FM / trocr

/usr/local/lib/python3.6/site-packages/transformers/models/trocr/

/usr/local/lib/python3.6/site-packages/transformers/models/trocr

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
configuration_trocr.py	6891	0644	edit dl rm
modeling_trocr.py	46634	0644	edit dl rm
processing_trocr.py	3239	0644	edit dl rm
__init__.py	1707	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/transformers/models/trocr/processing_trocr.py (3239B)

from ...processing_utils import ProcessorMixin

class TrOCRProcessor(ProcessorMixin):
    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 [`ViTFeatureExtractor`/`DeiTFeatureExtractor`] and
    [`RobertaTokenizer`/`XLMRobertaTokenizer`]. See the [`~TrOCRProcessor.__call__`] and [`~TrOCRProcessor.decode`] for
    more information.

Args:
        feature_extractor ([`ViTFeatureExtractor`/`DeiTFeatureExtractor`]):
            An instance of [`ViTFeatureExtractor`/`DeiTFeatureExtractor`]. The feature extractor is a required input.
        tokenizer ([`RobertaTokenizer`/`XLMRobertaTokenizer`]):
            An instance of [`RobertaTokenizer`/`XLMRobertaTokenizer`]. The tokenizer is a required input.
    """
    feature_extractor_class = "AutoFeatureExtractor"
    tokenizer_class = "AutoTokenizer"

def __init__(self, feature_extractor, tokenizer):
        super().__init__(feature_extractor, tokenizer)
        self.current_processor = self.feature_extractor

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

# 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 ...processing_utils import ProcessorMixin class TrOCRProcessor(ProcessorMixin): 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 [`ViTFeatureExtractor`/`DeiTFeatureExtractor`] and [`RobertaTokenizer`/`XLMRobertaTokenizer`]. See the [`~TrOCRProcessor.__call__`] and [`~TrOCRProcessor.decode`] for more information. Args: feature_extractor ([`ViTFeatureExtractor`/`DeiTFeatureExtractor`]): An instance of [`ViTFeatureExtractor`/`DeiTFeatureExtractor`]. The feature extractor is a required input. tokenizer ([`RobertaTokenizer`/`XLMRobertaTokenizer`]): An instance of [`RobertaTokenizer`/`XLMRobertaTokenizer`]. The tokenizer is a required input. """ feature_extractor_class = "AutoFeatureExtractor" tokenizer_class = "AutoTokenizer" def __init__(self, feature_extractor, tokenizer): super().__init__(feature_extractor, tokenizer) self.current_processor = self.feature_extractor 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