o .wim@sddlmZddlmZmZmZmZddlZddlmZm Z ddl m Z m Z m Z ddlmZddlmZddlmZmZesAd gZGd d d eZdS) )Sequence)AnyListOptionalUnionN)Tensortensor) _ter_compute _ter_update_TercomTokenizer)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPETranslationEditRate.plotcs*eZdZUdZdZeed<dZeed<dZeed<dZ e ed<dZ e ed <e ed <e ed <d Z eee ed <     d"dededededededd ffdd Zdeeeefdeeeeefdd fddZdee ee e fffddZ d#deee ee fdeedefd d!ZZS)$TranslationEditRateauCalculate Translation edit rate (`TER`_) of machine translated text with one or more references. This implementation follows the one from `SacreBleu_ter`_, which is a near-exact reimplementation of the Tercom algorithm, produces identical results on all "sane" outputs. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~Sequence`): An iterable of hypothesis corpus - ``target`` (:class:`~Sequence`): An iterable of iterables of reference corpus As output of ``forward`` and ``compute`` the metric returns the following output: - ``ter`` (:class:`~torch.Tensor`): if ``return_sentence_level_score=True`` return a corpus-level translation edit rate with a list of sentence-level translation_edit_rate, else return a corpus-level translation edit rate Args: normalize: An indication whether a general tokenization to be applied. no_punctuation: An indication whteher a punctuation to be removed from the sentences. lowercase: An indication whether to enable case-insensitivity. asian_support: An indication whether asian characters to be processed. return_sentence_level_score: An indication whether a sentence-level TER to be returned. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.text import TranslationEditRate >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> ter = TranslationEditRate() >>> ter(preds, target) tensor(0.1538) Fis_differentiablehigher_is_betterfull_state_updateplot_lower_boundg?plot_upper_boundtotal_num_edits total_tgt_lenN sentence_terT normalizeno_punctuation lowercase asian_supportreturn_sentence_level_scorekwargsreturnc stjd i|t|tstd|dt|ts#td|dt|ts0td|dt|ts=td|dt|||||_||_|jdt ddd |jd t ddd |jri|jd gd d dSdS)Nz>> # Example plotting a single value >>> from torchmetrics.text import TranslationEditRate >>> metric = TranslationEditRate() >>> 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 TranslationEditRate >>> metric = TranslationEditRate() >>> 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.r8r9r&r&r1plotws *r)FFTFF)NN)__name__ __module__ __qualname____doc__rr*__annotations__rrrfloatrrrrrrr(rstrrr4tupler7rrr; __classcell__r&r&r/r1rsP  !    2 r)collections.abcrtypingrrrrr5rr torchmetrics.functional.text.terr r r torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotrr__doctest_skip__rr&r&r&r1s