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@@ -967,7 +967,13 @@ class XverseModel(Model):
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
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- assert max(tokenizer.vocab.values()) < vocab_size
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+ # Since we are checking the maximum index, we need to ensure it's strictly less than vocab_size,
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+ # because vocab_size is the count of items, and indexes start at 0.
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+ max_vocab_index = max(tokenizer.get_vocab().values())
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+ if max_vocab_index >= vocab_size:
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+ raise ValueError("Vocabulary size exceeds expected maximum size.")
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+
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+
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reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
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added_vocab = tokenizer.get_added_vocab()
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