1543 lines
70 KiB
Python
1543 lines
70 KiB
Python
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# coding=utf-8
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# Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved.
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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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"""Tokenization class for LayoutLMv2."""
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import collections
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import os
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import sys
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import unicodedata
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from typing import Dict, List, Optional, Tuple, Union
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
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from ...tokenization_utils_base import (
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BatchEncoding,
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EncodedInput,
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PreTokenizedInput,
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TextInput,
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TextInputPair,
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TruncationStrategy,
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)
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from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
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LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING = r"""
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add_special_tokens (`bool`, *optional*, defaults to `True`):
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Whether or not to encode the sequences with the special tokens relative to their model.
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padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
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Activates and controls padding. Accepts the following values:
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- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
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sequence if provided).
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- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
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acceptable input length for the model if that argument is not provided.
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- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
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lengths).
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truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
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Activates and controls truncation. Accepts the following values:
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- `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
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to the maximum acceptable input length for the model if that argument is not provided. This will
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truncate token by token, removing a token from the longest sequence in the pair if a pair of
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sequences (or a batch of pairs) is provided.
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- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
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maximum acceptable input length for the model if that argument is not provided. This will only
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truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
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- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
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maximum acceptable input length for the model if that argument is not provided. This will only
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truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
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- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
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greater than the model maximum admissible input size).
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max_length (`int`, *optional*):
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Controls the maximum length to use by one of the truncation/padding parameters.
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If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
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is required by one of the truncation/padding parameters. If the model has no specific maximum input
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length (like XLNet) truncation/padding to a maximum length will be deactivated.
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stride (`int`, *optional*, defaults to 0):
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If set to a number along with `max_length`, the overflowing tokens returned when
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`return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
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returned to provide some overlap between truncated and overflowing sequences. The value of this
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argument defines the number of overlapping tokens.
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pad_to_multiple_of (`int`, *optional*):
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If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
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the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
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return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
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If set, will return tensors instead of list of python integers. Acceptable values are:
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- `'tf'`: Return TensorFlow `tf.constant` objects.
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- `'pt'`: Return PyTorch `torch.Tensor` objects.
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- `'np'`: Return Numpy `np.ndarray` objects.
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"""
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LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
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return_token_type_ids (`bool`, *optional*):
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Whether to return token type IDs. If left to the default, will return the token type IDs according to
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the specific tokenizer's default, defined by the `return_outputs` attribute.
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[What are token type IDs?](../glossary#token-type-ids)
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return_attention_mask (`bool`, *optional*):
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Whether to return the attention mask. If left to the default, will return the attention mask according
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to the specific tokenizer's default, defined by the `return_outputs` attribute.
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[What are attention masks?](../glossary#attention-mask)
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return_overflowing_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
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of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
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of returning overflowing tokens.
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return_special_tokens_mask (`bool`, *optional*, defaults to `False`):
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Whether or not to return special tokens mask information.
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return_offsets_mapping (`bool`, *optional*, defaults to `False`):
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Whether or not to return `(char_start, char_end)` for each token.
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This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using
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Python's tokenizer, this method will raise `NotImplementedError`.
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return_length (`bool`, *optional*, defaults to `False`):
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Whether or not to return the lengths of the encoded inputs.
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verbose (`bool`, *optional*, defaults to `True`):
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Whether or not to print more information and warnings.
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**kwargs: passed to the `self.tokenize()` method
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Return:
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[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
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- **input_ids** -- List of token ids to be fed to a model.
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[What are input IDs?](../glossary#input-ids)
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- **bbox** -- List of bounding boxes to be fed to a model.
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- **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or
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if *"token_type_ids"* is in `self.model_input_names`).
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[What are token type IDs?](../glossary#token-type-ids)
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
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[What are attention masks?](../glossary#attention-mask)
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- **labels** -- List of labels to be fed to a model. (when `word_labels` is specified).
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- **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
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`return_overflowing_tokens=True`).
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- **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
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`return_overflowing_tokens=True`).
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- **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
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regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
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- **length** -- The length of the inputs (when `return_length=True`).
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"""
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def load_vocab(vocab_file):
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"""Loads a vocabulary file into a dictionary."""
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vocab = collections.OrderedDict()
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with open(vocab_file, "r", encoding="utf-8") as reader:
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tokens = reader.readlines()
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for index, token in enumerate(tokens):
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token = token.rstrip("\n")
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vocab[token] = index
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return vocab
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def whitespace_tokenize(text):
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"""Runs basic whitespace cleaning and splitting on a piece of text."""
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text = text.strip()
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if not text:
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return []
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tokens = text.split()
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return tokens
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table = dict.fromkeys(i for i in range(sys.maxunicode) if unicodedata.category(chr(i)).startswith("P"))
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def subfinder(mylist, pattern):
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matches = []
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indices = []
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for idx, i in enumerate(range(len(mylist))):
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if mylist[i] == pattern[0] and mylist[i : i + len(pattern)] == pattern:
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matches.append(pattern)
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indices.append(idx)
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if matches:
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return matches[0], indices[0]
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else:
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return None, 0
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class LayoutLMv2Tokenizer(PreTrainedTokenizer):
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r"""
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Construct a LayoutLMv2 tokenizer. Based on WordPiece. [`LayoutLMv2Tokenizer`] can be used to turn words, word-level
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bounding boxes and optional word labels to token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`, and
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optional `labels` (for token classification).
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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[`LayoutLMv2Tokenizer`] runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the
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word-level bounding boxes into token-level bounding boxes.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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def __init__(
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self,
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vocab_file,
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do_lower_case=True,
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do_basic_tokenize=True,
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never_split=None,
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unk_token="[UNK]",
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sep_token="[SEP]",
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pad_token="[PAD]",
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cls_token="[CLS]",
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mask_token="[MASK]",
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cls_token_box=[0, 0, 0, 0],
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sep_token_box=[1000, 1000, 1000, 1000],
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pad_token_box=[0, 0, 0, 0],
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pad_token_label=-100,
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only_label_first_subword=True,
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tokenize_chinese_chars=True,
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strip_accents=None,
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model_max_length: int = 512,
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additional_special_tokens: Optional[List[str]] = None,
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**kwargs,
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):
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sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token
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unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
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pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
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cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token
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mask_token = AddedToken(mask_token, special=True) if isinstance(mask_token, str) else mask_token
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if not os.path.isfile(vocab_file):
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raise ValueError(
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f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
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" model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
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)
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self.vocab = load_vocab(vocab_file)
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self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
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self.do_basic_tokenize = do_basic_tokenize
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if do_basic_tokenize:
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self.basic_tokenizer = BasicTokenizer(
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do_lower_case=do_lower_case,
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never_split=never_split,
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tokenize_chinese_chars=tokenize_chinese_chars,
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strip_accents=strip_accents,
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)
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
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# additional properties
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self.cls_token_box = cls_token_box
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self.sep_token_box = sep_token_box
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self.pad_token_box = pad_token_box
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self.pad_token_label = pad_token_label
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self.only_label_first_subword = only_label_first_subword
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super().__init__(
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do_lower_case=do_lower_case,
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do_basic_tokenize=do_basic_tokenize,
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never_split=never_split,
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unk_token=unk_token,
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sep_token=sep_token,
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pad_token=pad_token,
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cls_token=cls_token,
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mask_token=mask_token,
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cls_token_box=cls_token_box,
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sep_token_box=sep_token_box,
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pad_token_box=pad_token_box,
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pad_token_label=pad_token_label,
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only_label_first_subword=only_label_first_subword,
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tokenize_chinese_chars=tokenize_chinese_chars,
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strip_accents=strip_accents,
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model_max_length=model_max_length,
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additional_special_tokens=additional_special_tokens,
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**kwargs,
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)
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@property
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def do_lower_case(self):
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return self.basic_tokenizer.do_lower_case
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@property
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def vocab_size(self):
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return len(self.vocab)
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def get_vocab(self):
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return dict(self.vocab, **self.added_tokens_encoder)
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def _tokenize(self, text):
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split_tokens = []
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if self.do_basic_tokenize:
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for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
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# If the token is part of the never_split set
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if token in self.basic_tokenizer.never_split:
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split_tokens.append(token)
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else:
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split_tokens += self.wordpiece_tokenizer.tokenize(token)
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else:
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split_tokens = self.wordpiece_tokenizer.tokenize(text)
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return split_tokens
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.vocab.get(token, self.vocab.get(self.unk_token))
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.ids_to_tokens.get(index, self.unk_token)
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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out_string = " ".join(tokens).replace(" ##", "").strip()
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return out_string
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. A BERT sequence has the following format:
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- single sequence: `[CLS] X [SEP]`
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- pair of sequences: `[CLS] A [SEP] B [SEP]`
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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|
if token_ids_1 is None:
|
||
|
|
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
|
||
|
|
cls = [self.cls_token_id]
|
||
|
|
sep = [self.sep_token_id]
|
||
|
|
return cls + token_ids_0 + sep + token_ids_1 + sep
|
||
|
|
|
||
|
|
def get_special_tokens_mask(
|
||
|
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
||
|
|
) -> List[int]:
|
||
|
|
"""
|
||
|
|
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
||
|
|
special tokens using the tokenizer `prepare_for_model` method.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
token_ids_0 (`List[int]`):
|
||
|
|
List of IDs.
