359 lines
17 KiB
Python
359 lines
17 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" AutoProcessor class."""
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import importlib
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import inspect
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import json
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import os
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import warnings
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from collections import OrderedDict
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# Build the list of all feature extractors
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from ...configuration_utils import PretrainedConfig
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from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
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from ...feature_extraction_utils import FeatureExtractionMixin
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from ...image_processing_utils import ImageProcessingMixin
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from ...processing_utils import ProcessorMixin
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from ...tokenization_utils import TOKENIZER_CONFIG_FILE
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from ...utils import FEATURE_EXTRACTOR_NAME, PROCESSOR_NAME, get_file_from_repo, logging
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from .auto_factory import _LazyAutoMapping
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from .configuration_auto import (
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CONFIG_MAPPING_NAMES,
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AutoConfig,
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model_type_to_module_name,
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replace_list_option_in_docstrings,
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)
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from .feature_extraction_auto import AutoFeatureExtractor
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from .image_processing_auto import AutoImageProcessor
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from .tokenization_auto import AutoTokenizer
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logger = logging.get_logger(__name__)
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PROCESSOR_MAPPING_NAMES = OrderedDict(
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[
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("align", "AlignProcessor"),
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("altclip", "AltCLIPProcessor"),
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("bark", "BarkProcessor"),
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("blip", "BlipProcessor"),
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("blip-2", "Blip2Processor"),
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("bridgetower", "BridgeTowerProcessor"),
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("chinese_clip", "ChineseCLIPProcessor"),
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("clap", "ClapProcessor"),
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("clip", "CLIPProcessor"),
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("clipseg", "CLIPSegProcessor"),
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("clvp", "ClvpProcessor"),
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("flava", "FlavaProcessor"),
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("fuyu", "FuyuProcessor"),
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("git", "GitProcessor"),
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("groupvit", "CLIPProcessor"),
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("hubert", "Wav2Vec2Processor"),
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("idefics", "IdeficsProcessor"),
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("idefics2", "Idefics2Processor"),
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("instructblip", "InstructBlipProcessor"),
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("kosmos-2", "Kosmos2Processor"),
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("layoutlmv2", "LayoutLMv2Processor"),
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("layoutlmv3", "LayoutLMv3Processor"),
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("llava", "LlavaProcessor"),
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("llava_next", "LlavaNextProcessor"),
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("markuplm", "MarkupLMProcessor"),
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("mctct", "MCTCTProcessor"),
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("mgp-str", "MgpstrProcessor"),
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("oneformer", "OneFormerProcessor"),
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("owlv2", "Owlv2Processor"),
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("owlvit", "OwlViTProcessor"),
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("pix2struct", "Pix2StructProcessor"),
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("pop2piano", "Pop2PianoProcessor"),
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("sam", "SamProcessor"),
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("seamless_m4t", "SeamlessM4TProcessor"),
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("sew", "Wav2Vec2Processor"),
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("sew-d", "Wav2Vec2Processor"),
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("siglip", "SiglipProcessor"),
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("speech_to_text", "Speech2TextProcessor"),
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("speech_to_text_2", "Speech2Text2Processor"),
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("speecht5", "SpeechT5Processor"),
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("trocr", "TrOCRProcessor"),
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("tvlt", "TvltProcessor"),
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("tvp", "TvpProcessor"),
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("unispeech", "Wav2Vec2Processor"),
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("unispeech-sat", "Wav2Vec2Processor"),
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("vilt", "ViltProcessor"),
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("vipllava", "LlavaProcessor"),
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("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
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("wav2vec2", "Wav2Vec2Processor"),
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("wav2vec2-bert", "Wav2Vec2Processor"),
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("wav2vec2-conformer", "Wav2Vec2Processor"),
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("wavlm", "Wav2Vec2Processor"),
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("whisper", "WhisperProcessor"),
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("xclip", "XCLIPProcessor"),
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]
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)
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PROCESSOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
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def processor_class_from_name(class_name: str):
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for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
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if class_name in processors:
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module_name = model_type_to_module_name(module_name)
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module = importlib.import_module(f".{module_name}", "transformers.models")
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try:
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return getattr(module, class_name)
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except AttributeError:
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continue
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for processor in PROCESSOR_MAPPING._extra_content.values():
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if getattr(processor, "__name__", None) == class_name:
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return processor
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# 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
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# init and we return the proper dummy to get an appropriate error message.
