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def __call__(self,
                a: Union[np.ndarray, tf.Tensor],
                x: Union[np.ndarray, tf.Tensor],
                y: Union[np.ndarray, tf.Tensor, None] = None) -> tuple:
    
    a_new = tf.math.tanh(tf.linalg.matmul(tf.concat([a, x], axis=1), self.wa)+self.ba)
    y_logits = tf.linalg.matmul(a_new, self.wy)+self.by
    if y is None:
        # during prediction return softmax probabilities
        return (a_new, tf.nn.softmax(y_logits))

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class Model:
    def __init__(self, vocabulary: list = [], a_size: int = 0):
        self.vocab = vocabulary
        self.vocab_size = len(vocabulary)
        self.a_size = a_size
        self.combined_size = self.vocab_size + self.a_size
        
        # weights and bias used to compute the new a
        # (a = vector that is passes to the next time step)
        self.wa = tf.Variable(tf.random.normal(

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def sample_word(vocabulary: list, prob: np.ndarray) -> str:
    # sample a word from the vocabulary according to 'prob'
    # probability distribution (the softmax output of our model)
    # until it is != <UNK>
    while True:
        word = np.random.choice(vocabulary, p=prob)
        if word != UNK:
            return word

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def word2index(vocabulary: list, word: str) -> int:
    # returns the index of 'word' in the vocabulary
    return vocabulary.index(word)

def words2onehot(vocabulary: list, words: list) -> np.ndarray:
    # transforms the list of words given as argument into
    # a one-hot matrix representation using the index in the vocabulary
    n_words = len(words)
    n_voc = len(vocabulary)
    indices = np.array([word2index(vocabulary, word) for word in words])

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def build_sentences(vocabulary: list, sentences: list) -> list:
    # transforms the list of sentences into a list of lists of words
    # replacing words that are not in the vocabulary with <UNK>
    # and appending <EOS> at the end of each sentence
    processed_sent = []
    n = len(sentences)
    for i, sent in enumerate(sentences):
        print('Creating sentences list: %05.2f%%' % (100*(i+1)/n,), end='\r')
        s = []
        for word in sent.strip().split():

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UNK = '<UNK>' # Unknown word
EOS = '<EOS>' # End of sentence

def build_vocabulary(sentences: list, words_to_keep: int) -> list:
    # builds a vocabulary using 'words_to_keep' most frequent words
    # encountered in the list of sentences
    vocabulary = {}
    n = len(sentences)
    for i, s in enumerate(sentences):
        print('Creating vocabulary: %05.2f%%' % (100*(i+1)/n,), end='\r')

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# Execute this with "pypy script_name.py"

from timeit import timeit

n_iter = 100000

py_time = timeit(
    stmt='py_fib(20)',
    setup='from fib import fib as py_fib',
    number=n_iter)

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from timeit import timeit

n_iter = 100000

py_time = timeit(
    stmt='py_fib(20)',
    setup='from fib import fib as py_fib',
    number=n_iter)

c_module_time = timeit(

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import platform
from fib import fib as py_fib
from my_c_module import fib as c_module_fib
from ctypes import *

def print_c_array(ptr, n):
    print('[', end='')
    for i in range(n):
        if i == n-1:
            print(ptr[i], end=']\n')

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def fib(n):
    fib_lst = []
    if n >= 1:
        fib_lst.append(0)
    if n >= 2:
        fib_lst.append(1)
    
    for i in range(2, n):
        fib_lst.append(fib_lst[-2]+fib_lst[-1])