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adapt_tokenizer.py ADDED
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+ from typing import Union
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+ from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
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+ Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
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+ NUM_SENTINEL_TOKENS: int = 100
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+
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+ def adapt_tokenizer_for_denoising(tokenizer: Tokenizer):
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+ """Adds sentinel tokens and padding token (if missing).
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+
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+ Expands the tokenizer vocabulary to include sentinel tokens
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+ used in mixture-of-denoiser tasks as well as a padding token.
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+
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+ All added tokens are added as special tokens. No tokens are
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+ added if sentinel tokens and padding token already exist.
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+ """
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+ sentinels_to_add = [f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)]
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+ tokenizer.add_tokens(sentinels_to_add, special_tokens=True)
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+ if tokenizer.pad_token is None:
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+ tokenizer.add_tokens('<pad>', special_tokens=True)
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+ tokenizer.pad_token = '<pad>'
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+ assert tokenizer.pad_token_id is not None
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+ sentinels = ''.join([f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)])
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+ _sentinel_token_ids = tokenizer(sentinels, add_special_tokens=False).input_ids
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+ tokenizer.sentinel_token_ids = _sentinel_token_ids
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+
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+ class AutoTokenizerForMOD(AutoTokenizer):
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+ """AutoTokenizer + Adaptation for MOD.
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+
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+ A simple wrapper around AutoTokenizer to make instantiating
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+ an MOD-adapted tokenizer a bit easier.
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+
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+ MOD-adapted tokenizers have sentinel tokens (e.g., <extra_id_0>),
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+ a padding token, and a property to get the token ids of the
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+ sentinel tokens.
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+ """
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+
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+ @classmethod
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+ def from_pretrained(cls, *args, **kwargs):
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+ """See `AutoTokenizer.from_pretrained` docstring."""
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+ tokenizer = super().from_pretrained(*args, **kwargs)
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+ adapt_tokenizer_for_denoising(tokenizer)
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+ return tokenizer