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main_module = importlib.import_module("transformers")
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if hasattr(main_module, class_name):
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return getattr(main_module, class_name)
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return None
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class AutoProcessor:
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r"""
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This is a generic processor class that will be instantiated as one of the processor classes of the library when
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created with the [`AutoProcessor.from_pretrained`] class method.
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This class cannot be instantiated directly using `__init__()` (throws an error).
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"""
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def __init__(self):
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raise EnvironmentError(
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"AutoProcessor is designed to be instantiated "
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"using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."
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)
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@classmethod
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@replace_list_option_in_docstrings(PROCESSOR_MAPPING_NAMES)
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def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
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r"""
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Instantiate one of the processor classes of the library from a pretrained model vocabulary.
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The processor class to instantiate is selected based on the `model_type` property of the config object (either
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passed as an argument or loaded from `pretrained_model_name_or_path` if possible):
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List options
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Params:
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pretrained_model_name_or_path (`str` or `os.PathLike`):
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This can be either:
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- a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on
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huggingface.co.
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- a path to a *directory* containing a processor files saved using the `save_pretrained()` method,
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e.g., `./my_model_directory/`.
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cache_dir (`str` or `os.PathLike`, *optional*):
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Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
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standard cache should not be used.
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force_download (`bool`, *optional*, defaults to `False`):
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Whether or not to force to (re-)download the feature extractor files and override the cached versions
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if they exist.
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resume_download (`bool`, *optional*, defaults to `False`):
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Whether or not to delete incompletely received file. Attempts to resume the download if such a file
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exists.
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proxies (`Dict[str, str]`, *optional*):
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A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
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'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
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token (`str` or *bool*, *optional*):
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The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
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when running `huggingface-cli login` (stored in `~/.huggingface`).
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revision (`str`, *optional*, defaults to `"main"`):
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
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identifier allowed by git.
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return_unused_kwargs (`bool`, *optional*, defaults to `False`):
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If `False`, then this function returns just the final feature extractor object. If `True`, then this
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functions returns a `Tuple(feature_extractor, unused_kwargs)` where *unused_kwargs* is a dictionary
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consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
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`kwargs` which has not been used to update `feature_extractor` and is otherwise ignored.
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trust_remote_code (`bool`, *optional*, defaults to `False`):
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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should only be set to `True` for repositories you trust and in which you have read the code, as it will
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execute code present on the Hub on your local machine.
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kwargs (`Dict[str, Any]`, *optional*):
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The values in kwargs of any keys which are feature extractor attributes will be used to override the
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loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is
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controlled by the `return_unused_kwargs` keyword parameter.
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<Tip>
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Passing `token=True` is required when you want to use a private model.
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</Tip>
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Examples:
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```python
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>>> from transformers import AutoProcessor
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>>> # Download processor from huggingface.co and cache.
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>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
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>>> # If processor files are in a directory (e.g. processor was saved using *save_pretrained('./test/saved_model/')*)
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>>> # processor = AutoProcessor.from_pretrained("./test/saved_model/")
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```"""
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use_auth_token = kwargs.pop("use_auth_token", None)
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if use_auth_token is not None:
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warnings.warn(
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"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
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FutureWarning,
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)
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if kwargs.get("token", None) is not None:
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raise ValueError(
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"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
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)
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kwargs["token"] = use_auth_token
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config = kwargs.pop("config", None)
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trust_remote_code = kwargs.pop("trust_remote_code", None)
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kwargs["_from_auto"] = True
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processor_class = None
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processor_auto_map = None
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# First, let's see if we have a processor or preprocessor config.
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# Filter the kwargs for `get_file_from_repo`.
