# 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
class TrOCRProcessor:
r"""
Constructs a TrOCR processor which wraps a vision feature extractor and a TrOCR tokenizer into a single processor.
[`TrOCRProcessor`] offers all the functionalities of [`AutoFeatureExtractor`]
and [`RobertaTokenizer`]. See the [`~TrOCRProcessor.__call__`] and
[`~TrOCRProcessor.decode`] for more information.
Args:
feature_extractor ([`AutoFeatureExtractor`]):
An instance of [`AutoFeatureExtractor`]. The feature extractor is a required input.
tokenizer ([`RobertaTokenizer`]):
An instance of [`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
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 [`~TrOCRProcessor.from_pretrained`] class method.
<Tip>
This class method is simply calling [`~PreTrainedFeatureExtractor.save_pretrained`] and
[`~tokenization_utils_base.PreTrainedTokenizer.save_pretrained`]. Please refer to the
docstrings of the methods above for more information.
</Tip>
Args:
save_directory (`str` or `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)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r"""
Instantiate a [`TrOCRProcessor`] from a pretrained TrOCR processor.
<Tip>
This class method is simply calling AutoFeatureExtractor's
[`~PreTrainedFeatureExtractor.from_pretrained`] and TrOCRTokenizer's
[`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`]. Please refer to the
docstrings of the methods above for more information.
</Tip>
Args:
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
[`~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 [`PreTrainedFeatureExtractor`] and
[`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)
def __call__(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to AutoFeatureExtractor's
[`~AutoFeatureExtractor.__call__`] and returns its output. If used in the context
[`~TrOCRProcessor.as_target_processor`] this method forwards all its arguments to
TrOCRTokenizer's [`~TrOCRTokenizer.__call__`]. Please refer to the doctsring of the above two
methods for more information.
"""
return self.current_processor(*args, **kwargs)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to TrOCRTokenizer's
[`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more
information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to TrOCRTokenizer's [`~PreTrainedTokenizer.decode`].
Please refer to the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@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