|
||
|
|
token_ids_1 (`List[int]`, *optional*):
|
||
|
|
Optional second list of IDs for sequence pairs.
|
||
|
|
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether or not the token list is already formatted with special tokens for the model.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
||
|
|
"""
|
||
|
|
|
||
|
|
if already_has_special_tokens:
|
||
|
|
return super().get_special_tokens_mask(
|
||
|
|
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
||
|
|
)
|
||
|
|
|
||
|
|
if token_ids_1 is not None:
|
||
|
|
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
|
||
|
|
return [1] + ([0] * len(token_ids_0)) + [1]
|
||
|
|
|
||
|
|
def create_token_type_ids_from_sequences(
|
||
|
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||
|
|
) -> List[int]:
|
||
|
|
"""
|
||
|
|
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
|
||
|
|
pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second
|
||
|
|
sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
token_ids_0 (`List[int]`):
|
||
|
|
List of IDs.
|
||
|
|
token_ids_1 (`List[int]`, *optional*):
|
||
|
|
Optional second list of IDs for sequence pairs.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
||
|
|
"""
|
||
|
|
sep = [self.sep_token_id]
|
||
|
|
cls = [self.cls_token_id]
|
||
|
|
if token_ids_1 is None:
|
||
|
|
return len(cls + token_ids_0 + sep) * [0]
|
||
|
|
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
|
||
|
|
|
||
|
|
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||
|
|
index = 0
|
||
|
|
if os.path.isdir(save_directory):
|
||
|
|
vocab_file = os.path.join(
|
||
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
|
||
|
|
with open(vocab_file, "w", encoding="utf-8") as writer:
|
||
|
|
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
|
||
|
|
if index != token_index:
|
||
|
|
logger.warning(
|
||
|
|
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
|
||
|
|
" Please check that the vocabulary is not corrupted!"
|
||
|
|
)
|
||
|
|
index = token_index
|
||
|
|
writer.write(token + "\n")
|
||
|
|
index += 1
|
||
|
|
return (vocab_file,)
|
||
|
|
|
||
|
|
@add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
||
|
|
def __call__(
|
||
|
|
self,
|
||
|
|
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
|
||
|
|
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
|
||
|
|
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
|
||
|
|
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
|
||
|
|
add_special_tokens: bool = True,
|
||
|
|
padding: Union[bool, str, PaddingStrategy] = False,
|
||
|
|
truncation: Union[bool, str, TruncationStrategy] = None,
|
||
|
|
max_length: Optional[int] = None,
|
||
|
|
stride: int = 0,
|
||
|
|
pad_to_multiple_of: Optional[int] = None,
|
||
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
||
|
|
return_token_type_ids: Optional[bool] = None,
|
||
|
|
return_attention_mask: Optional[bool] = None,
|
||
|
|
return_overflowing_tokens: bool = False,
|
||
|
|
return_special_tokens_mask: bool = False,
|
||
|
|
return_offsets_mapping: bool = False,
|
||
|
|
return_length: bool = False,
|
||
|
|
verbose: bool = True,
|
||
|
|
**kwargs,
|
||
|
|
) -> BatchEncoding:
|
||
|
|
"""
|
||
|
|
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
|
||
|
|
sequences with word-level normalized bounding boxes and optional labels.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
text (`str`, `List[str]`, `List[List[str]]`):
|
||
|
|
The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
|
||
|
|
(words of a single example or questions of a batch of examples) or a list of list of strings (batch of
|
||
|
|
words).
|
||
|
|
text_pair (`List[str]`, `List[List[str]]`):
|
||
|
|
The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
|
||
|
|
(pretokenized string).
|
||
|
|
boxes (`List[List[int]]`, `List[List[List[int]]]`):
|
||
|
|
Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
|
||
|
|
word_labels (`List[int]`, `List[List[int]]`, *optional*):
|
||
|
|
Word-level integer labels (for token classification tasks such as FUNSD, CORD).
|
||
|
|
"""
|
||
|
|
|
||
|
|
# Input type checking for clearer error
|
||
|
|
def _is_valid_text_input(t):
|
||
|
|
if isinstance(t, str):
|
||
|
|
# Strings are fine
|
||
|
|
return True
|
||
|
|
elif isinstance(t, (list, tuple)):
|
||
|
|
# List are fine as long as they are...
|
||
|
|
if len(t) == 0:
|
||
|
|
# ... empty
|
||
|
|
return True
|
||
|
|
elif isinstance(t[0], str):
|
||
|
|
# ... list of strings
|
||
|
|
return True
|
||
|
|
elif isinstance(t[0], (list, tuple)):
|
||
|
|
# ... list with an empty list or with a list of strings
|
||
|
|
return len(t[0]) == 0 or isinstance(t[0][0], str)
|
||
|
|
else:
|
||
|
|
return False
|
||
|
|
else:
|
||
|
|
return False
|
||
|
|
|
||
|
|
if text_pair is not None:
|
||
|
|
# in case text + text_pair are provided, text = questions, text_pair = words
|
||
|
|
if not _is_valid_text_input(text):
|
||
|
|
raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
|
||
|
|
if not isinstance(text_pair, (list, tuple)):
|
||
|
|
raise ValueError(
|
||
|
|
"Words must be of type `List[str]` (single pretokenized example), "
|
||
|
|
"or `List[List[str]]` (batch of pretokenized examples)."
