# 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.
""" AutoProcessor class. """
import importlib
from collections import OrderedDict
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...file_utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_list_of_files
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
config_class_to_model_type,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
PROCESSOR_MAPPING_NAMES = OrderedDict(
[
("clip", "CLIPProcessor"),
("layoutlmv2", "LayoutLMv2Processor"),
("layoutxlm", "LayoutXLMProcessor"),
("speech_to_text", "Speech2TextProcessor"),
("speech_to_text_2", "Speech2Text2Processor"),
("trocr", "TrOCRProcessor"),
("wav2vec2", "Wav2Vec2Processor"),
("wav2vec2_with_lm", "Wav2Vec2ProcessorWithLM"),
("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
]
)
PROCESSOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def processor_class_from_name(class_name: str):
for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
if class_name in processors:
module_name = model_type_to_module_name(module_name)
module = importlib.import_module(f".{module_name}", "transformers.models")
return getattr(module, class_name)
break
return None
class AutoProcessor:
r"""
This is a generic processor class that will be instantiated as one of the processor classes of the library when
created with the [`AutoProcessor.from_pretrained`] class method.
This class cannot be instantiated directly using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"AutoProcessor is designed to be instantiated "
"using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."
)
@classmethod
@replace_list_option_in_docstrings(PROCESSOR_MAPPING_NAMES)
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r"""
Instantiate one of the processor classes of the library from a pretrained model vocabulary.
The processor 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):
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 processor files saved using the `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 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.
use_auth_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 `transformers-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.
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 `use_auth_token=True` is required when you want to use a private model.
</Tip>
Examples:
```python
>>> from transformers import AutoProcessor
>>> # Download processor from huggingface.co and cache.
>>> processor = AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h')
>>> # If processor files are in a directory (e.g. processor was saved using *save_pretrained('./test/saved_model/')*)
>>> processor = AutoProcessor.from_pretrained('./test/saved_model/')
```"""
config = kwargs.pop("config", None)
kwargs["_from_auto"] = True
# First, let's see if we have a preprocessor config.
# get_list_of_files only takes three of the kwargs we have, so we filter them.
get_list_of_files_kwargs = {
key: kwargs[key] for key in ["revision", "use_auth_token", "local_files_only"] if key in kwargs
}
model_files = get_list_of_files(pretrained_model_name_or_path, **get_list_of_files_kwargs)
# strip to file name
model_files = [f.split("/")[-1] for f in model_files]
if FEATURE_EXTRACTOR_NAME in model_files:
config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
if "processor_class" in config_dict:
processor_class = processor_class_from_name(config_dict["processor_class"])
return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
# Otherwise, load config, if it can be loaded.
if not isinstance(config, PretrainedConfig):
config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
model_type = config_class_to_model_type(type(config).__name__)
if getattr(config, "processor_class", None) is not None:
processor_class = config.processor_class
return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
model_type = config_class_to_model_type(type(config).__name__)
if model_type is not None:
return PROCESSOR_MAPPING[type(config)].from_pretrained(pretrained_model_name_or_path, **kwargs)
raise ValueError(
f"Unrecognized processor in {pretrained_model_name_or_path}. Should have a `processor_type` key in "
f"its {FEATURE_EXTRACTOR_NAME}, or one of the following `model_type` keys in its {CONFIG_NAME}: "
f"{', '.join(c for c in PROCESSOR_MAPPING_NAMES.keys())}"
)