import collections import types import numpy as np from ..utils import ( add_end_docstrings, is_tf_available, is_torch_available, requires_backends, ) from .base import ArgumentHandler, Dataset, Pipeline, PipelineException, build_pipeline_init_args if is_torch_available(): import torch from ..models.auto.modeling_auto import ( MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES, ) if is_tf_available(): import tensorflow as tf from ..models.auto.modeling_tf_auto import ( TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES, ) class TableQuestionAnsweringArgumentHandler(ArgumentHandler): """ Handles arguments for the TableQuestionAnsweringPipeline """ def __call__(self, table=None, query=None, **kwargs): # Returns tqa_pipeline_inputs of shape: # [ # {"table": pd.DataFrame, "query": List[str]}, # ..., # {"table": pd.DataFrame, "query" : List[str]} # ] requires_backends(self, "pandas") import pandas as pd if table is None: raise ValueError("Keyword argument `table` cannot be None.") elif query is None: if isinstance(table, dict) and table.get("query") is not None and table.get("table") is not None: tqa_pipeline_inputs = [table] elif isinstance(table, list) and len(table) > 0: if not all(isinstance(d, dict) for d in table): raise ValueError( f"Keyword argument `table` should be a list of dict, but is {(type(d) for d in table)}" ) if table[0].get("query") is not None and table[0].get("table") is not None: tqa_pipeline_inputs = table else: raise ValueError( "If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`" f" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys." ) elif Dataset is not None and isinstance(table, Dataset) or isinstance(table, types.GeneratorType): return table else: raise ValueError( "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but " f"is {type(table)})" ) else: tqa_pipeline_inputs = [{"table": table, "query": query}] for tqa_pipeline_input in tqa_pipeline_inputs: if not isinstance(tqa_pipeline_input["table"], pd.DataFrame): if tqa_pipeline_input["table"] is None: raise ValueError("Table cannot be None.") tqa_pipeline_input["table"] = pd.DataFrame(tqa_pipeline_input["table"]) return tqa_pipeline_inputs @add_end_docstrings(build_pipeline_init_args(has_tokenizer=True)) class TableQuestionAnsweringPipeline(Pipeline): """ Table Question Answering pipeline using a `ModelForTableQuestionAnswering`. This pipeline is only available in PyTorch. 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). """ default_input_names = "table,query" def __init__(self, args_parser=TableQuestionAnsweringArgumentHandler(), *args, **kwargs): super().__init__(*args, **kwargs) self._args_parser = args_parser if self.framework == "tf": mapping = TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES.copy() mapping.update(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES) else: mapping = MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES.copy() mapping.update(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES) self.check_model_type(mapping) self.aggregate = bool(getattr(self.model.config, "aggregation_labels", None)) and bool( getattr(self.model.config, "num_aggregation_labels", None) ) self.type = "tapas" if hasattr(self.model.config, "aggregation_labels") else None def batch_inference(self, **inputs): return self.model(**inputs) def sequential_inference(self, **inputs): """ 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. """ if self.framework == "pt": all_logits = [] all_aggregations = [] prev_answers = None batch_size = inputs["input_ids"].shape[0] input_ids = inputs["input_ids"].to(self.device) attention_mask = inputs["attention_mask"].to(self.device) token_type_ids = inputs["token_type_ids"].to(self.device) token_type_ids_example = None for index in range(batch_size): # If sequences have already been processed, the token type IDs will be created according to the previous # answer. if prev_answers is not None: prev_labels_example = token_type_ids_example[:, 3] # shape (seq_len,) model_labels = np.zeros_like(prev_labels_example.cpu().numpy()) # shape (seq_len,) token_type_ids_example = token_type_ids[index] # shape (seq_len, 7) for i in range(model_labels.shape[0]): segment_id = token_type_ids_example[:, 0].tolist()[i] col_id = token_type_ids_example[:, 1].tolist()[i] - 1 row_id = token_type_ids_example[:, 2].tolist()[i] - 1 if row_id >= 0 and col_id >= 0 and segment_id == 1: model_labels[i] = int(prev_answers[(col_id, row_id)]) token_type_ids_example[:, 3] = torch.from_numpy(model_labels).type(torch.long).to(self.device) input_ids_example = input_ids[index] attention_mask_example = attention_mask[index] # shape (seq_len,) token_type_ids_example = token_type_ids[index] # shape (seq_len, 7) outputs = self.model( input_ids=input_ids_example.unsqueeze(0), attention_mask=attention_mask_example.unsqueeze(0), token_type_ids=token_type_ids_example.unsqueeze(0), ) logits = outputs.logits if self.aggregate: all_aggregations.append(outputs.logits_aggregation) all_logits.append(logits) dist_per_token = torch.distributions.Bernoulli(logits=logits) probabilities = dist_per_token.probs * attention_mask_example.type(torch.float32).to( dist_per_token.probs.device ) coords_to_probs = collections.defaultdict(list) for i, p in enumerate(probabilities.squeeze().tolist()): segment_id = token_type_ids_example[:, 0].tolist()[i] col = token_type_ids_example[:, 1].tolist()[i] - 1 row = token_type_ids_example[:, 2].tolist()[i] - 1 if col >= 0 and row >= 0 and segment_id == 1: coords_to_probs[(col, row)].append(p) prev_answers = {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs} logits_batch = torch.cat(tuple(all_logits), 0) return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(all_aggregations), 0)) else: all_logits = [] all_aggregations = [] prev_answers = None batch_size = inputs["input_ids"].shape[0] input_ids = inputs["input_ids"] attention_mask = inputs["attention_mask"] token_type_ids = inputs["token_type_ids"].numpy() token_type_ids_example = None for index in range(batch_size): # If sequences have already been processed, the token type IDs will be created according to the previous # answer. if prev_answers is not None: prev_labels_example = token_type_ids_example[:, 3] # shape (seq_len,) model_labels = np.zeros_like(prev_labels_example, dtype=np.int32) # shape (seq_len,) token_type_ids_example = token_type_ids[index] # shape (seq_len, 7) for i in range(model_labels.shape[0]): segment_id = token_type_ids_example[:, 0].tolist()[i] col_id = token_type_ids_example[:, 1].tolist()[i] - 1 row_id = token_type_ids_example[:, 2].tolist()[i] - 1 if row_id >= 0 and col_id >= 0 and segment_id == 1: model_labels[i] = int(prev_answers[(col_id, row_id)]) token_type_ids_example[:, 3] = model_labels input_ids_example = input_ids[index] attention_mask_example = attention_mask[index] # shape (seq_len,) token_type_ids_example = token_type_ids[index] # shape (seq_len, 7) outputs = self.model( input_ids=np.expand_dims(input_ids_example, axis=0), attention_mask=np.expand_dims(attention_mask_example, axis=0), token_type_ids=np.expand_dims(token_type_ids_example, axis=0), ) logits = outputs.logits if self.aggregate: all_aggregations.append(outputs.logits_aggregation) all_logits.append(logits) probabilities = tf.math.sigmoid(tf.cast(logits, tf.float32)) * tf.cast( attention_mask_example, tf.float32 ) coords_to_probs = collections.defaultdict(list) token_type_ids_example = token_type_ids_example for i, p in enumerate(tf.squeeze(probabilities).numpy().tolist()): segment_id = token_type_ids_example[:, 0].tolist()[i] col = token_type_ids_example[:, 1].tolist()[i] - 1 row = token_type_ids_example[:, 2].tolist()[i] - 1 if col >= 0 and row >= 0 and segment_id == 1: coords_to_probs[(col, row)].append(p) prev_answers = {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs} logits_batch = tf.concat(tuple(all_logits), 0) return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(all_aggregations), 0)) def __call__(self, *args, **kwargs): r""" 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. If there is an aggregator, the answer will be preceded by `AGGREGATOR >`. - **coordinates** (`List[Tuple[int, int]]`) -- Coordinates of the cells of the answers. - **cells** (`List[str]`) -- List of strings made up of the answer cell values. - **aggregator** (`str`) -- If the model has an aggregator, this returns the aggregator. """ pipeline_inputs = self._args_parser(*args, **kwargs) results = super().__call__(pipeline_inputs, **kwargs) if len(results) == 1: return results[0] return results def _sanitize_parameters(self, sequential=None, padding=None, truncation=None, **kwargs): preprocess_params = {} if padding is not None: preprocess_params["padding"] = padding if truncation is not None: preprocess_params["truncation"] = truncation forward_params = {} if sequential is not None: forward_params["sequential"] = sequential return preprocess_params, forward_params, {} def preprocess(self, pipeline_input, sequential=None, padding=True, truncation=None): if truncation is None: if self.type == "tapas": truncation = "drop_rows_to_fit" else: truncation = "do_not_truncate" table, query = pipeline_input["table"], pipeline_input["query"] if table.empty: raise ValueError("table is empty") if query is None or query == "": raise ValueError("query is empty") inputs = self.tokenizer(table, query, return_tensors=self.framework, truncation=truncation, padding=padding) inputs["table"] = table return inputs def _forward(self, model_inputs, sequential=False, **generate_kwargs): table = model_inputs.pop("table") if self.type == "tapas": if sequential: outputs = self.sequential_inference(**model_inputs) else: outputs = self.batch_inference(**model_inputs) else: # User-defined `generation_config` passed to the pipeline call take precedence if "generation_config" not in generate_kwargs: generate_kwargs["generation_config"] = self.generation_config outputs = self.model.generate(**model_inputs, **generate_kwargs) model_outputs = {"model_inputs": model_inputs, "table": table, "outputs": outputs} return model_outputs def postprocess(self, model_outputs): inputs = model_outputs["model_inputs"] table = model_outputs["table"] outputs = model_outputs["outputs"] if self.type == "tapas": if self.aggregate: logits, logits_agg = outputs[:2] predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits, logits_agg) answer_coordinates_batch, agg_predictions = predictions aggregators = {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(agg_predictions)} no_agg_label_index = self.model.config.no_aggregation_label_index aggregators_prefix = { i: aggregators[i] + " > " for i, pred in enumerate(agg_predictions) if pred != no_agg_label_index } else: logits = outputs[0] predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits) answer_coordinates_batch = predictions[0] aggregators = {} aggregators_prefix = {} answers = [] for index, coordinates in enumerate(answer_coordinates_batch): cells = [table.iat[coordinate] for coordinate in coordinates] aggregator = aggregators.get(index, "") aggregator_prefix = aggregators_prefix.get(index, "") answer = { "answer": aggregator_prefix + ", ".join(cells), "coordinates": coordinates, "cells": [table.iat[coordinate] for coordinate in coordinates], } if aggregator: answer["aggregator"] = aggregator answers.append(answer) if len(answer) == 0: raise PipelineException("Empty answer") else: answers = [{"answer": answer} for answer in self.tokenizer.batch_decode(outputs, skip_special_tokens=True)] return answers if len(answers) > 1 else answers[0]