o .wi@sddlmZddlmZmZmZddlZddlmZmZddl m Z ddl m Z m Z mZddlmZddlmZmZes?d gZGd d d e ZdS) )Sequence)AnyOptionalUnionN)Tensortensor)Metric)_bleu_score_compute_bleu_score_update _tokenize_fn)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEBLEUScore.plotc seZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed <e ed <e ed <e ed<   d"de dedeee deddf fdd ZdeedeeeddfddZde fddZ d#deee ee fdeedefd d!ZZS)$ 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.text 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_updateplot_lower_bound?plot_upper_bound preds_len target_len numerator denominatorNn_gramsmoothweightskwargsreturnc 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 != rrrsum)dist_reduce_fxrrr) super__init__rrlen ValueErrorr add_statertorchzeros)selfrrrr  __class__r$S/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/text/bleu.pyr&NszBLEUScore.__init__predstargetc Cs,t|||j|j|j|j|jt\|_|_dS)z*Update state with predictions and targets.N)r rrrrrr )r,r0r1r$r$r/updateaszBLEUScore.updatecCs"t|j|j|j|j|j|j|jS)zCalculate BLEU score.)r rrrrrrr)r,r$r$r/computenszBLEUScore.computevalaxcCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> from torchmetrics.text import BLEUScore >>> metric = BLEUScore() >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torchmetrics.text import BLEUScore >>> metric = BLEUScore() >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r,r4r5r$r$r/plotts *r)rFN)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrintrrrr&strr2r3rr rr7 __classcell__r$r$r-r/r"sF      " r)collections.abcrtypingrrrr*rr torchmetricsr!torchmetrics.functional.text.bleur r r torchmetrics.utilities.importsr torchmetrics.utilities.plotr r__doctest_skip__rr$r$r$r/s