i,AdUddlmZddlmZmZmZmZmZmZm Z ddl Z ddl m Z ddl m Z ddlmZmZmZddlmZddlmZdd lmZdd lmZmZdd lmZdd lmZmZdd l m!Z!m"Z"esdgZ#dZ$e%e&d<ererddl'm(Z(m)Z)ddZ*ee*s ddgZ#nddgZ#dee dee de+e%e ffdZ,GddeZ-y))Sequence)AnyCallableListOptionalTupleUnioncastN)Tensor)Module) _postprocess_multiple_references_preprocess_multiple_references bert_score)_preprocess_text)Metric)rank_zero_warn)_SKIP_SLOW_DOCTEST_try_proceed_with_timeout) dim_zero_cat)_MATPLOTLIB_AVAILABLE_TRANSFORMERS_GREATER_EQUAL_4_4)_AX_TYPE_PLOT_OUT_TYPEzBERTScore.plotz roberta-large_DEFAULT_MODEL) AutoModel AutoTokenizerreturncptjtdtjtdy)zDownload intensive operations.T)resume_downloadN)rfrom_pretrainedrrk/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/text/bert.py_download_model_for_bert_scorer$+s"%%ndK!!.$Gr" BERTScore input_idsattention_maskcXtj|tj|dS)z]Create an input dictionary of ``input_ids`` and ``attention_mask`` for BERTScore calculation.r&r')torchcatr)s r#_get_input_dictr,6s9->AZ [[r"c)&eZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e e ed <e e ed <e e ed <e e ed< d,deedeededeedeedeeeeee fge fdededeeeej.fdededededededeed eed!ed"ed#df(fd$ Zd%eeeefd&eeeeeeefd#dfd'Zd#eeee e e efffd(Z d-d)eee ee fd*eed#efd+ZxZS).r%a`Bert_score Evaluating Text Generation`_ for measuring text similarity. BERT leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language generation tasks. This implementation follows the original implementation from `BERT_score`_. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds``: Predicted sentence(s). Can be one of: * A single predicted sentence as a string (``str``) * A sequence of predicted sentences (``Sequence[str]``) - ``target``: Target/reference sentence(s). Can be one of: * A single reference sentence as a string (``str``) * A sequence of reference sentences (``Sequence[str]``) * A sequence of sequences of reference sentences for multi-reference evaluation (``Sequence[Sequence[str]]``) As output of ``forward`` and ``compute`` the metric returns the following output: - ``score`` (:class:`~Dict`): A dictionary containing the keys ``precision``, ``recall`` and ``f1`` with corresponding values Args: preds (Union[str, Sequence[str]]): A single predicted sentence or a sequence of predicted sentences. target (Union[str, Sequence[str], Sequence[Sequence[str]]]): A single target sentence, a sequence of target sentences, or a sequence of sequences of target sentences for multiple references per prediction. model_type: A name or a model path used to load ``transformers`` pretrained model. num_layers: A layer of representation to use. all_layers: An indication of whether the representation from all model's layers should be used. If ``all_layers=True``, the argument ``num_layers`` is ignored. model: A user's own model. Must be of `torch.nn.Module` instance. user_tokenizer: A user's own tokenizer used with the own model. This must be an instance with the ``__call__`` method. This method must take an iterable of sentences (`List[str]`) and must return a python dictionary containing `"input_ids"` and `"attention_mask"` represented by :class:`~torch.Tensor`. It is up to the user's model of whether `"input_ids"` is a :class:`~torch.Tensor` of input ids or embedding vectors. This tokenizer must prepend an equivalent of ``[CLS]`` token and append an equivalent of ``[SEP]`` token as ``transformers`` tokenizer does. user_forward_fn: A user's own forward function used in a combination with ``user_model``. This function must take ``user_model`` and a python dictionary of containing ``"input_ids"`` and ``"attention_mask"`` represented by :class:`~torch.Tensor` as an input and return the model's output represented by the single :class:`~torch.Tensor`. verbose: An indication of whether a progress bar to be displayed during the embeddings' calculation. idf: An indication whether normalization using inverse document frequencies should be used. device: A device to be used for calculation. max_length: A maximum length of input sequences. Sequences longer than ``max_length`` are to be trimmed. batch_size: A batch size used for model processing. num_threads: A number of threads to use for a dataloader. return_hash: An indication of whether the correspodning ``hash_code`` should be returned. lang: A language of input sentences. rescale_with_baseline: An indication of whether bertscore should be rescaled with a pre-computed baseline. When a pretrained model from ``transformers`` model is used, the corresponding baseline is downloaded from the original ``bert-score`` package from `BERT_score`_ if available. In other cases, please specify a path to the baseline csv/tsv file, which must follow the formatting of the files from `BERT_score`_. baseline_path: A path to the user's own local csv/tsv file with the baseline scale. baseline_url: A url path to the user's own csv/tsv file with the baseline scale. truncation: An indication of whether the input sequences should be truncated to the ``max_length``. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from pprint import pprint >>> from torchmetrics.text.bert import BERTScore >>> preds = ["hello there", "general kenobi"] >>> target = ["hello there", "master kenobi"] >>> bertscore = BERTScore() >>> pprint(bertscore(preds, target)) {'f1': tensor([1.0000, 0.9961]), 'precision': tensor([1.0000, 0.9961]), 'recall': tensor([1.0000, 0.9961])} Example: >>> from pprint import pprint >>> from torchmetrics.text.bert import BERTScore >>> preds = ["hello there", "general kenobi"] >>> target = [["hello there", "master kenobi"], ["hello there", "master kenobi"]] >>> bertscore = BERTScore() >>> pprint(bertscore(preds, target)) {'f1': tensor([1.0000, 0.9961]), 'precision': tensor([1.0000, 0.9961]), 'recall': tensor([1.0000, 0.9961])} Fis_differentiableThigher_is_betterfull_state_updategplot_lower_boundg?plot_upper_boundpreds_input_idspreds_attention_masktarget_input_idstarget_attention_maskNmodel_name_or_path num_layers all_layersmodeluser_tokenizeruser_forward_fnverboseidfdevice max_length batch_size num_threads return_hashlangrescale_with_baseline baseline_path baseline_url truncationkwargsrc t|di||xst|_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_||_d|_|r||_d|_nTt.s t1dddlm}|t7dtd|j8|j|_d|_|j;dgd |j;d gd |j;d gd |j;d gd y)NTz`BERTScore` metric with default tokenizers requires `transformers` package be installed. Either install with `pip install transformers>=4.4` or `pip install torchmetrics[text]`.r)rzThe argument `model_name_or_path` was not specified while it is required when the default `transformers` model is used. It will use the default recommended model - .Fr3r+)dist_reduce_fxr4r5r6r!)super__init__rr7r8r9r:r<r=r>embedding_devicer@rArBrCrDrErFrGrHref_group_boundaries tokenizerr;rModuleNotFoundError transformersrrr add_state)selfr7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIr __class__s r#rNzBERTScore.__init__sa, "6""4"F$$ .  &$$&& %:"*($EI! +DN"&D 2)p 3!)DDRCUUVX ;]::4;R;RSDN"'D  ("UC -r%H )2eD .5Ir"predstargetct|tr|g}t|tr|g}t|ts t|}t|ts t|}t|t|k7r#t dt|dt|t|trAt|dkDr3t|tr#t|dkDrt ||\}}|_t||j|j|jd|j\}}tttt||j|j|jd|j\}}|jj|d|j j|d|j"j|d|j$j|dy) zStore predictions/references for computing BERT scores. It is necessary to store sentences in a tokenized form to ensure the DDP mode working. zLExpected number of predicted and reference sentences to be the same, but gotz and rF)rHsort_according_length own_tokenizerr&r'N) isinstancestrlistlen ValueErrorrrPrrQr@rHr;r rr3appendr4r5r6)rUrWrX preds_dict_ target_dicts r#updatezBERTScore.updates eS !GE fc "XF%&KE&$'&\F u:V $^u:,eCK=2  eT "s5zA~*VT:RWZ[aWbefWf7VW\^d7e 4E644(  NN OO"'--  A* cF # NN OO"'--   Q ##J{$;< !!((4D)EF $$[%=> ""))+6F*GHr"ct|jt|jd}t|jt|jd}t did|d|d|j d|jd|jd|jd|jr |jnd d |jd |jd |jd |jd|j d|j"d|j$d|j&d|j(d|j*d|j,d|j.}|j0gt3|dt4rTt3|dt4rAt3|dt4r.t7|d|d|d|j0\|d<|d<|d<|S)zCalculate BERT scores.r)rWrXr7r8r9r:r;Nr<r=r>r?r@rArBrCrDrErFrG precisionrecallf1r!)rr3r4r5r6rr7r8r9r:r;rQr<r=r>rOr@rArBrCrDrErFrGrPr\r r )rUrWrX output_dicts r#computezBERTScore.computes&d&:&:;*4+D+DE  &d&;&;<*4+E+EF  !    $66     **  .2-@-@4>>d !00 LL  ((   (( (( ! "#'"<"<# $,,% &**' .  % % 1;{3V<;x0&9;t,f5QqK(+h*?TARTXTmTmR NK $k(&;[=Nr"valaxc|T|j}|jDcic])\}}|tj|j +}}}|j ||Scc}}w)a,Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> from torchmetrics.text.bert import BERTScore >>> preds = ["hello there", "general kenobi"] >>> target = ["hello there", "master kenobi"] >>> metric = BERTScore() >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import tensor >>> from torchmetrics.text.bert import BERTScore >>> preds = ["hello there", "general kenobi"] >>> target = ["hello there", "master kenobi"] >>> metric = BERTScore() >>> values = [] >>> for _ in range(10): ... val = metric(preds, target) ... val = {k: tensor(v).mean() for k,v in val.items()} # convert into single value per key ... values.append(val) >>> fig_, ax_ = metric.plot(values) )rkitemsr*tensormean_plot)rUrlrmkvs r#plotzBERTScore.plot>s`Z ;,,.C9<EA1ell1o**,,CEzz#r""Fs.A))NNFNNNFFNi@rFenFNNF)NN) __name__ __module__ __qualname____doc__r.bool__annotations__r/r0r1floatr2rr rr]intr rrdictr r*r?rNrrerkrrru __classcell__)rVs@r#r%r%;sUn$t#!d!#t#!e!!e!&\!v,&6l"<'-1$( "&(,SW59!&+'+&* 'AJ$SMAJSMAJ AJ  AJ ! 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