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
# in case only text is provided => must be words
|
||
|
|
if not isinstance(text, (list, tuple)):
|
||
|
|
raise ValueError(
|
||
|
|
"Words must be of type `List[str]` (single pretokenized example), "
|
||
|
|
"or `List[List[str]]` (batch of pretokenized examples)."
|
||
|
|
)
|
||
|
|
|
||
|
|
if text_pair is not None:
|
||
|
|
is_batched = isinstance(text, (list, tuple))
|
||
|
|
else:
|
||
|
|
is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
|
||
|
|
|
||
|
|
words = text if text_pair is None else text_pair
|
||
|
|
if boxes is None:
|
||
|
|
raise ValueError("You must provide corresponding bounding boxes")
|
||
|
|
if is_batched:
|
||
|
|
if len(words) != len(boxes):
|
||
|
|
raise ValueError("You must provide words and boxes for an equal amount of examples")
|
||
|
|
for words_example, boxes_example in zip(words, boxes):
|
||
|
|
if len(words_example) != len(boxes_example):
|
||
|
|
raise ValueError("You must provide as many words as there are bounding boxes")
|
||
|
|
else:
|
||
|
|
if len(words) != len(boxes):
|
||
|
|
raise ValueError("You must provide as many words as there are bounding boxes")
|
||
|
|
|
||
|
|
if is_batched:
|
||
|
|
if text_pair is not None and len(text) != len(text_pair):
|
||
|
|
raise ValueError(
|
||
|
|
f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
|
||
|
|
f" {len(text_pair)}."
|
||
|
|
)
|
||
|
|
batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
|
||
|
|
is_pair = bool(text_pair is not None)
|
||
|
|
return self.batch_encode_plus(
|
||
|
|
batch_text_or_text_pairs=batch_text_or_text_pairs,
|
||
|
|
is_pair=is_pair,
|
||
|
|
boxes=boxes,
|
||
|
|
word_labels=word_labels,
|
||
|
|
add_special_tokens=add_special_tokens,
|
||
|
|
padding=padding,
|
||
|
|
truncation=truncation,
|
||
|
|
max_length=max_length,
|
||
|
|
stride=stride,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
return_tensors=return_tensors,
|
||
|
|
return_token_type_ids=return_token_type_ids,
|
||
|
|
return_attention_mask=return_attention_mask,
|
||
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
||
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
||
|
|
return_offsets_mapping=return_offsets_mapping,
|
||
|
|
return_length=return_length,
|
||
|
|
verbose=verbose,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
return self.encode_plus(
|
||
|
|
text=text,
|
||
|
|
text_pair=text_pair,
|
||
|
|
boxes=boxes,
|
||
|
|
word_labels=word_labels,
|
||
|
|
add_special_tokens=add_special_tokens,
|
||
|
|
padding=padding,
|
||
|
|
truncation=truncation,
|
||
|
|
max_length=max_length,
|
||
|
|
stride=stride,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
return_tensors=return_tensors,
|
||
|
|
return_token_type_ids=return_token_type_ids,
|
||
|
|
return_attention_mask=return_attention_mask,
|
||
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
||
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
||
|
|
return_offsets_mapping=return_offsets_mapping,
|
||
|
|
return_length=return_length,
|
||
|
|
verbose=verbose,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
@add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
||
|
|
def batch_encode_plus(
|
||
|
|
self,
|
||
|
|
batch_text_or_text_pairs: Union[
|
||
|
|
List[TextInput],
|
||
|
|
List[TextInputPair],
|
||
|
|
List[PreTokenizedInput],
|
||
|
|
],
|
||
|
|
is_pair: bool = None,
|
||
|
|
boxes: Optional[List[List[List[int]]]] = None,
|
||
|
|
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
|
||
|
|
add_special_tokens: bool = True,
|
||
|
|
padding: Union[bool, str, PaddingStrategy] = False,
|
||
|
|
truncation: Union[bool, str, TruncationStrategy] = None,
|
||
|
|
max_length: Optional[int] = None,
|
||
|
|
stride: int = 0,
|
||
|
|
pad_to_multiple_of: Optional[int] = None,
|
||
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
||
|
|
return_token_type_ids: Optional[bool] = None,
|
||
|
|
return_attention_mask: Optional[bool] = None,
|
||
|
|
return_overflowing_tokens: bool = False,
|
||
|
|
return_special_tokens_mask: bool = False,
|
||
|
|
return_offsets_mapping: bool = False,
|
||
|
|
return_length: bool = False,
|
||
|
|
verbose: bool = True,
|
||
|
|
**kwargs,
|
||
|
|
) -> BatchEncoding:
|
||
|
|
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
|
||
|
|
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
|
||
|
|
padding=padding,
|
||
|
|
truncation=truncation,
|
||
|
|
max_length=max_length,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
verbose=verbose,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
return self._batch_encode_plus(
|
||
|
|
batch_text_or_text_pairs=batch_text_or_text_pairs,
|
||
|
|
is_pair=is_pair,
|
||
|
|
boxes=boxes,
|
||
|
|
word_labels=word_labels,
|
||
|
|
add_special_tokens=add_special_tokens,
|
||
|
|
padding_strategy=padding_strategy,
|
||
|
|
truncation_strategy=truncation_strategy,
|
||
|
|
max_length=max_length,
|
||
|
|
stride=stride,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
return_tensors=return_tensors,
|
||
|
|
return_token_type_ids=return_token_type_ids,
|
||
|
|
return_attention_mask=return_attention_mask,
|
||
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
||
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
||
|
|
return_offsets_mapping=return_offsets_mapping,
|
||
|
|
return_length=return_length,
|
||
|
|
verbose=verbose,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
def _batch_encode_plus(
|
||
|
|
self,
|
||
|
|
batch_text_or_text_pairs: Union[
|
||
|
|
List[TextInput],
|
||
|
|
List[TextInputPair],
|
||
|
|
List[PreTokenizedInput],
|
||
|
|
],
|
||
|
|
is_pair: bool = None,
|
||
|
|
boxes: Optional[List[List[List[int]]]] = None,
|
||
|
|
word_labels: Optional[List[List[int]]] = None,
|
||
|
|
add_special_tokens: bool = True,
|
||
|
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
||
|
|
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
|
||
|
|
max_length: Optional[int] = None,
|
||
|
|
stride: int = 0,
|
||
|
|
pad_to_multiple_of: Optional[int] = None,
|
||
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
||
|
|
return_token_type_ids: Optional[bool] = None,
|
||
|
|
return_attention_mask: Optional[bool] = None,
|
||
|
|
return_overflowing_tokens: bool = False,
|
||
|
|
return_special_tokens_mask: bool = False,
|
||
|
|
return_offsets_mapping: bool = False,
|
||
|
|
return_length: bool = False,
|
||
|
|
verbose: bool = True,
|
||
|
|
**kwargs,
|
||
|
|
) -> BatchEncoding:
|
||
|
|
if return_offsets_mapping:
|
||
|
|
raise NotImplementedError(
|
||
|
|
"return_offset_mapping is not available when using Python tokenizers. "
|
||
|
|
"To use this feature, change your tokenizer to one deriving from "
|
||
|
|
"transformers.PreTrainedTokenizerFast."
