






"""simple docstring"""






import numpy





class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

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

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

					# Real output values provided.
					A__							 =       output_array

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       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.
					A__							 =       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.
					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       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						)  ,					)
					A__							 =       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						)  ,					)
					A__							 =       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       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a						):


					"""simple docstring"""

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






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       input_arr

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

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

					A__							 =       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  __lowerCamelCase					(					lowerCAmelCase__				):
				return 1 / (1 + numpy.exp(-value				))



def  __lowerCamelCase					(					lowerCAmelCase__				):
				return (value) * (1 - (value))



def  __lowerCamelCase					(					):
				A__							 =       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.
				A__							 =       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])       ,dtype=numpy.floataa				)

				# Calling neural network class.
				A__							 =       TwoHiddenLayerNeuralNetwork(
				    input_array=lowerCAmelCase__       ,output_array=lowerCAmelCase__				)

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

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


if __name__ == "__main__":
					example()










"""simple docstring"""






from math import ceil



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(0       ,lowerCAmelCase__				)				)

				A__							 =       [item for sublist in list(device_map.values()				) for item in sublist]

				# Duplicate check
				A__							 =       []
				for i in device_map_blocks:
								if device_map_blocks.count(lowerCAmelCase__				) > 1 and i not in duplicate_blocks:
												duplicate_blocks.append(lowerCAmelCase__				)
    # Missing blocks
				A__							 =       [i for i in blocks if i not in device_map_blocks]
				A__							 =       [i for i in device_map_blocks if i not in blocks]

				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
								    ' These attention blocks were specified more than once: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'There are attention blocks for this model that are not specified in the device_map. Add these attention '
								    'blocks to a device on the device_map: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
								    + str(lowerCAmelCase__				)				)







def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(lowerCAmelCase__				)				)
				A__							 =       int(ceil(n_layers / len(lowerCAmelCase__				)				)				)
				A__							 =       [layers[i : i + n_blocks] for i in range(0       ,lowerCAmelCase__       ,lowerCAmelCase__				)]

				return dict(zip(lowerCAmelCase__       ,lowerCAmelCase__				)				)


