






import numpy



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __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        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''







    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        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __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 		a__			(		_UpperCamelCase			: Union[str, Any]							):
   return 1 / (1 + numpy.exp(-value							))





def 		a__			(		_UpperCamelCase			: str							):
   return (value) * (1 - (value))





def 		a__			(		):
   __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=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()











import torch

from diffusers import StableDiffusionPipeline


a_			=					"""path-to-your-trained-model"""
a_			=					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

a_			=					"""A photo of sks dog in a bucket"""
a_			=					pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






