from tokenizers import Tokenizer, decoders, models, normalizers, pre_tokenizers, trainers

def train_tokenizer(iterator, vocab_size=50000, min_frequency=3):
    tokenizer = Tokenizer(models.BPE(unk_token="<unk>"))
    
    # Normalization για ελληνικά και unicode
    tokenizer.normalizer = normalizers.Sequence([
        normalizers.NFC(),
        normalizers.StripAccents()
    ])
    
    # Προχωρημένος pre-tokenizer για μικτά κείμενα
    tokenizer.pre_tokenizer = pre_tokenizers.Sequence([
        pre_tokenizers.WhitespaceSplit(),
        pre_tokenizers.Punctuation(),
        pre_tokenizers.Digits(individual_digits=True)
    ])
    
    # Ειδικά tokens για ιστορικά κείμενα
    trainer = trainers.BpeTrainer(
        vocab_size=vocab_size,
        min_frequency=min_frequency,
        special_tokens=["<|endoftext|>", "<pad>", "<unk>", "<mask>", "[CITATION]"],
        continuing_subword_prefix=""
    )
    
    tokenizer.train_from_iterator(iterator, trainer=trainer)
    tokenizer.decoder = decoders.ByteLevel()
    return tokenizer