1171 lines
56 KiB
Python
1171 lines
56 KiB
Python
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# coding=utf-8
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# Copyright 2021 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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""" Tokenization classes for LayoutXLM model."""
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import os
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from shutil import copyfile
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from typing import Any, Dict, List, Optional, Tuple, Union
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import sentencepiece as spm
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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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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from ..xlm_roberta.tokenization_xlm_roberta import (
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SPIECE_UNDERLINE,
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VOCAB_FILES_NAMES,
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)
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logger = logging.get_logger(__name__)
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LAYOUTXLM_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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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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class LayoutXLMTokenizer(PreTrainedTokenizer):
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"""
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Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
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[SentencePiece](https://github.com/google/sentencepiece).
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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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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the beginning of
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sequence. The token used is the `cls_token`.
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</Tip>
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of sequence.
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The token used is the `sep_token`.
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</Tip>
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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cls_token (`str`, *optional*, defaults to `"<s>"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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mask_token (`str`, *optional*, defaults to `"<mask>"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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cls_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
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The bounding box to use for the special [CLS] token.
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sep_token_box (`List[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`):
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The bounding box to use for the special [SEP] token.
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pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
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The bounding box to use for the special [PAD] token.
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pad_token_label (`int`, *optional*, defaults to -100):
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The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
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CrossEntropyLoss.
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only_label_first_subword (`bool`, *optional*, defaults to `True`):
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Whether or not to only label the first subword, in case word labels are provided.
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sp_model_kwargs (`dict`, *optional*):
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Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
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SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
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to set:
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- `enable_sampling`: Enable subword regularization.
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- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
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- `nbest_size = {0,1}`: No sampling is performed.
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- `nbest_size > 1`: samples from the nbest_size results.
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- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
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using forward-filtering-and-backward-sampling algorithm.
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- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
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BPE-dropout.
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Attributes:
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sp_model (`SentencePieceProcessor`):
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The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(
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self,
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vocab_file,
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bos_token="<s>",
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eos_token="</s>",
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sep_token="</s>",
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cls_token="<s>",
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unk_token="<unk>",
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pad_token="<pad>",
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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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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs,
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) -> None:
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# Mask token behave like a normal word, i.e. include the space before it
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mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(str(vocab_file))
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self.vocab_file = vocab_file
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# Original fairseq vocab and spm vocab must be "aligned":
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# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
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# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
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# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
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# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
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# Mimic fairseq token-to-id alignment for the first 4 token
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self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
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# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
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self.fairseq_offset = 1
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self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + self.fairseq_offset
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self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
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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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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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sep_token=sep_token,
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cls_token=cls_token,
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pad_token=pad_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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sp_model_kwargs=self.sp_model_kwargs,
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**kwargs,
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)
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def __getstate__(self):
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state = self.__dict__.copy()
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state["sp_model"] = None
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state["sp_model_proto"] = self.sp_model.serialized_model_proto()
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return state
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def __setstate__(self, d):
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self.__dict__ = d
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# for backward compatibility
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if not hasattr(self, "sp_model_kwargs"):
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self.sp_model_kwargs = {}
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
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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. An XLM-RoBERTa sequence has the following format:
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|
- single sequence: `<s> X </s>`
|
||
|
|
- pair of sequences: `<s> A </s></s> B </s>`
|
||
|
|
|
||
|
|
Args:
|
||
|
|
token_ids_0 (`List[int]`):
|
||
|
|
List of IDs to which the special tokens will be added.
|
||
|
|
token_ids_1 (`List[int]`, *optional*):
|
||
|
|
Optional second list of IDs for sequence pairs.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
||
|
|
"""
|
||
|
|
|
||
|
|
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 + 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 None:
|
||
|
|
return [1] + ([0] * len(token_ids_0)) + [1]
|
||
|
|
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [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. XLM-RoBERTa does
|
||
|
|
not make use of token type ids, therefore a list of zeros is returned.
