[tokenizer.prepare_seq2seq_batch] change deprecation to be easily actionable (#12669)
* change deprecation to be easily actionable * Update src/transformers/tokenization_utils_base.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * rework as suggested * one warning together * fix format Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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@@ -3274,13 +3274,23 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
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The full set of keys ``[input_ids, attention_mask, labels]``, will only be returned if tgt_texts is passed.
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The full set of keys ``[input_ids, attention_mask, labels]``, will only be returned if tgt_texts is passed.
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Otherwise, input_ids, attention_mask will be the only keys.
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Otherwise, input_ids, attention_mask will be the only keys.
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"""
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"""
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warnings.warn(
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# docstyle-ignore
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"`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
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formatted_warning = """
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"regular `__call__` method to prepare your inputs and the tokenizer under the `as_target_tokenizer` "
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`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of HuggingFace Transformers. Use the regular
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"context manager to prepare your targets. See the documentation of your specific tokenizer for more "
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`__call__` method to prepare your inputs and the tokenizer under the `as_target_tokenizer` context manager to prepare
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"details",
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your targets.
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FutureWarning,
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)
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Here is a short example:
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model_inputs = tokenizer(src_texts, ...)
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with tokenizer.as_target_tokenizer():
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labels = tokenizer(tgt_texts, ...)
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model_inputs["labels"] = labels["input_ids"]
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See the documentation of your specific tokenizer for more details on the specific arguments to the tokenizer of choice.
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For a more complete example, see the implementation of `prepare_seq2seq_batch`.
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"""
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warnings.warn(formatted_warning, FutureWarning)
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# mBART-specific kwargs that should be ignored by other models.
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# mBART-specific kwargs that should be ignored by other models.
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kwargs.pop("src_lang", None)
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kwargs.pop("src_lang", None)
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kwargs.pop("tgt_lang", None)
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kwargs.pop("tgt_lang", None)
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