import numpy







class __snake_case    :




   def __init__(   self      :    str	,     _lowercase      :    numpy.ndarray	,     _lowercase      :    numpy.ndarray				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				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.
     SCREAMING_SNAKE_CASE__      							=				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.
     SCREAMING_SNAKE_CASE__      							=				numpy.random.rand(
         4	,     3				)

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

     # Real output values provided.
     SCREAMING_SNAKE_CASE__      							=				output_array

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




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				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.
     SCREAMING_SNAKE_CASE__      							=				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.
     SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				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				)	,     )
     SCREAMING_SNAKE_CASE__      							=				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				)	,     )
     SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    List[Any]	,     _lowercase      :    numpy.ndarray	,     _lowercase      :    int	,     _lowercase      :    bool				):



     """simple docstring"""






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




   def      __a						(   self      :    List[Any]	,     _lowercase      :    numpy.ndarray				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				input_arr

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

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

     SCREAMING_SNAKE_CASE__      							=				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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	numpy.ndarray     )							-> numpy.ndarray:







  """simple docstring"""





  return 1 / (1 + numpy.exp(-value     ))



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	numpy.ndarray     )							-> numpy.ndarray:







  """simple docstring"""





  return (value) * (1 - (value))



def      __SCREAMING_SNAKE_CASE  ( )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				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.
  SCREAMING_SNAKE_CASE__      							=				numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])			,					dtype=numpy.floataa     )

  # Calling neural network class.
  SCREAMING_SNAKE_CASE__      							=				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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]     )							-> Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[0] * len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[1] * len(__UpperCamelCase     )

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(__UpperCamelCase     )     ):
    if indegree[i] == 0:
      queue.append(__UpperCamelCase     )

  while queue:
    SCREAMING_SNAKE_CASE__      							=				queue.pop(0     )
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        SCREAMING_SNAKE_CASE__      							=				long_dist[vertex] + 1

      if indegree[x] == 0:
        queue.append(__UpperCamelCase     )

  print(max(__UpperCamelCase     )     )


# Adjacency list of Graph
__lowerCamelCase      :			int							       =		{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}






longest_distance(graph)
