/usr/local/lib/python3.6/site-packages/transformers/models/auto
Edit: /usr/local/lib/python3.6/site-packages/transformers/models/auto/processing_auto.py (12289B)
# 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
import inspect
import json
from collections import OrderedDict
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
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__)
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)
for processor in PROCESSOR_MAPPING._extra_content.values():
if getattr(processor, "__name__", None) == class_name:
return processor
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.
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.
Passing `use_auth_token=True` is required when you want to use a private model.
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)
trust_remote_code = kwargs.pop("trust_remote_code", False)
kwargs["_from_auto"] = True
processor_class = None
processor_auto_map = None
# First, let's see if we have a preprocessor config.
# Filter the kwargs for `get_file_from_repo`.
get_file_from_repo_kwargs = {
key: kwargs[key] for key in inspect.signature(get_file_from_repo).parameters.keys() if key in kwargs
}
# Let's start by checking whether the processor class is saved in a feature extractor
preprocessor_config_file = get_file_from_repo(
pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME, **get_file_from_repo_kwargs
)
if preprocessor_config_file is not None:
config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
processor_class = config_dict.get("processor_class", None)
if "AutoProcessor" in config_dict.get("auto_map", {}):
processor_auto_map = config_dict["auto_map"]["AutoProcessor"]
if processor_class is None:
# Next, let's check whether the processor class is saved in a tokenizer
tokenizer_config_file = get_file_from_repo(
pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE, **get_file_from_repo_kwargs
)
if tokenizer_config_file is not None:
with open(tokenizer_config_file, encoding="utf-8") as reader:
config_dict = json.load(reader)
processor_class = config_dict.get("processor_class", None)
if "AutoProcessor" in config_dict.get("auto_map", {}):
processor_auto_map = config_dict["auto_map"]["AutoProcessor"]
if processor_class is None:
# Otherwise, load config, if it can be loaded.
if not isinstance(config, PretrainedConfig):
config = AutoConfig.from_pretrained(
pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
)
# And check if the config contains the processor class.
processor_class = getattr(config, "processor_class", None)
if hasattr(config, "auto_map") and "AutoProcessor" in config.auto_map:
processor_auto_map = config.auto_map["AutoProcessor"]
if processor_class is not None:
# If we have custom code for a feature extractor, we get the proper class.
if processor_auto_map is not None:
if not trust_remote_code:
raise ValueError(
f"Loading {pretrained_model_name_or_path} requires you to execute the feature extractor file "
"in that repo on your local machine. Make sure you have read the code there to avoid "
"malicious use, then set the option `trust_remote_code=True` to remove this error."
)
if kwargs.get("revision", None) is None:
logger.warning(
"Explicitly passing a `revision` is encouraged when loading a feature extractor with custom "
"code to ensure no malicious code has been contributed in a newer revision."
)
module_file, class_name = processor_auto_map.split(".")
processor_class = get_class_from_dynamic_module(
pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
)
else:
processor_class = processor_class_from_name(processor_class)
return processor_class.from_pretrained(
pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
)
# Last try: we use the PROCESSOR_MAPPING.
if type(config) in PROCESSOR_MAPPING:
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())}"
)
@staticmethod
def register(config_class, processor_class):
"""
Register a new processor for this class.
Args:
config_class ([`PretrainedConfig`]):
The configuration corresponding to the model to register.
processor_class ([`FeatureExtractorMixin`]): The processor to register.
"""
PROCESSOR_MAPPING.register(config_class, processor_class)