TF Bert inference - support np.ndarray optional arguments (#15074)

* TF Bert inference - support np.ndarray optional arguments

* apply np input tests to all TF architectures

src/transformers/modeling_tf_utils.py

@@ -1941,16 +1941,19 @@ class TFSequenceSummary(tf.keras.layers.Layer):
         return output
 
 
-def shape_list(tensor: tf.Tensor) -> List[int]:
+def shape_list(tensor: Union[tf.Tensor, np.ndarray]) -> List[int]:
     """
     Deal with dynamic shape in tensorflow cleanly.
 
     Args:
-        tensor (`tf.Tensor`): The tensor we want the shape of.
+        tensor (`tf.Tensor` or `np.ndarray`): The tensor we want the shape of.
 
     Returns:
         `List[int]`: The shape of the tensor as a list.
     """
+    if isinstance(tensor, np.ndarray):
+        return list(tensor.shape)
+
     dynamic = tf.shape(tensor)
 
     if tensor.shape == tf.TensorShape(None):

tests/test_modeling_tf_common.py

@@ -846,7 +846,9 @@ class TFModelTesterMixin:
             inputs = self._prepare_for_class(inputs_dict, model_class)
             inputs_np = prepare_numpy_arrays(inputs)
 
-            model(inputs_np)
+            output_for_dict_input = model(inputs_np)
+            output_for_kw_input = model(**inputs_np)
+            self.assert_outputs_same(output_for_dict_input, output_for_kw_input)
 
     def test_resize_token_embeddings(self):
         if not self.test_resize_embeddings: