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parser = argparse.ArgumentParser()
parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point')
parser.add_argument('--batch-size', type=int, dest='batch_size', default=50, help='mini batch size for training')
parser.add_argument('--x_filename', type=str, dest='x_filename', help='Filename with training data')
parser.add_argument('--y_filename', type=str, dest='y_filename', help='Filename with label data')
parser.add_argument('--training_size', type=str, dest='training_size', help='Size of training dataset')
parser.add_argument('--n_epochs', type=int, dest='n_epochs', help='Number of epochs')

args = parser.parse_args()

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# start an Azure ML run
run = Run.get_context()

class LogRunMetrics(Callback):
    # callback at the end of every epoch
    def on_epoch_end(self, epoch, log):
        # log a value repeated which creates a list
        run.log('Loss', log['loss'])
        run.log('Accuracy', log['acc'])
        run.log('Val_Loss', log['val_loss'])

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class CharVocab: 
    ''' Create a Vocabulary for '''
    def __init__(self, type_vocab,pad_token='<PAD>', eos_token='<EOS>', unk_token='<UNK>'): #Initialization of the type of vocabulary
        self.type = type_vocab
        #self.int2char ={}
        self.int2char = []
        if pad_token !=None:
            self.int2char += [pad_token]
        if eos_token !=None:
            self.int2char += [eos_token]

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def one_hot_encode(indices, dict_size):
    ''' Define one hot encode matrix for our sequences'''
    # Creating a multi-dimensional array with the desired output shape
    # Encode every integer with its one hot representation
    features = np.eye(dict_size, dtype=np.float32)[indices.flatten()]
    
    # Finally reshape it to get back to the original array
    features = features.reshape((*indices.shape, dict_size))
            
    return features

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import sagemaker

# Get the session id 
sagemaker_session = sagemaker.Session()
# Get the bucet, in our example the default buack
bucket = sagemaker_session.default_bucket()
# Set the S3 subfolder where our data will be stored 
prefix = 'sagemaker/char_level_rnn'
# Get the role for permission
role = sagemaker.get_execution_role()

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import torch
from torch import nn
from torch.autograd import Variable

class RNNModel(nn.Module):
    def __init__(self, vocab_size, embedding_size, hidden_dim, n_layers, drop_rate=0.2):
        
        super(RNNModel, self).__init__()

        # Defining some parameters

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def batch_generator_sequence(features_seq, label_seq, batch_size, seq_len):
    """Generator function that yields batches of data (input and target)

    Args:
        batch_size (int): number of examples (in this case, sentences) per batch.
        max_length (int): maximum length of the output tensor.
        NOTE: max_length includes the end-of-sentence character that will be added
                to the tensor.  
                Keep in mind that the length of the tensor is always 1 + the length
                of the original line of characters.

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from sagemaker.pytorch import PyTorch

# Select the type of instance to use for training
#instance_type='ml.m4.4xlarge' # CPU instance
instance_type='ml.p2.xlarge' # GPU instance
#instance_type='local'
#Create the estimator object
estimator = PyTorch(entry_point="train.py",
                    source_dir="train",
                    role=role,

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def sample_from_probs(probs, top_n=10):
    """
    truncated weighted random choice.
    """
    _, indices = torch.sort(probs)
    # set probabilities after top_n to 0
    probs[indices.data[:-top_n]] = 0
    # Sampling the index of the predicted next char
    sampled_index = torch.multinomial(probs, 1)
    return sampled_index

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from sagemaker.predictor import RealTimePredictor
from sagemaker.pytorch import PyTorchModel

class StringPredictor(RealTimePredictor):
    def __init__(self, endpoint_name, sagemaker_session):
        super(StringPredictor, self).__init__(endpoint_name, sagemaker_session, content_type='text/plain')
# Create a model in Sagemaker 
model = PyTorchModel(model_data=estimator.model_data,
                     role = role,
                     framework_version='0.4.0',