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| # Copyright 2023 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 # noqa: E402 | |
| 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_DIFFUSERS = os.path.join(git_repo_path, "src", "diffusers") | |
| class CheckDummiesTester(unittest.TestCase): | |
| def test_find_backend(self): | |
| simple_backend = find_backend(" if not is_torch_available():") | |
| self.assertEqual(simple_backend, "torch") | |
| # 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_torch_available() and is_transformers_available()):") | |
| self.assertEqual(double_backend, "torch_and_transformers") | |
| # 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_torch_available() and is_transformers_available() and is_onnx_available()):" | |
| ) | |
| self.assertEqual(triple_backend, "torch_and_transformers_and_onnx") | |
| 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("torch_and_transformers", objects) | |
| self.assertIn("flax_and_transformers", objects) | |
| self.assertIn("torch_and_transformers_and_onnx", objects) | |
| # Likewise, we can't assert on the exact content of a key | |
| self.assertIn("UNet2DModel", objects["torch"]) | |
| self.assertIn("FlaxUNet2DConditionModel", objects["flax"]) | |
| self.assertIn("StableDiffusionPipeline", objects["torch_and_transformers"]) | |
| self.assertIn("FlaxStableDiffusionPipeline", objects["flax_and_transformers"]) | |
| self.assertIn("LMSDiscreteScheduler", objects["torch_and_scipy"]) | |
| self.assertIn("OnnxStableDiffusionPipeline", objects["torch_and_transformers_and_onnx"]) | |
| 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') | |
| @classmethod | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, 'torch') | |
| @classmethod | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, '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. | |
| 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"]) | |
| @classmethod | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["torch"]) | |
| @classmethod | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["torch"]) | |
| """ | |
| dummy_files = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}) | |
| self.assertEqual(dummy_files["torch"], expected_dummy_pytorch_file) | |