o yi|@s`ddlmZmZmZddlZddlmZmZddlmZddl m Z m Z m Z GdddeZ dS))AnyOptionalSequenceN)Tensortensor)Metric)_bleu_score_compute_bleu_score_update _tokenize_fnc seZdZUdZdZeed<dZeed<dZeed<e ed<e ed<e ed <e ed <  dd e dede e e deffdd Zde ede e edd fddZde fddZZS) BLEUScoreaCalculate `BLEU score`_ of machine translated text with one or more references. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~Sequence`): An iterable of machine translated corpus - ``target`` (:class:`~Sequence`): An iterable of iterables of reference corpus As output of ``forward`` and ``update`` the metric returns the following output: - ``bleu`` (:class:`~torch.Tensor`): A tensor with the BLEU Score Args: n_gram: Gram value ranged from 1 to 4 smooth: Whether or not to apply smoothing, see `Machine Translation Evolution`_ kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. weights: Weights used for unigrams, bigrams, etc. to calculate BLEU score. If not provided, uniform weights are used. Raises: ValueError: If a length of a list of weights is not ``None`` and not equal to ``n_gram``. Example: >>> from torchmetrics import BLEUScore >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> bleu = BLEUScore() >>> bleu(preds, target) tensor(0.7598) Fis_differentiableThigher_is_betterfull_state_update preds_len target_len numerator denominatorNn_gramsmoothweightskwargsc stjd i|||_||_|dur%t||kr%tdt|d||dur+|nd|g||_|jdtddd|jdtddd|jd t |jdd|jd t |jdddS) Nz5List of weights has different weights than `n_gram`: z != g?rgsum)dist_reduce_fxrrr) super__init__rrlen ValueErrorr add_statertorchzeros)selfrrrr __class__rJ/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/text/bleu.pyrEszBLEUScore.__init__predstargetreturnc Cs,t|||j|j|j|j|jt\|_|_dS)z*Update state with predictions and targets.N)r rrrrrr )r"r&r'rrr%updateXszBLEUScore.updatecCs"t|j|j|j|j|j|j|jS)zCalculate BLEU score.)rrrrrrrr)r"rrr%computeeszBLEUScore.compute)rFN)__name__ __module__ __qualname____doc__r bool__annotations__r rrintrrfloatrrstrr)r* __classcell__rrr#r%r s.     " r )typingrrrr rr torchmetricsr!torchmetrics.functional.text.bleurr r r rrrr%s