`iIddlZddlZddlmZmZddlmZddlmZddl m Z m Z m Z m Z ddlmZmZe r ddlZdd lmZe rdd lmZe jeZGd d ejZe ed GddeZe ed GddeZe ed GddeZdS)N)AnyUnion)GenerationConfig)TruncationStrategy)add_end_docstringsis_tf_availableis_torch_availablelogging)Pipelinebuild_pipeline_init_args)/TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES),MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMESceZdZdZdZdS) ReturnTyperr N)__name__ __module__ __qualname__TENSORSTEXT/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/pipelines/text2text_generation.pyrrsG DDDrrT) has_tokenizerc eZdZdZdZdZdZdZdZe ddZ dZ fdZ dd Z d ed ed efdZdZdeeeefdedeeeefffd ZejfdZdZejdfdZxZS)Text2TextGenerationPipelinea9 Pipeline for text to text generation using seq2seq models. Unless the model you're using explicitly sets these generation parameters in its configuration files (`generation_config.json`), the following default values will be used: - max_new_tokens: 256 - num_beams: 4 Example: ```python >>> from transformers import pipeline >>> generator = pipeline(model="mrm8488/t5-base-finetuned-question-generation-ap") >>> generator( ... "answer: Manuel context: Manuel has created RuPERTa-base with the support of HF-Transformers and Google" ... ) [{'generated_text': 'question: Who created the RuPERTa-base?'}] ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial). You can pass text generation parameters to this pipeline to control stopping criteria, decoding strategy, and more. Learn more about text generation parameters in [Text generation strategies](../generation_strategies) and [Text generation](text_generation). This Text2TextGenerationPipeline pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"text2text-generation"`. The models that this pipeline can use are models that have been fine-tuned on a translation task. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=text2text-generation). For a list of available parameters, see the [following documentation](https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.generation.GenerationMixin.generate) Usage: ```python text2text_generator = pipeline("text2text-generation") text2text_generator("question: What is 42 ? context: 42 is the answer to life, the universe and everything") ```TF)max_new_tokens num_beams generatedctj|i|||jdkrtnt dS)Ntf)super__init__check_model_type frameworkrrselfargskwargs __class__s rr&z$Text2TextGenerationPipeline.__init__TsY$)&))) ~%% < ;=     rNc vi}|||d<|} i} |||r tjn tj}||| d<||| d<|N|j|d} t | dkrt jd| d|d <|j |j| d <|j |j| d <|j | d <|| | fS) N truncation return_typeclean_up_tokenization_spacesF)add_special_tokensr zStopping on a multiple token sequence is not yet supported on transformers. The first token of the stop sequence will be used as the stop sequence string in the interim.r eos_token_idassistant_model tokenizerassistant_tokenizer) rrrr5encodelenwarningswarnr4r6) r*return_tensors return_textr0r1r/ stop_sequencegenerate_kwargspreprocess_paramsforward_paramspostprocess_paramsstop_sequence_idss r_sanitize_parametersz0Text2TextGenerationPipeline._sanitize_parameters]s  !.8 l +(  %+*=0>S*,,JOK  "0; } - ' 3A] = >  $ $ 5 5mX] 5 ^ ^ $%%)) b/@.BON +   +040DN, -  # /*..N; '484LN0 1 .2DDDr input_length min_length max_lengthcdS)j Checks whether there might be something wrong with given input with regard to the model. Trr*rDrErFs r check_inputsz(Text2TextGenerationPipeline.check_inputss trc|j|jndt|dtr3|jjt dfd|dDf}d}nCt|dt r|dzf}d}ntd|dd|j||||jd }d |vr|d =|S) NrzOPlease make sure that the tokenizer has a pad_token_id when using a batch inputcg|]}|zSrr).0argprefixs r zCText2TextGenerationPipeline._parse_and_tokenize..s555cVc\555rTFz `args[0]`: zI have the wrong format. The should be either of type `str` or type `list`)paddingr/r;token_type_ids) rP isinstancelistr5 pad_token_id ValueErrorstr TypeErrorr()r*r/r+rRinputsrPs @r_parse_and_tokenizez/Text2TextGenerationPipeline._parse_and_tokenizes $ 7R d1gt $ $ ~*2 !rsss5555T!W5557DGG Q % % T!W$&DGGqtAwqqq  w:^b^lmmm v % %'( rr+r,returnctj|i|}t|dtrDt d|dDr%t d|Dr d|DS|S)a Generate the output text(s) using text(s) given as inputs. Args: args (`str` or `list[str]`): Input text for the encoder. return_tensors (`bool`, *optional*, defaults to `False`): Whether or not to include the tensors of predictions (as token indices) in the outputs. return_text (`bool`, *optional*, defaults to `True`): Whether or not to include the decoded texts in the outputs. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to clean up the potential extra spaces in the text output. truncation (`TruncationStrategy`, *optional*, defaults to `TruncationStrategy.DO_NOT_TRUNCATE`): The truncation strategy for the tokenization within the pipeline. `TruncationStrategy.DO_NOT_TRUNCATE` (default) will never truncate, but it is sometimes desirable to truncate the input to fit the model's max_length instead of throwing an error down the line. generate_kwargs: Additional keyword arguments to pass along to the generate method of the model (see the generate method corresponding to your framework [here](./text_generation)). Return: A list or a list of list of `dict`: Each result comes as a dictionary with the following keys: - **generated_text** (`str`, present when `return_text=True`) -- The generated text. - **generated_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The token ids of the generated text. rc3@K|]}t|tVdS)N)rTrX)rNels r z7Text2TextGenerationPipeline.__call__..s,::BJr3''::::::rc3<K|]}t|dkVdS)r N)r8rNress rr`z7Text2TextGenerationPipeline.__call__..s,44cCHHM444444rcg|] }|d S)rrrbs rrQz8Text2TextGenerationPipeline.__call__..s---sCF---r)r%__call__rTrUall)r*r+r,resultr-s rrez$Text2TextGenerationPipeline.__call__s:"!42622 tAw % % .::$q'::::: .44V44444 . .-f--- - rc &|j|fd|i|}|S)Nr/)r[)r*rZr/r,s r preprocessz&Text2TextGenerationPipeline.preprocesss&))&RRZR6RR rc z|jdkr|dj\}}n:|jdkr/tj|d\}}|||d|jj|d|jjd|vr |j|d<|j j d i||}|jd}|jdkr |j |||zg|jddR}n5|jdkr*tj ||||zg|jddR}d |iS) Npt input_idsr$rErFgeneration_configrr output_idsr) r(shaper$numpyrJgetrmrErFmodelgeneratereshape)r* model_inputsr>in_brDrnout_bs r_forwardz$Text2TextGenerationPipeline._forwardsp >T ! !!-k!:!@ D,, ^t # #!#,{*C!D!D!J!J!L!L D,      d.D.O P P    d.D.O P P    o 5 5373IO/ 0(TZ(KK<K?KK  # >T ! !++D%4-W*BRSTSUSUBVWWWJJ ^t # #Ju}0\zGWXYXZXZG[0\0\]]Jj))rcg}|ddD]k}|tjkr |jd|i}n7|tjkr'|jd|j|d|i}||l|S)Nrnr _token_ids_textT)skip_special_tokensr1)rr return_namerr5decodeappend)r* model_outputsr0r1recordsrnrecords r postprocessz'Text2TextGenerationPipeline.postprocesss' 5a8 # #Jj000!-999:F //'...0E0E",05Q1F11 NN6 " " " "r)NNNNNN)rrr__doc___pipeline_calls_generate_load_processor_load_image_processor_load_feature_extractor_load_tokenizerr_default_generation_configr}r&rCintrJr[rrXrUrdictrerDO_NOT_TRUNCATErirxrrr __classcell__r-s@rrrs' ' R $O!#O!1!1""" K     %)(E(E(E(ET#3 *$eCcN3$s$tDQTVYQYNG[$$$$$$L-?,N***06@_chrrc>eZdZdZdZfdZdedededefdZxZ S) SummarizationPipelinea Summarize news articles and other documents. This summarizing pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"summarization"`. The models that this pipeline can use are models that have been fine-tuned on a summarization task, which is currently, '*bart-large-cnn*', '*google-t5/t5-small*', '*google-t5/t5-base*', '*google-t5/t5-large*', '*google-t5/t5-3b*', '*google-t5/t5-11b*'. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=summarization). For a list of available parameters, see the [following documentation](https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.generation.GenerationMixin.generate) Unless the model you're using explicitly sets these generation parameters in its configuration files (`generation_config.json`), the following default values will be used: - max_new_tokens: 256 - num_beams: 4 Usage: ```python # use bart in pytorch summarizer = pipeline("summarization") summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20) # use t5 in tf summarizer = pipeline("summarization", model="google-t5/t5-base", tokenizer="google-t5/t5-base", framework="tf") summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20) ```summaryc6tj|i|S)a Summarize the text(s) given as inputs. Args: documents (*str* or `list[str]`): One or several articles (or one list of articles) to summarize. return_text (`bool`, *optional*, defaults to `True`): Whether or not to include the decoded texts in the outputs return_tensors (`bool`, *optional*, defaults to `False`): Whether or not to include the tensors of predictions (as token indices) in the outputs. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to clean up the potential extra spaces in the text output. generate_kwargs: Additional keyword arguments to pass along to the generate method of the model (see the generate method corresponding to your framework [here](./text_generation)). Return: A list or a list of list of `dict`: Each result comes as a dictionary with the following keys: - **summary_text** (`str`, present when `return_text=True`) -- The summary of the corresponding input. - **summary_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The token ids of the summary. r%rer)s rrezSummarizationPipeline.__call__s!0 uww0000rrDrErFr\c ||kr!td|d|d||kr)td|d|d|dzdd Sd S) rHzYour min_length=z' must be inferior than your max_length=.zYour max_length is set to z , but your input_length is only z. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=r)NloggerwarningrIs rrJz"SummarizationPipeline.check_inputs1s  " " NNnjnnaknnn o o o * $ $ NNsZssYess^jno^osss      % $r) rrrrr}rerboolrJrrs@rrrsz  <K111114  # 3 SW        rrcdeZdZdZdZdededefdZejdddfd Z d fd Z fd Z xZ S) TranslationPipelinea Translates from one language to another. This translation pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"translation_xx_to_yy"`. The models that this pipeline can use are models that have been fine-tuned on a translation task. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=translation). For a list of available parameters, see the [following documentation](https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.generation.GenerationMixin.generate) Unless the model you're using explicitly sets these generation parameters in its configuration files (`generation_config.json`), the following default values will be used: - max_new_tokens: 256 - num_beams: 4 Usage: ```python en_fr_translator = pipeline("translation_en_to_fr") en_fr_translator("How old are you?") ``` translationrDrErFcZ|d|zkr!td|d|ddS)Ng?zYour input_length: z" is bigger than 0.9 * max_length: z`. You might consider increasing your max_length manually, e.g. translator('...', max_length=400)TrrIs rrJz TranslationPipeline.check_inputs\sX # * * * NN^l^^V`^^^   trN)r/src_langtgt_langct|jddr|jj||j|||dSt j|d|iS)N_build_translation_inputs)r;r/rrr/)getattrr5rr(r%r[)r*r/rrr+r-s rrizTranslationPipeline.preprocessdsf 4>#> E E M;4>;dnV^iq /577.LLL Lrc "tjdi|\}}}|||d<|||d<|]|[|d|j}|d}|r)t |dkr|d|d<|d|d<|||fS) Nrrtask_rr r)r%rCrqrsplitr8) r*rrr,r?r@rAritemsr-s rrCz(TranslationPipeline._sanitize_parametersls@\@\@f@f_e@f@f=>+=  ,4 j )  ,4 j )   0::fdi00DJJsOOE 9E a05a!*-05a!*- .2DDDrc6tj|i|S)a Translate the text(s) given as inputs. Args: args (`str` or `list[str]`): Texts to be translated. return_tensors (`bool`, *optional*, defaults to `False`): Whether or not to include the tensors of predictions (as token indices) in the outputs. return_text (`bool`, *optional*, defaults to `True`): Whether or not to include the decoded texts in the outputs. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to clean up the potential extra spaces in the text output. src_lang (`str`, *optional*): The language of the input. Might be required for multilingual models. Will not have any effect for single pair translation models tgt_lang (`str`, *optional*): The language of the desired output. Might be required for multilingual models. Will not have any effect for single pair translation models generate_kwargs: Additional keyword arguments to pass along to the generate method of the model (see the generate method corresponding to your framework [here](./text_generation)). Return: A list or a list of list of `dict`: Each result comes as a dictionary with the following keys: - **translation_text** (`str`, present when `return_text=True`) -- The translation. - **translation_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The token ids of the translation. rr)s rrezTranslationPipeline.__call__|s!< uww0000r)NN) rrrrr}rrJrrrirCrerrs@rrr@s  0 K#3,>+MX\gkMMMMMMMEEEEEE 111111111rr)enumr9typingrr generationrtokenization_utilsrutilsrr r r baser r tensorflowr$models.auto.modeling_tf_autormodels.auto.modeling_autor get_loggerrrEnumrrrrrrrrs@ ))))))333333TTTTTTTTTTTT44444444?_^^^^^^YXXXXXX  H % %        ,,4@@@AAVVVVV(VVBAVr,,4@@@AAGGGGG7GGBAGT,,4@@@AAY1Y1Y1Y1Y15Y1Y1BAY1Y1Y1r