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| # 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. | |
| # 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. | |
| """ AutoFeatureExtractor class.""" | |
| import importlib | |
| import json | |
| import os | |
| import warnings | |
| from collections import OrderedDict | |
| from typing import Dict, Optional, Union | |
| # Build the list of all feature extractors | |
| from ...configuration_utils import PretrainedConfig | |
| from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code | |
| from ...feature_extraction_utils import FeatureExtractionMixin | |
| from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging | |
| from .auto_factory import _LazyAutoMapping | |
| from .configuration_auto import ( | |
| CONFIG_MAPPING_NAMES, | |
| AutoConfig, | |
| model_type_to_module_name, | |
| replace_list_option_in_docstrings, | |
| ) | |
| logger = logging.get_logger(__name__) | |
| FEATURE_EXTRACTOR_MAPPING_NAMES = OrderedDict( | |
| [ | |
| ("audio-spectrogram-transformer", "ASTFeatureExtractor"), | |
| ("beit", "BeitFeatureExtractor"), | |
| ("chinese_clip", "ChineseCLIPFeatureExtractor"), | |
| ("clap", "ClapFeatureExtractor"), | |
| ("clip", "CLIPFeatureExtractor"), | |
| ("clipseg", "ViTFeatureExtractor"), | |
| ("conditional_detr", "ConditionalDetrFeatureExtractor"), | |
| ("convnext", "ConvNextFeatureExtractor"), | |
| ("cvt", "ConvNextFeatureExtractor"), | |
| ("data2vec-audio", "Wav2Vec2FeatureExtractor"), | |
| ("data2vec-vision", "BeitFeatureExtractor"), | |
| ("deformable_detr", "DeformableDetrFeatureExtractor"), | |
| ("deit", "DeiTFeatureExtractor"), | |
| ("detr", "DetrFeatureExtractor"), | |
| ("dinat", "ViTFeatureExtractor"), | |
| ("donut-swin", "DonutFeatureExtractor"), | |
| ("dpt", "DPTFeatureExtractor"), | |
| ("encodec", "EncodecFeatureExtractor"), | |
| ("flava", "FlavaFeatureExtractor"), | |
| ("glpn", "GLPNFeatureExtractor"), | |
| ("groupvit", "CLIPFeatureExtractor"), | |
| ("hubert", "Wav2Vec2FeatureExtractor"), | |
| ("imagegpt", "ImageGPTFeatureExtractor"), | |
| ("layoutlmv2", "LayoutLMv2FeatureExtractor"), | |
| ("layoutlmv3", "LayoutLMv3FeatureExtractor"), | |
| ("levit", "LevitFeatureExtractor"), | |
| ("maskformer", "MaskFormerFeatureExtractor"), | |
| ("mctct", "MCTCTFeatureExtractor"), | |
| ("mobilenet_v1", "MobileNetV1FeatureExtractor"), | |
| ("mobilenet_v2", "MobileNetV2FeatureExtractor"), | |
| ("mobilevit", "MobileViTFeatureExtractor"), | |
| ("nat", "ViTFeatureExtractor"), | |
| ("owlvit", "OwlViTFeatureExtractor"), | |
| ("perceiver", "PerceiverFeatureExtractor"), | |
| ("poolformer", "PoolFormerFeatureExtractor"), | |
| ("pop2piano", "Pop2PianoFeatureExtractor"), | |
| ("regnet", "ConvNextFeatureExtractor"), | |
| ("resnet", "ConvNextFeatureExtractor"), | |
| ("segformer", "SegformerFeatureExtractor"), | |
| ("sew", "Wav2Vec2FeatureExtractor"), | |
| ("sew-d", "Wav2Vec2FeatureExtractor"), | |
| ("speech_to_text", "Speech2TextFeatureExtractor"), | |
| ("speecht5", "SpeechT5FeatureExtractor"), | |
| ("swiftformer", "ViTFeatureExtractor"), | |
| ("swin", "ViTFeatureExtractor"), | |
| ("swinv2", "ViTFeatureExtractor"), | |
| ("table-transformer", "DetrFeatureExtractor"), | |
| ("timesformer", "VideoMAEFeatureExtractor"), | |
| ("tvlt", "TvltFeatureExtractor"), | |
| ("unispeech", "Wav2Vec2FeatureExtractor"), | |
| ("unispeech-sat", "Wav2Vec2FeatureExtractor"), | |
| ("van", "ConvNextFeatureExtractor"), | |
| ("videomae", "VideoMAEFeatureExtractor"), | |
| ("vilt", "ViltFeatureExtractor"), | |
| ("vit", "ViTFeatureExtractor"), | |
| ("vit_mae", "ViTFeatureExtractor"), | |
| ("vit_msn", "ViTFeatureExtractor"), | |
| ("wav2vec2", "Wav2Vec2FeatureExtractor"), | |
| ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"), | |
| ("wavlm", "Wav2Vec2FeatureExtractor"), | |
| ("whisper", "WhisperFeatureExtractor"), | |
| ("xclip", "CLIPFeatureExtractor"), | |
| ("yolos", "YolosFeatureExtractor"), | |
| ] | |
| ) | |
| FEATURE_EXTRACTOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES) | |
| def feature_extractor_class_from_name(class_name: str): | |
| for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items(): | |
| if class_name in extractors: | |
| module_name = model_type_to_module_name(module_name) | |
| module = importlib.import_module(f".{module_name}", "transformers.models") | |
| try: | |
| return getattr(module, class_name) | |
| except AttributeError: | |
| continue | |
| for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items(): | |
| if getattr(extractor, "__name__", None) == class_name: | |
| return extractor | |
| # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main | |
