Source code for

# coding=utf-8
# Copyright 2021 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.
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# distributed under the License is distributed on an "AS IS" BASIS,
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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

        (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 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': '', 'http://hostname': ''}.` 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, 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 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())}" )