






import numpy

class 		lowerCAmelCase     :

					'''simple docstring'''

					def __init__(  self				:	Dict		,     __a				:	numpy.ndarray		,     __a				:	numpy.ndarray							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     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.
												__lowercase      :							Dict				=     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.
												__lowercase      :							Dict				=     numpy.random.rand(
												    4		,     3							)

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

												# Real output values provided.
												__lowercase      :							Union[str, Any]				=     output_array

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



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							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.
												__lowercase      :							Any				=     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.
												__lowercase      :							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       lowerCAmelCase				(  self				:	Any							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     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							)		,     )
												__lowercase      :							Dict				=     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							)		,     )
												__lowercase      :							int				=     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				:	Dict		,     __a				:	numpy.ndarray		,     __a				:	int		,     __a				:	bool							)			->  Any:


												"""simple docstring"""

												for iteration in range(1		,     iterations + 1							):
																			__lowercase      :							Union[str, Any]				=     self.feedforward()
																			self.back_propagation()
																			if give_loss:
																										__lowercase      :							Tuple				=     numpy.mean(numpy.square(output - self.feedforward()							)							)
																										print(F"Iteration {iteration} Loss: {loss}"							)



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	numpy.ndarray							)			->  str:


												"""simple docstring"""

												__lowercase      :							int				=     input_arr

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

												__lowercase      :							Optional[Any]				=     sigmoid(
												    numpy.dot(
												        self.layer_between_input_and_first_hidden_layer		,     self.first_hidden_layer_and_second_hidden_layer_weights		,     )							)

												__lowercase      :							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       snake_case_		(     lowerCAmelCase_       :						numpy.ndarray      ):
							return 1 / (1 + numpy.exp(-value      ))
def       snake_case_		(     lowerCAmelCase_       :						numpy.ndarray      ):
							return (value) * (1 - (value))
def       snake_case_		(     ):
							__lowercase      :							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.
							__lowercase      :							int				=     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])       ,       dtype=numpy.floataa      )

							# Calling neural network class.
							__lowercase      :							List[Any]				=     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()







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase : Union[str, Any]						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	int		,     *__a				:	Dict		,     **__a				:	Optional[Any]							)			->  None:


												"""simple docstring"""

												warnings.warn(
												    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
												    """ use BeitImageProcessor instead."""		,     __a		,     )
												super().__init__(*__a		,     **__a							)