iddlmZddlmZmZmZddlmZddlm Z ddl m Z m Z ddl mZddlmZddlmZmZesd gZGd d eZy ) )Sequence)AnyOptionalUnion)Tensor)_bleu_score_update)_SacreBLEUTokenizer_TokenizersLiteral) BLEUScore)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzSacreBLEUScore.plotceZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed < dd e d ede dedeee dedd ffd Zdeedeeedd fdZ ddeeeeefdeedefdZxZS)SacreBLEUScorea Calculate `BLEU score`_ of machine translated text with one or more references. This implementation follows the behaviour of `SacreBLEU`_. The SacreBLEU implementation differs from the NLTK BLEU implementation in tokenization techniques. 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 ``compute`` the metric returns the following output: - ``sacre_bleu`` (:class:`~torch.Tensor`): A tensor with the SacreBLEU Score .. note:: In the original SacreBLEU, references are passed as a list of reference sets (grouped by reference index). In TorchMetrics, references are passed grouped per prediction (each prediction has its own list of references). For example:: # Predictions preds = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'] # Original SacreBLEU: refs = [ ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], # First set ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'], # Second set ] # TorchMetrics SacreBLEU: target = [ ['The dog bit the man.', 'The dog had bit the man.'], # References for first prediction ['It was not unexpected.', 'No one was surprised.'], # References for second prediction ['The man bit him first.', 'The man had bitten the dog.'], # References for third prediction ] Args: n_gram: Gram value ranged from 1 to 4 smooth: Whether to apply smoothing, see `SacreBLEU`_ tokenize: Tokenization technique to be used. Choose between ``'none'``, ``'13a'``, ``'zh'``, ``'intl'``, ``'char'``, ``'ja-mecab'``, ``'ko-mecab'``, ``'flores101'`` and ``'flores200'``. lowercase: If ``True``, BLEU score over lowercased text is calculated. 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 ``tokenize`` not one of 'none', '13a', 'zh', 'intl' or 'char' ValueError: If ``tokenize`` is set to 'intl' and `regex` is not installed ValueError: If a length of a list of weights is not ``None`` and not equal to ``n_gram``. Example: >>> from torchmetrics.text import SacreBLEUScore >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> sacre_bleu = SacreBLEUScore() >>> sacre_bleu(preds, target) tensor(0.7598) Additional References: - Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics by Chin-Yew Lin and Franz Josef Och `Machine Translation Evolution`_ Fis_differentiableThigher_is_betterfull_state_updategplot_lower_boundg?plot_upper_boundNn_gramsmoothtokenize lowercaseweightskwargsreturnc Nt|d|||d|t|||_y)N)rrr)super__init__r tokenizer)selfrrrrrr __class__s q/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/text/sacre_bleu.pyr zSacreBLEUScore.__init__qs, QvwQ&Q,XyApredstargetc t|||j|j|j|j|j |j \|_|_y)z*Update state with predictions and targets.N)r numerator denominator preds_len target_lenrr!)r"r&r's r$updatezSacreBLEUScore.update}sG*<   NN    NN OO KK NN + 'r%valaxc&|j||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> from torchmetrics.text import SacreBLEUScore >>> metric = SacreBLEUScore() >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torchmetrics.text import SacreBLEUScore >>> metric = SacreBLEUScore() >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r"r.r/s r$plotzSacreBLEUScore.plotsTzz#r""r%)F13aFN)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrintr rrrr strr-rrr rr2 __classcell__)r#s@r$rr#sEN$t#!d!"t"!e!!e!',-1 B B B% B  B (5/* B B  B  HSM  8HSM3J  t  _c*#E&(6*:":;<*#IQRZI[*# *#r%rN)collections.abcrtypingrrrtorchr!torchmetrics.functional.text.bleur'torchmetrics.functional.text.sacre_bleur r torchmetrics.text.bleur torchmetrics.utilities.importsr torchmetrics.utilities.plotr r__doctest_skip__rrr%r$rHs<(%''@[,@@-.Q#YQ#r%