"""simple docstring"""




import numpy
class 		__A							:






					def __init__(	self       ,  a__       ,  a__						):
							_lowerCAmelCase		: int						    =  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		: Union[str, Any]						    =  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		: List[str]						    =  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		: List[Any]						    =  numpy.random.rand(3       ,  1						)

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

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






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






					def 					__A						(	self       ,  a__						):
							_lowerCAmelCase		: str						    =  input_arr

							_lowerCAmelCase		: List[Any]						    =  sigmoid(
							    numpy.dot(self.array       ,  self.input_layer_and_first_hidden_layer_weights						)						)

							_lowerCAmelCase		: Tuple						    =  sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,  self.first_hidden_layer_and_second_hidden_layer_weights       ,  )						)

							_lowerCAmelCase		: 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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: numpy.ndarray			) ->   numpy.ndarray:
		return 1 / (1 + numpy.exp(-value			))





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: numpy.ndarray			) ->   numpy.ndarray:
		return (value) * (1 - (value))





def 							SCREAMING_SNAKE_CASE  (    ) ->   int:
		_lowerCAmelCase		: List[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.
		_lowerCAmelCase		: Optional[int]						    =  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,dtype=numpy.floataa			)

		# Calling neural network class.
		_lowerCAmelCase		: str						    =  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=10    ,give_loss=_lowerCamelCase			)

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


if __name__ == "__main__":
					example()





"""simple docstring"""




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)




