I have the following code

import keras

v = {
    "deck": ['a','B','C','D','E','F','G','H','I','J','K','L']
}

print(len(v["deck"]))

l = keras.layers.TextVectorization(
    max_tokens=len(v["deck"])+2,
    vocabulary=v["deck"],
    output_mode='count',
    name="deck")

print(l.vocabulary_size())
print(l.get_vocabulary())

print(l('a A b B'))

the output is:

12
13
['[UNK]', 'a', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L']
tf.Tensor([2. 2. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.], shape=(13,), dtype=float32)

I would expect at least one of the b's to be counted.

If I use l.adapt(v["deck"]), things seem to work accordingly, but the vocab is all in lowercase.

Like this:

import keras

v = {
    "deck": ['a','B','C','D','E','F','G','H','I','J','K','L']
}

print(len(v["deck"]))

l = keras.layers.TextVectorization(
    max_tokens=len(v["deck"])+2,
    # vocabulary=v["deck"],
    output_mode='count',
    name="deck")

l.adapt(v['deck'])

print(l.vocabulary_size())
print(l.get_vocabulary())

print(l('a A b B'))

and output:

12
13
['[UNK]', 'l', 'k', 'j', 'i', 'h', 'g', 'f', 'e', 'd', 'c', 'b', 'a']
tf.Tensor([0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 2. 2.], shape=(13,), dtype=float32)