| # init and we return the proper dummy to get an appropriate error message. | |
| main_module = importlib.import_module("transformers") | |
| if hasattr(main_module, class_name): | |
| return getattr(main_module, class_name) | |
| return None | |
| def get_feature_extractor_config( | |
| pretrained_model_name_or_path: Union[str, os.PathLike], | |
| cache_dir: Optional[Union[str, os.PathLike]] = None, | |
| force_download: bool = False, | |
| resume_download: bool = False, | |
| proxies: Optional[Dict[str, str]] = None, | |
| token: Optional[Union[bool, str]] = None, | |
| revision: Optional[str] = None, | |
| local_files_only: bool = False, | |
| **kwargs, | |
| ): | |
| """ | |
| Loads the tokenizer configuration from a pretrained model tokenizer configuration. | |
| Args: | |
| pretrained_model_name_or_path (`str` or `os.PathLike`): | |
| This can be either: | |
| - a string, the *model id* of a pretrained model configuration 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 configuration file saved using the | |
| [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. | |
| cache_dir (`str` or `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. | |
| force_download (`bool`, *optional*, defaults to `False`): | |
| Whether or not to force to (re-)download the configuration files and override the cached versions if they | |
| exist. | |
| resume_download (`bool`, *optional*, defaults to `False`): | |
| Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. | |
| proxies (`Dict[str, str]`, *optional*): | |
| A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', | |
| 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. | |
| token (`str` or *bool*, *optional*): | |
| The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated | |
| when running `huggingface-cli login` (stored in `~/.huggingface`). | |
| revision (`str`, *optional*, defaults to `"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. | |
| local_files_only (`bool`, *optional*, defaults to `False`): | |
| If `True`, will only try to load the tokenizer configuration from local files. | |
| <Tip> | |
| Passing `token=True` is required when you want to use a private model. | |
| </Tip> | |
| Returns: | |
| `Dict`: The configuration of the tokenizer. | |
| Examples: | |
| ```python | |
| # Download configuration from huggingface.co and cache. | |
| tokenizer_config = get_tokenizer_config("bert-base-uncased") | |
| # This model does not have a tokenizer config so the result will be an empty dict. | |
| tokenizer_config = get_tokenizer_config("xlm-roberta-base") | |
| # Save a pretrained tokenizer locally and you can reload its config | |
| from transformers import AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") | |
| tokenizer.save_pretrained("tokenizer-test") | |
| tokenizer_config = get_tokenizer_config("tokenizer-test") | |
| ```""" | |
| 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.", 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 | |
| resolved_config_file = get_file_from_repo( | |
| pretrained_model_name_or_path, | |
| FEATURE_EXTRACTOR_NAME, | |
| cache_dir=cache_dir, | |
| force_download=force_download, | |
| resume_download=resume_download, | |
| proxies=proxies, | |
| token=token, | |
| revision=revision, | |
| local_files_only=local_files_only, | |
| ) | |
| if resolved_config_file is None: | |
| logger.info( | |
| "Could not locate the feature extractor configuration file, will try to use the model config instead." | |
| ) | |
| return {} | |
| with open(resolved_config_file, encoding="utf-8") as reader: | |
| return json.load(reader) | |
| 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 [`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." | |
| ) | |
| 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 feature extractor class to instantiate is selected based on the `model_type` property of the config object | |
| (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's | |
| missing, by falling back to using pattern matching on `pretrained_model_name_or_path`: | |
| List options | |
| Params: | |
| 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. 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 | |
| [`~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 (`str` or `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 (`bool`, *optional*, defaults to `False`): | |
| Whether or not to force to (re-)download the feature extractor files and override the cached versions | |
| if they exist. | |
| resume_download (`bool`, *optional*, defaults to `False`): | |
| Whether or not to delete incompletely received file. Attempts to resume the download if such a file | |