|
||
|
|
|
||
|
|
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 zeros.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
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 + sep + token_ids_1 + sep) * [0]
|
||
|
|
|
||
|
|
@property
|
||
|
|
def vocab_size(self):
|
||
|
|
return len(self.sp_model) + self.fairseq_offset + 1 # Add the <mask> token
|
||
|
|
|
||
|
|
def get_vocab(self):
|
||
|
|
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
||
|
|
vocab.update(self.added_tokens_encoder)
|
||
|
|
return vocab
|
||
|
|
|
||
|
|
def _tokenize(self, text: str) -> List[str]:
|
||
|
|
return self.sp_model.encode(text, out_type=str)
|
||
|
|
|
||
|
|
def _convert_token_to_id(self, token):
|
||
|
|
"""Converts a token (str) in an id using the vocab."""
|
||
|
|
if token in self.fairseq_tokens_to_ids:
|
||
|
|
return self.fairseq_tokens_to_ids[token]
|
||
|
|
spm_id = self.sp_model.PieceToId(token)
|
||
|
|
|
||
|
|
# Need to return unknown token if the SP model returned 0
|
||
|
|
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
|
||
|
|
|
||
|
|
def _convert_id_to_token(self, index):
|
||
|
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
||
|
|
if index in self.fairseq_ids_to_tokens:
|
||
|
|
return self.fairseq_ids_to_tokens[index]
|
||
|
|
return self.sp_model.IdToPiece(index - self.fairseq_offset)
|
||
|
|
|
||
|
|
def convert_tokens_to_string(self, tokens):
|
||
|
|
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
|
||
|
|
out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
|
||
|
|
return out_string
|
||
|
|
|
||
|
|
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||
|
|
if not os.path.isdir(save_directory):
|
||
|
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
||
|
|
return
|
||
|
|
out_vocab_file = os.path.join(
|
||
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||
|
|
)
|
||
|
|
|
||
|
|
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
||
|
|
copyfile(self.vocab_file, out_vocab_file)
|
||
|
|
elif not os.path.isfile(self.vocab_file):
|
||
|
|
with open(out_vocab_file, "wb") as fi:
|
||
|
|
content_spiece_model = self.sp_model.serialized_model_proto()
|
||
|
|
fi.write(content_spiece_model)
|
||
|
|
|
||
|
|
return (out_vocab_file,)
|
||
|
|
|
||
|
|
@add_end_docstrings(LAYOUTXLM_ENCODE_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 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 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,
|
||
|
|
)
|
||
|
|
|
||
|
|
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(LAYOUTXLM_ENCODE_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
|
||
|
|
|
||
|
|
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(LAYOUTXLM_ENCODE_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.
|
||
|
|
|
||
|
|
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))] + [self.sep_token_box]
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
# 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.
|
||
|
|
"""
|
||
|
|
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.LONGEST_FIRST:
|
||
|
|
for _ in range(num_tokens_to_remove):
|
||
|
|
if pair_ids is None or len(ids) > len(pair_ids):
|
||
|
|
if not overflowing_tokens:
|
||
|
|
window_len = min(len(ids), stride + 1)
|
||
|
|
else:
|
||
|
|
window_len = 1
|
||
|
|
overflowing_tokens.extend(ids[-window_len:])
|
||
|
|
overflowing_token_boxes.extend(token_boxes[-window_len:])
|
||
|
|
overflowing_labels.extend(labels[-window_len:])
|
||
|
|
ids = ids[:-1]
|
||
|
|
token_boxes = token_boxes[:-1]
|
||
|
|
labels = labels[:-1]
|
||
|
|
else:
|
||
|
|
if not overflowing_tokens:
|
||
|
|
window_len = min(len(pair_ids), stride + 1)
|
||
|
|
else:
|
||
|
|
window_len = 1
|
||
|
|
overflowing_tokens.extend(pair_ids[-window_len:])
|
||
|
|
overflowing_token_boxes.extend(pair_token_boxes[-window_len:])
|
||
|
|
pair_ids = pair_ids[:-1]
|
||
|
|
pair_token_boxes = pair_token_boxes[:-1]
|
||
|
|
elif truncation_strategy == TruncationStrategy.ONLY_FIRST:
|
||
|
|
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:
|
||
|
|
logger.error(
|
||
|
|
f"We need to remove {num_tokens_to_remove} to truncate the input "
|
||
|
|
f"but the first sequence has a length {len(ids)}. "
|
||
|
|
f"Please select another truncation strategy than {truncation_strategy}, "
|
||
|
|
"for instance 'longest_first' or 'only_second'."
|
||
|
|
)
|
||
|
|
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
|