}-j.ddlZddlZddlmZddlmZmZddlm Z m Z m Z ej e ZGdde Zee d Gd d e ZdS) N)TruncationStrategy)add_end_docstringslogging)ArgumentHandler ChunkPipelinebuild_pipeline_init_argsceZdZdZdZdZdS)%ZeroShotClassificationArgumentHandlerz Handles arguments for zero-shot for text classification by turning each possible label into an NLI premise/hypothesis pair. cnt|trd|dD}|S)Nc^g|]*}||+S)strip).0labels o/var/www/html/banglarbhumi/venv/lib/python3.11/site-packages/transformers/pipelines/zero_shot_classification.py zGZeroShotClassificationArgumentHandler._parse_labels..s-TTTekkmmTekkmmTTT,) isinstancestrsplit)selflabelss r _parse_labelsz3ZeroShotClassificationArgumentHandler._parse_labelss9 fc " " UTTc1B1BTTTF rc^t|dkst|dkrtd|dkrtddt|tr|g}g}|D]$|fd|D%||fS)Nrz>You must include at least one label and at least one sequence.z"The provided hypothesis_template "z" was not able to be formatted with the target labels. Make sure the passed template includes formatting syntax such as {} where the label should go.c>g|]}|gSr)format)rrhypothesis_templatesequences rrzBZeroShotClassificationArgumentHandler.__call__..&s."e"e"eUZH.A.H.H.O.O#P"e"e"er)len ValueErrorrrrextend)r sequencesrr sequence_pairsr!s ` @r__call__z.ZeroShotClassificationArgumentHandler.__call__s v;;!  s9~~22]^^ ^  % %fQi 0 04G G Gq5Hqqq  i % % $" I! g gH  ! !"e"e"e"e"e^d"e"e"e f f f fy((rN)__name__ __module__ __qualname____doc__rr'rrrr r s<  )))))rr T) has_tokenizerceZdZdZdZdZdZdZeffd Z e dZ dde j fdZdZdeeezffd Zdd Zd ZddZxZS)ZeroShotClassificationPipelinea NLI-based zero-shot classification pipeline using a `ModelForSequenceClassification` trained on NLI (natural language inference) tasks. Equivalent of `text-classification` pipelines, but these models don't require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is **much** more flexible. Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model. Then, the logit for *entailment* is taken as the logit for the candidate label being valid. Any NLI model can be used, but the id of the *entailment* label must be included in the model config's :attr:*~transformers.PreTrainedConfig.label2id*. Example: ```python >>> from transformers import pipeline >>> oracle = pipeline(model="facebook/bart-large-mnli") >>> oracle( ... "I have a problem with my iphone that needs to be resolved asap!!", ... candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"], ... ) {'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'], 'scores': [0.504, 0.479, 0.013, 0.003, 0.002]} >>> oracle( ... "I have a problem with my iphone that needs to be resolved asap!!", ... candidate_labels=["english", "german"], ... ) {'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['english', 'german'], 'scores': [0.814, 0.186]} ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This NLI pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"zero-shot-classification"`. The models that this pipeline can use are models that have been fine-tuned on an NLI task. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?search=nli). FTc ||_tjdi||jdkrtddSdS)NzFailed to determine 'entailment' label id from the label2id mapping in the model config. Setting to -1. Define a descriptive label2id mapping in the model config to ensure correct outputs.r) _args_parsersuper__init__ entailment_idloggerwarning)r args_parserkwargs __class__s rr3z'ZeroShotClassificationPipeline.__init__Ysd'""6"""   # # NNk      $ #rc|jjjD]0\}}|dr|cS1dS)Nentailr0)modelconfiglabel2iditemslower startswith)rrinds rr4z,ZeroShotClassificationPipeline.entailment_idbsZ*+4::<<  JE3{{}}''11   rrc Xd}|jj0td|jj|j_ ||||||}nO#t $rB}dt |vr%|||||tj}n|Yd}~nd}~wwxYw|S)ze Parse arguments and tokenize only_first so that hypothesis (label) is not truncated ptNzfTokenizer was not supporting padding necessary for zero-shot, attempting to use `pad_token=eos_token`)add_special_tokensreturn_tensorspadding truncationz too short) tokenizer pad_tokenr5error eos_token ExceptionrrDO_NOT_TRUNCATE) rr&rGrErHr8rFinputses r_parse_and_tokenizez2ZeroShotClassificationPipeline._parse_and_tokenizeis  > # + LL)   (,~'?DN $ ^^#5-% $FF   c!ff$$"'9#1#1A ( $ sA B'%8B""B'c i}d|vr#|j|d|d<d|vr |d|d<i}d|vr |d|d<|i|fS)Ncandidate_labelsr multi_label)r1r)rr8preprocess_paramspostprocess_paramss r_sanitize_parametersz3ZeroShotClassificationPipeline._sanitize_parameterss  ' '484E4S4STZ[mTn4o4o 0 1 F * *7=>S7T 3 4 F " "06}0E } - "&888rr%ct|dkrn5t|dkrd|vr |d|d<ntd|tj|fi|S)a Classify the sequence(s) given as inputs. See the [`ZeroShotClassificationPipeline`] documentation for more information. Args: sequences (`str` or `list[str]`): The sequence(s) to classify, will be truncated if the model input is too large. candidate_labels (`str` or `list[str]`): The set of possible class labels to classify each sequence into. Can be a single label, a string of comma-separated labels, or a list of labels. hypothesis_template (`str`, *optional*, defaults to `"This example is {}."`): The template used to turn each label into an NLI-style hypothesis. This template must include a {} or similar syntax for the candidate label to be inserted into the template. For example, the default template is `"This example is {}."` With the candidate label `"sports"`, this would be fed into the model like `" sequence to classify This example is sports . "`. The default template works well in many cases, but it may be worthwhile to experiment with different templates depending on the task setting. multi_label (`bool`, *optional*, defaults to `False`): Whether or not multiple candidate labels can be true. If `False`, the scores are normalized such that the sum of the label likelihoods for each sequence is 1. If `True`, the labels are considered independent and probabilities are normalized for each candidate by doing a softmax of the entailment score vs. the contradiction score. Return: A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys: - **sequence** (`str`) -- The sequence for which this is the output. - **labels** (`list[str]`) -- The labels sorted by order of likelihood. - **scores** (`list[float]`) -- The probabilities for each of the labels. rrrSz%Unable to understand extra arguments )r"r#r2r')rr%argsr8r9s rr'z'ZeroShotClassificationPipeline.__call__szH t99>>  YY!^^ 2& @ @)-aF% & &KTKKLL Luww 44V444rNThis example is {}.c#K||||\}}tt||D]B\}\}}||g} ||d|t |dz kd| VCdS)Nrrcandidate_labelr!is_last)r1 enumerateziprQr") rrOrSr r&r%ir] sequence_pair model_inputs r preprocessz)ZeroShotClassificationPipeline.preprocesss$($5$5f>NPc$d$d! 3z;ZeroShotClassificationPipeline._forward..sOOO6!9OOOr use_cacheFr^r\r)rImodel_input_namesr<forwardinspect signature parameters)rrOr]r! model_inputs model_forwardoutputs model_outputss ` r_forwardz'ZeroShotClassificationPipeline._forwards !23*%OOOOdn.NOOO  * '+M::E E E(-L %$*,,|,, / i(    rc:d|Dd|D}tjd|D}|jd}t}||z}|||df}|stdkrd|j} | dkrdnd} |d| | gf} tj| tj| dd z } | d } nM|d|jf} tj| tj| dd z } tt| d }|dfd |D| d|f d S) Ncg|] }|d S)r]rrrqs rrz>ZeroShotClassificationPipeline.postprocess..sTTT7G$56TTTrcg|] }|d S)r!rrvs rrz>ZeroShotClassificationPipeline.postprocess..sFFFWWZ(FFFrcfg|].}|d/S)logits)floatnumpy)routputs rrz>ZeroShotClassificationPipeline.postprocess..s5 ^ ^ ^f!1!7!7!9!9!?!?!A!A ^ ^ ^rrr0r.T)keepdims).rc g|] }| Srr)rrarSs rrz>ZeroShotClassificationPipeline.postprocess..s===q'*===r)r!rscores) np concatenateshaper"reshaper4expsumlistreversedargsorttolist)rrrrTr%ryNn num_sequencesreshaped_outputsr4contradiction_identail_contr_logitsr entail_logitstop_indsrSs @r postprocessz*ZeroShotClassificationPipeline.postprocesssTTmTTTFF FFF  ^ ^P] ^ ^ ^__ LO  ! !Q !>>=!R*@AA  Z#.//144 .M%2a%7%7rrQ "239I=8Y3Y"Z V/0026:M3N3N3R3RSU`d3R3e3eeFF^FF-S$2D-DEMVM**RVM-B-B-F-FrTX-F-Y-YYF!2!2!4!45566!! ====H===Q[)0022   r)NrZ)F)r(r)r*r+_load_processor_load_image_processor_load_feature_extractor_load_tokenizerr r3propertyr4r ONLY_FIRSTrQrWrrr'rdrsr __classcell__)r9s@rr.r.+s%%NO!#O#H#H#J#JX'+tPbPm((((T 9 9 9+5c?+5+5+5+5+5+5Z    $        rr.)rlr{rtokenization_pythonrutilsrrbaserr r get_loggerr(r5r r.rrrrs444444////////JJJJJJJJJJ  H % %)))))O)))<,,4@@@AAY Y Y Y Y ]Y Y BAY Y Y r