__main__":
		main()




"""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))
