a  d@sddlmZmZmZmZmZmZddlmZm Z ddl m Z ddl m Z mZddlmZddlmZddlmZmZes~dgZGd d d eZd S) )AnyListOptionalSequenceTupleUnion)Tensorstack)Literal) _eed_compute _eed_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEExtendedEditDistance.plotc seZdZUdZdZeed<dZeed<dZeed<dZ e ed<dZ e ed <e e ed <dedee e e e eddfdd ZeeeefeeeeefddddZee ee e ffdddZdeee ee feeedddZZS)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?rjaN)languagereturn_sentence_level_scorealpharhodeletion insertionkwargsreturnc  stjfi||dvr(td|||_||_tgd||||gD]4\}} t| trnt| trJ| dkrJtd|dqJ||_||_ ||_ ||_ |j dgdd dS) Nrz?Expected argument `language` to either be `en` or `ja` but got )r"r#r$r%rz Parameter `z)` is expected to be a non-negative float.rcat)Zdist_reduce_fx) super__init__ ValueErrorr r!zip isinstancefloatr"r#r$r%Z add_state) selfr r!r"r#r$r%r& param_nameparam __class__^/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/text/eed.pyr*Es zExtendedEditDistance.__init__)predstargetr'c Cs(t|||j|j|j|j|j|j|_dS)z*Update state with predictions and targets.N)r r r"r#r$r%r)r/r6r7r4r4r5updatebszExtendedEditDistance.update)r'cCs"t|j}|jr|t|jfS|S)z'Calculate extended edit distance score.)r rr!r )r/Zaverager4r4r5computess zExtendedEditDistance.compute)valaxr'cCs |||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) )Z_plot)r/r:r;r4r4r5plot{s*r)rFrrrr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrr.rrrr rr*rstrrr8rr9rrrr< __classcell__r4r4r2r5rsB         rN)typingrrrrrrZtorchrr Ztyping_extensionsr Z torchmetrics.functional.text.eedr r Ztorchmetrics.metricr Ztorchmetrics.utilities.importsrZtorchmetrics.utilities.plotrrZ__doctest_skip__rr4r4r4r5s