|
||
|
|
)
|
||
|
|
|
||
|
|
batch_outputs = self._batch_prepare_for_model(
|
||
|
|
batch_text_or_text_pairs=batch_text_or_text_pairs,
|
||
|
|
is_pair=is_pair,
|
||
|
|
boxes=boxes,
|
||
|
|
word_labels=word_labels,
|
||
|
|
add_special_tokens=add_special_tokens,
|
||
|
|
padding_strategy=padding_strategy,
|
||
|
|
truncation_strategy=truncation_strategy,
|
||
|
|
max_length=max_length,
|
||
|
|
stride=stride,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
return_attention_mask=return_attention_mask,
|
||
|
|
return_token_type_ids=return_token_type_ids,
|
||
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
||
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
||
|
|
return_length=return_length,
|
||
|
|
return_tensors=return_tensors,
|
||
|
|
verbose=verbose,
|
||
|
|
)
|
||
|
|
|
||
|
|
return BatchEncoding(batch_outputs)
|
||
|
|
|
||
|
|
@add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
||
|
|
def _batch_prepare_for_model(
|
||
|
|
self,
|
||
|
|
batch_text_or_text_pairs,
|
||
|
|
is_pair: bool = None,
|
||
|
|
boxes: Optional[List[List[int]]] = None,
|
||
|
|
word_labels: Optional[List[List[int]]] = None,
|
||
|
|
add_special_tokens: bool = True,
|
||
|
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
||
|
|
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
|
||
|
|
max_length: Optional[int] = None,
|
||
|
|
stride: int = 0,
|
||
|
|
pad_to_multiple_of: Optional[int] = None,
|
||
|
|
return_tensors: Optional[str] = None,
|
||
|
|
return_token_type_ids: Optional[bool] = None,
|
||
|
|
return_attention_mask: Optional[bool] = None,
|
||
|
|
return_overflowing_tokens: bool = False,
|
||
|
|
return_special_tokens_mask: bool = False,
|
||
|
|
return_length: bool = False,
|
||
|
|
verbose: bool = True,
|
||
|
|
) -> BatchEncoding:
|
||
|
|
"""
|
||
|
|
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
|
||
|
|
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
|
||
|
|
manages a moving window (with user defined stride) for overflowing tokens.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
batch_ids_pairs: list of tokenized input ids or input ids pairs
|
||
|
|
"""
|
||
|
|
|
||
|
|
batch_outputs = {}
|
||
|
|
for idx, example in enumerate(zip(batch_text_or_text_pairs, boxes)):
|
||
|
|
batch_text_or_text_pair, boxes_example = example
|
||
|
|
outputs = self.prepare_for_model(
|
||
|
|
batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair,
|
||
|
|
batch_text_or_text_pair[1] if is_pair else None,
|
||
|
|
boxes_example,
|
||
|
|
word_labels=word_labels[idx] if word_labels is not None else None,
|
||
|
|
add_special_tokens=add_special_tokens,
|
||
|
|
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
|
||
|
|
truncation=truncation_strategy.value,
|
||
|
|
max_length=max_length,
|
||
|
|
stride=stride,
|
||
|
|
pad_to_multiple_of=None, # we pad in batch afterward
|
||
|
|
return_attention_mask=False, # we pad in batch afterward
|
||
|
|
return_token_type_ids=return_token_type_ids,
|
||
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
||
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
||
|
|
return_length=return_length,
|
||
|
|
return_tensors=None, # We convert the whole batch to tensors at the end
|
||
|
|
prepend_batch_axis=False,
|
||
|
|
verbose=verbose,
|
||
|
|
)
|
||
|
|
|
||
|
|
for key, value in outputs.items():
|
||
|
|
if key not in batch_outputs:
|
||
|
|
batch_outputs[key] = []
|
||
|
|
batch_outputs[key].append(value)
|
||
|
|
|
||
|
|
batch_outputs = self.pad(
|
||
|
|
batch_outputs,
|
||
|
|
padding=padding_strategy.value,
|
||
|
|
max_length=max_length,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
return_attention_mask=return_attention_mask,
|
||
|
|
)
|
||
|
|
|
||
|
|
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
|
||
|
|
|
||
|
|
return batch_outputs
|
||
|
|
|
||
|
|
@add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING)
|
||
|
|
def encode(
|
||
|
|
self,
|
||
|
|
text: Union[TextInput, PreTokenizedInput],
|
||
|
|
text_pair: Optional[PreTokenizedInput] = None,
|
||
|
|
boxes: Optional[List[List[int]]] = None,
|
||
|
|
word_labels: Optional[List[int]] = None,
|
||
|
|
add_special_tokens: bool = True,
|
||
|
|
padding: Union[bool, str, PaddingStrategy] = False,
|
||
|
|
truncation: Union[bool, str, TruncationStrategy] = None,
|
||
|
|
max_length: Optional[int] = None,
|
||
|
|
stride: int = 0,
|
||
|
|
pad_to_multiple_of: Optional[int] = None,
|
||
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
||
|
|
return_token_type_ids: Optional[bool] = None,
|
||
|
|
return_attention_mask: Optional[bool] = None,
|
||
|
|
return_overflowing_tokens: bool = False,
|
||
|
|
return_special_tokens_mask: bool = False,
|
||
|
|
return_offsets_mapping: bool = False,
|
||
|
|
return_length: bool = False,
|
||
|
|
verbose: bool = True,
|
||
|
|
**kwargs,
|
||
|
|
) -> List[int]:
|
||
|
|
encoded_inputs = self.encode_plus(
|
||
|
|
text=text,
|
||
|
|
text_pair=text_pair,
|
||
|
|
boxes=boxes,
|
||
|
|
word_labels=word_labels,
|
||
|
|
add_special_tokens=add_special_tokens,
|
||
|
|
padding=padding,
|
||
|
|
truncation=truncation,
|
||
|
|
max_length=max_length,
|
||
|
|
stride=stride,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
return_tensors=return_tensors,
|
||
|
|
return_token_type_ids=return_token_type_ids,
|
||
|
|
return_attention_mask=return_attention_mask,
|
||
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
||
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
||
|
|
return_offsets_mapping=return_offsets_mapping,
|
||
|
|
return_length=return_length,
|
||
|
|
verbose=verbose,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
return encoded_inputs["input_ids"]
|
||
|
|
|
||
|
|
@add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
||
|
|
def encode_plus(
|
||
|
|
self,
|
||
|
|
text: Union[TextInput, PreTokenizedInput],
|
||
|
|
text_pair: Optional[PreTokenizedInput] = None,
|
||
|
|
boxes: Optional[List[List[int]]] = None,
|
||
|
|
word_labels: Optional[List[int]] = None,
|
||
|
|
add_special_tokens: bool = True,
|
||
|
|
padding: Union[bool, str, PaddingStrategy] = False,
|
||
|
|
truncation: Union[bool, str, TruncationStrategy] = None,
|
||
|
|
max_length: Optional[int] = None,
|
||
|
|
stride: int = 0,
|
||
|
|
pad_to_multiple_of: Optional[int] = None,
|
||
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
||
|
|
return_token_type_ids: Optional[bool] = None,
|
||
|
|
return_attention_mask: Optional[bool] = None,
|
||
|
|
return_overflowing_tokens: bool = False,
|
||
|
|
return_special_tokens_mask: bool = False,
|
||
|
|
return_offsets_mapping: bool = False,
|
||
|
|
return_length: bool = False,
|
||
|
|
verbose: bool = True,
|
||
|
|
**kwargs,
|
||
|
|
) -> BatchEncoding:
|
||
|
|
"""
|
||
|
|
Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
|
||
|
|
`__call__` should be used instead.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
text (`str`, `List[str]`, `List[List[str]]`):
|
||
|
|
The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
|
||
|
|
text_pair (`List[str]` or `List[int]`, *optional*):
|
||
|
|
Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
|
||
|
|
list of list of strings (words of a batch of examples).
