a  d$@sddlmZmZmZmZmZmZmZmZddl m Z ddl m Z ddl mZddlmZmZmZmZddlmZmZddlmZmZesdgZd d giZGd d d eZd S))AnyCallableDictListOptionalSequenceTupleUnion)Tensor)Literal)Metric)ALLOWED_ACCUMULATE_VALUESALLOWED_ROUGE_KEYS_rouge_score_compute_rouge_score_update)_MATPLOTLIB_AVAILABLE_NLTK_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEROUGEScore.plot) ROUGEScorenltkc s&eZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <dee e egefe e egeefedeeeedffed dfdd Zeeeefeeeeeeefd dddZeeefdddZedddZde eeeefe eedddZZS) 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``, spliting 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_boundNbestZrouge1Zrouge2ZrougeL rougeLsum)avgr.) use_stemmer normalizer tokenizer accumulate rouge_keyskwargsreturnc  stjfi||sd|vr2ts*tdddl}t|tsB|f}|D] }|tvrFtd|dtqF|t vrtd|dt ||_ dd|D|_ |r|j j nd|_||_||_||_|j D](} d D]} |j| d | gdd qqdS) NrzUStemmer 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 cSsg|] }t|qS)r).0keyr(r(`/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/text/rouge.py z'ROUGEScore.__init__..ZfmeasureZ precisionZrecall_)Zdist_reduce_fx)super__init__rModuleNotFoundErrorr isinstancetupler ValueErrorr r%rouge_keys_valuesstemZporterZ PorterStemmerstemmerr"r#r$Z add_state) selfr!r"r#r$r%r&rr* rouge_keyZscore __class__r(r+r1is2    zROUGEScore.__init__)predstargetr'c Cst|tr:tdd|Dr:t|tr,|gn dd|D}t|trJ|g}t|tr\|gg}t|||j|j|j|j|j d}| D]H\}}|D]:}| D],\}}t |d|d| | |jqqqdS) z*Update state with predictions and targets.css|]}t|tVqdS)N)r3strr)Ztgtr(r(r+ r-z$ROUGEScore.update..cSsg|] }|gqSr(r(r@r(r(r+r,r-z%ROUGEScore.update..)r8r"r#r$rouger/N)r3listallr?rr6r8r"r#r$itemsgetattrappendtoZdevice) r9r=r>outputr:ZmetricsZmetrictpvaluer(r(r+updates&   zROUGEScore.update)r'cCsHi}|jD]4}dD]*}t|d|d||d|d|<qq t|S)zCCalculate (Aggregate and provide confidence intervals) ROUGE score.r.rBr/)r6rFr)r9Z update_outputr:rJr(r(r+computes  *zROUGEScore.computecCsH|jjg}|jD]*}t||}t|tr0t|}||qtt|S)z>Return a unique hash for the specific instance of this metric.) r<__name__ _defaultsrFr3rCr4rGhash)r9Z hash_valsr*rKr(r(r+__hash__s     zROUGEScore.__hash__)valaxr'cCs |||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) )Z_plot)r9rRrSr(r(r+plots*r)FNNrr)NN)rN __module__ __qualname____doc__rbool__annotations__rrrfloatrrrr?rr r rrr1rLrr rMintrQrrrT __classcell__r(r(r;r+r$s< >     )&  rN)typingrrrrrrrr Ztorchr Ztyping_extensionsr Z torchmetricsr Z"torchmetrics.functional.text.rouger rrrZtorchmetrics.utilities.importsrrZtorchmetrics.utilities.plotrrZ__doctest_skip__Z__doctest_requires__rr(r(r(r+s(