Add some tests for check_dummies (#19146)

tests/repo_utils/test_check_dummies.py

+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import sys
+import unittest
+
+
+git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
+sys.path.append(os.path.join(git_repo_path, "utils"))
+
+import check_dummies
+from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402
+
+
+# Align TRANSFORMERS_PATH in check_dummies with the current path
+check_dummies.PATH_TO_TRANSFORMERS = os.path.join(git_repo_path, "src", "transformers")
+
+DUMMY_CONSTANT = """
+{0} = None
+"""
+
+DUMMY_CLASS = """
+class {0}(metaclass=DummyObject):
+    _backends = {1}
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, {1})
+"""
+
+
+DUMMY_FUNCTION = """
+def {0}(*args, **kwargs):
+    requires_backends({0}, {1})
+"""
+
+
+class CheckDummiesTester(unittest.TestCase):
+    def test_find_backend(self):
+        no_backend = find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")')
+        self.assertIsNone(no_backend)
+
+        simple_backend = find_backend("    if not is_tokenizers_available():")
+        self.assertEqual(simple_backend, "tokenizers")
+
+        backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
+        self.assertEqual(backend_with_underscore, "tensorflow_text")
+
+        double_backend = find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):")
+        self.assertEqual(double_backend, "sentencepiece_and_tokenizers")
+
+        double_backend_with_underscore = find_backend(
+            "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
+        )
+        self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
+
+        triple_backend = find_backend(
+            "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"
+        )
+        self.assertEqual(triple_backend, "sentencepiece_and_tokenizers_and_vision")
+
+    def test_read_init(self):
+        objects = read_init()
+        # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
+        self.assertIn("torch", objects)
+        self.assertIn("tensorflow_text", objects)
+        self.assertIn("sentencepiece_and_tokenizers", objects)
+
+        # Likewise, we can't assert on the exact content of a key
+        self.assertIn("BertModel", objects["torch"])
+        self.assertIn("TFBertModel", objects["tf"])
+        self.assertIn("FlaxBertModel", objects["flax"])
+        self.assertIn("BertModel", objects["torch"])
+        self.assertIn("TFBertTokenizer", objects["tensorflow_text"])
+        self.assertIn("convert_slow_tokenizer", objects["sentencepiece_and_tokenizers"])
+
+    def test_create_dummy_object(self):
+        dummy_constant = create_dummy_object("CONSTANT", "'torch'")
+        self.assertEqual(dummy_constant, "\nCONSTANT = None\n")
+
+        dummy_function = create_dummy_object("function", "'torch'")
+        self.assertEqual(
+            dummy_function, "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"
+        )
+
+        expected_dummy_class = """
+class FakeClass(metaclass=DummyObject):
+    _backends = 'torch'
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, 'torch')
+"""
+        dummy_class = create_dummy_object("FakeClass", "'torch'")
+        self.assertEqual(dummy_class, expected_dummy_class)
+
+    def test_create_dummy_files(self):
+        expected_dummy_pytorch_file = """# This file is autogenerated by the command `make fix-copies`, do not edit.
+# flake8: noqa
+from ..utils import DummyObject, requires_backends
+
+
+CONSTANT = None
+
+
+def function(*args, **kwargs):
+    requires_backends(function, ["torch"])
+
+
+class FakeClass(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+"""
+        dummy_files = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
+        self.assertEqual(dummy_files["torch"], expected_dummy_pytorch_file)

utils/check_dummies.py

@@ -105,9 +105,10 @@ def create_dummy_object(name, backend_name):
         return DUMMY_CLASS.format(name, backend_name)
 
 
-def create_dummy_files():
+def create_dummy_files(backend_specific_objects=None):
     """Create the content of the dummy files."""
-    backend_specific_objects = read_init()
+    if backend_specific_objects is None:
+        backend_specific_objects = read_init()
     # For special correspondence backend to module name as used in the function requires_modulename
     dummy_files = {}
 

utils/tests_fetcher.py

@@ -437,7 +437,7 @@ def module_to_test_file(module_fname):
         return ["tests/onnx/test_features.py", "tests/onnx/test_onnx.py", "tests/onnx/test_onnx_v2.py"]
     # Special case for utils (not the one in src/transformers, the ones at the root of the repo).
     elif len(splits) > 0 and splits[0] == "utils":
-        default_test_file = f"tests/utils/test_utils_{module_name}"
+        default_test_file = f"tests/repo_utils/test_{module_name}"
     elif len(splits) > 4 and splits[2] == "models":
         default_test_file = f"tests/models/{splits[3]}/test_{module_name}"
     elif len(splits) > 2 and splits[2].startswith("generation"):