





import numpy






class        _a:







		def __init__(    self ,						__snake_case ,						__snake_case				)      ->      None:


							'''simple docstring'''

							_snake_case       :					int				       =   input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							_snake_case       :					Optional[Any]				       =   numpy.random.rand(
							    self.input_array.shape[1] ,						4				)

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							_snake_case       :					Union[str, Any]				       =   numpy.random.rand(
							    4 ,						3				)

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							_snake_case       :					Optional[Any]				       =   numpy.random.rand(3 ,						1				)

							# Real output values provided.
							_snake_case       :					Dict				       =   output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							_snake_case       :					Optional[int]				       =   numpy.zeros(output_array.shape				)







		def        lowercase       (    self				)      ->      numpy.ndarray:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   sigmoid(
							    numpy.dot(self.input_array ,						self.input_layer_and_first_hidden_layer_weights				)				)

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							_snake_case       :					List[str]				       =   sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer ,						self.first_hidden_layer_and_second_hidden_layer_weights ,						)				)

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							_snake_case       :					Dict				       =   sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer ,						self.second_hidden_layer_and_output_layer_weights ,						)				)

							return self.layer_between_second_hidden_layer_and_output







		def        lowercase       (    self				)      ->      None:


							'''simple docstring'''

							_snake_case       :					str				       =   numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T ,						2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output				) ,						)
							_snake_case       :					str				       =   numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T ,						numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output				) ,						self.second_hidden_layer_and_output_layer_weights.T ,						)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer				) ,						)
							_snake_case       :					Optional[Any]				       =   numpy.dot(
							    self.input_array.T ,						numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output				) ,						self.second_hidden_layer_and_output_layer_weights.T ,						)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer				) ,						self.first_hidden_layer_and_second_hidden_layer_weights.T ,						)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer				) ,						)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      None:


							'''simple docstring'''

							for iteration in range(1 ,						iterations + 1				):
												_snake_case       :					Optional[int]				       =   self.feedforward()
												self.back_propagation()
												if give_loss:
																	_snake_case       :					int				       =   numpy.mean(numpy.square(output - self.feedforward()				)				)
																	print(f"""Iteration {iteration} Loss: {loss}"""				)







		def        lowercase       (    self ,						__snake_case				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   input_arr

							_snake_case       :					Dict				       =   sigmoid(
							    numpy.dot(self.array ,						self.input_layer_and_first_hidden_layer_weights				)				)

							_snake_case       :					Tuple				       =   sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer ,						self.first_hidden_layer_and_second_hidden_layer_weights ,						)				)

							_snake_case       :					Optional[int]				       =   sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer ,						self.second_hidden_layer_and_output_layer_weights ,						)				)

							return int(self.layer_between_second_hidden_layer_and_output > 0.6				)







def    A				(							UpperCAmelCase				):
					return 1 / (1 + numpy.exp(-value				))







def    A				(							UpperCAmelCase				):
					return (value) * (1 - (value))







def    A				(							):
					_snake_case       :					Optional[Any]				       =   numpy.array(
					    (
					        [0, 0, 0],
					        [0, 0, 1],
					        [0, 1, 0],
					        [0, 1, 1],
					        [1, 0, 0],
					        [1, 0, 1],
					        [1, 1, 0],
					        [1, 1, 1],
					    )  ,  dtype=numpy.floataa  ,  )

					# True output values for the given input values.
					_snake_case       :					str				       =   numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])  ,  dtype=numpy.floataa				)

					# Calling neural network class.
					_snake_case       :					int				       =   TwoHiddenLayerNeuralNetwork(
					    input_array=UpperCAmelCase  ,  output_array=UpperCAmelCase				)

					# Calling training function.
					# Set give_loss to True if you want to see loss in every iteration.
					neural_network.train(output=UpperCAmelCase  ,  iterations=10  ,  give_loss=UpperCAmelCase				)

					return neural_network.predict(numpy.array(([1, 1, 1])  ,  dtype=numpy.floataa				)				)


if __name__ == "__main__":
							example()






def    A				(							UpperCAmelCase				):
					if n == 1 or not isinstance(UpperCAmelCase  ,  UpperCAmelCase				):
										return 0
					elif n == 2:
										return 1
					else:
										_snake_case       :					List[Any]				       =   [0, 1]
										for i in range(2  ,  n + 1				):
															sequence.append(sequence[i - 1] + sequence[i - 2]				)

										return sequence[n]







def    A				(							UpperCAmelCase				):
					_snake_case       :					Tuple				       =   0
					_snake_case       :					Optional[Any]				       =   2

					while digits < n:
										index += 1
										_snake_case       :					str				       =   len(str(fibonacci(UpperCAmelCase				)				)				)

					return index







def    A				(							UpperCAmelCase = 1_000				):
					return fibonacci_digits_index(UpperCAmelCase				)


if __name__ == "__main__":
							print(solution(int(str(input()).strip())))