# 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.
"""
Processor class for TrOCR.
"""
from contextlib import contextmanager
from transformers.feature_extraction_utils import FeatureExtractionMixin
from transformers.models.roberta.tokenization_roberta import RobertaTokenizer
from transformers.models.roberta.tokenization_roberta_fast import RobertaTokenizerFast
from ..auto.feature_extraction_auto import AutoFeatureExtractor
[docs]class TrOCRProcessor:
r"""
Constructs a TrOCR processor which wraps a vision feature extractor and a TrOCR tokenizer into a single processor.
:class:`~transformers.TrOCRProcessor` offers all the functionalities of :class:`~transformers.AutoFeatureExtractor`
and :class:`~transformers.RobertaTokenizer`. See the :meth:`~transformers.TrOCRProcessor.__call__` and
:meth:`~transformers.TrOCRProcessor.decode` for more information.
Args:
feature_extractor (:class:`~transformers.AutoFeatureExtractor`):
An instance of :class:`~transformers.AutoFeatureExtractor`. The feature extractor is a required input.
tokenizer (:class:`~transformers.RobertaTokenizer`):
An instance of :class:`~transformers.RobertaTokenizer`. The tokenizer is a required input.
"""
def __init__(self, feature_extractor, tokenizer):
if not isinstance(feature_extractor, FeatureExtractionMixin):
raise ValueError(
f"`feature_extractor` has to be of type {FeatureExtractionMixin.__class__}, but is {type(feature_extractor)}"
)
if not (isinstance(tokenizer, RobertaTokenizer) or (isinstance(tokenizer, RobertaTokenizerFast))):
raise ValueError(
f"`tokenizer` has to be of type {RobertaTokenizer.__class__} or {RobertaTokenizerFast.__class__}, but is {type(tokenizer)}"
)
self.feature_extractor = feature_extractor
self.tokenizer = tokenizer
self.current_processor = self.feature_extractor
[docs] def save_pretrained(self, save_directory):
"""
Save a TrOCR feature extractor object and TrOCR tokenizer object to the directory ``save_directory``, so that
it can be re-loaded using the :func:`~transformers.TrOCRProcessor.from_pretrained` class method.
.. note::
This class method is simply calling :meth:`~transformers.PreTrainedFeatureExtractor.save_pretrained` and
:meth:`~transformers.tokenization_utils_base.PreTrainedTokenizer.save_pretrained`. Please refer to the
docstrings of the methods above for more information.
Args:
save_directory (:obj:`str` or :obj:`os.PathLike`):
Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
be created if it does not exist).
"""
self.feature_extractor.save_pretrained(save_directory)
self.tokenizer.save_pretrained(save_directory)
[docs] @classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r"""
Instantiate a :class:`~transformers.TrOCRProcessor` from a pretrained TrOCR processor.
.. note::
This class method is simply calling AutoFeatureExtractor's
:meth:`~transformers.PreTrainedFeatureExtractor.from_pretrained` and TrOCRTokenizer's
:meth:`~transformers.tokenization_utils_base.PreTrainedTokenizer.from_pretrained`. Please refer to the
docstrings of the methods above for more information.
Args:
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
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
:meth:`~transformers.PreTrainedFeatureExtractor.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``.
**kwargs
Additional keyword arguments passed along to both :class:`~transformers.PreTrainedFeatureExtractor` and
:class:`~transformers.PreTrainedTokenizer`
"""
feature_extractor = AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
tokenizer = RobertaTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
[docs] def __call__(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to AutoFeatureExtractor's
:meth:`~transformers.AutoFeatureExtractor.__call__` and returns its output. If used in the context
:meth:`~transformers.TrOCRProcessor.as_target_processor` this method forwards all its arguments to
TrOCRTokenizer's :meth:`~transformers.TrOCRTokenizer.__call__`. Please refer to the doctsring of the above two
methods for more information.
"""
return self.current_processor(*args, **kwargs)
[docs] def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to TrOCRTokenizer's
:meth:`~transformers.PreTrainedTokenizer.batch_decode`. Please refer to the docstring of this method for more
information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
[docs] def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to TrOCRTokenizer's :meth:`~transformers.PreTrainedTokenizer.decode`.
Please refer to the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
[docs] @contextmanager
def as_target_processor(self):
"""
Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning TrOCR.
"""
self.current_processor = self.tokenizer
yield
self.current_processor = self.feature_extractor