finish transformers removal

setup.py

-# Copyright 2021 The HuggingFace Team. All rights reserved.
+# 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.
@@ -52,11 +52,11 @@ To create the package for pypi.
    twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/
 
    Check that you can install it in a virtualenv by running:
-   pip install -i https://testpypi.python.org/pypi transformers
+   pip install -i https://testpypi.python.org/pypi diffusers
 
    Check you can run the following commands:
-   python -c "from transformers import pipeline; classifier = pipeline('text-classification'); print(classifier('What a nice release'))"
-   python -c "from transformers import *"
+   python -c "from diffusers import pipeline; classifier = pipeline('text-classification'); print(classifier('What a nice release'))"
+   python -c "from diffusers import *"
 
 9. Upload the final version to actual pypi:
    twine upload dist/* -r pypi
@@ -77,36 +77,21 @@ from setuptools import find_packages, setup
 # 2. once modified, run: `make deps_table_update` to update src/diffusers/dependency_versions_table.py
 _deps = [
     "Pillow",
-    "accelerate>=0.9.0",
     "black~=22.0,>=22.3",
-    "codecarbon==1.2.0",
-    "dataclasses",
-    "datasets",
-    "GitPython<3.1.19",
-    "hf-doc-builder>=0.3.0",
-    "huggingface-hub>=0.1.0,<1.0",
-    "importlib_metadata",
+    "filelock",
+    "flake8>=3.8.3",
+    "huggingface-hub",
     "isort>=5.5.4",
-    "numpy>=1.17",
+    "numpy",
     "pytest",
-    "pytest-timeout",
-    "pytest-xdist",
-    "python>=3.7.0",
-    "regex!=2019.12.17",
     "requests",
-    "sagemaker>=2.31.0",
-    "tokenizers>=0.11.1,!=0.11.3,<0.13",
     "torch>=1.4",
-    "torchaudio",
-    "tqdm>=4.27",
-    "unidic>=1.0.2",
-    "unidic_lite>=1.0.7",
-    "uvicorn",
+    "torchvision",
 ]
 
 # this is a lookup table with items like:
 #
-# tokenizers: "tokenizers==0.9.4"
+# tokenizers: "huggingface-hub==0.8.0"
 # packaging: "packaging"
 #
 # some of the values are versioned whereas others aren't.
@@ -176,15 +161,17 @@ extras["quality"] = ["black ~= 22.0", "isort >= 5.5.4", "flake8 >= 3.8.3"]
 extras["docs"] = []
 extras["test"] = [
     "pytest",
-    "pytest-xdist",
-    "pytest-subtests",
-    "datasets",
-    "transformers",
 ]
 extras["dev"] = extras["quality"] + extras["test"]
 
-extras["sagemaker"] = [
-    "sagemaker",  # boto3 is a required package in sagemaker
+install_requires = [
+    deps["filelock"],
+    deps["huggingface-hub"],
+    deps["numpy"],
+    deps["requests"],
+    deps["torch"],
+    deps["torchvision"],
+    deps["Pillow"],
 ]
 
 setup(
@@ -201,7 +188,7 @@ setup(
     package_dir={"": "src"},
     packages=find_packages("src"),
     python_requires=">=3.6.0",
-    install_requires=["numpy>=1.17", "packaging>=20.0", "pyyaml", "torch>=1.4.0"],
+    install_requires=install_requires,
     extras_require=extras,
     classifiers=[
         "Development Status :: 5 - Production/Stable",

src/diffusers/configuration_utils.py

@@ -57,6 +57,8 @@ class ConfigMixin:
         if self.config_name is None:
             raise NotImplementedError(f"Make sure that {self.__class__} has defined a class name `config_name`")
         kwargs["_class_name"] = self.__class__.__name__
+        kwargs["_diffusers_version"] = __version__
+
         for key, value in kwargs.items():
             try:
                 setattr(self, key, value)
@@ -91,6 +93,21 @@ class ConfigMixin:
         self.to_json_file(output_config_file)
         logger.info(f"ConfigMixinuration saved in {output_config_file}")
 
+    @classmethod
+    def from_config(cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, **kwargs):
+        config_dict = cls.get_config_dict(
+            pretrained_model_name_or_path=pretrained_model_name_or_path, **kwargs
+        )
+
+        init_dict, unused_kwargs = cls.extract_init_dict(config_dict, **kwargs)
+
+        model = cls(**init_dict)
+
+        if return_unused_kwargs:
+            return model, unused_kwargs
+        else:
+            return model
+
     @classmethod
     def get_config_dict(
         cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
@@ -107,6 +124,12 @@ class ConfigMixin:
 
         pretrained_model_name_or_path = str(pretrained_model_name_or_path)
 
+        if cls.config_name is None:
+            raise ValueError(
+                "`self.config_name` is not defined. Note that one should not load a config from "
+                "`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`"
+            )
+
         if os.path.isfile(pretrained_model_name_or_path):
             config_file = pretrained_model_name_or_path
         elif os.path.isdir(pretrained_model_name_or_path):
@@ -168,13 +191,13 @@ class ConfigMixin:
                     f"containing a {cls.config_name} file"
                 )
 
-            try:
-                # Load config dict
-                config_dict = cls._dict_from_json_file(config_file)
-            except (json.JSONDecodeError, UnicodeDecodeError):
-                raise EnvironmentError(
-                    f"It looks like the config file at '{config_file}' is not a valid JSON file."
-                )
+        try:
+            # Load config dict
+            config_dict = cls._dict_from_json_file(config_file)
+        except (json.JSONDecodeError, UnicodeDecodeError):
+            raise EnvironmentError(
+                f"It looks like the config file at '{config_file}' is not a valid JSON file."
+            )
 
         return config_dict
 
@@ -200,21 +223,6 @@ class ConfigMixin:
 
         return init_dict, unused_kwargs
 
-    @classmethod
-    def from_config(cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, **kwargs):
-        config_dict = cls.get_config_dict(
-            pretrained_model_name_or_path=pretrained_model_name_or_path, **kwargs
-        )
-
-        init_dict, unused_kwargs = cls.extract_init_dict(config_dict, **kwargs)
-
-        model = cls(**init_dict)
-
-        if return_unused_kwargs:
-            return model, unused_kwargs
-        else:
-            return model
-
     @classmethod
     def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
         with open(json_file, "r", encoding="utf-8") as reader:
@@ -227,18 +235,9 @@ class ConfigMixin:
     def __repr__(self):
         return f"{self.__class__.__name__} {self.to_json_string()}"
 
-    def to_dict(self) -> Dict[str, Any]:
-        """
-        Serializes this instance to a Python dictionary.
-
-        Returns:
-            `Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
-        """
-        output = copy.deepcopy(self.__dict__)
-
-        # Diffusion version when serializing the model
-        output["diffusers_version"] = __version__
-
+    @property
+    def config(self) -> Dict[str, Any]:
+        output = copy.deepcopy(self._dict_to_save)
         return output
 
     def to_json_string(self) -> str:

src/diffusers/modeling_utils.py

@@ -401,14 +401,14 @@ class ModelMixin(torch.nn.Module):
                 )
 
             # restore default dtype
-            state_dict = load_state_dict(model_file)
-            model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
-                model,
-                state_dict,
-                model_file,
-                pretrained_model_name_or_path,
-                ignore_mismatched_sizes=ignore_mismatched_sizes,
-            )
+        state_dict = load_state_dict(model_file)
+        model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
+            model,
+            state_dict,
+            model_file,
+            pretrained_model_name_or_path,
+            ignore_mismatched_sizes=ignore_mismatched_sizes,
+        )
 
         # Set model in evaluation mode to deactivate DropOut modules by default
         model.eval()

src/diffusers/pipeline_utils.py

@@ -67,11 +67,12 @@ class DiffusionPipeline(ConfigMixin):
     def save_pretrained(self, save_directory: Union[str, os.PathLike]):
         self.save_config(save_directory)
 
-        model_index_dict = self._dict_to_save
+        model_index_dict = self.config
         model_index_dict.pop("_class_name")
+        model_index_dict.pop("_diffusers_version")
         model_index_dict.pop("_module")
 
-        for name, (library_name, class_name) in self._dict_to_save.items():
+        for name, (library_name, class_name) in model_index_dict.items():
             importable_classes = LOADABLE_CLASSES[library_name]
 
             # TODO: Suraj

src/diffusers/utils/__init__.py

@@ -19,6 +19,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from requests.exceptions import HTTPError
+import os
 
 hf_cache_home = os.path.expanduser(
     os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))

tests/test_modeling_utils.py

@@ -24,6 +24,7 @@ import torch
 
 from diffusers import GaussianDDPMScheduler, UNetModel
 from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.configuration_utils import ConfigMixin
 from models.vision.ddpm.modeling_ddpm import DDPM
 
 
@@ -77,6 +78,45 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None):
     return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous()
 
 
+class ConfigTester(unittest.TestCase):
+    def test_load_not_from_mixin(self):
+        with self.assertRaises(ValueError):
+            ConfigMixin.from_config("dummy_path")
+
+    def test_save_load(self):
+
+        class SampleObject(ConfigMixin):
+            config_name = "config.json"
+
+            def __init__(
+                self,
+                a=2,
+                b=5,
+                c=(2, 5),
+                d="for diffusion",
+                e=[1, 3],
+            ):
+                self.register(a=a, b=b, c=c, d=d, e=e)
+
+        obj = SampleObject()
+        config = obj.config
+
+        assert config["a"] == 2
+        assert config["b"] == 5
+        assert config["c"] == (2, 5)
+        assert config["d"] == "for diffusion"
+        assert config["e"] == [1, 3]
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            obj.save_config(tmpdirname)
+            new_obj = SampleObject.from_config(tmpdirname)
+            new_config = new_obj.config
+
+        assert config.pop("c") == (2, 5)  # instantiated as tuple
+        assert new_config.pop("c") == [2, 5]  # saved & loaded as list because of json
+        assert config == new_config
+
+
 class ModelTesterMixin(unittest.TestCase):
     @property
     def dummy_input(self):