


import numpy






class     _lowerCamelCase					:




       """simple docstring"""



       def __init__( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE						)->Tuple:




         '''simple docstring'''





         A_ :							Tuple							=   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_ :							Optional[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.
         A_ :							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.
         A_ :							List[Any]							=   numpy.random.rand(3							,			1						)

         # Real output values provided.
         A_ :							Union[str, Any]							=   output_array

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



       def 			_snake_case			( self						)->Optional[Any]:




         '''simple docstring'''





         A_ :							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_ :							List[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 			_snake_case			( self						)->Any:




         '''simple docstring'''





         A_ :							Optional[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						)							,			)
         A_ :							Union[str, 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_ :							List[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 			_snake_case			( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE						)->List[Any]:




         '''simple docstring'''





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







       def 			_snake_case			( self							,			_SCREAMING_SNAKE_CASE						)->List[Any]:




         '''simple docstring'''





         A_ :							Union[str, Any]							=   input_arr

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

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

         A_ :							List[str]							=   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      (					SCREAMING_SNAKE_CASE			):
  return 1 / (1 + numpy.exp(-value			))


def     _SCREAMING_SNAKE_CASE      (					SCREAMING_SNAKE_CASE			):
  return (value) * (1 - (value))


def     _SCREAMING_SNAKE_CASE      (					):
  A_ :							Union[str, 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=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()












from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
							import datasets
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				@slow
				@require_torch
				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''',    '''prajjwal1/bert-tiny'''    )
								lowercase__  					=     BertTokenizer.from_pretrained('''bert-base-uncased'''    )

								lowercase__  					=     bertabert.config.encoder.vocab_size
								lowercase__  					=     tokenizer.sep_token_id
								lowercase__  					=     tokenizer.cls_token_id
								lowercase__  					=     128

								lowercase__  					=     datasets.load_dataset('''cnn_dailymail''',    '''3.0.0''',    split='''train[:1%]'''    )
								lowercase__  					=     datasets.load_dataset('''cnn_dailymail''',    '''3.0.0''',    split='''validation[:1%]'''    )

								lowercase__  					=     train_dataset.select(range(32    )    )
								lowercase__  					=     val_dataset.select(range(16    )    )

								lowercase__  					=     4

								def _map_to_encoder_decoder_inputs(lowerCamelCase		:       str    ):
												# Tokenizer will automatically set [BOS] <text> [EOS]
												lowercase__  					=     tokenizer(batch['''article'''],    padding='''max_length''',    truncation=lowerCamelCase,    max_length=512    )
												lowercase__  					=     tokenizer(batch['''highlights'''],    padding='''max_length''',    truncation=lowerCamelCase,    max_length=128    )
												lowercase__  					=     inputs.input_ids
												lowercase__  					=     inputs.attention_mask

												lowercase__  					=     outputs.input_ids
												lowercase__  					=     outputs.input_ids.copy()
												lowercase__  					=     [
												    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
												]
												lowercase__  					=     outputs.attention_mask

												assert all(len(lowerCamelCase    ) == 512 for x in inputs.input_ids    )
												assert all(len(lowerCamelCase    ) == 128 for x in outputs.input_ids    )

												return batch

								def _compute_metrics(lowerCamelCase		:       Any    ):
												lowercase__  					=     pred.label_ids
												lowercase__  					=     pred.predictions

												# all unnecessary tokens are removed
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )

												lowercase__  					=     sum([int(pred_str[i] == label_str[i]    ) for i in range(len(lowerCamelCase    )    )]    ) / len(lowerCamelCase    )

												return {"accuracy": accuracy}

								# map train dataset
								lowercase__  					=     train_dataset.map(
								    _map_to_encoder_decoder_inputs,    batched=lowerCamelCase,    batch_size=lowerCamelCase,    remove_columns=['''article''', '''highlights'''],    )
								train_dataset.set_format(
								    type='''torch''',    columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''],    )

								# same for validation dataset
								lowercase__  					=     val_dataset.map(
								    _map_to_encoder_decoder_inputs,    batched=lowerCamelCase,    batch_size=lowerCamelCase,    remove_columns=['''article''', '''highlights'''],    )
								val_dataset.set_format(
								    type='''torch''',    columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''],    )

								lowercase__  					=     self.get_auto_remove_tmp_dir()

								lowercase__  					=     SeqaSeqTrainingArguments(
								    output_dir=lowerCamelCase,    per_device_train_batch_size=lowerCamelCase,    per_device_eval_batch_size=lowerCamelCase,    predict_with_generate=lowerCamelCase,    evaluation_strategy='''steps''',    do_train=lowerCamelCase,    do_eval=lowerCamelCase,    warmup_steps=0,    eval_steps=2,    logging_steps=2,    )

								# instantiate trainer
								lowercase__  					=     SeqaSeqTrainer(
								    model=lowerCamelCase,    args=lowerCamelCase,    compute_metrics=_compute_metrics,    train_dataset=lowerCamelCase,    eval_dataset=lowerCamelCase,    tokenizer=lowerCamelCase,    )

								# start training
								trainer.train()




