





"""simple docstring"""





import numpy





class        _lowerCamelCase      :


						def __init__(self   ,							__a   ,							__a		) ->   None:
								UpperCamelCase   =	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.
								UpperCamelCase   =	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.
								UpperCamelCase   =	numpy.random.rand(
								    4   ,							3		)

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

								# Real output values provided.
								UpperCamelCase   =	output_array

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


						def     snake_case_   (self		) ->   numpy.ndarray:
								UpperCamelCase   =	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.
								UpperCamelCase   =	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.
								UpperCamelCase   =	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		) ->   None:
								UpperCamelCase   =	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		)   ,							)
								UpperCamelCase   =	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		)   ,							)
								UpperCamelCase   =	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   ,							__a   ,							__a   ,							__a		) ->   None:
								for iteration in range(1   ,							iterations + 1		):
										UpperCamelCase   =	self.feedforward()
										self.back_propagation()
										if give_loss:
												UpperCamelCase   =	numpy.mean(numpy.square(output - self.feedforward()		)		)
												print(F"Iteration {iteration} Loss: {loss}"		)




						def     snake_case_   (self   ,							__a		) ->   int:
								UpperCamelCase   =	input_arr

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

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

								UpperCamelCase   =	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__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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


def       a__					(					):




		"""simple docstring"""


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

		# Calling neural network class.
		UpperCamelCase   =	TwoHiddenLayerNeuralNetwork(
		    input_array=_SCREAMING_SNAKE_CASE  ,	output_array=_SCREAMING_SNAKE_CASE    )

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

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


if __name__ == "__main__":
						example()













"""simple docstring"""





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer





@require_torch
@require_sentencepiece
@require_tokenizers
class        _lowerCamelCase      (       unittest.TestCase ):


						@slow
						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"   ,							return_dict=__a		).to(__a		)
								UpperCamelCase   =	AutoTokenizer.from_pretrained("google/mt5-small"		)

								UpperCamelCase   =	tokenizer("Hello there"   ,							return_tensors="pt"		).input_ids
								UpperCamelCase   =	tokenizer("Hi I am"   ,							return_tensors="pt"		).input_ids

								UpperCamelCase   =	model(input_ids.to(__a		)   ,							labels=labels.to(__a		)		).loss
								UpperCamelCase   =	-(labels.shape[-1] * loss.item())

								UpperCamelCase   =	-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1e-4		)






