


"""simple docstring"""







import numpy






class 				__A    :






 """simple docstring"""




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

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

     # Real output values provided.
     a					=output_array

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					numpy.ndarray:
     a					=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.
     a					=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.
     a					=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 				SCREAMING_SNAKE_CASE							(		self						) ->					None:
     a					=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						)			,   )
     a					=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						)			,   )
     a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					None:
     for iteration in range(1			,   iterations + 1						):
         a					=self.feedforward()
         self.back_propagation()
         if give_loss:
             a					=numpy.mean(numpy.square(output - self.feedforward()						)						)
             print(f'''Iteration {iteration} Loss: {loss}'''						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					int:
     a					=input_arr

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

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

     a					=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						(						lowercase		):







    """simple docstring"""
    return 1 / (1 + numpy.exp(-value		))
def _A						(						lowercase		):







    """simple docstring"""
    return (value) * (1 - (value))
def _A						(						):







    """simple docstring"""
    a					=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.
    a					=numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])	,   dtype=numpy.floataa		)

    # Calling neural network class.
    a					=TwoHiddenLayerNeuralNetwork(
        input_array=lowercase	,   output_array=lowercase		)

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

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


if __name__ == "__main__":
       example()



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_       :       Union[str, Any]      						=  {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Optional[int]      						=  ["""LayoutXLMTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       lowerCamelCase_       :       Union[str, Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)