|
||
|
|
"""
|
||
|
|
|
||
|
|
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
|
||
|
|
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
|
||
|
|
padding=padding,
|
||
|
|
truncation=truncation,
|
||
|
|
max_length=max_length,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
verbose=verbose,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
return self._encode_plus(
|
||
|
|
text=text,
|
||
|
|
boxes=boxes,
|
||
|
|
text_pair=text_pair,
|
||
|
|
word_labels=word_labels,
|
||
|
|
add_special_tokens=add_special_tokens,
|
||
|
|
padding_strategy=padding_strategy,
|
||
|
|
truncation_strategy=truncation_strategy,
|
||
|
|
max_length=max_length,
|
||
|
|
stride=stride,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
return_tensors=return_tensors,
|
||
|
|
return_token_type_ids=return_token_type_ids,
|
||
|
|
return_attention_mask=return_attention_mask,
|
||
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
||
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
||
|
|
return_offsets_mapping=return_offsets_mapping,
|
||
|
|
return_length=return_length,
|
||
|
|
verbose=verbose,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
def _encode_plus(
|
||
|
|
self,
|
||
|
|
text: Union[TextInput, PreTokenizedInput],
|
||
|
|
text_pair: Optional[PreTokenizedInput] = None,
|
||
|
|
boxes: Optional[List[List[int]]] = None,
|
||
|
|
word_labels: Optional[List[int]] = None,
|
||
|
|
add_special_tokens: bool = True,
|
||
|
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
||
|
|
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
|
||
|
|
max_length: Optional[int] = None,
|
||
|
|
stride: int = 0,
|
||
|
|
pad_to_multiple_of: Optional[int] = None,
|
||
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
||
|
|
return_token_type_ids: Optional[bool] = None,
|
||
|
|
return_attention_mask: Optional[bool] = None,
|
||
|
|
return_overflowing_tokens: bool = False,
|
||
|
|
return_special_tokens_mask: bool = False,
|
||
|
|
return_offsets_mapping: bool = False,
|
||
|
|
return_length: bool = False,
|
||
|
|
verbose: bool = True,
|
||
|
|
**kwargs,
|
||
|
|
) -> BatchEncoding:
|
||
|
|
if return_offsets_mapping:
|
||
|
|
raise NotImplementedError(
|
||
|
|
"return_offset_mapping is not available when using Python tokenizers. "
|
||
|
|
"To use this feature, change your tokenizer to one deriving from "
|
||
|
|
"transformers.PreTrainedTokenizerFast. "
|
||
|
|
"More information on available tokenizers at "
|
||
|
|
"https://github.com/huggingface/transformers/pull/2674"
|
||
|
|
)
|
||
|
|
|
||
|
|
return self.prepare_for_model(
|
||
|
|
text=text,
|
||
|
|
text_pair=text_pair,
|
||
|
|
boxes=boxes,
|
||
|
|
word_labels=word_labels,
|
||
|
|
add_special_tokens=add_special_tokens,
|
||
|
|
padding=padding_strategy.value,
|
||
|
|
truncation=truncation_strategy.value,
|
||
|
|
max_length=max_length,
|
||
|
|
stride=stride,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
return_tensors=return_tensors,
|
||
|
|
prepend_batch_axis=True,
|
||
|
|
return_attention_mask=return_attention_mask,
|
||
|
|
return_token_type_ids=return_token_type_ids,
|
||
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
||
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
||
|
|
return_length=return_length,
|
||
|
|
verbose=verbose,
|
||
|
|
)
|
||
|
|
|
||
|
|
@add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
||
|
|
def prepare_for_model(
|
||
|
|
self,
|
||
|
|
text: Union[TextInput, PreTokenizedInput],
|
||
|
|
text_pair: Optional[PreTokenizedInput] = None,
|
||
|
|
boxes: Optional[List[List[int]]] = None,
|
||
|
|
word_labels: Optional[List[int]] = None,
|
||
|
|
add_special_tokens: bool = True,
|
||
|
|
padding: Union[bool, str, PaddingStrategy] = False,
|
||
|
|
truncation: Union[bool, str, TruncationStrategy] = None,
|
||
|
|
max_length: Optional[int] = None,
|
||
|
|
stride: int = 0,
|
||
|
|
pad_to_multiple_of: Optional[int] = None,
|
||
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
||
|
|
return_token_type_ids: Optional[bool] = None,
|
||
|
|
return_attention_mask: Optional[bool] = None,
|
||
|
|
return_overflowing_tokens: bool = False,
|
||
|
|
return_special_tokens_mask: bool = False,
|
||
|
|
return_offsets_mapping: bool = False,
|
||
|
|
return_length: bool = False,
|
||
|
|
verbose: bool = True,
|
||
|
|
prepend_batch_axis: bool = False,
|
||
|
|
**kwargs,
|
||
|
|
) -> BatchEncoding:
|
||
|
|
"""
|
||
|
|
Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
|
||
|
|
truncates sequences if overflowing while taking into account the special tokens and manages a moving window
|
||
|
|
(with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and
|
||
|
|
*truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a
|
||
|
|
combination of arguments will raise an error.
|
||
|
|
|
||
|
|
Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into
|
||
|
|
token-level `labels`. The word label is used for the first token of the word, while remaining tokens are
|
||
|
|
labeled with -100, such that they will be ignored by the loss function.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
text (`str`, `List[str]`, `List[List[str]]`):
|
||
|
|
The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
|
||
|
|
text_pair (`List[str]` or `List[int]`, *optional*):
|
||
|
|
Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
|
||
|
|
list of list of strings (words of a batch of examples).
|
||
|
|
"""
|
||
|
|
|
||
|
|
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
|
||
|
|
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
|
||
|
|
padding=padding,
|
||
|
|
truncation=truncation,
|
||
|
|
max_length=max_length,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
verbose=verbose,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
tokens = []
|
||
|
|
pair_tokens = []
|
||
|
|
token_boxes = []
|
||
|
|
pair_token_boxes = []
|
||
|
|
labels = []
|
||
|
|
|
||
|
|
if text_pair is None:
|
||
|
|
if word_labels is None:
|
||
|
|
# CASE 1: document image classification (training + inference) + CASE 2: token classification (inference)
|
||
|
|
for word, box in zip(text, boxes):
|
||
|
|
if len(word) < 1: # skip empty words
|
||
|
|
continue
|
||
|
|
word_tokens = self.tokenize(word)
|
||
|
|
tokens.extend(word_tokens)
|
||
|
|
token_boxes.extend([box] * len(word_tokens))
|
||
|
|
else:
|
||
|
|
# CASE 2: token classification (training)
|
||
|
|
for word, box, label in zip(text, boxes, word_labels):
|
||
|
|
if len(word) < 1: # skip empty words
|
||
|
|
continue
|
||
|
|
word_tokens = self.tokenize(word)
|
||
|
|
tokens.extend(word_tokens)
|
||
|
|
token_boxes.extend([box] * len(word_tokens))
|
||
|
|
if self.only_label_first_subword:
|
||
|
|
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
|
||
|
|
labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
|
||
|
|
else:
|
||
|
|
labels.extend([label] * len(word_tokens))
|
||
|
|
else:
|
||
|
|
# CASE 3: document visual question answering (inference)
|
||
|
|
# text = question
|
||
|
|
# text_pair = words
|
||
|
|
tokens = self.tokenize(text)
|
||
|
|
token_boxes = [self.pad_token_box for _ in range(len(tokens))]
|
||
|
|
|
||
|
|
for word, box in zip(text_pair, boxes):
|
||
|
|
if len(word) < 1: # skip empty words
|
||
|
|
continue
|
||
|
|
word_tokens = self.tokenize(word)
|
||
|
|
pair_tokens.extend(word_tokens)
|
||
|
|
pair_token_boxes.extend([box] * len(word_tokens))
|
||
|
|
|
||
|
|
# Create ids + pair_ids
|
||
|
|
ids = self.convert_tokens_to_ids(tokens)
|
||
|
|
pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None
|
||
|
|
|
||
|
|
if (
|
||
|
|
return_overflowing_tokens
|
||
|
|
and truncation_strategy == TruncationStrategy.LONGEST_FIRST
|
||
|
|
and pair_ids is not None
|
||
|
|
):
|
||
|
|
raise ValueError(
|
||
|
|
"Not possible to return overflowing tokens for pair of sequences with the "
|
||
|
|
"`longest_first`. Please select another truncation strategy than `longest_first`, "
|
||
|
|
"for instance `only_second` or `only_first`."
|
||
|
|
)
|
||
|
|
|
||
|
|
# Compute the total size of the returned encodings
|
||
|
|
pair = bool(pair_ids is not None)
|
||
|
|
len_ids = len(ids)
|
||
|
|
len_pair_ids = len(pair_ids) if pair else 0
|
||
|
|
total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
|
||
|
|
|
||
|
|
# Truncation: Handle max sequence length
|
||
|
|
overflowing_tokens = []
|
||
|
|
overflowing_token_boxes = []
|
||
|
|
overflowing_labels = []
|
||
|
|
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
|
||
|
|
(
|
||
|
|
ids,
|
||
|
|
token_boxes,
|
||
|
|
pair_ids,
|
||
|
|
pair_token_boxes,
|
||
|
|
labels,
|
||
|
|
overflowing_tokens,
|
||
|
|
overflowing_token_boxes,
|
||
|
|
overflowing_labels,
|
||
|
|
) = self.truncate_sequences(
|
||
|
|
ids,
|
||
|
|
token_boxes,
|
||
|
|
pair_ids=pair_ids,
|
||
|
|
pair_token_boxes=pair_token_boxes,
|
||
|
|
labels=labels,
|
||
|
|
num_tokens_to_remove=total_len - max_length,
|
||
|
|
truncation_strategy=truncation_strategy,
|
||
|
|
stride=stride,
|
||
|
|
)
|
||
|
|
|
||
|
|
if return_token_type_ids and not add_special_tokens:
|
||
|
|
raise ValueError(
|
||
|
|
"Asking to return token_type_ids while setting add_special_tokens to False "
|
||
|
|
"results in an undefined behavior. Please set add_special_tokens to True or "
|
||
|
|
"set return_token_type_ids to None."
|
||
|
|
)
|
||
|
|
|
||
|
|
# Load from model defaults
|
||
|
|
if return_token_type_ids is None:
|
||
|
|
return_token_type_ids = "token_type_ids" in self.model_input_names
|
||
|
|
if return_attention_mask is None:
|
||
|
|
return_attention_mask = "attention_mask" in self.model_input_names
|
||
|
|
|
||
|
|
encoded_inputs = {}
|
||
|
|
|
||
|
|
if return_overflowing_tokens:
|
||
|
|
encoded_inputs["overflowing_tokens"] = overflowing_tokens
|
||
|
|
encoded_inputs["overflowing_token_boxes"] = overflowing_token_boxes
|
||
|
|
encoded_inputs["overflowing_labels"] = overflowing_labels
|
||
|
|
encoded_inputs["num_truncated_tokens"] = total_len - max_length
|
||
|
|
|
||
|
|
# Add special tokens
|
||
|
|
if add_special_tokens:
|
||
|
|
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
|
||
|
|
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
|
||
|
|
token_boxes = [self.cls_token_box] + token_boxes + [self.sep_token_box]
|
||
|
|
if pair_token_boxes:
|
||
|
|
pair_token_boxes = pair_token_boxes + [self.sep_token_box]
|
||
|
|
if labels:
|
||
|
|
labels = [self.pad_token_label] + labels + [self.pad_token_label]
|
||
|
|
else:
|
||
|
|
sequence = ids + pair_ids if pair else ids
|
||
|
|
token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
|
||
|
|
|
||
|
|
# Build output dictionary
|
||
|
|
encoded_inputs["input_ids"] = sequence
|
||
|
|
encoded_inputs["bbox"] = token_boxes + pair_token_boxes
|
||
|
|
if return_token_type_ids:
|
||
|
|
encoded_inputs["token_type_ids"] = token_type_ids
|
||
|
|
if return_special_tokens_mask:
|
||
|
|
if add_special_tokens:
|
||
|
|
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
|
||
|
|
else:
|
||
|
|
encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
|
||
|
|
|
||
|
|
if labels:
|
||
|
|
encoded_inputs["labels"] = labels
|
||
|
|
|
||
|
|
# Check lengths
|
||
|
|
self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
|
||
|
|
|
||
|
|
# Padding
|
||
|
|
if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
|
||
|
|
encoded_inputs = self.pad(
|
||
|
|
encoded_inputs,
|
||
|
|
max_length=max_length,
|
||
|
|
padding=padding_strategy.value,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
return_attention_mask=return_attention_mask,
|
||
|
|
)
|
||
|
|
|
||
|
|
if return_length:
|
||
|
|
encoded_inputs["length"] = len(encoded_inputs["input_ids"])
|
||
|
|
|
||
|
|
batch_outputs = BatchEncoding(
|
||
|
|
encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
|
||
|
|
)
|
||
|
|
|
||
|
|
return batch_outputs
|
||
|
|
|
||
|
|
def truncate_sequences(
|
||
|
|
self,
|
||
|
|
ids: List[int],
|
||
|
|
token_boxes: List[List[int]],
|
||
|
|
pair_ids: Optional[List[int]] = None,
|
||
|
|
pair_token_boxes: Optional[List[List[int]]] = None,
|
||
|
|
labels: Optional[List[int]] = None,
|
||
|
|
num_tokens_to_remove: int = 0,
|
||
|
|
truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
|
||
|
|
stride: int = 0,
|
||
|
|
) -> Tuple[List[int], List[int], List[int]]:
|
||
|
|
"""
|
||
|
|
Truncates a sequence pair in-place following the strategy.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
ids (`List[int]`):
|
||
|
|
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
|
||
|
|
`convert_tokens_to_ids` methods.
|
||
|
|
token_boxes (`List[List[int]]`):
|
||
|
|
Bounding boxes of the first sequence.
|
||
|
|
pair_ids (`List[int]`, *optional*):
|
||
|
|
Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
|
||
|
|
and `convert_tokens_to_ids` methods.
|
||
|
|
pair_token_boxes (`List[List[int]]`, *optional*):
|
||
|
|
Bounding boxes of the second sequence.
|
||
|
|
labels (`List[int]`, *optional*):
|
||
|
|
Labels of the first sequence (for token classification tasks).
|
||
|
|
num_tokens_to_remove (`int`, *optional*, defaults to 0):
|
||
|
|
Number of tokens to remove using the truncation strategy.
|
||
|
|
truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
|
||
|
|
The strategy to follow for truncation. Can be:
|
||
|
|
|
||
|
|
- `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
|
||
|
|
maximum acceptable input length for the model if that argument is not provided. This will truncate
|
||
|
|
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
|
||
|
|
batch of pairs) is provided.
|
||
|
|
- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
|
||
|
|
maximum acceptable input length for the model if that argument is not provided. This will only
|
||
|
|
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
|
||
|
|
- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
|
||
|
|
maximum acceptable input length for the model if that argument is not provided. This will only
|
||
|
|
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
|
||
|
|
- `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
|
||
|
|
than the model maximum admissible input size).
|
||
|
|
stride (`int`, *optional*, defaults to 0):
|
||
|
|
If set to a positive number, the overflowing tokens returned will contain some tokens from the main
|
||
|
|
sequence returned. The value of this argument defines the number of additional tokens.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
|
||
|
|
overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair
|
||
|
|
of sequences (or a batch of pairs) is provided.
|
||
|
|
"""
|
||
|
|
if num_tokens_to_remove <= 0:
|
||
|
|
return ids, token_boxes, pair_ids, pair_token_boxes, labels, [], [], []
|
||
|
|
|
||
|
|
if not isinstance(truncation_strategy, TruncationStrategy):
|
||
|
|
truncation_strategy = TruncationStrategy(truncation_strategy)
|
||
|
|
|
||
|
|
overflowing_tokens = []
|
||
|
|
overflowing_token_boxes = []
|
||
|
|
overflowing_labels = []
|
||
|
|
if truncation_strategy == TruncationStrategy.ONLY_FIRST or (
|
||
|
|
truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None
|
||
|
|
):
|
||
|
|
if len(ids) > num_tokens_to_remove:
|
||
|
|
window_len = min(len(ids), stride + num_tokens_to_remove)
|
||
|
|
overflowing_tokens = ids[-window_len:]
|
||
|
|
overflowing_token_boxes = token_boxes[-window_len:]
|
||
|
|
overflowing_labels = labels[-window_len:]
|
||
|
|
ids = ids[:-num_tokens_to_remove]
|
||
|
|
token_boxes = token_boxes[:-num_tokens_to_remove]
|
||
|
|
labels = labels[:-num_tokens_to_remove]
|
||
|
|
else:
|
||
|
|
error_msg = (
|
||
|
|
f"We need to remove {num_tokens_to_remove} to truncate the input "
|
||
|
|
f"but the first sequence has a length {len(ids)}. "
|
||
|
|
)
|
||
|
|
if truncation_strategy == TruncationStrategy.ONLY_FIRST:
|
||
|
|
error_msg = (
|
||
|
|
error_msg + "Please select another truncation strategy than "
|
||
|
|
f"{truncation_strategy}, for instance 'longest_first' or 'only_second'."
|
||
|
|
)
|
||
|
|
logger.error(error_msg)
|
||
|
|
elif truncation_strategy == TruncationStrategy.LONGEST_FIRST:
|
||
|
|
logger.warning(
|
||
|
|
"Be aware, overflowing tokens are not returned for the setting you have chosen,"
|
||
|
|
f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
|
||
|
|
"truncation strategy. So the returned list will always be empty even if some "
|
||
|
|
"tokens have been removed."
|
||
|
|
)
|
||
|
|
for _ in range(num_tokens_to_remove):
|
||
|
|
if pair_ids is None or len(ids) > len(pair_ids):
|
||
|
|
ids = ids[:-1]
|
||
|
|
token_boxes = token_boxes[:-1]
|
||
|
|
labels = labels[:-1]
|
||
|
|
else:
|
||
|
|
pair_ids = pair_ids[:-1]
|
||
|
|
pair_token_boxes = pair_token_boxes[:-1]
|
||
|
|
elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
|
||
|
|
if len(pair_ids) > num_tokens_to_remove:
|
||
|
|
window_len = min(len(pair_ids), stride + num_tokens_to_remove)
|
||
|
|
overflowing_tokens = pair_ids[-window_len:]
|
||
|
|
overflowing_token_boxes = pair_token_boxes[-window_len:]
|
||
|
|
pair_ids = pair_ids[:-num_tokens_to_remove]
|
||
|
|
pair_token_boxes = pair_token_boxes[:-num_tokens_to_remove]
|
||
|
|
else:
|
||
|
|
logger.error(
|
||
|
|
f"We need to remove {num_tokens_to_remove} to truncate the input "
|
||
|
|
f"but the second sequence has a length {len(pair_ids)}. "
|
||
|
|
f"Please select another truncation strategy than {truncation_strategy}, "
|
||
|
|
"for instance 'longest_first' or 'only_first'."
|
||
|
|
)
|
||
|
|
|
||
|
|
return (
|
||
|
|
ids,
|
||
|
|
token_boxes,
|
||
|
|
pair_ids,
|
||
|
|
pair_token_boxes,
|
||
|
|
labels,
|
||
|
|
overflowing_tokens,
|
||
|
|
overflowing_token_boxes,
|
||
|
|
overflowing_labels,
|
||
|
|
)
|
||
|
|
|
||
|
|
def _pad(
|
||
|
|
self,
|
||
|
|
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
|
||
|
|
max_length: Optional[int] = None,
|
||
|
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
||
|
|
pad_to_multiple_of: Optional[int] = None,
|
||
|
|
return_attention_mask: Optional[bool] = None,
|
||
|
|
) -> dict:
|
||
|
|
"""
|
||
|
|
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
|
||
|
|
|
||
|
|
Args:
|
||
|
|
encoded_inputs:
|
||
|
|
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
|
||
|
|
max_length: maximum length of the returned list and optionally padding length (see below).
|
||
|
|
Will truncate by taking into account the special tokens.
|
||
|
|
padding_strategy: PaddingStrategy to use for padding.
|
||
|
|
|
||
|
|
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
|
||
|
|
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
|
||
|
|
- PaddingStrategy.DO_NOT_PAD: Do not pad
|
||
|
|
The tokenizer padding sides are defined in self.padding_side:
|
||
|
|
|
||
|
|
- 'left': pads on the left of the sequences
|
||
|
|
- 'right': pads on the right of the sequences
|
||
|
|
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
|
||
|
|
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
|
||
|
|
`>= 7.5` (Volta).
|
||
|
|
return_attention_mask:
|
||
|
|
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
|
||
|
|
"""
|
||
|
|
# Load from model defaults
|
||
|
|
if return_attention_mask is None:
|
||
|
|
return_attention_mask = "attention_mask" in self.model_input_names
|
||
|
|
|
||
|
|
required_input = encoded_inputs[self.model_input_names[0]]
|
||
|
|
|
||
|
|
if padding_strategy == PaddingStrategy.LONGEST:
|
||
|
|
max_length = len(required_input)
|
||
|
|
|
||
|
|
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
|
||
|
|
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
|
||
|
|
|
||
|
|
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
|
||
|
|
|
||
|
|
# Initialize attention mask if not present.
|
||
|
|
if return_attention_mask and "attention_mask" not in encoded_inputs:
|
||
|
|
encoded_inputs["attention_mask"] = [1] * len(required_input)
|
||
|
|
|
||
|
|
if needs_to_be_padded:
|
||
|
|
difference = max_length - len(required_input)
|
||
|
|
if self.padding_side == "right":
|
||
|
|
if return_attention_mask:
|
||
|
|
encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
|
||
|
|
if "token_type_ids" in encoded_inputs:
|
||
|
|
encoded_inputs["token_type_ids"] = (
|
||
|
|
encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
|
||
|
|
)
|
||
|
|
if "bbox" in encoded_inputs:
|
||
|
|
encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
|
||
|
|
if "labels" in encoded_inputs:
|
||
|
|
encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
|
||
|
|
if "special_tokens_mask" in encoded_inputs:
|
||
|
|
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
|
||
|
|
encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
|
||
|
|
elif self.padding_side == "left":
|
||
|
|
if return_attention_mask:
|
||
|
|
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
|
||
|
|
if "token_type_ids" in encoded_inputs:
|
||
|
|
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
|
||
|
|
"token_type_ids"
|
||
|
|
]
|
||
|
|
if "bbox" in encoded_inputs:
|
||
|
|
encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
|
||
|
|
if "labels" in encoded_inputs:
|
||
|
|
encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
|
||
|
|
if "special_tokens_mask" in encoded_inputs:
|
||
|
|
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
|
||
|
|
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
|
||
|
|
else:
|
||
|
|
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
|
||
|
|
|
||
|
|
return encoded_inputs
|
||
|
|
|
||
|
|
|
||
|
|
# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
|
||
|
|
class BasicTokenizer(object):
|
||
|
|
"""
|
||
|
|
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
do_lower_case (`bool`, *optional*, defaults to `True`):
|
||
|
|
Whether or not to lowercase the input when tokenizing.
|
||
|
|
never_split (`Iterable`, *optional*):
|
||
|
|
Collection of tokens which will never be split during tokenization. Only has an effect when
|
||
|
|
`do_basic_tokenize=True`
|
||
|
|
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
|
||
|
|
Whether or not to tokenize Chinese characters.
|
||
|
|
|
||
|
|
This should likely be deactivated for Japanese (see this
|
||
|
|
[issue](https://github.com/huggingface/transformers/issues/328)).
|
||
|
|
strip_accents (`bool`, *optional*):
|
||
|
|
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
|
||
|
|
value for `lowercase` (as in the original BERT).
