iddlmZddlmZmZmZddlZddlmZmZddl m Z ddl m Z m Z mZddlmZddlmZmZesd gZGd d e Zy) )Sequence)AnyOptionalUnionN)Tensortensor)Metric)_bleu_score_compute_bleu_score_update _tokenize_fn)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzBLEUScore.plotc "eZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed <e ed <e ed <e ed< dde dedeee deddf fd ZdeedeeeddfdZde fdZ ddeee ee fdeedefdZxZS) 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 t|d i|||_||_|(t ||k7rt dt |d|||nd|z g|z|_|jdtdd|jdtdd|jd tj|jd|jd tj|jdy) Nz5List of weights has different weights than `n_gram`: z != rrrsum)dist_reduce_fxrrr) super__init__rrlen ValueErrorr add_statertorchzeros)selfrrrr __class__s k/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/text/bleu.pyr'zBLEUScore.__init__Ns "6"   3w<6#9TUXY`UaTbbfgmfnop p")"5wC&L>F;R  {F3KF |VC[G {EKK $uUpredstargetc t|||j|j|j|j|j t \|_|_y)z*Update state with predictions and targets.N)r rrrrrr )r-r1r2s r/updatezBLEUScore.updateasC*<   NN    NN OO KK  + 'r0c t|j|j|j|j|j |j |jS)zCalculate BLEU score.)r rrrrrrr)r-s r/computezBLEUScore.computensB" NNDOOT^^T=M=Mt{{\`\h\hjnjuju  r0valaxc&|j||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> from torchmetrics.text import BLEUScore >>> metric = BLEUScore() >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torchmetrics.text import BLEUScore >>> metric = BLEUScore() >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r-r7r8s r/plotzBLEUScore.plottsTzz#r""r0)FN)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrintrrrr'strr4r6rrrr; __classcell__)r.s@r/rr"s@$t#!d!"t"!e!!e!-1 VVV(5/* V  V  V&  HSM  8HSM3J  t    _c*#E&(6*:":;<*#IQRZI[*# *#r0r)collections.abcrtypingrrrr+rr torchmetricsr !torchmetrics.functional.text.bleur r r torchmetrics.utilities.importsr torchmetrics.utilities.plotrr__doctest_skip__rr%r0r/rNs?&%'' cc@@()|#|#r0