


'''simple docstring'''
import numpy






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      Optional[int] ,  lowerCAmelCase							:      numpy.ndarray ,  lowerCAmelCase							:      numpy.ndarray	):
													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							:      Any	):
													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							:      Optional[int]	):
													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							:      List[Any] ,  lowerCAmelCase							:      numpy.ndarray ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      bool	):
													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							:      int ,  lowerCAmelCase							:      numpy.ndarray	):
													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       a_				(	UpperCamelCase_ ):
							return 1 / (1 + numpy.exp(-value ))







def       a_				(	UpperCamelCase_ ):
							return (value) * (1 - (value))







def       a_				(	):
							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=1_0						,  give_loss=lowerCAmelCase__ )

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


if __name__ == "__main__":
							example()












import os
import sys

_lowercase		:  List[str]			       =os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_lowercase		:  Optional[int]			       =[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]


@add_start_docstrings(AutoConfig.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoConfig.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoTokenizer.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoTokenizer.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModel.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModel.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModelForCausalLM.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForCausalLM.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModelForMaskedLM.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForMaskedLM.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModelForSequenceClassification.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForSequenceClassification.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForQuestionAnswering.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)
