ppdiffusers/configuration_utils.py

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 The HuggingFace Team. All rights reserved.
# Copyright (c) 2022, NVIDIA CORPORATION.  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.
""" ConfigMixin base class and utilities."""
import functools
import importlib
import inspect
import json
import os
import re
import tempfile
from collections import OrderedDict
from typing import Any, Dict, Optional, Tuple, Union

import numpy as np
from huggingface_hub import (
    create_repo,
    get_hf_file_metadata,
    hf_hub_download,
    hf_hub_url,
    repo_type_and_id_from_hf_id,
    upload_folder,
)
from huggingface_hub.utils import EntryNotFoundError
from requests import HTTPError

from .download_utils import ppdiffusers_bos_download
from .utils import (
    DOWNLOAD_SERVER,
    HF_CACHE,
    PPDIFFUSERS_CACHE,
    DummyObject,
    deprecate,
    logging,
)
from .version import VERSION as __version__

logger = logging.get_logger(__name__)

_re_configuration_file = re.compile(r"config\.(.*)\.json")


class FrozenDict(OrderedDict):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        for key, value in self.items():
            setattr(self, key, value)

        self.__frozen = True

    def __delitem__(self, *args, **kwargs):
        raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")

    def setdefault(self, *args, **kwargs):
        raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")

    def pop(self, *args, **kwargs):
        raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")

    def update(self, *args, **kwargs):
        raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")

    def __setattr__(self, name, value):
        if hasattr(self, "__frozen") and self.__frozen:
            raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
        super().__setattr__(name, value)

    def __setitem__(self, name, value):
        if hasattr(self, "__frozen") and self.__frozen:
            raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
        super().__setitem__(name, value)


class ConfigMixin:
    r"""
    Base class for all configuration classes. Stores all configuration parameters under `self.config` Also handles all
    methods for loading/downloading/saving classes inheriting from [`ConfigMixin`] with
        - [`~ConfigMixin.from_config`]
        - [`~ConfigMixin.save_config`]

    Class attributes:
        - **config_name** (`str`) -- A filename under which the config should stored when calling
          [`~ConfigMixin.save_config`] (should be overridden by parent class).
        - **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be
          overridden by subclass).
        - **has_compatibles** (`bool`) -- Whether the class has compatible classes (should be overridden by subclass).
        - **_deprecated_kwargs** (`List[str]`) -- Keyword arguments that are deprecated. Note that the init function
          should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by
          subclass).
    """
    config_name = None
    ignore_for_config = []
    has_compatibles = False
    _deprecated_kwargs = []

    def register_to_config(self, **kwargs):
        if self.config_name is None:
            raise NotImplementedError(f"Make sure that {self.__class__} has defined a class name `config_name`")

        # Special case for `kwargs` used in deprecation warning added to schedulers
        # TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,
        # or solve in a more general way.
        kwargs.pop("kwargs", None)
        for key, value in kwargs.items():
            try:
                setattr(self, key, value)
            except AttributeError as err:
                logger.error(f"Can't set {key} with value {value} for {self}")
                raise err

        if not hasattr(self, "_internal_dict"):
            internal_dict = kwargs
        else:
            previous_dict = dict(self._internal_dict)
            internal_dict = {**self._internal_dict, **kwargs}
            logger.debug(f"Updating config from {previous_dict} to {internal_dict}")

        self._internal_dict = FrozenDict(internal_dict)

    def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
        """
        Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
        [`~ConfigMixin.from_config`] class method.

        Args:
            save_directory (`str` or `os.PathLike`):
                Directory where the configuration JSON file will be saved (will be created if it does not exist).
        """
        if os.path.isfile(save_directory):
            raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")

        os.makedirs(save_directory, exist_ok=True)

        # If we save using the predefined names, we can load using `from_config`
        output_config_file = os.path.join(save_directory, self.config_name)

        self.to_json_file(output_config_file)
        logger.info(f"Configuration saved in {output_config_file}")

    def save_to_hf_hub(
        self,
        repo_id: str,
        private: Optional[bool] = None,
        subfolder: Optional[str] = None,
        commit_message: Optional[str] = None,
        revision: Optional[str] = None,
        create_pr: bool = False,
    ):
        """
        Uploads all elements of this config to a new HuggingFace Hub repository.
        Args:
            repo_id (str): Repository name for your model/tokenizer in the Hub.
            private (bool, optional): Whether the model/tokenizer is set to private
            subfolder (str, optional): Push to a subfolder of the repo instead of the root
            commit_message (str, optional): The summary / title / first line of the generated commit. Defaults to: f"Upload {path_in_repo} with huggingface_hub"
            revision (str, optional): The git revision to commit from. Defaults to the head of the "main" branch.
            create_pr (boolean, optional): Whether or not to create a Pull Request with that commit. Defaults to False.
                If revision is not set, PR is opened against the "main" branch. If revision is set and is a branch, PR is opened against this branch.
                If revision is set and is not a branch name (example: a commit oid), an RevisionNotFoundError is returned by the server.

        Returns: The url of the commit of your model in the given repository.
        """
        repo_url = create_repo(repo_id, private=private, exist_ok=True)

        # Infer complete repo_id from repo_url
        # Can be different from the input `repo_id` if repo_owner was implicit
        _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url)

        repo_id = f"{repo_owner}/{repo_name}"

        # Check if README file already exist in repo
        try:
            get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision))
            has_readme = True
        except EntryNotFoundError:
            has_readme = False

        with tempfile.TemporaryDirectory() as root_dir:
            if subfolder is not None:
                save_dir = os.path.join(root_dir, subfolder)
            else:
                save_dir = root_dir
            # save config
            self.save_config(save_dir)
            # Add readme if does not exist
            logger.info("README.md not found, adding the default README.md")
            if not has_readme:
                with open(os.path.join(root_dir, "README.md"), "w") as f:
                    f.write(f"---\nlibrary_name: ppdiffusers\n---\n# {repo_id}")

            # Upload model and return
            logger.info(f"Pushing to the {repo_id}. This might take a while")
            return upload_folder(
                repo_id=repo_id,
                repo_type="model",
                folder_path=root_dir,
                commit_message=commit_message,
                revision=revision,
                create_pr=create_pr,
            )

    @classmethod
    def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):
        r"""
        Instantiate a Python class from a config dictionary

        Parameters:
            config (`Dict[str, Any]`):
                A config dictionary from which the Python class will be instantiated. Make sure to only load
                configuration files of compatible classes.
            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                Whether kwargs that are not consumed by the Python class should be returned or not.

            kwargs (remaining dictionary of keyword arguments, *optional*):
                Can be used to update the configuration object (after it being loaded) and initiate the Python class.
                `**kwargs` will be directly passed to the underlying scheduler/model's `__init__` method and eventually
                overwrite same named arguments of `config`.

        Examples:

        ```python
        >>> from ppdiffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler

        >>> # Download scheduler from BOS and cache.
        >>> scheduler = DDPMScheduler.from_pretrained("google/ddpm-cifar10-32")

        >>> # Instantiate DDIM scheduler class with same config as DDPM
        >>> scheduler = DDIMScheduler.from_config(scheduler.config)

        >>> # Instantiate PNDM scheduler class with same config as DDPM
        >>> scheduler = PNDMScheduler.from_config(scheduler.config)
        ```
        """
        # <===== TO BE REMOVED WITH DEPRECATION
        # TODO(Patrick) - make sure to remove the following lines when config=="model_path" is deprecated
        if "pretrained_model_name_or_path" in kwargs:
            config = kwargs.pop("pretrained_model_name_or_path")

        if config is None:
            raise ValueError("Please make sure to provide a config as the first positional argument.")
        # ======>

        if not isinstance(config, dict):
            deprecation_message = "It is deprecated to pass a pretrained model name or path to `from_config`."
            if "Scheduler" in cls.__name__:
                deprecation_message += (
                    f"If you were trying to load a scheduler, please use {cls}.from_pretrained(...) instead."
                    " Otherwise, please make sure to pass a configuration dictionary instead. This functionality will"
                    " be removed in v1.0.0."
                )
            elif "Model" in cls.__name__:
                deprecation_message += (
                    f"If you were trying to load a model, please use {cls}.load_config(...) followed by"
                    f" {cls}.from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary"
                    " instead. This functionality will be removed in v1.0.0."
                )
            deprecate("config-passed-as-path", "1.0.0", deprecation_message, standard_warn=False)
            config, kwargs = cls.load_config(pretrained_model_name_or_path=config, return_unused_kwargs=True, **kwargs)

        init_dict, unused_kwargs, hidden_dict = cls.extract_init_dict(config, **kwargs)

        # Allow dtype to be specified on initialization
        if "dtype" in unused_kwargs:
            # (TODO junnyu, donot use dtype)
            unused_kwargs.pop("dtype")
            # init_dict["dtype"] = unused_kwargs.pop("dtype")

        # add possible deprecated kwargs
        for deprecated_kwarg in cls._deprecated_kwargs:
            if deprecated_kwarg in unused_kwargs:
                init_dict[deprecated_kwarg] = unused_kwargs.pop(deprecated_kwarg)

        # Return model and optionally state and/or unused_kwargs
        model = cls(**init_dict)

        # make sure to also save config parameters that might be used for compatible classes
        model.register_to_config(**hidden_dict)

        # add hidden kwargs of compatible classes to unused_kwargs
        unused_kwargs = {**unused_kwargs, **hidden_dict}

        if return_unused_kwargs:
            return (model, unused_kwargs)
        else:
            return model

    @classmethod
    def get_config_dict(cls, *args, **kwargs):
        deprecation_message = (
            f" The function get_config_dict is deprecated. Please use {cls}.load_config instead. This function will be"
            " removed in version v1.0.0"
        )
        deprecate("get_config_dict", "1.0.0", deprecation_message, standard_warn=False)
        return cls.load_config(*args, **kwargs)

    @classmethod
    def load_config(
        cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, **kwargs
    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
        r"""
        Instantiate a Python class from a config dictionary

        Parameters:
            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
                Can be either:

                    - A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an
                      organization name, like `google/ddpm-celebahq-256`.
                    - A path to a *directory* containing model weights saved using [`~ConfigMixin.save_config`], e.g.,
                      `./my_model_directory/`.

            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo (either remote in
                huggingface.co or downloaded locally), you can specify the folder name here.
            from_hf_hub (bool, *optional*):
                Whether to load from Hugging Face Hub. Defaults to False
        """
        from_hf_hub = kwargs.pop("from_hf_hub", False)
        if from_hf_hub:
            cache_dir = kwargs.pop("cache_dir", HF_CACHE)
        else:
            cache_dir = kwargs.pop("cache_dir", PPDIFFUSERS_CACHE)
        subfolder = kwargs.pop("subfolder", None)

        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):
            if os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):
                # Load from a Paddle checkpoint
                config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)
            elif subfolder is not None and os.path.isfile(
                os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
            ):
                config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
            else:
                raise EnvironmentError(
                    f"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}."
                )
        elif from_hf_hub:
            config_file = hf_hub_download(
                repo_id=pretrained_model_name_or_path,
                filename=cls.config_name,
                cache_dir=cache_dir,
                subfolder=subfolder,
                library_name="PPDiffusers",
                library_version=__version__,
            )
        else:
            try:
                config_file = ppdiffusers_bos_download(
                    pretrained_model_name_or_path,
                    filename=cls.config_name,
                    subfolder=subfolder,
                    cache_dir=cache_dir,
                )
            except HTTPError as err:
                raise EnvironmentError(
                    "There was a specific connection error when trying to load"
                    f" {pretrained_model_name_or_path}:\n{err}"
                )
            except ValueError:
                raise EnvironmentError(
                    f"We couldn't connect to '{DOWNLOAD_SERVER}' to load this model, couldn't find it"
                    f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
                    f" directory containing a {cls.config_name} file.\nCheckout your internet connection or see how to"
                    " run the library in offline mode at"
                    " 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
                )
            except EnvironmentError:
                raise EnvironmentError(
                    f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from "
                    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
                    f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                    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.")

        if return_unused_kwargs:
            return config_dict, kwargs

        return config_dict

    @staticmethod
    def _get_init_keys(cls):
        return set(dict(inspect.signature(cls.__init__).parameters).keys())

    @classmethod
    def extract_init_dict(cls, config_dict, **kwargs):
        # 0. Copy origin config dict
        original_dict = {k: v for k, v in config_dict.items()}

        # 1. Retrieve expected config attributes from __init__ signature
        expected_keys = cls._get_init_keys(cls)
        expected_keys.remove("self")
        # remove general kwargs if present in dict
        if "kwargs" in expected_keys:
            expected_keys.remove("kwargs")

        # 2. Remove attributes that cannot be expected from expected config attributes
        # remove keys to be ignored
        if len(cls.ignore_for_config) > 0:
            expected_keys = expected_keys - set(cls.ignore_for_config)

        # load ppdiffusers library to import compatible and original scheduler
        ppdiffusers_library = importlib.import_module(__name__.split(".")[0])

        if cls.has_compatibles:
            compatible_classes = [c for c in cls._get_compatibles() if not isinstance(c, DummyObject)]
        else:
            compatible_classes = []

        expected_keys_comp_cls = set()
        for c in compatible_classes:
            expected_keys_c = cls._get_init_keys(c)
            expected_keys_comp_cls = expected_keys_comp_cls.union(expected_keys_c)
        expected_keys_comp_cls = expected_keys_comp_cls - cls._get_init_keys(cls)
        config_dict = {k: v for k, v in config_dict.items() if k not in expected_keys_comp_cls}

        # remove attributes from orig class that cannot be expected
        orig_cls_name = config_dict.pop("_class_name", cls.__name__)
        if orig_cls_name != cls.__name__ and hasattr(ppdiffusers_library, orig_cls_name):
            orig_cls = getattr(ppdiffusers_library, orig_cls_name)
            unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys
            config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig}

        # remove private attributes
        config_dict = {k: v for k, v in config_dict.items() if not k.startswith("_")}

        # 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments
        init_dict = {}
        for key in expected_keys:
            # if config param is passed to kwarg and is present in config dict
            # it should overwrite existing config dict key
            if key in kwargs and key in config_dict:
                config_dict[key] = kwargs.pop(key)

            if key in kwargs:
                # overwrite key
                init_dict[key] = kwargs.pop(key)
            elif key in config_dict:
                # use value from config dict
                init_dict[key] = config_dict.pop(key)

        # 4. Give nice warning if unexpected values have been passed
        if len(config_dict) > 0:
            logger.warning(
                f"The config attributes {config_dict} were passed to {cls.__name__}, "
                "but are not expected and will be ignored. Please verify your "
                f"{cls.config_name} configuration file."
            )

        # 5. Give nice info if config attributes are initiliazed to default because they have not been passed
        passed_keys = set(init_dict.keys())
        if len(expected_keys - passed_keys) > 0:
            logger.info(
                f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
            )

        # 6. Define unused keyword arguments
        unused_kwargs = {**config_dict, **kwargs}

        # 7. Define "hidden" config parameters that were saved for compatible classes
        hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict}

        return init_dict, unused_kwargs, hidden_config_dict

    @classmethod
    def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
        with open(json_file, "r", encoding="utf-8") as reader:
            text = reader.read()
        return json.loads(text)

    def __repr__(self):
        return f"{self.__class__.__name__} {self.to_json_string()}"

    @property
    def config(self) -> Dict[str, Any]:
        """
        Returns the config of the class as a frozen dictionary

        Returns:
            `Dict[str, Any]`: Config of the class.
        """
        return self._internal_dict

    def to_json_string(self) -> str:
        """
        Serializes this instance to a JSON string.

        Returns:
            `str`: String containing all the attributes that make up this configuration instance in JSON format.
        """
        config_dict = self._internal_dict if hasattr(self, "_internal_dict") else {}
        config_dict["_class_name"] = self.__class__.__name__
        config_dict["_ppdiffusers_version"] = __version__

        def to_json_saveable(value):
            if isinstance(value, np.ndarray):
                value = value.tolist()
            return value

        config_dict = {k: to_json_saveable(v) for k, v in config_dict.items()}
        return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"

    def to_json_file(self, json_file_path: Union[str, os.PathLike]):
        """
        Save this instance to a JSON file.

        Args:
            json_file_path (`str` or `os.PathLike`):
                Path to the JSON file in which this configuration instance's parameters will be saved.
        """
        with open(json_file_path, "w", encoding="utf-8") as writer:
            writer.write(self.to_json_string())


def register_to_config(init):
    r"""
    Decorator to apply on the init of classes inheriting from [`ConfigMixin`] so that all the arguments are
    automatically sent to `self.register_for_config`. To ignore a specific argument accepted by the init but that
    shouldn't be registered in the config, use the `ignore_for_config` class variable

    Warning: Once decorated, all private arguments (beginning with an underscore) are trashed and not sent to the init!
    """

    @functools.wraps(init)
    def inner_init(self, *args, **kwargs):
        # Ignore private kwargs in the init.
        init_kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")}
        config_init_kwargs = {k: v for k, v in kwargs.items() if k.startswith("_")}

        if not isinstance(self, ConfigMixin):
            raise RuntimeError(
                f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
                "not inherit from `ConfigMixin`."
            )

        ignore = getattr(self, "ignore_for_config", [])
        # Get positional arguments aligned with kwargs
        new_kwargs = {}
        signature = inspect.signature(init)
        parameters = {
            name: p.default for i, (name, p) in enumerate(signature.parameters.items()) if i > 0 and name not in ignore
        }
        for arg, name in zip(args, parameters.keys()):
            new_kwargs[name] = arg

        # Then add all kwargs
        new_kwargs.update(
            {
                k: init_kwargs.get(k, default)
                for k, default in parameters.items()
                if k not in ignore and k not in new_kwargs
            }
        )
        new_kwargs = {**config_init_kwargs, **new_kwargs}
        getattr(self, "register_to_config")(**new_kwargs)
        init(self, *args, **init_kwargs)

    return inner_init