




'''simple docstring'''






import numpy

class  UpperCAmelCase__						:



    def __init__(	self      :       Dict,__A      :       numpy.ndarray,__A      :       numpy.ndarray     ):
          _lowerCamelCase				:   Dict								=   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				:   Dict								=   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				:   Dict								=   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				:   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				:   List[str]								=   numpy.zeros(output_array.shape     )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   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.
          _lowerCamelCase				:   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.
          _lowerCamelCase				:   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   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   Union[str, 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     ),)
          _lowerCamelCase				:   Dict								=   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				:   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   lowerCamelCase_  (	self      :       Dict,__A      :       numpy.ndarray,__A      :       int,__A      :       bool     ):
          for iteration in range(1,iterations + 1     ):
                _lowerCamelCase				:   Union[str, Any]								=   self.feedforward()
                self.back_propagation()
                if give_loss:
                      _lowerCamelCase				:   Tuple								=   numpy.mean(numpy.square(output - self.feedforward()     )     )
                      print(f'Iteration {iteration} Loss: {loss}'     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       numpy.ndarray     ):
          _lowerCamelCase				:   int								=   input_arr

          _lowerCamelCase				:   Union[str, Any]								=   sigmoid(
              numpy.dot(self.array,self.input_layer_and_first_hidden_layer_weights     )     )

          _lowerCamelCase				:   Optional[Any]								=   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 		A_       (	_lowerCAmelCase   :    numpy.ndarray							):



      """simple docstring"""




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







def 		A_       (	_lowerCAmelCase   :    numpy.ndarray							):



      """simple docstring"""




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







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   Optional[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				:   int								=   numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])				,	dtype=numpy.floataa							)

      # Calling neural network class.
      _lowerCamelCase				:   List[Any]								=   TwoHiddenLayerNeuralNetwork(
          input_array=a_				,	output_array=a_							)

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

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


if __name__ == "__main__":
 example()

'''simple docstring'''






import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )

class  UpperCAmelCase__						(       unittest.TestCase ):



    def __init__(	self      :       Union[str, Any],__A      :       Dict,__A      :       List[str]=1_3,__A      :       Any=7,__A      :       str=True,__A      :       Optional[int]=True,__A      :       Optional[Any]=True,__A      :       Any=True,__A      :       List[str]=9_9,__A      :       str=3_2,__A      :       List[str]=5,__A      :       Optional[Any]=4,__A      :       Any=3_7,__A      :       Optional[Any]="gelu",__A      :       List[Any]=0.1,__A      :       Any=0.1,__A      :       Dict=5_1_2,__A      :       Tuple=1_6,__A      :       Tuple=2,__A      :       List[Any]=0.02,__A      :       Any=4,):
          _lowerCamelCase				:   List[Any]								=   parent
          _lowerCamelCase				:   Optional[int]								=   batch_size
          _lowerCamelCase				:   Tuple								=   seq_length
          _lowerCamelCase				:   Tuple								=   is_training
          _lowerCamelCase				:   Union[str, Any]								=   use_attention_mask
          _lowerCamelCase				:   Optional[Any]								=   use_token_type_ids
          _lowerCamelCase				:   List[Any]								=   use_labels
          _lowerCamelCase				:   str								=   vocab_size
          _lowerCamelCase				:   List[str]								=   hidden_size
          _lowerCamelCase				:   Union[str, Any]								=   num_hidden_layers
          _lowerCamelCase				:   Tuple								=   num_attention_heads
          _lowerCamelCase				:   Union[str, Any]								=   intermediate_size
          _lowerCamelCase				:   Optional[Any]								=   hidden_act
          _lowerCamelCase				:   Tuple								=   hidden_dropout_prob
          _lowerCamelCase				:   Optional[Any]								=   attention_probs_dropout_prob
          _lowerCamelCase				:   List[Any]								=   max_position_embeddings
          _lowerCamelCase				:   Union[str, Any]								=   type_vocab_size
          _lowerCamelCase				:   Union[str, Any]								=   type_sequence_label_size
          _lowerCamelCase				:   str								=   initializer_range
          _lowerCamelCase				:   List[Any]								=   num_choices



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

          _lowerCamelCase				:   Dict								=   None
          if self.use_attention_mask:
                _lowerCamelCase				:   Optional[int]								=   random_attention_mask([self.batch_size, self.seq_length]     )

          _lowerCamelCase				:   List[str]								=   None
          if self.use_token_type_ids:
                _lowerCamelCase				:   Tuple								=   ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size     )

          _lowerCamelCase				:   Optional[int]								=   RobertaConfig(
              vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,is_decoder=__A,initializer_range=self.initializer_range,)

          return config, input_ids, token_type_ids, attention_mask



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   List[str]								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   str								=   config_and_inputs
          _lowerCamelCase				:   List[Any]								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Any								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Dict								=   config_and_inputs

          _lowerCamelCase				:   int								=   True
          _lowerCamelCase				:   List[Any]								=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],vocab_size=2     )

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )


@require_flax
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		True

    lowerCAmelCase_				    =		(
        (
            FlaxRobertaModel,
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[str]								=   FlaxRobertaModelTester(self     )



    @slow
    def   lowerCamelCase_  (	self      :       Any     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   Union[str, Any]								=   model_class_name.from_pretrained("roberta-base",from_pt=__A     )
                _lowerCamelCase				:   Union[str, Any]								=   model(np.ones((1, 1)     )     )
                self.assertIsNotNone(__A     )