Fix the behaviour of DefaultArgumentHandler (removing it). (#8180)

* Some work to fix the behaviour of DefaultArgumentHandler by removing it.

* Fixing specific pipelines argument checking.

tests/test_pipelines_fill_mask.py

@@ -1,5 +1,7 @@
 import unittest
 
+import pytest
+
 from transformers import pipeline
 from transformers.testing_utils import require_tf, require_torch, slow
 
@@ -37,7 +39,7 @@ EXPECTED_FILL_MASK_TARGET_RESULT = [
 
 class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
     pipeline_task = "fill-mask"
-    pipeline_loading_kwargs = {"topk": 2}
+    pipeline_loading_kwargs = {"top_k": 2}
     small_models = ["sshleifer/tiny-distilroberta-base"]  # Models tested without the @slow decorator
     large_models = ["distilroberta-base"]  # Models tested with the @slow decorator
     mandatory_keys = {"sequence", "score", "token"}
@@ -51,6 +53,28 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
     ]
     expected_check_keys = ["sequence"]
 
+    @require_torch
+    def test_torch_topk_deprecation(self):
+        # At pipeline initialization only it was not enabled at pipeline
+        # call site before
+        with pytest.warns(FutureWarning, match=r".*use `top_k`.*"):
+            pipeline(task="fill-mask", model=self.small_models[0], topk=1)
+
+    @require_torch
+    def test_torch_fill_mask(self):
+        valid_inputs = "My name is <mask>"
+        nlp = pipeline(task="fill-mask", model=self.small_models[0])
+        outputs = nlp(valid_inputs)
+        self.assertIsInstance(outputs, list)
+
+        # This passes
+        outputs = nlp(valid_inputs, targets=[" Patrick", " Clara"])
+        self.assertIsInstance(outputs, list)
+
+        # This used to fail with `cannot mix args and kwargs`
+        outputs = nlp(valid_inputs, something=False)
+        self.assertIsInstance(outputs, list)
+
     @require_torch
     def test_torch_fill_mask_with_targets(self):
         valid_inputs = ["My name is <mask>"]
@@ -94,7 +118,7 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
                 model=model_name,
                 tokenizer=model_name,
                 framework="pt",
-                topk=2,
+                top_k=2,
             )
 
             mono_result = nlp(valid_inputs[0], targets=valid_targets)