|
||
|
|
do_split_on_punc (`bool`, *optional*, defaults to `True`):
|
||
|
|
In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
|
||
|
|
the full context of the words, such as contractions.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
do_lower_case=True,
|
||
|
|
never_split=None,
|
||
|
|
tokenize_chinese_chars=True,
|
||
|
|
strip_accents=None,
|
||
|
|
do_split_on_punc=True,
|
||
|
|
):
|
||
|
|
if never_split is None:
|
||
|
|
never_split = []
|
||
|
|
self.do_lower_case = do_lower_case
|
||
|
|
self.never_split = set(never_split)
|
||
|
|
self.tokenize_chinese_chars = tokenize_chinese_chars
|
||
|
|
self.strip_accents = strip_accents
|
||
|
|
self.do_split_on_punc = do_split_on_punc
|
||
|
|
|
||
|
|
def tokenize(self, text, never_split=None):
|
||
|
|
"""
|
||
|
|
Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
never_split (`List[str]`, *optional*)
|
||
|
|
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
|
||
|
|
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
|
||
|
|
"""
|
||
|
|
# union() returns a new set by concatenating the two sets.
|
||
|
|
never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
|
||
|
|
text = self._clean_text(text)
|
||
|
|
|
||
|
|
# This was added on November 1st, 2018 for the multilingual and Chinese
|
||
|
|
# models. This is also applied to the English models now, but it doesn't
|
||
|
|
# matter since the English models were not trained on any Chinese data
|
||
|
|
# and generally don't have any Chinese data in them (there are Chinese
|
||
|
|
# characters in the vocabulary because Wikipedia does have some Chinese
|
||
|
|
# words in the English Wikipedia.).
|
||
|
|
if self.tokenize_chinese_chars:
|
||
|
|
text = self._tokenize_chinese_chars(text)
|
||
|
|
# prevents treating the same character with different unicode codepoints as different characters
|
||
|
|
unicode_normalized_text = unicodedata.normalize("NFC", text)
|
||
|
|
orig_tokens = whitespace_tokenize(unicode_normalized_text)
|
||
|
|
split_tokens = []
|
||
|
|
for token in orig_tokens:
|
||
|
|
if token not in never_split:
|
||
|
|
if self.do_lower_case:
|
||
|
|
token = token.lower()
|
||
|
|
if self.strip_accents is not False:
|
||
|
|
token = self._run_strip_accents(token)
|
||
|
|
elif self.strip_accents:
|
||
|
|
token = self._run_strip_accents(token)
|
||
|
|
split_tokens.extend(self._run_split_on_punc(token, never_split))
|
||
|
|
|
||
|
|
output_tokens = whitespace_tokenize(" ".join(split_tokens))
|
||
|
|
return output_tokens
|
||
|
|
|
||
|
|
def _run_strip_accents(self, text):
|
||
|
|
"""Strips accents from a piece of text."""
|
||
|
|
text = unicodedata.normalize("NFD", text)
|
||
|
|
output = []
|
||
|
|
for char in text:
|
||
|
|
cat = unicodedata.category(char)
|
||
|
|
if cat == "Mn":
|
||
|
|
continue
|
||
|
|
output.append(char)
|
||
|
|
return "".join(output)
|
||
|
|
|
||
|
|
def _run_split_on_punc(self, text, never_split=None):
|
||
|
|
"""Splits punctuation on a piece of text."""
|
||
|
|
if not self.do_split_on_punc or (never_split is not None and text in never_split):
|
||
|
|
return [text]
|
||
|
|
chars = list(text)
|
||
|
|
i = 0
|
||
|
|
start_new_word = True
|
||
|
|
output = []
|
||
|
|
while i < len(chars):
|
||
|
|
char = chars[i]
|
||
|
|
if _is_punctuation(char):
|
||
|
|
output.append([char])
|
||
|
|
start_new_word = True
|
||
|
|
else:
|
||
|
|
if start_new_word:
|
||
|
|
output.append([])
|
||
|
|
start_new_word = False
|
||
|
|
output[-1].append(char)
|
||
|
|
i += 1
|
||
|
|
|
||
|
|
return ["".join(x) for x in output]
|
||
|
|
|
||
|
|
def _tokenize_chinese_chars(self, text):
|
||
|
|
"""Adds whitespace around any CJK character."""
|
||
|
|
output = []
|
||
|
|
for char in text:
|
||
|
|
cp = ord(char)
|
||
|
|
if self._is_chinese_char(cp):
|
||
|
|
output.append(" ")
|
||
|
|
output.append(char)
|
||
|
|
output.append(" ")
|
||
|
|
else:
|
||
|
|
output.append(char)
|
||
|
|
return "".join(output)
|
||
|
|
|
||
|
|
def _is_chinese_char(self, cp):
|
||
|
|
"""Checks whether CP is the codepoint of a CJK character."""
|
||
|
|
# This defines a "chinese character" as anything in the CJK Unicode block:
|
||
|
|
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
|
||
|
|
#
|
||
|
|
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
|
||
|
|
# despite its name. The modern Korean Hangul alphabet is a different block,
|
||
|
|
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
|
||
|
|
# space-separated words, so they are not treated specially and handled
|
||
|
|
# like the all of the other languages.
|
||
|
|
if (
|
||
|
|
(cp >= 0x4E00 and cp <= 0x9FFF)
|
||
|
|
or (cp >= 0x3400 and cp <= 0x4DBF) #
|
||
|
|
or (cp >= 0x20000 and cp <= 0x2A6DF) #
|
||
|
|
or (cp >= 0x2A700 and cp <= 0x2B73F) #
|
||
|
|
or (cp >= 0x2B740 and cp <= 0x2B81F) #
|
||
|
|
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
|
||
|
|
or (cp >= 0xF900 and cp <= 0xFAFF)
|
||
|
|
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
|
||
|
|
): #
|
||
|
|
return True
|
||
|
|
|
||
|
|
return False
|
||
|
|
|
||
|
|
def _clean_text(self, text):
|
||
|
|
"""Performs invalid character removal and whitespace cleanup on text."""
|
||
|
|
output = []
|
||
|
|
for char in text:
|
||
|
|
cp = ord(char)
|
||
|
|
if cp == 0 or cp == 0xFFFD or _is_control(char):
|
||
|
|
continue
|
||
|
|
if _is_whitespace(char):
|
||
|
|
output.append(" ")
|
||
|
|
else:
|
||
|
|
output.append(char)
|
||
|
|
return "".join(output)
|
||
|
|
|
||
|
|
|
||
|
|
# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
|
||
|
|
class WordpieceTokenizer(object):
|
||
|
|
"""Runs WordPiece tokenization."""
|
||
|
|
|
||
|
|
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
|
||
|
|
self.vocab = vocab
|
||
|
|
self.unk_token = unk_token
|
||
|
|
self.max_input_chars_per_word = max_input_chars_per_word
|
||
|
|
|
||
|
|
def tokenize(self, text):
|
||
|
|
"""
|
||
|
|
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
|
||
|
|
tokenization using the given vocabulary.
|
||
|
|
|
||
|
|
For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
text: A single token or whitespace separated tokens. This should have
|
||
|
|
already been passed through *BasicTokenizer*.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A list of wordpiece tokens.
|
||
|
|
"""
|
||
|
|
|
||
|
|
output_tokens = []
|
||
|
|
for token in whitespace_tokenize(text):
|
||
|
|
chars = list(token)
|
||
|
|
if len(chars) > self.max_input_chars_per_word:
|
||
|
|
output_tokens.append(self.unk_token)
|
||
|
|
continue
|
||
|
|
|
||
|
|
is_bad = False
|
||
|
|
start = 0
|
||
|
|
sub_tokens = []
|
||
|
|
while start < len(chars):
|
||
|
|
end = len(chars)
|
||
|
|
cur_substr = None
|
||
|
|
while start < end:
|
||
|
|
substr = "".join(chars[start:end])
|
||
|
|
if start > 0:
|
||
|
|
substr = "##" + substr
|
||
|
|
if substr in self.vocab:
|
||
|
|
cur_substr = substr
|
||
|
|
break
|
||
|
|
end -= 1
|
||
|
|
if cur_substr is None:
|
||
|
|
is_bad = True
|
||
|
|
break
|
||
|
|
sub_tokens.append(cur_substr)
|
||
|
|
start = end
|
||
|
|
|
||
|
|
if is_bad:
|
||
|
|
output_tokens.append(self.unk_token)
|
||
|
|
else:
|
||
|
|
output_tokens.extend(sub_tokens)
|
||
|
|
return output_tokens
|