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110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
# Copyright 2026 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
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sys.path.append(os.path.join(git_repo_path, "utils"))
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import check_dummies # noqa: E402
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from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
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# Align TRANSFORMERS_PATH in check_dummies with the current path
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check_dummies.PATH_TO_DIFFUSERS = os.path.join(git_repo_path, "src", "diffusers")
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class TestCheckDummies:
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def test_find_backend(self):
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simple_backend = find_backend(" if not is_torch_available():")
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assert simple_backend == "torch"
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# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
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# assert backend_with_underscore == "tensorflow_text"
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double_backend = find_backend(" if not (is_torch_available() and is_transformers_available()):")
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assert double_backend == "torch_and_transformers"
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# double_backend_with_underscore = find_backend(
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# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
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# )
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# assert double_backend_with_underscore == "sentencepiece_and_tensorflow_text"
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def test_read_init(self):
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objects = read_init()
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# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
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assert "torch" in objects
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assert "torch_and_transformers" in objects
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# Likewise, we can't assert on the exact content of a key
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assert "UNet2DModel" in objects["torch"]
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assert "StableDiffusionPipeline" in objects["torch_and_transformers"]
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assert "LMSDiscreteScheduler" in objects["torch_and_scipy"]
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def test_create_dummy_object(self):
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dummy_constant = create_dummy_object("CONSTANT", "'torch'")
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assert dummy_constant == "\nCONSTANT = None\n"
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dummy_function = create_dummy_object("function", "'torch'")
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assert dummy_function == "\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n"
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expected_dummy_class = """
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class FakeClass(metaclass=DummyObject):
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_backends = 'torch'
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def __init__(self, *args, **kwargs):
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requires_backends(self, 'torch')
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, 'torch')
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, 'torch')
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"""
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dummy_class = create_dummy_object("FakeClass", "'torch'")
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assert dummy_class == expected_dummy_class
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def test_create_dummy_files(self):
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expected_dummy_pytorch_file = """# This file is autogenerated by the command `make fix-copies`, do not edit.
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from ..utils import DummyObject, requires_backends
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CONSTANT = None
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def function(*args, **kwargs):
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requires_backends(function, ["torch"])
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class FakeClass(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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@classmethod
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def from_config(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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requires_backends(cls, ["torch"])
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"""
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dummy_files = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
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assert dummy_files["torch"] == expected_dummy_pytorch_file
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