Adding a new align_to_words param to qa pipeline. (huggingface#18010)

* Adding a new `align_to_words` param to qa pipeline.

* Update src/transformers/pipelines/question_answering.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Import protection.

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/pipelines/question_answering.py

@@ -603,6 +603,48 @@ def get_indices(
             end_index = enc.offsets[e][1]
         return start_index, end_index
 
+    def decode(
+        self, start: np.ndarray, end: np.ndarray, topk: int, max_answer_len: int, undesired_tokens: np.ndarray
+    ) -> Tuple:
+        """
+        Take the output of any `ModelForQuestionAnswering` and will generate probabilities for each span to be the
+        actual answer.
+
+        In addition, it filters out some unwanted/impossible cases like answer len being greater than max_answer_len or
+        answer end position being before the starting position. The method supports output the k-best answer through
+        the topk argument.
+
+        Args:
+            start (`np.ndarray`): Individual start probabilities for each token.
+            end (`np.ndarray`): Individual end probabilities for each token.
+            topk (`int`): Indicates how many possible answer span(s) to extract from the model output.
+            max_answer_len (`int`): Maximum size of the answer to extract from the model's output.
+            undesired_tokens (`np.ndarray`): Mask determining tokens that can be part of the answer
+        """
+        # Ensure we have batch axis
+        if start.ndim == 1:
+            start = start[None]
+
+        if end.ndim == 1:
+            end = end[None]
+
+        # Compute the score of each tuple(start, end) to be the real answer
+        outer = np.matmul(np.expand_dims(start, -1), np.expand_dims(end, 1))
+
+        # Remove candidate with end < start and end - start > max_answer_len
+        candidates = np.tril(np.triu(outer), max_answer_len - 1)
+
+        #  Inspired by Chen & al. (https://github.com/facebookresearch/DrQA)
+        scores_flat = candidates.flatten()
+        if topk == 1:
+            idx_sort = [np.argmax(scores_flat)]
+        elif len(scores_flat) < topk:
+            idx_sort = np.argsort(-scores_flat)
+        else:
+            start_index = enc.offsets[s][0]
+            end_index = enc.offsets[e][1]
+        return start_index, end_index
+
     def span_to_answer(self, text: str, start: int, end: int) -> Dict[str, Union[str, int]]:
         """
         When decoding from token probabilities, this method maps token indexes to actual word in the initial context.