224 lines
9 KiB
Python
224 lines
9 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Processor class for Donut.
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"""
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import re
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import warnings
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from contextlib import contextmanager
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from typing import Optional, Union
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from ...image_utils import ImageInput
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from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
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from ...tokenization_utils_base import PreTokenizedInput, TextInput
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from ...utils import logging
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class DonutProcessorKwargs(ProcessingKwargs, total=False):
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_defaults = {}
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logger = logging.get_logger(__name__)
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class DonutProcessor(ProcessorMixin):
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r"""
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Constructs a Donut processor which wraps a Donut image processor and an XLMRoBERTa tokenizer into a single
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processor.
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[`DonutProcessor`] offers all the functionalities of [`DonutImageProcessor`] and
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[`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`]. See the [`~DonutProcessor.__call__`] and
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[`~DonutProcessor.decode`] for more information.
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Args:
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image_processor ([`DonutImageProcessor`], *optional*):
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An instance of [`DonutImageProcessor`]. The image processor is a required input.
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tokenizer ([`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`], *optional*):
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An instance of [`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`]. The tokenizer is a required input.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "AutoImageProcessor"
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tokenizer_class = "AutoTokenizer"
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def __init__(self, image_processor=None, tokenizer=None, **kwargs):
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feature_extractor = None
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if "feature_extractor" in kwargs:
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warnings.warn(
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"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
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" instead.",
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FutureWarning,
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)
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feature_extractor = kwargs.pop("feature_extractor")
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image_processor = image_processor if image_processor is not None else feature_extractor
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if image_processor is None:
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raise ValueError("You need to specify an `image_processor`.")
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if tokenizer is None:
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raise ValueError("You need to specify a `tokenizer`.")
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super().__init__(image_processor, tokenizer)
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self.current_processor = self.image_processor
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self._in_target_context_manager = False
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def __call__(
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self,
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images: ImageInput = None,
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text: Optional[Union[str, list[str], TextInput, PreTokenizedInput]] = None,
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audio=None,
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videos=None,
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**kwargs: Unpack[DonutProcessorKwargs],
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):
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"""
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When used in normal mode, this method forwards all its arguments to AutoImageProcessor's
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[`~AutoImageProcessor.__call__`] and returns its output. If used in the context
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[`~DonutProcessor.as_target_processor`] this method forwards all its arguments to DonutTokenizer's
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[`~DonutTokenizer.__call__`]. Please refer to the docstring of the above two methods for more information.
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"""
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if self._in_target_context_manager:
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return self.current_processor(images, text, **kwargs)
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if images is None and text is None:
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raise ValueError("You need to specify either an `images` or `text` input to process.")
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output_kwargs = self._merge_kwargs(
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DonutProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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if images is not None:
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inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
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if text is not None:
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if images is not None:
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output_kwargs["text_kwargs"].setdefault("add_special_tokens", False)
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encodings = self.tokenizer(text, **output_kwargs["text_kwargs"])
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if text is None:
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return inputs
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elif images is None:
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return encodings
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else:
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inputs["labels"] = encodings["input_ids"] # for BC
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inputs["input_ids"] = encodings["input_ids"]
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return inputs
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to DonutTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer
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to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to DonutTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the
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docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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@contextmanager
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def as_target_processor(self):
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"""
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Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning TrOCR.
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"""
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warnings.warn(
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"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
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"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
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"your images inputs, or in a separate call."
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)
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self._in_target_context_manager = True
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self.current_processor = self.tokenizer
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yield
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self.current_processor = self.image_processor
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self._in_target_context_manager = False
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def token2json(self, tokens, is_inner_value=False, added_vocab=None):
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"""
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Convert a (generated) token sequence into an ordered JSON format.
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"""
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if added_vocab is None:
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added_vocab = self.tokenizer.get_added_vocab()
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output = {}
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while tokens:
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# We want r"<s_(.*?)>" but without ReDOS risk, so do it manually in two parts
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potential_start = re.search(r"<s_", tokens, re.IGNORECASE)
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if potential_start is None:
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break
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start_token = tokens[potential_start.start() :]
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if ">" not in start_token:
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break
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start_token = start_token[: start_token.index(">") + 1]
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key = start_token[len("<s_") : -len(">")]
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key_escaped = re.escape(key)
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end_token = re.search(rf"</s_{key_escaped}>", tokens, re.IGNORECASE)
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if end_token is None:
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tokens = tokens.replace(start_token, "")
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else:
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end_token = end_token.group()
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start_token_escaped = re.escape(start_token)
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end_token_escaped = re.escape(end_token)
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content = re.search(
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f"{start_token_escaped}(.*?){end_token_escaped}", tokens, re.IGNORECASE | re.DOTALL
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)
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if content is not None:
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content = content.group(1).strip()
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if r"<s_" in content and r"</s_" in content: # non-leaf node
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value = self.token2json(content, is_inner_value=True, added_vocab=added_vocab)
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if value:
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if len(value) == 1:
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value = value[0]
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output[key] = value
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else: # leaf nodes
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output[key] = []
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for leaf in content.split(r"<sep/>"):
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leaf = leaf.strip()
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if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
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leaf = leaf[1:-2] # for categorical special tokens
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output[key].append(leaf)
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if len(output[key]) == 1:
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output[key] = output[key][0]
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tokens = tokens[tokens.find(end_token) + len(end_token) :].strip()
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if tokens[:6] == r"<sep/>": # non-leaf nodes
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return [output] + self.token2json(tokens[6:], is_inner_value=True, added_vocab=added_vocab)
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if output:
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return [output] if is_inner_value else output
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else:
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return [] if is_inner_value else {"text_sequence": tokens}
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@property
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def feature_extractor_class(self):
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warnings.warn(
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"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
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FutureWarning,
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)
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return self.image_processor_class
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@property
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def feature_extractor(self):
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warnings.warn(
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"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
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FutureWarning,
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)
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return self.image_processor
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__all__ = ["DonutProcessor"]
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