147 lines
6.2 KiB
Python
147 lines
6.2 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 MarkupLM.
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"""
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from typing import Optional, Union
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from ...file_utils import TensorType
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from ...processing_utils import ProcessorMixin
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from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
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class MarkupLMProcessor(ProcessorMixin):
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r"""
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Constructs a MarkupLM processor which combines a MarkupLM feature extractor and a MarkupLM tokenizer into a single
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processor.
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[`MarkupLMProcessor`] offers all the functionalities you need to prepare data for the model.
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It first uses [`MarkupLMFeatureExtractor`] to extract nodes and corresponding xpaths from one or more HTML strings.
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Next, these are provided to [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`], which turns them into token-level
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`input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and `xpath_subs_seq`.
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Args:
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feature_extractor (`MarkupLMFeatureExtractor`):
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An instance of [`MarkupLMFeatureExtractor`]. The feature extractor is a required input.
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tokenizer (`MarkupLMTokenizer` or `MarkupLMTokenizerFast`):
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An instance of [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`]. The tokenizer is a required input.
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parse_html (`bool`, *optional*, defaults to `True`):
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Whether or not to use `MarkupLMFeatureExtractor` to parse HTML strings into nodes and corresponding xpaths.
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"""
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feature_extractor_class = "MarkupLMFeatureExtractor"
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tokenizer_class = ("MarkupLMTokenizer", "MarkupLMTokenizerFast")
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parse_html = True
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def __call__(
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self,
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html_strings=None,
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nodes=None,
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xpaths=None,
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node_labels=None,
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questions=None,
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add_special_tokens: bool = True,
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padding: Union[bool, str, PaddingStrategy] = False,
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truncation: Union[bool, str, TruncationStrategy] = None,
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max_length: Optional[int] = None,
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stride: int = 0,
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pad_to_multiple_of: Optional[int] = None,
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return_token_type_ids: Optional[bool] = None,
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return_attention_mask: Optional[bool] = None,
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return_overflowing_tokens: bool = False,
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return_special_tokens_mask: bool = False,
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return_offsets_mapping: bool = False,
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return_length: bool = False,
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verbose: bool = True,
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return_tensors: Optional[Union[str, TensorType]] = None,
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**kwargs,
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) -> BatchEncoding:
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"""
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This method first forwards the `html_strings` argument to [`~MarkupLMFeatureExtractor.__call__`]. Next, it
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passes the `nodes` and `xpaths` along with the additional arguments to [`~MarkupLMTokenizer.__call__`] and
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returns the output.
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Optionally, one can also provide a `text` argument which is passed along as first sequence.
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Please refer to the docstring of the above two methods for more information.
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"""
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# first, create nodes and xpaths
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if self.parse_html:
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if html_strings is None:
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raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`")
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if nodes is not None or xpaths is not None or node_labels is not None:
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raise ValueError(
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"Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"
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)
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features = self.feature_extractor(html_strings)
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nodes = features["nodes"]
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xpaths = features["xpaths"]
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else:
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if html_strings is not None:
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raise ValueError("You have passed HTML strings but `parse_html` is set to `False`.")
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if nodes is None or xpaths is None:
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raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`")
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# # second, apply the tokenizer
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if questions is not None and self.parse_html:
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if isinstance(questions, str):
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questions = [questions] # add batch dimension (as the feature extractor always adds a batch dimension)
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encoded_inputs = self.tokenizer(
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text=questions if questions is not None else nodes,
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text_pair=nodes if questions is not None else None,
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xpaths=xpaths,
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node_labels=node_labels,
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add_special_tokens=add_special_tokens,
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padding=padding,
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truncation=truncation,
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max_length=max_length,
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stride=stride,
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pad_to_multiple_of=pad_to_multiple_of,
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return_token_type_ids=return_token_type_ids,
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return_attention_mask=return_attention_mask,
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return_overflowing_tokens=return_overflowing_tokens,
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return_special_tokens_mask=return_special_tokens_mask,
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return_offsets_mapping=return_offsets_mapping,
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return_length=return_length,
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verbose=verbose,
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return_tensors=return_tensors,
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**kwargs,
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)
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return encoded_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 TrOCRTokenizer'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 TrOCRTokenizer'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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@property
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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return tokenizer_input_names
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