| exists. | |
| proxies (`Dict[str, str]`, *optional*): | |
| A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', | |
| 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. | |
| token (`str` or *bool*, *optional*): | |
| The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated | |
| when running `huggingface-cli login` (stored in `~/.huggingface`). | |
| revision (`str`, *optional*, defaults to `"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 (`bool`, *optional*, defaults to `False`): | |
| If `False`, then this function returns just the final feature extractor object. If `True`, then this | |
| functions returns a `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. | |
| trust_remote_code (`bool`, *optional*, defaults to `False`): | |
| Whether or not to allow for custom models defined on the Hub in their own modeling files. This option | |
| should only be set to `True` for repositories you trust and in which you have read the code, as it will | |
| execute code present on the Hub on your local machine. | |
| kwargs (`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. | |
| <Tip> | |
| Passing `token=True` is required when you want to use a private model. | |
| </Tip> | |
| Examples: | |
| ```python | |
| >>> from transformers import AutoFeatureExtractor | |
| >>> # Download feature extractor from huggingface.co and cache. | |
| >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h") | |
| >>> # If feature extractor 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/") | |
| ```""" | |
| 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.", 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 | |
| config = kwargs.pop("config", None) | |
| trust_remote_code = kwargs.pop("trust_remote_code", None) | |
| kwargs["_from_auto"] = True | |
| config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs) | |
| feature_extractor_class = config_dict.get("feature_extractor_type", None) | |
| feature_extractor_auto_map = None | |
| if "AutoFeatureExtractor" in config_dict.get("auto_map", {}): | |
| feature_extractor_auto_map = config_dict["auto_map"]["AutoFeatureExtractor"] | |
| # If we don't find the feature extractor class in the feature extractor config, let's try the model config. | |
| if feature_extractor_class is None and feature_extractor_auto_map is None: | |
| if not isinstance(config, PretrainedConfig): | |
| config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) | |
| # It could be in `config.feature_extractor_type`` | |
| feature_extractor_class = getattr(config, "feature_extractor_type", None) | |
| if hasattr(config, "auto_map") and "AutoFeatureExtractor" in config.auto_map: | |
| feature_extractor_auto_map = config.auto_map["AutoFeatureExtractor"] | |
| if feature_extractor_class is not None: | |
| feature_extractor_class = feature_extractor_class_from_name(feature_extractor_class) | |
| has_remote_code = feature_extractor_auto_map is not None | |
| has_local_code = feature_extractor_class is not None or type(config) in FEATURE_EXTRACTOR_MAPPING | |
| trust_remote_code = resolve_trust_remote_code( | |
| trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code | |
| ) | |
| if has_remote_code and trust_remote_code: | |
| feature_extractor_class = get_class_from_dynamic_module( | |
| feature_extractor_auto_map, pretrained_model_name_or_path, **kwargs | |
| ) | |
| _ = kwargs.pop("code_revision", None) | |
| if os.path.isdir(pretrained_model_name_or_path): | |
| feature_extractor_class.register_for_auto_class() | |
| return feature_extractor_class.from_dict(config_dict, **kwargs) | |
| elif feature_extractor_class is not None: | |
| return feature_extractor_class.from_dict(config_dict, **kwargs) | |
| # Last try: we use the FEATURE_EXTRACTOR_MAPPING. | |
| elif type(config) in FEATURE_EXTRACTOR_MAPPING: | |
| feature_extractor_class = FEATURE_EXTRACTOR_MAPPING[type(config)] | |
| return feature_extractor_class.from_dict(config_dict, **kwargs) | |
| raise ValueError( | |
| f"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a " | |
| f"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following " | |
| f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}" | |
| ) | |
| def register(config_class, feature_extractor_class, exist_ok=False): | |
| """ | |
| Register a new feature extractor for this class. | |
| Args: | |
| config_class ([`PretrainedConfig`]): | |
| The configuration corresponding to the model to register. | |
| feature_extractor_class ([`FeatureExtractorMixin`]): The feature extractor to register. | |
| """ | |
| FEATURE_EXTRACTOR_MAPPING.register(config_class, feature_extractor_class, exist_ok=exist_ok) | |