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get_file_from_repo_kwargs = {
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key: kwargs[key] for key in inspect.signature(get_file_from_repo).parameters.keys() if key in kwargs
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}
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# Let's start by checking whether the processor class is saved in a processor config
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processor_config_file = get_file_from_repo(
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pretrained_model_name_or_path, PROCESSOR_NAME, **get_file_from_repo_kwargs
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)
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if processor_config_file is not None:
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config_dict, _ = ProcessorMixin.get_processor_dict(pretrained_model_name_or_path, **kwargs)
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processor_class = config_dict.get("processor_class", None)
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if "AutoProcessor" in config_dict.get("auto_map", {}):
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processor_auto_map = config_dict["auto_map"]["AutoProcessor"]
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if processor_class is None:
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# If not found, let's check whether the processor class is saved in an image processor config
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preprocessor_config_file = get_file_from_repo(
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pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME, **get_file_from_repo_kwargs
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)
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if preprocessor_config_file is not None:
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config_dict, _ = ImageProcessingMixin.get_image_processor_dict(pretrained_model_name_or_path, **kwargs)
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processor_class = config_dict.get("processor_class", None)
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if "AutoProcessor" in config_dict.get("auto_map", {}):
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processor_auto_map = config_dict["auto_map"]["AutoProcessor"]
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# If not found, let's check whether the processor class is saved in a feature extractor config
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if preprocessor_config_file is not None and processor_class is None:
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config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(
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pretrained_model_name_or_path, **kwargs
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)
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processor_class = config_dict.get("processor_class", None)
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if "AutoProcessor" in config_dict.get("auto_map", {}):
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processor_auto_map = config_dict["auto_map"]["AutoProcessor"]
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if processor_class is None:
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# Next, let's check whether the processor class is saved in a tokenizer
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tokenizer_config_file = get_file_from_repo(
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pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE, **get_file_from_repo_kwargs
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)
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if tokenizer_config_file is not None:
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with open(tokenizer_config_file, encoding="utf-8") as reader:
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config_dict = json.load(reader)
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processor_class = config_dict.get("processor_class", None)
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if "AutoProcessor" in config_dict.get("auto_map", {}):
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processor_auto_map = config_dict["auto_map"]["AutoProcessor"]
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if processor_class is None:
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# Otherwise, load config, if it can be loaded.
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if not isinstance(config, PretrainedConfig):
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config = AutoConfig.from_pretrained(
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pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
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)
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# And check if the config contains the processor class.
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processor_class = getattr(config, "processor_class", None)
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if hasattr(config, "auto_map") and "AutoProcessor" in config.auto_map:
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processor_auto_map = config.auto_map["AutoProcessor"]
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if processor_class is not None:
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processor_class = processor_class_from_name(processor_class)
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has_remote_code = processor_auto_map is not None
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has_local_code = processor_class is not None or type(config) in PROCESSOR_MAPPING
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trust_remote_code = resolve_trust_remote_code(
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trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
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)
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if has_remote_code and trust_remote_code:
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processor_class = get_class_from_dynamic_module(
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processor_auto_map, pretrained_model_name_or_path, **kwargs
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)
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_ = kwargs.pop("code_revision", None)
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if os.path.isdir(pretrained_model_name_or_path):
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processor_class.register_for_auto_class()
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return processor_class.from_pretrained(
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pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
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)
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elif processor_class is not None:
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return processor_class.from_pretrained(
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pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
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)
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# Last try: we use the PROCESSOR_MAPPING.
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elif type(config) in PROCESSOR_MAPPING:
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return PROCESSOR_MAPPING[type(config)].from_pretrained(pretrained_model_name_or_path, **kwargs)
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# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
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# tokenizer.
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try:
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return AutoTokenizer.from_pretrained(
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pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
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)
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except Exception:
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try:
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return AutoImageProcessor.from_pretrained(
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pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
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)
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except Exception:
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pass
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try:
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return AutoFeatureExtractor.from_pretrained(
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pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
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)
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except Exception:
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pass
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raise ValueError(
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f"Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a "
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"tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains "
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"the files of at least one of those processing classes."
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)
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@staticmethod
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def register(config_class, processor_class, exist_ok=False):
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"""
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Register a new processor for this class.
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Args:
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config_class ([`PretrainedConfig`]):
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The configuration corresponding to the model to register.
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processor_class ([`FeatureExtractorMixin`]): The processor to register.
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"""
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PROCESSOR_MAPPING.register(config_class, processor_class, exist_ok=exist_ok)
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