





import numpy







class 				__lowerCAmelCase       :







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					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.
        a__	:					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.
        a__	:					int										=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__	:					Any										=numpy.random.rand(3		,      1							)

        # Real output values provided.
        a__	:					str										=output_array

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







      def    _lowercase  (    self							)   ->						numpy.ndarray:



        '''simple docstring'''




        a__	:					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.
        a__	:					Optional[Any]										=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__	:					Dict										=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    _lowercase  (    self							)   ->						None:



        '''simple docstring'''




        a__	:					Any										=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__	:					Any										=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__	:					Optional[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    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




        a__	:					List[Any]										=input_arr

        a__	:					Optional[int]										=sigmoid(
            numpy.dot(self.array		,      self.input_layer_and_first_hidden_layer_weights							)							)

        a__	:					Union[str, Any]										=sigmoid(
            numpy.dot(
                self.layer_between_input_and_first_hidden_layer		,      self.first_hidden_layer_and_second_hidden_layer_weights		,      )							)

        a__	:					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      _A       (		SCREAMING_SNAKE_CASE     :   numpy.ndarray						):





  """simple docstring"""



  return 1 / (1 + numpy.exp(-value						))
def      _A       (		SCREAMING_SNAKE_CASE     :   numpy.ndarray						):





  """simple docstring"""



  return (value) * (1 - (value))
def      _A       (		):





  """simple docstring"""



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

  # Calling neural network class.
  a__	:					Dict										=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()













from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A    :					Union[str, Any] =      logging.get_logger(__name__)

A    :					int =      {
    '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
    '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
    '''xlm-roberta-large-finetuned-conll02-dutch''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll02-spanish''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-english''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-german''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
    ),
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Any							=    '''xlm-roberta'''






			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    List[Any]=3_05_22							,	__lowerCAmelCase   :    int=7_68							,	__lowerCAmelCase   :    Tuple=12							,	__lowerCAmelCase   :    str=12							,	__lowerCAmelCase   :    Union[str, Any]=30_72							,	__lowerCAmelCase   :    Union[str, Any]="gelu"							,	__lowerCAmelCase   :    Optional[int]=0.1							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[str]=5_12							,	__lowerCAmelCase   :    Tuple=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-12							,	__lowerCAmelCase   :    Union[str, Any]=1							,	__lowerCAmelCase   :    str=0							,	__lowerCAmelCase   :    Optional[int]=2							,	__lowerCAmelCase   :    Tuple="absolute"							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Any=None							,	**__lowerCAmelCase   :    str							,	)	->	Optional[Any]:







					"""simple docstring"""






					super().__init__(pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase   )

					A__        =   vocab_size
					A__        =   hidden_size
					A__        =   num_hidden_layers
					A__        =   num_attention_heads
					A__        =   hidden_act
					A__        =   intermediate_size
					A__        =   hidden_dropout_prob
					A__        =   attention_probs_dropout_prob
					A__        =   max_position_embeddings
					A__        =   type_vocab_size
					A__        =   initializer_range
					A__        =   layer_norm_eps
					A__        =   position_embedding_type
					A__        =   use_cache
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task == "multiple-choice":
							A__        =   {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							A__        =   {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]   )



