



from string import ascii_lowercase, ascii_uppercase
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[int]				)->str:


      if not sentence:
            return ""
      _lowerCAmelCase 			=  dict(zip(lowerCamelCase__     ,				lowerCamelCase__				)				)
      return lower_to_upper.get(sentence[0]     ,				sentence[0]				) + sentence[1:]


if __name__ == "__main__":
       from doctest import testmod

       testmod()




import numpy





class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  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.
         _lowerCAmelCase 			=  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.
         _lowerCAmelCase 			=  numpy.random.rand(
             4       ,     3			)

         # Random initial values for the second hidden layer.
         # Second hidden layer has 3 nodes.
         # Output layer has 1 node.
         _lowerCAmelCase 			=  numpy.random.rand(3       ,     1			)

         # Real output values provided.
         _lowerCAmelCase 			=  output_array

         # Predicted output values by the neural network.
         # Predicted_output array initially consists of zeroes.
         _lowerCAmelCase 			=  numpy.zeros(output_array.shape			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  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.
         _lowerCAmelCase 			=  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.
         _lowerCAmelCase 			=  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 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  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			)       ,     )
         _lowerCAmelCase 			=  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			)       ,     )
         _lowerCAmelCase 			=  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 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         for iteration in range(1       ,     iterations + 1			):
               _lowerCAmelCase 			=  self.feedforward()
               self.back_propagation()
               if give_loss:
                     _lowerCAmelCase 			=  numpy.mean(numpy.square(output - self.feedforward()			)			)
                     print(F'''Iteration {iteration} Loss: {loss}'''			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  input_arr

         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(self.array       ,     self.input_layer_and_first_hidden_layer_weights			)			)

         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer       ,     self.first_hidden_layer_and_second_hidden_layer_weights       ,     )			)

         _lowerCAmelCase 			=  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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	numpy.ndarray				)->numpy.ndarray:
      return 1 / (1 + numpy.exp(-value				))
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	numpy.ndarray				)->numpy.ndarray:
      return (value) * (1 - (value))
def 							UpperCAmelCase__		(				)->int:
      _lowerCAmelCase 			=  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.
      _lowerCAmelCase 			=  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,				dtype=numpy.floataa				)

      # Calling neural network class.
      _lowerCAmelCase 			=  TwoHiddenLayerNeuralNetwork(
          input_array=_SCREAMING_SNAKE_CASE     ,				output_array=_SCREAMING_SNAKE_CASE				)

      # Calling training function.
      # Set give_loss to True if you want to see loss in every iteration.
      neural_network.train(output=_SCREAMING_SNAKE_CASE     ,				iterations=1_0     ,				give_loss=_SCREAMING_SNAKE_CASE				)

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


if __name__ == "__main__":
    example()