import numpy as np


def read_text(filename):
    return open(filename, 'r', encoding='UTF-8').read().lower()


def peek_random_seed(data):
    return np.random.randint(0, len(data) - 1)


def prepare_text(text, ids, seq_length):
    dataX = []
    dataY = []

    for i in range(0, len(text) - seq_length, 1):
        seq_in = text[i:i + seq_length]
        seq_out = text[i + seq_length]

        dataX.append([ids[char] for char in seq_in])
        dataY.append(ids[seq_out])

    return dataX, dataY


def generate_text(model, pattern, chars, n_vocab, prediction_len):
    text = ""

    for i in range(prediction_len):
        x = np.reshape(pattern, (1, len(pattern), 1)) / float(n_vocab)
        prediction = model.predict(x, verbose=0)
        index = np.argmax(prediction)

        text += chars[index]

        pattern.append(index)
        pattern = pattern[1:len(pattern)]

    return text