




import math

import tensorflow as tf
from packaging import version


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     tf.convert_to_tensor(_A							)
	snake_case_		    =     0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0							)     ,	x.dtype							)							))

	return x * cdf


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     tf.convert_to_tensor(_A							)
	snake_case_		    =     tf.cast(math.pi     ,	x.dtype							)
	snake_case_		    =     tf.cast(0.04_47_15     ,	x.dtype							)
	snake_case_		    =     0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi							) * (x + coeff * tf.pow(_A     ,	3							))							))

	return x * cdf


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     tf.convert_to_tensor(_A							)

	return x * tf.tanh(tf.math.softplus(_A							)							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     tf.convert_to_tensor(_A							)
	snake_case_		    =     tf.cast(0.04_47_15     ,	x.dtype							)
	snake_case_		    =     tf.cast(0.79_78_84_56_08     ,	x.dtype							)

	return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)							))


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     tf.convert_to_tensor(_A							)
	snake_case_		    =     tf.cast(1.7_02     ,	x.dtype							)
	return x * tf.math.sigmoid(coeff * x							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return tf.clip_by_value(_gelu(_A							)     ,	-10     ,	10							)


def 						lowerCamelCase__    (		_A     ,	_A=-1							):







	'''simple docstring'''





	snake_case_    ,      snake_case_		    =     tf.split(_A     ,	2     ,	axis=_A							)
	return a * tf.math.sigmoid(_A							)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):


		def 						lowerCamelCase__    (		_A							):







			'''simple docstring'''





			return tf.keras.activations.gelu(_A     ,	approximate=_A							)

		lowercase__				:     List[Any] 		=						tf.keras.activations.gelu






		lowercase__				:     int 		=						approximate_gelu_wrap
else:
		lowercase__				:     List[str] 		=						_gelu
		lowercase__				:     Union[str, Any] 		=						_gelu_new


lowercase__				:     Any 		=						{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if activation_string in ACTaFN:
		return ACTaFN[activation_string]
	else:
		raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()							)}"							)






import numpy
class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							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.
							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.
							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.
							snake_case_		    =     numpy.random.rand(3       ,	1						)

							# Real output values provided.
							snake_case_		    =     output_array

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






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							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.
							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.
							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        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							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						)       ,	)
							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						)       ,	)
							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        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     int       ,	__lowercase							:     bool						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     input_arr

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

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

							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 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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


def 						lowerCamelCase__    (		):







	'''simple docstring'''





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

	# Calling neural network class.
	snake_case_		    =     TwoHiddenLayerNeuralNetwork(
	    input_array=_A     ,	output_array=_A							)

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

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


if __name__ == "__main__":
		example()
