a  d( @sddlZddlmZmZmZmZmZmZmZm Z ddl Z ddl m Z m Z ddl mZddlmZmZmZddlmZddlmZmZesdgZd Zd Zd Zeeee feee feee feee feee feee ffZGd d d eZdS)N)AnyDictIteratorListOptionalSequenceTupleUnion)Tensortensor)Metric)_chrf_score_compute_chrf_score_update_prepare_n_grams_dicts)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPECHRFScore.plot)charword)predstargetZmatching)Ztotal_preds_char_n_gramsZtotal_preds_word_n_gramsZtotal_target_char_n_gramsZtotal_target_word_n_gramsZtotal_matching_char_n_gramsZtotal_matching_word_n_gramsc sZeZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <d Z e eeed <d)eee eeeed dfdd Zeeeeed dddZeeeeeffdddZedddZed dddZeeeeddd Zeeeeed!d"d#Zeeeeefefdd$d%Z d*e eeeefe e!e"d&d'd(Z#Z$S)+ CHRFScoreaCalculate `chrf score`_ of machine translated text with one or more references. This implementation supports both ChrF score computation introduced in `chrF score`_ and `chrF++ score`_ introduced in `chrF++ score`_. This implementation follows the implmenetaions from https://github.com/m-popovic/chrF and https://github.com/mjpost/sacrebleu/blob/master/sacrebleu/metrics/chrf.py. 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: - ``chrf`` (:class:`~torch.Tensor`): If `return_sentence_level_score=True` return a list of sentence-level chrF/chrF++ scores, else return a corpus-level chrF/chrF++ score Args: n_char_order: A character n-gram order. If ``n_char_order=6``, the metrics refers to the official chrF/chrF++. n_word_order: A word n-gram order. If ``n_word_order=2``, the metric refers to the official chrF++. If ``n_word_order=0``, the metric is equivalent to the original ChrF. beta: parameter determining an importance of recall w.r.t. precision. If ``beta=1``, their importance is equal. lowercase: An indication whether to enable case-insesitivity. whitespace: An indication whether keep whitespaces during n-gram extraction. return_sentence_level_score: An indication whether a sentence-level chrF/chrF++ score to be returned. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``n_char_order`` is not an integer greater than or equal to 1. ValueError: If ``n_word_order`` is not an integer greater than or equal to 0. ValueError: If ``beta`` is smaller than 0. Example: >>> from torchmetrics.text import CHRFScore >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> chrf = CHRFScore() >>> chrf(preds, target) tensor(0.8640) Fis_differentiableThigher_is_betterfull_state_updateplot_lower_boundg?plot_upper_boundNsentence_chrf_score@) n_char_order n_word_orderbeta lowercase whitespacereturn_sentence_level_scorekwargsreturnc  stjfi|t|tr$|dkr,td||_t|trD|dkrLtd||_|dkrbtd||_||_||_ ||_ t |||_ | D]B\\}} } td| dD]&} || || } |j| tdddqq|j r|jd gd ddS) NzMExpected argument `n_char_order` to be an integer greater than or equal to 1.rzMExpected argument `n_word_order` to be an integer greater than or equal to 0.z.Expected argument `beta` to be greater than 0.rsum)Zdist_reduce_fxrcat)super__init__ isinstanceint ValueErrorr#r$r%r&r'r(floatn_order_get_text_n_gram_iteratorrange_get_state_nameZ add_stater ) selfr#r$r%r&r'r(r) n_gram_level n_gram_ordertextn state_name __class___/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/text/chrf.pyr/is( zCHRFScore.__init__)rrr*cCstt||g||j|j|j|j|j|j|jr@|j ndR}| |dd|j durp|d|_ dS)z*Update state with predictions and targets.N) r_convert_states_to_dictsr#r$r4r%r&r'r(r_update_states_from_dicts)r8rrn_grams_dicts_tupler@r@rAupdates.    zCHRFScore.update)r*cCsR|jdur4tg||j|jRt|jfStg||j|jRS)zCalculate chrF/chrF++ score.N)rr rCr4r%torchr-r8r@r@rAcomputes   zCHRFScore.computecCsztttt|j|j}|D]L\\}}}td|dD]0}|||}| |||}t |||||<q:q t | S)z_Convert global metric states to the n-gram dictionaries to be passed in ``_chrf_score_update``.r+) dictzip_DICT_STATES_NAMESrr#r$r5r6_get_dict_namer7getattrtuplevalues)r8 n_grams_dictsr9r:r;r< dict_namer=r@r@rArCs z"CHRFScore._convert_states_to_dicts)rEr*c Csjttt|}|D]N\\}}}td|dD]2}|||}||||}t|||||q0qdS)z]Update global metric states based on the n-gram dictionaries calculated on the current batch.r+N)rJrKrLr5r6rMr7setattr) r8rErQr9r:r;r<rRr=r@r@rArDs  z#CHRFScore._update_states_from_dicts)r;r9r*cCsd|d|dS)z*Return a dictionary name w.r.t input args.total__Z_n_gramsr@)r;r9r@r@rArMszCHRFScore._get_dict_name)r;r9r<r*cCsd|d|d|dS)z,Return a metric state name w.r.t input args.rTrUZ_gramsr@)r;r9r<r@r@rAr7szCHRFScore._get_state_namecCsttt|j|jgtS)zKGet iterator over char/word and reference/hypothesis/matching n-gram level.) itertoolsproductrK_N_GRAM_LEVELSr#r$ _TEXT_LEVELSrHr@r@rAr5sz#CHRFScore._get_text_n_gram_iterator)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 CHRFScore >>> metric = CHRFScore() >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torchmetrics.text import CHRFScore >>> metric = CHRFScore() >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )Z_plot)r8rZr[r@r@rAplots*r)r r!r"FFF)NN)%__name__ __module__ __qualname____doc__rbool__annotations__rrrr3rrrrr r1rr/rstrrFr rrI_DICT_STATES_TYPESrCrD staticmethodrMr7rr5rrr\ __classcell__r@r@r>rAr4sJ ,     $  "r)rVtypingrrrrrrrr rGr r Z torchmetricsr Z!torchmetrics.functional.text.chrfr rrZtorchmetrics.utilities.importsrZtorchmetrics.utilities.plotrrZ__doctest_skip__rXrYrLr1rdrr@r@r@rAs (   >