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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)
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