# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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.
"""
Processing saving/loading class for common processors.
"""
import copy
import inspect
import json
import os
import warnings
from pathlib import Path
from typing import Any, Dict, Optional, Tuple, Union
from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import (
PROCESSOR_NAME,
PushToHubMixin,
add_model_info_to_auto_map,
cached_file,
copy_func,
direct_transformers_import,
download_url,
is_offline_mode,
is_remote_url,
logging,
)
logger = logging.get_logger(__name__)
# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
transformers_module = direct_transformers_import(Path(__file__).parent)
AUTO_TO_BASE_CLASS_MAPPING = {
"AutoTokenizer": "PreTrainedTokenizerBase",
"AutoFeatureExtractor": "FeatureExtractionMixin",
"AutoImageProcessor": "ImageProcessingMixin",
}
class ProcessorMixin(PushToHubMixin):
"""
This is a mixin used to provide saving/loading functionality for all processor classes.
"""
attributes = ["feature_extractor", "tokenizer"]
# Names need to be attr_class for attr in attributes
feature_extractor_class = None
tokenizer_class = None
_auto_class = None
# args have to match the attributes class attribute
def __init__(self, *args, **kwargs):
# Sanitize args and kwargs
for key in kwargs:
if key not in self.attributes:
raise TypeError(f"Unexpected keyword argument {key}.")
for arg, attribute_name in zip(args, self.attributes):
if attribute_name in kwargs:
raise TypeError(f"Got multiple values for argument {attribute_name}.")
else:
kwargs[attribute_name] = arg
if len(kwargs) != len(self.attributes):
raise ValueError(
f"This processor requires {len(self.attributes)} arguments: {', '.join(self.attributes)}. Got "
f"{len(args)} arguments instead."
)
# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
for attribute_name, arg in kwargs.items():
class_name = getattr(self, f"{attribute_name}_class")
# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
class_name = AUTO_TO_BASE_CLASS_MAPPING.get(class_name, class_name)
if isinstance(class_name, tuple):
proper_class = tuple(getattr(transformers_module, n) for n in class_name if n is not None)
else:
proper_class = getattr(transformers_module, class_name)
if not isinstance(arg, proper_class):
raise ValueError(
f"Received a {type(arg).__name__} for argument {attribute_name}, but a {class_name} was expected."
)
setattr(self, attribute_name, arg)
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 processor instance.
"""
output = copy.deepcopy(self.__dict__)
# Get the kwargs in `__init__`.
sig = inspect.signature(self.__init__)
# Only save the attributes that are presented in the kwargs of `__init__`.
attrs_to_save = sig.parameters
# Don't save attributes like `tokenizer`, `image processor` etc.
attrs_to_save = [x for x in attrs_to_save if x not in self.__class__.attributes]
# extra attributes to be kept
attrs_to_save += ["auto_map"]
output = {k: v for k, v in output.items() if k in attrs_to_save}
output["processor_class"] = self.__class__.__name__
if "tokenizer" in output:
del output["tokenizer"]
if "image_processor" in output:
del output["image_processor"]
if "feature_extractor" in output:
del output["feature_extractor"]
# Some attributes have different names but containing objects that are not simple strings
output = {
k: v
for k, v in output.items()
if not (isinstance(v, PushToHubMixin) or v.__class__.__name__ == "BeamSearchDecoderCTC")
}
return output
def to_json_string(self) -> str:
"""
Serializes this instance to a JSON string.
Returns:
`str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
"""
dictionary = self.to_dict()
return json.dumps(dictionary, 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 processor instance's parameters will be saved.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string())
def __repr__(self):
attributes_repr = [f"- {name}: {repr(getattr(self, name))}" for name in self.attributes]
attributes_repr = "\n".join(attributes_repr)
return f"{self.__class__.__name__}:\n{attributes_repr}\n\n{self.to_json_string()}"
def save_pretrained(self, save_directory, push_to_hub: bool = False, **kwargs):
"""
Saves the attributes of this processor (feature extractor, tokenizer...) in the specified directory so that it
can be reloaded using the [`~ProcessorMixin.from_pretrained`] method.
<Tip>
This class method is simply calling [`~feature_extraction_utils.FeatureExtractionMixin.save_pretrained`] and
[`~tokenization_utils_base.PreTrainedTokenizerBase.save_pretrained`]. Please refer to the docstrings of the
methods above for more information.
</Tip>
Args:
save_directory (`str` or `os.PathLike`):
Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
be created if it does not exist).
