#!python3
#!/usr/bin/env python3

import tensorflow as tf
import settings
#from tensorflow.keras import layers
from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector, Activation, Bidirectional, Reshape
from keras.models import Model, Sequential
from keras.utils.np_utils import to_categorical
import numpy as np
import sys
import pickle

def load_data(samples_path):
    print('Loading... {}'.format(train_data_path))
    train_X = np.load(train_data_path, allow_pickle=True)['arr_0']
    train_y = np.load(train_data_path, allow_pickle=True)['arr_1']
    return train_X, train_y

# TODO: make transformer class with fit, transform and reverse definitions
def preprocess_samples(train_X, train_y):
    vocab_X = np.unique(train_X)
    vocab_y = np.unique(train_y)
    vocab = np.concatenate([vocab_X, vocab_y])
    n_vocab = vocab.shape[0]
    note_to_int = dict((note, number) for number, note in enumerate(vocab))
    int_to_note = dict((number, note) for number, note in enumerate(vocab))

    _train_X = []
    _train_y = []
    for sample in train_X:
        # TODO: add normalizasion
        _train_X.append([note_to_int[note] for note in sample])

    train_X = np.array(_train_X).reshape(train_X.shape[0], train_X.shape[1], 1)
    train_y = np.array([note_to_int[note] for note in train_y]).reshape(-1,1)
    train_y = to_categorical(train_y)

    return train_X, train_y, n_vocab, int_to_note

train_data_path = sys.argv[1]

train_X, train_y = load_data(train_data_path)
train_X, train_y, n_vocab, int_to_note = preprocess_samples(train_X, train_y)

save_model_path = sys.argv[2]
epochs = int(sys.argv[3])

model = Sequential()
model.add(LSTM(512, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))
model.add(Dropout(0.3))
model.add(LSTM(512, return_sequences=True))
model.add(Dropout(0.3))
model.add(LSTM(512))
model.add(Dense(256))
model.add(Dropout(0.3))
model.add(Dense(n_vocab))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

# model training
print('Training...')
model.fit(train_X, train_y, epochs=epochs, batch_size=64)

# save trained model
pickle.dump(model, open(save_model_path,'wb'))
pickle.dump(int_to_note, open('{}_dict'.format(save_model_path),'wb'))
print('Done!')
print("Model saved to: {}".format(save_model_path))