"""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"""




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel