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
namespace).
kwargs (`Dict[str, Any]`, *optional*):
Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
use_auth_token = kwargs.pop("use_auth_token", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if kwargs.get("token", None) is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
kwargs["token"] = use_auth_token
os.makedirs(save_directory, exist_ok=True)
if push_to_hub:
commit_message = kwargs.pop("commit_message", None)
repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
repo_id = self._create_repo(repo_id, **kwargs)
files_timestamps = self._get_files_timestamps(save_directory)
# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
# loaded from the Hub.
if self._auto_class is not None:
attrs = [getattr(self, attribute_name) for attribute_name in self.attributes]
configs = [(a.init_kwargs if isinstance(a, PreTrainedTokenizerBase) else a) for a in attrs]
configs.append(self)
custom_object_save(self, save_directory, config=configs)
for attribute_name in self.attributes:
attribute = getattr(self, attribute_name)
# Include the processor class in the attribute config so this processor can then be reloaded with the
# `AutoProcessor` API.
if hasattr(attribute, "_set_processor_class"):
attribute._set_processor_class(self.__class__.__name__)
attribute.save_pretrained(save_directory)
if self._auto_class is not None:
# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
for attribute_name in self.attributes:
attribute = getattr(self, attribute_name)
if isinstance(attribute, PreTrainedTokenizerBase):
del attribute.init_kwargs["auto_map"]
# If we save using the predefined names, we can load using `from_pretrained`
output_processor_file = os.path.join(save_directory, PROCESSOR_NAME)
# For now, let's not save to `processor_config.json` if the processor doesn't have extra attributes and
# `auto_map` is not specified.
if set(self.to_dict().keys()) != {"processor_class"}:
self.to_json_file(output_processor_file)
logger.info(f"processor saved in {output_processor_file}")
if push_to_hub:
self._upload_modified_files(
save_directory,
repo_id,
files_timestamps,
commit_message=commit_message,
token=kwargs.get("token"),
)
if set(self.to_dict().keys()) == {"processor_class"}:
return []
return [output_processor_file]
@classmethod
def get_processor_dict(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""
From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
processor of type [`~processing_utils.ProcessingMixin`] using `from_args_and_dict`.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`):
The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
specify the folder name here.
Returns:
`Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the processor object.
"""
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
token = kwargs.pop("token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", "")
from_pipeline = kwargs.pop("_from_pipeline", None)
from_auto_class = kwargs.pop("_from_auto", False)
user_agent = {"file_type": "processor", "from_auto_class": from_auto_class}
if from_pipeline is not None:
user_agent["using_pipeline"] = from_pipeline
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
is_local = os.path.isdir(pretrained_model_name_or_path)
if os.path.isdir(pretrained_model_name_or_path):
processor_file = os.path.join(pretrained_model_name_or_path, PROCESSOR_NAME)
if os.path.isfile(pretrained_model_name_or_path):
resolved_processor_file = pretrained_model_name_or_path
is_local = True
elif is_remote_url(pretrained_model_name_or_path):
processor_file = pretrained_model_name_or_path
resolved_processor_file = download_url(pretrained_model_name_or_path)
else:
processor_file = PROCESSOR_NAME
try:
# Load from local folder or from cache or download from model Hub and cache
resolved_processor_file = cached_file(
pretrained_model_name_or_path,
processor_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
token=token,
user_agent=user_agent,
revision=revision,
subfolder=subfolder,
_raise_exceptions_for_missing_entries=False,
)
except EnvironmentError:
# Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
# the original exception.
raise
except Exception:
# For any other exception, we throw a generic error.
raise EnvironmentError(
f"Can't load processor 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"
f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
f" directory containing a {PROCESSOR_NAME} file"
)
# Existing processors on the Hub created before #27761 being merged don't have `processor_config.json` (if not
# updated afterward), and we need to keep `from_pretrained` work. So here it fallbacks to the empty dict.
# (`cached_file` called using `_raise_exceptions_for_missing_entries=False` to avoid exception)
# However, for models added in the future, we won't get the expected error if this file is missing.
if resolved_processor_file is None:
return {}, kwargs
try:
# Load processor dict
with open(resolved_processor_file, "r", encoding="utf-8") as reader:
text = reader.read()
processor_dict = json.loads(text)
except json.JSONDecodeError:
raise EnvironmentError(
f"It looks like the config file at '{resolved_processor_file}' is not a valid JSON file."
)
if is_local:
logger.info(f"loading configuration file {resolved_processor_file}")
else:
logger.info(f"loading configuration file {processor_file} from cache at {resolved_processor_file}")
if "auto_map" in processor_dict and not is_local:
processor_dict["auto_map"] = add_model_info_to_auto_map(
processor_dict["auto_map"], pretrained_model_name_or_path
)
return processor_dict, kwargs
@classmethod
def from_args_and_dict(cls, args, processor_dict: Dict[str, Any], **kwargs):
"""
Instantiates a type of [`~processing_utils.ProcessingMixin`] from a Python dictionary of parameters.
Args:
processor_dict (`Dict[str, Any]`):
Dictionary that will be used to instantiate the processor object. Such a dictionary can be
retrieved from a pretrained checkpoint by leveraging the
[`~processing_utils.ProcessingMixin.to_dict`] method.
kwargs (`Dict[str, Any]`):
Additional parameters from which to initialize the processor object.
Returns:
[`~processing_utils.ProcessingMixin`]: The processor object instantiated from those
parameters.
"""
processor_dict = processor_dict.copy()
return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
# Unlike image processors or feature extractors whose `__init__` accept `kwargs`, processor don't have `kwargs`.
# We have to pop up some unused (but specific) arguments to make it work.
if "processor_class" in processor_dict:
del processor_dict["processor_class"]
if "auto_map" in processor_dict:
del processor_dict["auto_map"]
processor = cls(*args, **processor_dict)
# Update processor with kwargs if needed
for key in set(kwargs.keys()):
if hasattr(processor, key):
setattr(processor, key, kwargs.pop(key))
logger.info(f"Processor {processor}")
if return_unused_kwargs:
return processor, kwargs
else:
return processor
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, os.PathLike],
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
local_files_only: bool = False,
token: Optional[Union[str, bool]] = None,
revision: str = "main",
**kwargs,
):
r"""
Instantiate a processor associated with a pretrained model.
<Tip>
This class method is simply calling the feature extractor
[`~feature_extraction_utils.FeatureExtractionMixin.from_pretrained`], image processor
[`~image_processing_utils.ImageProcessingMixin`] and the tokenizer
[`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`] methods. Please refer to the docstrings of the
methods above for more information.
</Tip>
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on
huggingface.co.
- a path to a *directory* containing a feature extractor file saved using the
[`~SequenceFeatureExtractor.save_pretrained`] method, e.g., `./my_model_directory/`.
- a path or url to a saved feature extractor JSON *file*, e.g.,
`./my_model_directory/preprocessor_config.json`.
**kwargs
Additional keyword arguments passed along to both
[`~feature_extraction_utils.FeatureExtractionMixin.from_pretrained`] and
[`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`].
"""
kwargs["cache_dir"] = cache_dir
kwargs["force_download"] = force_download
kwargs["local_files_only"] = local_files_only
kwargs["revision"] = revision
use_auth_token = kwargs.pop("use_auth_token", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if token is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
token = use_auth_token
if token is not None:
kwargs["token"] = token
args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
processor_dict, kwargs = cls.get_processor_dict(pretrained_model_name_or_path, **kwargs)
return cls.from_args_and_dict(args, processor_dict, **kwargs)
@classmethod
def register_for_auto_class(cls, auto_class="AutoProcessor"):
"""
Register this class with a given auto class. This should only be used for custom feature extractors as the ones
in the library are already mapped with `AutoProcessor`.
<Tip warning={true}>
This API is experimental and may have some slight breaking changes in the next releases.
</Tip>
Args:
auto_class (`str` or `type`, *optional*, defaults to `"AutoProcessor"`):
The auto class to register this new feature extractor with.
"""
if not isinstance(auto_class, str):
auto_class = auto_class.__name__
import transformers.models.auto as auto_module
if not hasattr(auto_module, auto_class):
raise ValueError(f"{auto_class} is not a valid auto class.")
cls._auto_class = auto_class
@classmethod
def _get_arguments_from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
args = []
for attribute_name in cls.attributes:
class_name = getattr(cls, f"{attribute_name}_class")
if isinstance(class_name, tuple):
classes = tuple(getattr(transformers_module, n) if n is not None else None for n in class_name)
use_fast = kwargs.get("use_fast", True)
if use_fast and classes[1] is not None:
attribute_class = classes[1]
else:
attribute_class = classes[0]
else:
attribute_class = getattr(transformers_module, class_name)
args.append(attribute_class.from_pretrained(pretrained_model_name_or_path, **kwargs))
return args
@property
def model_input_names(self):
first_attribute = getattr(self, self.attributes[0])
return getattr(first_attribute, "model_input_names", None)
ProcessorMixin.push_to_hub = copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
ProcessorMixin.push_to_hub.__doc__ = ProcessorMixin.push_to_hub.__doc__.format(
object="processor", object_class="AutoProcessor", object_files="processor files"
)