Source code for transformers.models.auto.feature_extraction_auto
# coding=utf-8
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""" AutoFeatureExtractor class. """
import os
from collections import OrderedDict
from transformers import DeiTFeatureExtractor, Speech2TextFeatureExtractor, ViTFeatureExtractor
from ... import DeiTConfig, PretrainedConfig, Speech2TextConfig, ViTConfig, Wav2Vec2Config
from ...feature_extraction_utils import FeatureExtractionMixin
# Build the list of all feature extractors
from ...file_utils import FEATURE_EXTRACTOR_NAME
from ..wav2vec2.feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
from .configuration_auto import AutoConfig, replace_list_option_in_docstrings
FEATURE_EXTRACTOR_MAPPING = OrderedDict(
[
(DeiTConfig, DeiTFeatureExtractor),
(Speech2TextConfig, Speech2TextFeatureExtractor),
(ViTConfig, ViTFeatureExtractor),
(Wav2Vec2Config, Wav2Vec2FeatureExtractor),
]
)
def feature_extractor_class_from_name(class_name: str):
for c in FEATURE_EXTRACTOR_MAPPING.values():
if c is not None and c.__name__ == class_name:
return c
[docs]class AutoFeatureExtractor:
r"""
This is a generic feature extractor class that will be instantiated as one of the feature extractor classes of the
library when created with the :meth:`AutoFeatureExtractor.from_pretrained` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"AutoFeatureExtractor is designed to be instantiated "
"using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."
)
[docs] @classmethod
@replace_list_option_in_docstrings(FEATURE_EXTRACTOR_MAPPING)
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r"""
Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary.
The tokenizer class to instantiate is selected based on the :obj:`model_type` property of the config object
(either passed as an argument or loaded from :obj:`pretrained_model_name_or_path` if possible), or when it's
missing, by falling back to using pattern matching on :obj:`pretrained_model_name_or_path`:
List options
Params:
pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
This can be either:
- a string, the `model id` of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like ``bert-base-uncased``, or
namespaced under a user or organization name, like ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing a feature extractor file saved using the
:func:`~transformers.feature_extraction_utils.FeatureExtractionMixin.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``.
cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.
force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.
resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.
proxies (:obj:`Dict[str, str]`, `optional`):
A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
use_auth_token (:obj:`str` or `bool`, `optional`):
The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
identifier allowed by git.
return_unused_kwargs (:obj:`bool`, `optional`, defaults to :obj:`False`):
If :obj:`False`, then this function returns just the final feature extractor object. If :obj:`True`,
then this functions returns a :obj:`Tuple(feature_extractor, unused_kwargs)` where `unused_kwargs` is a
dictionary consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the
part of ``kwargs`` which has not been used to update ``feature_extractor`` and is otherwise ignored.
kwargs (:obj:`Dict[str, Any]`, `optional`):
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is
controlled by the ``return_unused_kwargs`` keyword parameter.
.. note::
Passing :obj:`use_auth_token=True` is required when you want to use a private model.
Examples::
>>> from transformers import AutoFeatureExtractor
>>> # Download vocabulary from huggingface.co and cache.
>>> feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h')
>>> # If vocabulary files are in a directory (e.g. feature extractor was saved using `save_pretrained('./test/saved_model/')`)
>>> feature_extractor = AutoFeatureExtractor.from_pretrained('./test/saved_model/')
"""
config = kwargs.pop("config", None)
kwargs["_from_auto"] = True
is_feature_extraction_file = os.path.isfile(pretrained_model_name_or_path)
is_directory = os.path.isdir(pretrained_model_name_or_path) and os.path.exists(
os.path.join(pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME)
)
if not is_feature_extraction_file and not is_directory:
if not isinstance(config, PretrainedConfig):
config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
kwargs["_from_auto"] = True
config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
if type(config) in FEATURE_EXTRACTOR_MAPPING.keys():
return FEATURE_EXTRACTOR_MAPPING[type(config)].from_dict(config_dict, **kwargs)
elif "feature_extractor_type" in config_dict:
feature_extractor_class = feature_extractor_class_from_name(config_dict["feature_extractor_type"])
return feature_extractor_class.from_dict(config_dict, **kwargs)
raise ValueError(
f"Unrecognized model in {pretrained_model_name_or_path}. Should have a `feature_extractor_type` key in "
f"its {FEATURE_EXTRACTOR_NAME}, or contain one of the following strings "
f"in its name: {', '.join(FEATURE_EXTRACTOR_MAPPING.keys())}"
)