


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   __lowercase				(  UpperCamelCase_  ):





						@slow
						@require_torch
						def        _lowercase				(		self						:					Any       )    ->   Any:






									"""simple docstring"""







									UpperCAmelCase							=     EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""					,       """prajjwal1/bert-tiny"""       )
									UpperCAmelCase							=     BertTokenizer.from_pretrained("""bert-base-uncased"""       )

									UpperCAmelCase							=     bertabert.config.encoder.vocab_size
									UpperCAmelCase							=     tokenizer.sep_token_id
									UpperCAmelCase							=     tokenizer.cls_token_id
									UpperCAmelCase							=     1_2_8

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

									UpperCAmelCase							=     train_dataset.select(range(3_2       )       )
									UpperCAmelCase							=     val_dataset.select(range(1_6       )       )

									UpperCAmelCase							=     4

									def _map_to_encoder_decoder_inputs(__lowerCamelCase						:					Optional[int]       ):
												# Tokenizer will automatically set [BOS] <text> [EOS]
												UpperCAmelCase							=     tokenizer(batch["""article"""]					,       padding="""max_length"""					,       truncation=__A					,       max_length=5_1_2       )
												UpperCAmelCase							=     tokenizer(batch["""highlights"""]					,       padding="""max_length"""					,       truncation=__A					,       max_length=1_2_8       )
												UpperCAmelCase							=     inputs.input_ids
												UpperCAmelCase							=     inputs.attention_mask

												UpperCAmelCase							=     outputs.input_ids
												UpperCAmelCase							=     outputs.input_ids.copy()
												UpperCAmelCase							=     [
												    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
												]
												UpperCAmelCase							=     outputs.attention_mask

												assert all(len(__A       ) == 5_1_2 for x in inputs.input_ids       )
												assert all(len(__A       ) == 1_2_8 for x in outputs.input_ids       )

												return batch

									def _compute_metrics(__lowerCamelCase						:					Union[str, Any]       ):
												UpperCAmelCase							=     pred.label_ids
												UpperCAmelCase							=     pred.predictions

												# all unnecessary tokens are removed
												UpperCAmelCase							=     tokenizer.batch_decode(__A					,       skip_special_tokens=__A       )
												UpperCAmelCase							=     tokenizer.batch_decode(__A					,       skip_special_tokens=__A       )

												UpperCAmelCase							=     sum([int(pred_str[i] == label_str[i]       ) for i in range(len(__A       )       )]       ) / len(__A       )

												return {"accuracy": accuracy}

									# map train dataset
									UpperCAmelCase							=     train_dataset.map(
									    _map_to_encoder_decoder_inputs					,       batched=__A					,       batch_size=__A					,       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
									UpperCAmelCase							=     val_dataset.map(
									    _map_to_encoder_decoder_inputs					,       batched=__A					,       batch_size=__A					,       remove_columns=["""article""", """highlights"""]					,       )
									val_dataset.set_format(
									    type="""torch"""					,       columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]					,       )

									UpperCAmelCase							=     self.get_auto_remove_tmp_dir()

									UpperCAmelCase							=     SeqaSeqTrainingArguments(
									    output_dir=__A					,       per_device_train_batch_size=__A					,       per_device_eval_batch_size=__A					,       predict_with_generate=__A					,       evaluation_strategy="""steps"""					,       do_train=__A					,       do_eval=__A					,       warmup_steps=0					,       eval_steps=2					,       logging_steps=2					,       )

									# instantiate trainer
									UpperCAmelCase							=     SeqaSeqTrainer(
									    model=__A					,       args=__A					,       compute_metrics=_compute_metrics					,       train_dataset=__A					,       eval_dataset=__A					,       tokenizer=__A					,       )

									# start training
									trainer.train()







import numpy






class   __lowercase				:





      def __init__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					numpy.ndarray       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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.
         UpperCAmelCase							=     numpy.random.rand(
             4					,       3       )

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

         # Real output values provided.
         UpperCAmelCase							=     output_array

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





      def        _lowercase				(		self						:					List[str]       )    ->   numpy.ndarray:






         """simple docstring"""







         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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						:					Optional[Any]       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     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       )					,       )
         UpperCAmelCase							=     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       )					,       )
         UpperCAmelCase							=     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						:					Any					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					bool       )    ->   None:






         """simple docstring"""







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





      def        _lowercase				(		self						:					List[str]					,       __lowerCamelCase						:					numpy.ndarray       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     input_arr

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

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

         UpperCAmelCase							=     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   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return 1 / (1 + numpy.exp(-value   ))
def   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return (value) * (1 - (value))
def   _UpperCamelCase				(						)						->int:
   UpperCAmelCase							=     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.
   UpperCAmelCase							=     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])  ,						dtype=numpy.floataa   )

   # Calling neural network class.
   UpperCAmelCase							=     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=1_0  ,						give_loss=lowerCAmelCase_   )

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


if __name__ == "__main__":
 example()



