




'''simple docstring'''



import numpy






class 				_a							:





       '''simple docstring'''






       def __init__(	self,				A,				A   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[str]   =		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.
             SCREAMING_SNAKE_CASE					:	List[str]   =		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.
             SCREAMING_SNAKE_CASE					:	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.
             SCREAMING_SNAKE_CASE					:	Dict   =		numpy.random.rand(3,				1   )

             # Real output values provided.
             SCREAMING_SNAKE_CASE					:	Dict   =		output_array

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







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		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.
             SCREAMING_SNAKE_CASE					:	Tuple   =		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.
             SCREAMING_SNAKE_CASE					:	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     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	int   =		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   ),				)
             SCREAMING_SNAKE_CASE					:	List[str]   =		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   ),				)
             SCREAMING_SNAKE_CASE					:	int   =		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     UpperCamelCase_				(	self,				A,				A,				A   ):







             '''simple docstring'''







             for iteration in range(1,				iterations + 1   ):
                   SCREAMING_SNAKE_CASE					:	str   =		self.feedforward()
                   self.back_propagation()
                   if give_loss:
                         SCREAMING_SNAKE_CASE					:	Optional[int]   =		numpy.mean(numpy.square(output - self.feedforward()   )   )
                         print(F"Iteration {iteration} Loss: {loss}"   )







       def     UpperCamelCase_				(	self,				A   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[int]   =		input_arr

             SCREAMING_SNAKE_CASE					:	List[str]   =		sigmoid(
                 numpy.dot(self.array,				self.input_layer_and_first_hidden_layer_weights   )   )

             SCREAMING_SNAKE_CASE					:	int   =		sigmoid(
                 numpy.dot(
                     self.layer_between_input_and_first_hidden_layer,				self.first_hidden_layer_and_second_hidden_layer_weights,				)   )

             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		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    lowercase__(       __UpperCamelCase: Any  ):

      """simple docstring"""






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




def    lowercase__(       __UpperCamelCase: Union[str, Any]  ):

      """simple docstring"""






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




def    lowercase__(       ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	Tuple   =		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.
      SCREAMING_SNAKE_CASE					:	Dict   =		numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])	,dtype=numpy.floataa  )

      # Calling neural network class.
      SCREAMING_SNAKE_CASE					:	int   =		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()



from ....configuration_utils import PretrainedConfig
from ....utils import logging


A   :     str       =				logging.get_logger(__name__)

# TODO: upload to AWS
A   :     Dict       =				{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''retribert'''







  def __init__(							self       :				Optional[int]     ,   __magic_name__       :				Optional[Any]=30_522     ,   __magic_name__       :				int=768     ,   __magic_name__       :				Dict=8     ,   __magic_name__       :				List[Any]=12     ,   __magic_name__       :				Tuple=3_072     ,   __magic_name__       :				List[Any]="gelu"     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Tuple=512     ,   __magic_name__       :				Dict=2     ,   __magic_name__       :				int=0.02     ,   __magic_name__       :				List[Any]=1e-12     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Dict=128     ,   __magic_name__       :				Union[str, Any]=0     ,   **__magic_name__       :				List[Any]     ,   )     ->		Dict:
        super().__init__(pad_token_id=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     vocab_size
        SCREAMING_SNAKE_CASE_      =     hidden_size
        SCREAMING_SNAKE_CASE_      =     num_hidden_layers
        SCREAMING_SNAKE_CASE_      =     num_attention_heads
        SCREAMING_SNAKE_CASE_      =     hidden_act
        SCREAMING_SNAKE_CASE_      =     intermediate_size
        SCREAMING_SNAKE_CASE_      =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_      =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_      =     max_position_embeddings
        SCREAMING_SNAKE_CASE_      =     type_vocab_size
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     layer_norm_eps
        SCREAMING_SNAKE_CASE_      =     share_encoders
        SCREAMING_SNAKE_CASE_      =     projection_dim


