i$ddlmZddlmZmZmZmZddlmZddl m Z ddl m Z ddl mZmZmZmZddlmZmZddlmZmZesd gZd d giZGd d e Zy))Sequence)AnyCallableOptionalUnion)Tensor)Literal)Metric)ALLOWED_ACCUMULATE_VALUESALLOWED_ROUGE_KEYS_rouge_score_compute_rouge_score_update)_MATPLOTLIB_AVAILABLE_NLTK_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzROUGEScore.plot) ROUGEScorenltkcpeZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed < dd ed e e egefde e egeefdeddeeeedffdedd ffd Zdeeeefdeeeeeeefdd fdZdeeeffdZdefdZ dde eeeefde edefdZxZS) ra `Calculate Rouge Score`_, used for automatic summarization. This implementation should imitate the behaviour of the ``rouge-score`` package `Python ROUGE Implementation` As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~Sequence`): An iterable of predicted sentences or a single predicted sentence - ``target`` (:class:`~Sequence`): An iterable of target sentences or an iterable of interables of target sentences or a single target sentence As output of ``forward`` and ``compute`` the metric returns the following output: - ``rouge`` (:class:`~Dict`): A dictionary of tensor rouge scores for each input str rouge key Args: use_stemmer: Use Porter stemmer to strip word suffixes to improve matching. normalizer: A user's own normalizer function. If this is ``None``, replacing any non-alpha-numeric characters with spaces is default. This function must take a ``str`` and return a ``str``. tokenizer: A user's own tokenizer function. If this is ``None``, splitting by spaces is default This function must take a ``str`` and return ``Sequence[str]`` accumulate: Useful in case of multi-reference rouge score. - ``avg`` takes the avg of all references with respect to predictions - ``best`` takes the best fmeasure score obtained between prediction and multiple corresponding references. rouge_keys: A list of rouge types to calculate. Keys that are allowed are ``rougeL``, ``rougeLsum``, and ``rouge1`` through ``rouge9``. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.text.rouge import ROUGEScore >>> preds = "My name is John" >>> target = "Is your name John" >>> rouge = ROUGEScore() >>> from pprint import pprint >>> pprint(rouge(preds, target)) {'rouge1_fmeasure': tensor(0.7500), 'rouge1_precision': tensor(0.7500), 'rouge1_recall': tensor(0.7500), 'rouge2_fmeasure': tensor(0.), 'rouge2_precision': tensor(0.), 'rouge2_recall': tensor(0.), 'rougeL_fmeasure': tensor(0.5000), 'rougeL_precision': tensor(0.5000), 'rougeL_recall': tensor(0.5000), 'rougeLsum_fmeasure': tensor(0.5000), 'rougeLsum_precision': tensor(0.5000), 'rougeLsum_recall': tensor(0.5000)} Raises: ValueError: If the python packages ``nltk`` is not installed. ValueError: If any of the ``rouge_keys`` does not belong to the allowed set of keys. Fis_differentiableThigher_is_betterfull_state_updategplot_lower_boundg?plot_upper_boundN use_stemmer normalizer tokenizer accumulate)avgbest rouge_keys.kwargsreturnc 8t |d i||sd|vrts tdddl}t |t s|f}|D]}|tvs td|dt|tvrtd|dt||_ |Dcgc] }t| c}|_ |r$jjjnd|_||_||_||_|jD]"} dD]} |j'| d| gd $ycc}w) N rougeLsumzUStemmer and/or `rougeLsum` requires that `nltk` is installed. Use `pip install nltk`.rzGot unknown rouge key z. Expected to be one of zGot unknown accumulate value fmeasure precisionrecall_)dist_reduce_fx)super__init__rModuleNotFoundErrorr isinstancetupler ValueErrorr r!rouge_keys_valuesstemporter PorterStemmerstemmerrrr add_state) selfrrrrr!r"rkey rouge_keyscore __class__s l/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/text/rouge.pyr.zROUGEScore.__init__js8 "6" +3")k *e,$JC,, #9#>VWiVj!kll 6 6/ |;STmSno %EO!PZc"4S"9Z!P;Ftyy''557D $"$I<)AeW5r$O=)"Qs Dpredstargetc Rt|tr6td|Dr$t|tr|gn|Dcgc]}|gc}}t|tr|g}t|tr|gg}t |||j |j |j|j|j}|jD]^\}}|D]T}|jD]?\}} t|d|d|j| j|jAV`ycc}w)z*Update state with predictions and targets.c3<K|]}t|tyw)N)r0str).0tgts r> z$ROUGEScore.update..s+SFSJsC,@Fs)r7rrrrouger*N)r0listallrCrr3r7rrritemsgetattrappendtodevice) r9r?r@rEoutputr;metricsmetrictpvalues r>updatezROUGEScore.updates fd #+SF+S(S!+E3!7fXv=Vvsev=VF eS !GE fc "hZFAT    " "LLnnB #),,. Iw!!'IBDE)AbT":;BB588DKKCXY"0"#1#>Ws D$c i}|jD]$}dD]}t|d|d||d|d|<&t|S)zCCalculate (Aggregate and provide confidence intervals) ROUGE score.r&rGr*)r3rKr )r9 update_outputr;rRs r>computezROUGEScore.computes] //I9:A$%PY{Z[\^[_H`:a i["67:0$M22c|jjg}|jD]:}t||}t |t r t |}|j|<tt |S)z>Return a unique hash for the specific instance of this metric.) r=__name__ _defaultsrKr0rHr1rLhash)r9 hash_valsr:rSs r>__hash__zROUGEScore.__hash__sc^^,,- >>CD#&E%&e    U # " E)$%%rXvalaxc&|j||S)aFPlot 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.rouge import ROUGEScore >>> metric = ROUGEScore() >>> preds = "My name is John" >>> target = "Is your name John" >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torchmetrics.text.rouge import ROUGEScore >>> metric = ROUGEScore() >>> preds = "My name is John" >>> target = "Is your name John" >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r9r_r`s r>plotzROUGEScore.plotsTzz#r""rX)FNNr )rouge1rouge2rougeLr%)NN)rZ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrrCrr rr1rr.rTdictrrWintr^rrrc __classcell__)r=s@r>rr%s<|$t#!d!"t"!e!!e!"59>B-32] &P&PXseSj12&PHcUHSM%9:; &P M* &P #uS#X./ &P&P &PPZ3 -.Z8=c8C=RZ[cdg[hRi>i8jZ Z63c6k*3 &# &_c*#E&(6*:":;<*#IQRZI[*# *#rXrN)collections.abcrtypingrrrrtorchrtyping_extensionsr torchmetricsr "torchmetrics.functional.text.rouger r r rtorchmetrics.utilities.importsrrtorchmetrics.utilities.plotrr__doctest_skip____doctest_requires__rr,rXr>rzsU%11% R@)*(&2H#H#rX