o .wi@sddlmZddlmZmZmZmZddlmZm Z ddl m Z ddl m Z mZddlmZddlmZddlmZmZesAd gZGd d d eZd S) )Sequence)AnyListOptionalUnion)Tensorstack)Literal) _eed_compute _eed_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEExtendedEditDistance.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<dZ e ed <e e ed <   d%deddede de de de deddffdd ZdeeeefdeeeeefddfddZdee ee e fffdd Z d&d!eee ee fd"eedefd#d$ZZS)'ExtendedEditDistanceaCompute extended edit distance score (`ExtendedEditDistance`_) for strings or list of strings. The metric utilises the Levenshtein distance and extends it by adding a jump operation. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~Sequence`): An iterable of hypothesis corpus - ``target`` (:class:`~Sequence`): An iterable of iterables of reference corpus As output of ``forward`` and ``compute`` the metric returns the following output: - ``eed`` (:class:`~torch.Tensor`): A tensor with the extended edit distance score Args: language: Language used in sentences. Only supports English (en) and Japanese (ja) for now. return_sentence_level_score: An indication of whether sentence-level EED score is to be returned alpha: optimal jump penalty, penalty for jumps between characters rho: coverage cost, penalty for repetition of characters deletion: penalty for deletion of character insertion: penalty for insertion or substitution of character kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.text import ExtendedEditDistance >>> preds = ["this is the prediction", "here is an other sample"] >>> target = ["this is the reference", "here is another one"] >>> eed = ExtendedEditDistance() >>> eed(preds=preds, target=target) tensor(0.3078) Fhigher_is_betteris_differentiablefull_state_updategplot_lower_bound?plot_upper_bound sentence_eeden@333333?皙?languagerjareturn_sentence_level_scorealpharhodeletion insertionkwargsreturnNc  stjd i||dvrtd|||_||_tgd||||gD]\}} t| tr7t| tr?| dkr?td|dq%||_||_ ||_ ||_ |j dgdd dS) Nrz?Expected argument `language` to either be `en` or `ja` but got )r!r"r#r$rz Parameter `z)` is expected to be a non-negative float.rcat)dist_reduce_fx) super__init__ ValueErrorrr zip isinstancefloatr!r"r#r$ add_state) selfrr r!r"r#r$r% param_nameparam __class__r)R/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/text/eed.pyr+Fs zExtendedEditDistance.__init__predstargetc Cs(t|||j|j|j|j|j|j|_dS)z*Update state with predictions and targets.N)r rr!r"r#r$r)r1r7r8r)r)r6updatecs zExtendedEditDistance.updatecCs"t|j}|jr|t|jfS|S)z'Calculate extended edit distance score.)r rr r)r1averager)r)r6computets zExtendedEditDistance.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 ExtendedEditDistance >>> metric = ExtendedEditDistance() >>> preds = ["this is the prediction", "there is an other sample"] >>> target = ["this is the reference", "there is another one"] >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torchmetrics.text import ExtendedEditDistance >>> metric = ExtendedEditDistance() >>> preds = ["this is the prediction", "there is an other sample"] >>> target = ["this is the reference", "there is another one"] >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r1r<r=r)r)r6plot|s *r)rFrrrr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrr/rrrr rr+rstrrr9tupler;rrrr? __classcell__r)r)r4r6rs^         rN)collections.abcrtypingrrrrtorchrrtyping_extensionsr torchmetrics.functional.text.eedr r torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotrr__doctest_skip__rr)r)r)r6s