import numpy





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     Any     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	None:
				_lowerCamelCase				   =  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.
				_lowerCamelCase				   =  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.
				_lowerCamelCase				   =  numpy.random.rand(
				    4     ,  3		)

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

				# Real output values provided.
				_lowerCamelCase				   =  output_array

				# Predicted output values by the neural network.
				# Predicted_output array initially consists of zeroes.
				_lowerCamelCase				   =  numpy.zeros(output_array.shape		)
		def   _snake_case       (       self     :     List[str]		)      ->	numpy.ndarray:
				_lowerCamelCase				   =  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.
				_lowerCamelCase				   =  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.
				_lowerCamelCase				   =  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     :     List[str]		)      ->	None:
				_lowerCamelCase				   =  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		)     ,  )
				_lowerCamelCase				   =  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		)     ,  )
				_lowerCamelCase				   =  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     :     List[str]     ,  snake_case__     :     Optional[int]     ,  snake_case__     :     str     ,  snake_case__     :     int		)      ->	None:
				for iteration in range(1     ,  iterations + 1		):
						_lowerCamelCase				   =  self.feedforward()
						self.back_propagation()
						if give_loss:
								_lowerCamelCase				   =  numpy.mean(numpy.square(output - self.feedforward()		)		)
								print(f"""Iteration {iteration} Loss: {loss}"""		)



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     Union[str, Any]		)      ->	int:
				_lowerCamelCase				   =  input_arr

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

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

				_lowerCamelCase				   =  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					(	UpperCamelCase     :				numpy.ndarray							)   ->    numpy.ndarray:
		return 1 / (1 + numpy.exp(-value							))


def 	lowerCamelCase					(	UpperCamelCase     :				numpy.ndarray							)   ->    numpy.ndarray:
		return (value) * (1 - (value))


def 	lowerCamelCase					(	)   ->    int:
		_lowerCamelCase				   =  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.
		_lowerCamelCase				   =  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])			,       dtype=numpy.floataa							)

		# Calling neural network class.
		_lowerCamelCase				   =  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()

"""simple docstring"""

import os
import time

import numpy as np
import onnxruntime as ort


SCREAMING_SNAKE_CASE_            =    '''1'''
SCREAMING_SNAKE_CASE_            =    '''0'''
SCREAMING_SNAKE_CASE_            =    '''1'''

SCREAMING_SNAKE_CASE_            =    ort.SessionOptions()
SCREAMING_SNAKE_CASE_            =    ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
SCREAMING_SNAKE_CASE_            =    ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
SCREAMING_SNAKE_CASE_            =    ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
SCREAMING_SNAKE_CASE_            =    ort.RunOptions()

SCREAMING_SNAKE_CASE_            =    128
SCREAMING_SNAKE_CASE_            =    1
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
SCREAMING_SNAKE_CASE_            =    time.time()
SCREAMING_SNAKE_CASE_            =    2000
SCREAMING_SNAKE_CASE_            =    {}
for iter in range(max_iters):
   SCREAMING_SNAKE_CASE_            =    sess.run(
       None,
       {
           sess.get_inputs()[0].name: input_ids,
           sess.get_inputs()[1].name: attention_mask,
           sess.get_inputs()[2].name: token_type_ids,
       },
       run_options=run_opt,
   )
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1000 / max_iters))



