iddlmZddlmZmZmZmZddlmZm Z ddl m Z ddl m Z mZddlmZddlmZddlmZmZesd gZGd d eZy ) )Sequence)AnyListOptionalUnion)Tensorstack)Literal) _eed_compute _eed_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzExtendedEditDistance.plotc>eZdZUdZdZeed<dZeed<dZeed<dZ e ed<dZ e ed <e e ed < dd ed d ede de de de deddffd ZdeeeefdeeeeefddfdZdee ee e fffdZ ddeee ee fdeedefdZxZS)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_eedlanguageenjareturn_sentence_level_scorealpharhodeletion insertionkwargsreturnNc bt |d i||dvrtd|||_||_t gd||||gD]:\}} t | trt | ts'| dks-td|d||_||_ ||_ ||_ |jdgd y) Nrz?Expected argument `language` to either be `en` or `ja` but got )rr r!r"rz Parameter `z)` is expected to be a non-negative float.rcat)dist_reduce_fx) super__init__ ValueErrorrrzip isinstancefloatrr r!r" add_state) selfrrrr r!r"r# param_nameparam __class__s j/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/text/eed.pyr*zExtendedEditDistance.__init__Fs "6" < '^_g^hij j-5 +F("%%NQVX[]egpPq!r JeU+ 5%0HUUVY ;zl:c!dee"s   " ~r%@predstargetc t|||j|j|j|j|j |j |_y)z*Update state with predictions and targets.N)r rrr r!r"r)r0r6r7s r4updatezExtendedEditDistance.updatecsC (   MM JJ HH MM NN     r5cvt|j}|jr|t|jfS|S)z'Calculate extended edit distance score.)r rrr )r0averages r4computezExtendedEditDistance.computets6t001  + +E$"3"344 4r5valaxc&|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 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)r0r=r>s r4plotzExtendedEditDistance.plot|sTzz#r""r5)rFg@g333333?g?r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrr.rrrr rr*rstrrr9tupler<rrrrA __classcell__)r3s@r4rrs`@#d"#t##t#!e!!e!v,)-,1A*%A&*A A  A  AAA A: S(3-'( sHSM123    "vuVV^'<<=_c*#E&(6*:":;<*#IQRZI[*# *#r5rN)collections.abcrtypingrrrrtorchrr typing_extensionsr torchmetrics.functional.text.eedr r torchmetrics.metricr torchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr(r5r4rTs<%--%F&@@34I#6I#r5