Unnest QuestionAnsweringArgumentHandler

transformers/pipelines.py

@@ -333,6 +333,63 @@ class NerPipeline(Pipeline):
         return answers
 
 
+class QuestionAnsweringArgumentHandler(ArgumentHandler):
+    """
+    QuestionAnsweringPipeline requires the user to provide multiple arguments (i.e. question & context) to be mapped
+    to internal SquadExample / SquadFeature structures.
+
+    QuestionAnsweringArgumentHandler manages all the possible to create SquadExample from the command-line supplied
+    arguments.
+    """
+    def __call__(self, *args, **kwargs):
+        # Position args, handling is sensibly the same as X and data, so forwarding to avoid duplicating
+        if args is not None and len(args) > 0:
+            if len(args) == 1:
+                kwargs['X'] = args[0]
+            else:
+                kwargs['X'] = list(args)
+
+            # Generic compatibility with sklearn and Keras
+            # Batched data
+        if 'X' in kwargs or 'data' in kwargs:
+            data = kwargs['X'] if 'X' in kwargs else kwargs['data']
+
+            if not isinstance(data, list):
+                data = [data]
+
+            for i, item in enumerate(data):
+                if isinstance(item, dict):
+                    if any(k not in item for k in ['question', 'context']):
+                        raise KeyError('You need to provide a dictionary with keys {question:..., context:...}')
+                    data[i] = QuestionAnsweringPipeline.create_sample(**item)
+
+                elif isinstance(item, SquadExample):
+                    continue
+                else:
+                    raise ValueError(
+                        '{} argument needs to be of type (list[SquadExample | dict], SquadExample, dict)'
+                            .format('X' if 'X' in kwargs else 'data')
+                    )
+            inputs = data
+
+            # Tabular input
+        elif 'question' in kwargs and 'context' in kwargs:
+            if isinstance(kwargs['question'], str):
+                kwargs['question'] = [kwargs['question']]
+
+            if isinstance(kwargs['context'], str):
+                kwargs['context'] = [kwargs['context']]
+
+            inputs = [QuestionAnsweringPipeline.create_sample(q, c) for q, c in zip(kwargs['question'], kwargs['context'])]
+        else:
+            raise ValueError('Unknown arguments {}'.format(kwargs))
+
+        if not isinstance(inputs, list):
+            inputs = [inputs]
+
+        return inputs
+
+
 class QuestionAnsweringPipeline(Pipeline):
     """
     Question Answering pipeline using ModelForQuestionAnswering head.
@@ -403,8 +460,9 @@ class QuestionAnsweringPipeline(Pipeline):
         else:
             return SquadExample(None, question, context, None, None, None)
 
-    def __init__(self, model, tokenizer: Optional[PreTrainedTokenizer]):
-        super().__init__(model, tokenizer, args_parser=QuestionAnsweringPipeline.QuestionAnsweringArgumentHandler())
+    def __init__(self, model, tokenizer: Optional[PreTrainedTokenizer], device: int = -1, **kwargs):
+        super().__init__(model, tokenizer, args_parser=QuestionAnsweringArgumentHandler(),
+                         device=device, **kwargs)
 
     def inputs_for_model(self, features: Union[SquadExample, List[SquadExample]]) -> Dict:
         """