o eiC@sddlZddlZddlZddlmZddlmZmZm Z ddl m Z m Z m Z mZmZer9ddlZddlmZmZGdd d e Zeed d Gd d d e ZdS)N)GenerationConfig)add_end_docstringsis_torch_availablerequires_backends)ArgumentHandlerDatasetPipelinePipelineExceptionbuild_pipeline_init_args),MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES0MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMESc@seZdZdZdddZdS)%TableQuestionAnsweringArgumentHandlerzB Handles arguments for the TableQuestionAnsweringPipeline NcKs`t|dddl}|durtd|durt|tr,|ddur,|ddur,|g}nbt|trmt|dkrmtdd|DsLtdd d|D|dddura|dddura|}n-td |d d t durvt|t s|t|t j r~|Std t |d ||dg}|D]}t|d|js|ddurtd||d|d<q|S)Npandasrz(Keyword argument `table` cannot be None.querytablecss|]}t|tVqdSN) isinstancedict.0drm/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/pipelines/table_question_answering.py -szATableQuestionAnsweringArgumentHandler.__call__..z:Keyword argument `table` should be a list of dict, but is css|]}t|VqdSr)typerrrrr/szIf keyword argument `table` is a list of dictionaries, each dictionary should have a `table` and `query` key, but only dictionary has keys z `table` and `query` keys.zZInvalid input. Keyword argument `table` should be either of type `dict` or `list`, but is ))rrzTable cannot be None.)rr ValueErrorrrgetlistlenallkeysr types GeneratorTyper DataFrame)selfrrkwargspdtqa_pipeline_inputstqa_pipeline_inputrrr__call__sD &$   z.TableQuestionAnsweringArgumentHandler.__call__)NN)__name__ __module__ __qualname____doc__r,rrrrrsrT) has_tokenizercseZdZdZdZdZdZdZdZdZ e ddZ e ffdd Z d d Zd d Zfd dZdddZdddZdddZddZZS)TableQuestionAnsweringPipelinea Table Question Answering pipeline using a `ModelForTableQuestionAnswering`. This pipeline is only available in PyTorch. 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 Example: ```python >>> from transformers import pipeline >>> oracle = pipeline(model="google/tapas-base-finetuned-wtq") >>> table = { ... "Repository": ["Transformers", "Datasets", "Tokenizers"], ... "Stars": ["36542", "4512", "3934"], ... "Contributors": ["651", "77", "34"], ... "Programming language": ["Python", "Python", "Rust, Python and NodeJS"], ... } >>> oracle(query="How many stars does the transformers repository have?", table=table) {'answer': 'AVERAGE > 36542', 'coordinates': [(0, 1)], 'cells': ['36542'], 'aggregator': 'AVERAGE'} ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This tabular question answering pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"table-question-answering"`. The models that this pipeline can use are models that have been fine-tuned on a tabular question answering task. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=table-question-answering). z table,queryTF)max_new_tokensc sxtjdi|||_t}|t||t|j j ddo)t|j j dd|_ t |j j dr7d|_ dSd|_ dS)Naggregation_labelsnum_aggregation_labelstapasr)super__init__ _args_parserrcopyupdater check_model_typegetattrmodelconfig aggregatehasattrr)r' args_parserr(mapping __class__rrr9}s   "z'TableQuestionAnsweringPipeline.__init__cKs|jdi|S)Nr)r?)r'inputsrrrbatch_inferencesz.TableQuestionAnsweringPipeline.batch_inferencec sg}g}d}|djd}|d|j}|d|j}|d|j}d} t|D]} |dur| dddf} t| } || } t| jdD]@} | dddf| }| dddf| d}| dddf| d}|dkr|dkr|dkrt |||f| | <qNt |  t j |j| dddf<|| }|| }|| } |j|d|d| dd }|j}|jr||j||t jj|d }|j| t j|jj}ttt|D]D\} }| dddf| }| dddf| d}| dddf| d}|dkr6|dkr6|dkr6||f|qfd d D}q+t t|d}|jsP|fS|t t|dfS) z Inference used for models that need to process sequences in a sequential fashion, like the SQA models which handle conversational query related to a table. N input_idsrattention_masktoken_type_idsrr)rIrJrK)logitscs$i|]}|t|dkqS)g?)nparraymean)rkeycoords_to_probsrr s$zGTableQuestionAnsweringPipeline.sequential_inference..)shapetodevicerangerN zeros_likecpunumpytolistinttorch from_numpyrlongr? unsqueezerMrAappendlogits_aggregation distributions Bernoulliprobsfloat32 collections defaultdictr enumeratesqueezecattuple)r'rG all_logitsall_aggregations prev_answers batch_sizerIrJrKtoken_type_ids_exampleindexprev_labels_example model_labelsi segment_idcol_idrow_idinput_ids_exampleattention_mask_exampleoutputsrMdist_per_token probabilitiespcolrow logits_batchrrRrsequential_inferences`&   "z3TableQuestionAnsweringPipeline.sequential_inferencecs<|j|i|}tj|fi|}t|dkr|dS|S)a Answers queries according to a table. The pipeline accepts several types of inputs which are detailed below: - `pipeline(table, query)` - `pipeline(table, [query])` - `pipeline(table=table, query=query)` - `pipeline(table=table, query=[query])` - `pipeline({"table": table, "query": query})` - `pipeline({"table": table, "query": [query]})` - `pipeline([{"table": table, "query": query}, {"table": table, "query": query}])` The `table` argument should be a dict or a DataFrame built from that dict, containing the whole table: Example: ```python data = { "actors": ["brad pitt", "leonardo di caprio", "george clooney"], "age": ["56", "45", "59"], "number of movies": ["87", "53", "69"], "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"], } ``` This dictionary can be passed in as such, or can be converted to a pandas DataFrame: Example: ```python import pandas as pd table = pd.DataFrame.from_dict(data) ``` Args: table (`pd.DataFrame` or `Dict`): Pandas DataFrame or dictionary that will be converted to a DataFrame containing all the table values. See above for an example of dictionary. query (`str` or `list[str]`): Query or list of queries that will be sent to the model alongside the table. sequential (`bool`, *optional*, defaults to `False`): Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the inference to be done sequentially to extract relations within sequences, given their conversational nature. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (`bool`, `str` or [`TapasTruncationStrategy`], *optional*, defaults to `False`): Activates and controls truncation. Accepts the following values: - `True` or `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate row by row, removing rows from the table. - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). Return: A dictionary or a list of dictionaries containing results: Each result is a dictionary with the following keys: - **answer** (`str`) -- The answer of the query given the table. 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