import json
import os
from pathlib import Path
from pickle import DEFAULT_PROTOCOL, PicklingError
from typing import Any, Dict, List, Optional, Union
from packaging import version
from huggingface_hub import snapshot_download
from huggingface_hub.constants import CONFIG_NAME
from huggingface_hub.hf_api import HfApi
from huggingface_hub.utils import (
SoftTemporaryDirectory,
get_fastai_version,
get_fastcore_version,
get_python_version,
)
from .utils import logging, validate_hf_hub_args
from .utils._runtime import _PY_VERSION # noqa: F401 # for backward compatibility...
logger = logging.get_logger(__name__)
def _check_fastai_fastcore_versions(
fastai_min_version: str = "2.4",
fastcore_min_version: str = "1.3.27",
):
"""
Checks that the installed fastai and fastcore versions are compatible for pickle serialization.
Args:
fastai_min_version (`str`, *optional*):
The minimum fastai version supported.
fastcore_min_version (`str`, *optional*):
The minimum fastcore version supported.
<Tip>
Raises the following error:
- [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
if the fastai or fastcore libraries are not available or are of an invalid version.
</Tip>
"""
if (get_fastcore_version() or get_fastai_version()) == "N/A":
raise ImportError(
f"fastai>={fastai_min_version} and fastcore>={fastcore_min_version} are"
f" required. Currently using fastai=={get_fastai_version()} and"
f" fastcore=={get_fastcore_version()}."
)
current_fastai_version = version.Version(get_fastai_version())
current_fastcore_version = version.Version(get_fastcore_version())
if current_fastai_version < version.Version(fastai_min_version):
raise ImportError(
"`push_to_hub_fastai` and `from_pretrained_fastai` require a"
f" fastai>={fastai_min_version} version, but you are using fastai version"
f" {get_fastai_version()} which is incompatible. Upgrade with `pip install"
" fastai==2.5.6`."
)
if current_fastcore_version < version.Version(fastcore_min_version):
raise ImportError(
"`push_to_hub_fastai` and `from_pretrained_fastai` require a"
f" fastcore>={fastcore_min_version} version, but you are using fastcore"
f" version {get_fastcore_version()} which is incompatible. Upgrade with"
" `pip install fastcore==1.3.27`."
)
def _check_fastai_fastcore_pyproject_versions(
storage_folder: str,
fastai_min_version: str = "2.4",
fastcore_min_version: str = "1.3.27",
):
"""
Checks that the `pyproject.toml` file in the directory `storage_folder` has fastai and fastcore versions
that are compatible with `from_pretrained_fastai` and `push_to_hub_fastai`. If `pyproject.toml` does not exist
or does not contain versions for fastai and fastcore, then it logs a warning.
Args:
storage_folder (`str`):
Folder to look for the `pyproject.toml` file.
fastai_min_version (`str`, *optional*):
The minimum fastai version supported.
fastcore_min_version (`str`, *optional*):
The minimum fastcore version supported.
<Tip>
Raises the following errors:
- [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
if the `toml` module is not installed.
- [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
if the `pyproject.toml` indicates a lower than minimum supported version of fastai or fastcore.
</Tip>
"""
try:
import toml
except ModuleNotFoundError:
raise ImportError(
"`push_to_hub_fastai` and `from_pretrained_fastai` require the toml module."
" Install it with `pip install toml`."
)
# Checks that a `pyproject.toml`, with `build-system` and `requires` sections, exists in the repository. If so, get a list of required packages.
if not os.path.isfile(f"{storage_folder}/pyproject.toml"):
logger.warning(
"There is no `pyproject.toml` in the repository that contains the fastai"
" `Learner`. The `pyproject.toml` would allow us to verify that your fastai"
" and fastcore versions are compatible with those of the model you want to"
" load."
)
return
pyproject_toml = toml.load(f"{storage_folder}/pyproject.toml")
if "build-system" not in pyproject_toml.keys():
logger.warning(
"There is no `build-system` section in the pyproject.toml of the repository"
" that contains the fastai `Learner`. The `build-system` would allow us to"
" verify that your fastai and fastcore versions are compatible with those"
" of the model you want to load."
)
return
build_system_toml = pyproject_toml["build-system"]
if "requires" not in build_system_toml.keys():
logger.warning(
"There is no `requires` section in the pyproject.toml of the repository"
" that contains the fastai `Learner`. The `requires` would allow us to"
" verify that your fastai and fastcore versions are compatible with those"
" of the model you want to load."
)
return
package_versions = build_system_toml["requires"]
# Extracts contains fastai and fastcore versions from `pyproject.toml` if available.
# If the package is specified but not the version (e.g. "fastai" instead of "fastai=2.4"), the default versions are the highest.
fastai_packages = [pck for pck in package_versions if pck.startswith("fastai")]
if len(fastai_packages) == 0:
logger.warning("The repository does not have a fastai version specified in the `pyproject.toml`.")
# fastai_version is an empty string if not specified
else:
fastai_version = str(fastai_packages[0]).partition("=")[2]
if fastai_version != "" and version.Version(fastai_version) < version.Version(fastai_min_version):
raise ImportError(
"`from_pretrained_fastai` requires"
f" fastai>={fastai_min_version} version but the model to load uses"
f" {fastai_version} which is incompatible."
)
fastcore_packages = [pck for pck in package_versions if pck.startswith("fastcore")]
if len(fastcore_packages) == 0:
logger.warning("The repository does not have a fastcore version specified in the `pyproject.toml`.")
# fastcore_version is an empty string if not specified
else:
fastcore_version = str(fastcore_packages[0]).partition("=")[2]
if fastcore_version != "" and version.Version(fastcore_version) < version.Version(fastcore_min_version):
raise ImportError(
"`from_pretrained_fastai` requires"
f" fastcore>={fastcore_min_version} version, but you are using fastcore"
f" version {fastcore_version} which is incompatible."
)
README_TEMPLATE = """---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
"""
PYPROJECT_TEMPLATE = f"""[build-system]
requires = ["setuptools>=40.8.0", "wheel", "python={get_python_version()}", "fastai={get_fastai_version()}", "fastcore={get_fastcore_version()}"]
build-backend = "setuptools.build_meta:__legacy__"
"""
def _create_model_card(repo_dir: Path):
"""
Creates a model card for the repository.
Args:
repo_dir (`Path`):
Directory where model card is created.
"""
readme_path = repo_dir / "README.md"
if not readme_path.exists():
with readme_path.open("w", encoding="utf-8") as f:
f.write(README_TEMPLATE)
def _create_model_pyproject(repo_dir: Path):
"""
Creates a `pyproject.toml` for the repository.
Args:
repo_dir (`Path`):
Directory where `pyproject.toml` is created.
"""
pyproject_path = repo_dir / "pyproject.toml"
if not pyproject_path.exists():
with pyproject_path.open("w", encoding="utf-8") as f:
f.write(PYPROJECT_TEMPLATE)
def _save_pretrained_fastai(
learner,
save_directory: Union[str, Path],
config: Optional[Dict[str, Any]] = None,
):
"""
Saves a fastai learner to `save_directory` in pickle format using the default pickle protocol for the version of python used.
Args:
learner (`Learner`):
The `fastai.Learner` you'd like to save.
save_directory (`str` or `Path`):
Specific directory in which you want to save the fastai learner.
config (`dict`, *optional*):
Configuration object. Will be uploaded as a .json file. Example: 'https://huggingface.co/espejelomar/fastai-pet-breeds-classification/blob/main/config.json'.
<Tip>
Raises the following error:
- [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError)
if the config file provided is not a dictionary.
</Tip>
"""
_check_fastai_fastcore_versions()
os.makedirs(save_directory, exist_ok=True)
# if the user provides config then we update it with the fastai and fastcore versions in CONFIG_TEMPLATE.
if config is not None:
if not isinstance(config, dict):
raise RuntimeError(f"Provided config should be a dict. Got: '{type(config)}'")
path = os.path.join(save_directory, CONFIG_NAME)
with open(path, "w") as f:
json.dump(config, f)
_create_model_card(Path(save_directory))
_create_model_pyproject(Path(save_directory))
# learner.export saves the model in `self.path`.
learner.path = Path(save_directory)
os.makedirs(save_directory, exist_ok=True)
try:
learner.export(
fname="model.pkl",
pickle_protocol=DEFAULT_PROTOCOL,
)
except PicklingError:
raise PicklingError(
"You are using a lambda function, i.e., an anonymous function. `pickle`"
" cannot pickle function objects and requires that all functions have"
" names. One possible solution is to name the function."
)
@validate_hf_hub_args
def from_pretrained_fastai(
repo_id: str,
revision: Optional[str] = None,
):
"""
Load pretrained fastai model from the Hub or from a local directory.
Args:
repo_id (`str`):
The location where the pickled fastai.Learner is. It can be either of the two:
- Hosted on the Hugging Face Hub. E.g.: 'espejelomar/fatai-pet-breeds-classification' or 'distilgpt2'.
You can add a `revision` by appending `@` at the end of `repo_id`. E.g.: `dbmdz/bert-base-german-cased@main`.
Revision is the specific model version to use. Since we use a git-based system for storing models and other
artifacts on the Hugging Face Hub, it can be a branch name, a tag name, or a commit id.
- Hosted locally. `repo_id` would be a directory containing the pickle and a pyproject.toml
indicating the fastai and fastcore versions used to build the `fastai.Learner`. E.g.: `./my_model_directory/`.
revision (`str`, *optional*):
Revision at which the repo's files are downloaded. See documentation of `snapshot_download`.
Returns:
The `fastai.Learner` model in the `repo_id` repo.
"""
_check_fastai_fastcore_versions()
# Load the `repo_id` repo.
# `snapshot_download` returns the folder where the model was stored.
# `cache_dir` will be the default '/root/.cache/huggingface/hub'
if not os.path.isdir(repo_id):
storage_folder = snapshot_download(
repo_id=repo_id,
revision=revision,
library_name="fastai",
library_version=get_fastai_version(),
)
else:
storage_folder = repo_id
_check_fastai_fastcore_pyproject_versions(storage_folder)
from fastai.learner import load_learner # type: ignore
return load_learner(os.path.join(storage_folder, "model.pkl"))
@validate_hf_hub_args
def push_to_hub_fastai(
learner,
*,
repo_id: str,
commit_message: str = "Push FastAI model using huggingface_hub.",
private: bool = False,
token: Optional[str] = None,
config: Optional[dict] = None,
branch: Optional[str] = None,
create_pr: Optional[bool] = None,
allow_patterns: Optional[Union[List[str], str]] = None,
ignore_patterns: Optional[Union[List[str], str]] = None,
delete_patterns: Optional[Union[List[str], str]] = None,
api_endpoint: Optional[str] = None,
):
"""
Upload learner checkpoint files to the Hub.
Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use
`delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
details.
Args:
learner (`Learner`):
The `fastai.Learner' you'd like to push to the Hub.
repo_id (`str`):
The repository id for your model in Hub in the format of "namespace/repo_name". The namespace can be your individual account or an organization to which you have write access (for example, 'stanfordnlp/stanza-de').
commit_message (`str`, *optional*):
Message to commit while pushing. Will default to :obj:`"add model"`.
private (`bool`, *optional*, defaults to `False`):
Whether or not the repository created should be private.
token (`str`, *optional*):
The Hugging Face account token to use as HTTP bearer authorization for remote files. If :obj:`None`, the token will be asked by a prompt.
config (`dict`, *optional*):
Configuration object to be saved alongside the model weights.
branch (`str`, *optional*):
The git branch on which to push the model. This defaults to
the default branch as specified in your repository, which
defaults to `"main"`.
create_pr (`boolean`, *optional*):
Whether or not to create a Pull Request from `branch` with that commit.
Defaults to `False`.
api_endpoint (`str`, *optional*):
The API endpoint to use when pushing the model to the hub.
allow_patterns (`List[str]` or `str`, *optional*):
If provided, only files matching at least one pattern are pushed.
ignore_patterns (`List[str]` or `str`, *optional*):
If provided, files matching any of the patterns are not pushed.
delete_patterns (`List[str]` or `str`, *optional*):
If provided, remote files matching any of the patterns will be deleted from the repo.
Returns:
The url of the commit of your model in the given repository.
<Tip>
Raises the following error:
- [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
if the user is not log on to the Hugging Face Hub.
</Tip>
"""
_check_fastai_fastcore_versions()
api = HfApi(endpoint=api_endpoint)
repo_id = api.create_repo(repo_id=repo_id, token=token, private=private, exist_ok=True).repo_id
# Push the files to the repo in a single commit
with SoftTemporaryDirectory() as tmp:
saved_path = Path(tmp) / repo_id
_save_pretrained_fastai(learner, saved_path, config=config)
return api.upload_folder(
repo_id=repo_id,
token=token,
folder_path=saved_path,
commit_message=commit_message,
revision=branch,
create_pr=create_pr,
allow_patterns=allow_patterns,
ignore_patterns=ignore_patterns,
delete_patterns=delete_patterns,
)