# GRADED FUNCTION: sentences_to_indices

def sentences_to_indices(X, word_to_index, max_len):
    """
    Converts an array of sentences (strings) into an array of indices corresponding to words in the sentences.
    The output shape should be such that it can be given to `Embedding()` (described in Figure 4). 

    Arguments:
    X -- array of sentences (strings), of shape (m, 1)
    word_to_index -- a dictionary containing the each word mapped to its index
    max_len -- maximum number of words in a sentence. You can assume every sentence in X is no longer than this. 

    Returns:
    X_indices -- array of indices corresponding to words in the sentences from X, of shape (m, max_len)
    """

    m = X.shape[0]                                   # number of training examples

    ### START CODE HERE ###
    # Initialize X_indices as a numpy matrix of zeros and the correct shape (≈ 1 line)
    X_indices = np.zeros((m, max_len))

    for i in range(m):                               # loop over training examples

        # Convert the ith training sentence in lower case and split is into words. You should get a list of words.
        sentence_words = [word.lower() for word in X[i].strip().split(' ')]

        # Initialize j to 0
        j = 0

        # Loop over the words of sentence_words
        for w in sentence_words:
            # Set the (i,j)th entry of X_indices to the index of the correct word.
            X_indices[i, j] = word_to_index[w] if w != '' else 0
            # Increment j to j + 1
            j = j + 1

    ### END CODE HERE ###

    return X_indices

# GRADED FUNCTION: pretrained_embedding_layer

def pretrained_embedding_layer(word_to_vec_map, word_to_index):
    """
    Creates a Keras Embedding() layer and loads in pre-trained GloVe 50-dimensional vectors.

    Arguments:
    word_to_vec_map -- dictionary mapping words to their GloVe vector representation.
    word_to_index -- dictionary mapping from words to their indices in the vocabulary (400,001 words)

    Returns:
    embedding_layer -- pretrained layer Keras instance
    """

    vocab_len = len(word_to_index) + 1                  # adding 1 to fit Keras embedding (requirement)
    emb_dim = word_to_vec_map["cucumber"].shape[0]      # define dimensionality of your GloVe word vectors (= 50)

    ### START CODE HERE ###
    # Initialize the embedding matrix as a numpy array of zeros of shape (vocab_len, dimensions of word vectors = emb_dim)
    emb_matrix = np.zeros((vocab_len, emb_dim))

    # Set each row "index" of the embedding matrix to be the word vector representation of the "index"th word of the vocabulary
    for word, index in word_to_index.items():
        emb_matrix[index, :] = word_to_vec_map[word]

    # Define Keras embedding layer with the correct output/input sizes, make it trainable. Use Embedding(...). Make sure to set trainable=False. 
    embedding_layer = Embedding(vocab_len, emb_dim, trainable=False)
    ### END CODE HERE ###

    # Build the embedding layer, it is required before setting the weights of the embedding layer. Do not modify the "None".
    embedding_layer.build((None,))

    # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.
    embedding_layer.set_weights([emb_matrix])

    return embedding_layer


# GRADED FUNCTION: Emojify_V2

def Emojify_V2(input_shape, word_to_vec_map, word_to_index):
    """
    Function creating the Emojify-v2 model's graph.

    Arguments:
    input_shape -- shape of the input, usually (max_len,)
    word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation
    word_to_index -- dictionary mapping from words to their indices in the vocabulary (400,001 words)

    Returns:
    model -- a model instance in Keras
    """

    ### START CODE HERE ###
    # Define sentence_indices as the input of the graph, it should be of shape input_shape and dtype 'int32' (as it contains indices).
    sentence_indices = Input(shape=input_shape, dtype='int32')

    # Create the embedding layer pretrained with GloVe Vectors (≈1 line)
    embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index)

    # Propagate sentence_indices through your embedding layer, you get back the embeddings
    embeddings = embedding_layer(sentence_indices) 

    # Propagate the embeddings through an LSTM layer with 128-dimensional hidden state
    # Be careful, the returned output should be a batch of sequences.
    X = LSTM(128, return_sequences = True)(embeddings)
    # Add dropout with a probability of 0.5
    X = Dropout(0.5)(X)
    # Propagate X trough another LSTM layer with 128-dimensional hidden state
    # Be careful, the returned output should be a single hidden state, not a batch of sequences.
    X = LSTM(128)(X)
    # Add dropout with a probability of 0.5
    X = Dropout(0.5)(X)
    # Propagate X through a Dense layer with softmax activation to get back a batch of 5-dimensional vectors.
    X = Dense(5)(X)
    # Add a softmax activation
    X = Activation('softmax')(X)

    # Create Model instance which converts sentence_indices into X.
    model = Model(inputs=sentence_indices, outputs=X)

    ### END CODE HERE ###

    return model

'''
maxLen = 20
model = Emojify_V2((maxLen,), word_to_vec_map, word_to_index)
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
X_train_indices = sentences_to_indices(X_train, word_to_index, maxLen)
Y_train_oh = convert_to_one_hot(Y_train, C = 5)
model.fit(X_train_indices, Y_train_oh, epochs = 50, batch_size = 32, shuffle=True)
X_test_indices = sentences_to_indices(X_test, word_to_index, max_len = maxLen)
Y_test_oh = convert_to_one_hot(Y_test, C = 5)
loss, acc = model.evaluate(X_test_indices, Y_test_oh)
print()
print("Test accuracy = ", acc)
'''

'\nmaxLen = 20\nmodel = Emojify_V2((maxLen,), word_to_vec_map, word_to_index)\nmodel.summary()\nmodel.compile(loss=\'categorical_crossentropy\', optimizer=\'adam\', metrics=[\'accuracy\'])\nX_train_indices = sentences_to_indices(X_train, word_to_index, maxLen)\nY_train_oh = convert_to_one_hot(Y_train, C = 5)\nmodel.fit(X_train_indices, Y_train_oh, epochs = 50, batch_size = 32, shuffle=True)\nX_test_indices = sentences_to_indices(X_test, word_to_index, max_len = maxLen)\nY_test_oh = convert_to_one_hot(Y_test, C = 5)\nloss, acc = model.evaluate(X_test_indices, Y_test_oh)\nprint()\nprint("Test accuracy = ", acc)\n'