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866 lines
42 KiB
866 lines
42 KiB
# 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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"""
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Tokenization class for LayoutLMv2. Based on WordPiece.
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"""
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from tokenizers import Tokenizer, decoders, models, normalizers, pre_tokenizers, processors
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from ...tokenization_utils_base import (
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BatchEncoding,
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EncodedInput,
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PaddingStrategy,
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PreTokenizedInput,
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TensorType,
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TextInput,
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TextInputPair,
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TruncationStrategy,
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)
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from ...tokenization_utils_tokenizers import TokenizersBackend
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from ...utils import add_end_docstrings, logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
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# Docstring constants for encode methods
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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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"""
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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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"""
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class LayoutLMv2Tokenizer(TokenizersBackend):
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r"""
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Construct a "fast" LayoutLMv2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
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This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
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refer to this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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File containing the vocabulary.
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do_lower_case (`bool`, *optional*, defaults to `True`):
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Whether or not to lowercase the input when tokenizing.
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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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sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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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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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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cls_token (`str`, *optional*, defaults to `"[CLS]"`):
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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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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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tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
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Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
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issue](https://github.com/huggingface/transformers/issues/328)).
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strip_accents (`bool`, *optional*):
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Whether or not to strip all accents. If this option is not specified, then it will be determined by the
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value for `lowercase` (as in the original LayoutLMv2).
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model = models.WordPiece
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model_input_names = ["input_ids", "attention_mask", "token_type_ids"]
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def __init__(
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self,
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vocab: str | dict[str, int] | None = None,
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do_lower_case=True,
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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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**kwargs,
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):
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self.do_lower_case = do_lower_case
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if vocab is not None:
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self._vocab = vocab
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else:
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self._vocab = {
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str(pad_token): 0,
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str(unk_token): 1,
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str(cls_token): 2,
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str(sep_token): 3,
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str(mask_token): 4,
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}
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self._tokenizer = Tokenizer(models.WordPiece(vocab=self._vocab, unk_token=str(unk_token)))
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self._tokenizer.normalizer = normalizers.BertNormalizer(
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clean_text=True,
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handle_chinese_chars=tokenize_chinese_chars,
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strip_accents=strip_accents,
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lowercase=do_lower_case,
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)
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self._tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
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self._tokenizer.decoder = decoders.WordPiece(prefix="##")
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super().__init__(
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do_lower_case=do_lower_case,
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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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**kwargs,
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)
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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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# Now set post_processor with actual token IDs
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cls = str(self.cls_token)
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sep = str(self.sep_token)
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cls_token_id = self.cls_token_id
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sep_token_id = self.sep_token_id
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self._tokenizer.post_processor = processors.TemplateProcessing(
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single=f"{cls}:0 $A:0 {sep}:0",
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pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1",
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special_tokens=[
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(cls, cls_token_id),
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(sep, sep_token_id),
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],
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)
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@add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
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def __call__(
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self,
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text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput],
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text_pair: PreTokenizedInput | list[PreTokenizedInput] | None = None,
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boxes: list[list[int]] | list[list[list[int]]] | None = None,
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word_labels: list[int] | list[list[int]] | None = None,
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add_special_tokens: bool = True,
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padding: bool | str | PaddingStrategy = False,
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truncation: bool | str | TruncationStrategy = None,
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max_length: int | None = None,
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stride: int = 0,
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pad_to_multiple_of: int | None = None,
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padding_side: str | None = None,
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return_tensors: str | TensorType | None = None,
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return_token_type_ids: bool | None = None,
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return_attention_mask: bool | None = None,
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return_overflowing_tokens: bool = False,
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return_special_tokens_mask: bool = False,
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return_offsets_mapping: bool = False,
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return_length: bool = False,
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verbose: bool = True,
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**kwargs,
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) -> BatchEncoding:
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"""
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Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
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sequences with word-level normalized bounding boxes and optional labels.
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Args:
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text (`str`, `List[str]`, `List[List[str]]`):
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The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
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(words of a single example or questions of a batch of examples) or a list of list of strings (batch of
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words).
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text_pair (`List[str]`, `List[List[str]]`):
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The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
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(pretokenized string).
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boxes (`List[List[int]]`, `List[List[List[int]]]`):
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Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
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word_labels (`List[int]`, `List[List[int]]`, *optional*):
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Word-level integer labels (for token classification tasks such as FUNSD, CORD).
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"""
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# Input type checking for clearer error
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def _is_valid_text_input(t):
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if isinstance(t, str):
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# Strings are fine
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return True
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elif isinstance(t, (list, tuple)):
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# List are fine as long as they are...
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if len(t) == 0:
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# ... empty
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return True
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elif isinstance(t[0], str):
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# ... list of strings
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return True
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elif isinstance(t[0], (list, tuple)):
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# ... list with an empty list or with a list of strings
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return len(t[0]) == 0 or isinstance(t[0][0], str)
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else:
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return False
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else:
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return False
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if text_pair is not None:
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# in case text + text_pair are provided, text = questions, text_pair = words
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if not _is_valid_text_input(text):
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raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
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if not isinstance(text_pair, (list, tuple)):
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raise ValueError(
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"Words must be of type `List[str]` (single pretokenized example), "
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"or `List[List[str]]` (batch of pretokenized examples)."
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)
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else:
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# in case only text is provided => must be words
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if not isinstance(text, (list, tuple)):
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raise ValueError(
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"Words must be of type `List[str]` (single pretokenized example), "
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"or `List[List[str]]` (batch of pretokenized examples)."
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)
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if text_pair is not None:
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is_batched = isinstance(text, (list, tuple))
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else:
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is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
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words = text if text_pair is None else text_pair
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if boxes is None:
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raise ValueError("You must provide corresponding bounding boxes")
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if is_batched:
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if len(words) != len(boxes):
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raise ValueError("You must provide words and boxes for an equal amount of examples")
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for words_example, boxes_example in zip(words, boxes):
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if len(words_example) != len(boxes_example):
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raise ValueError("You must provide as many words as there are bounding boxes")
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else:
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if len(words) != len(boxes):
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raise ValueError("You must provide as many words as there are bounding boxes")
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if is_batched:
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if text_pair is not None and len(text) != len(text_pair):
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raise ValueError(
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f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
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f" {len(text_pair)}."
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)
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batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
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is_pair = bool(text_pair is not None)
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return self.batch_encode_plus(
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batch_text_or_text_pairs=batch_text_or_text_pairs,
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is_pair=is_pair,
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boxes=boxes,
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word_labels=word_labels,
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add_special_tokens=add_special_tokens,
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padding=padding,
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truncation=truncation,
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max_length=max_length,
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stride=stride,
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pad_to_multiple_of=pad_to_multiple_of,
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padding_side=padding_side,
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return_tensors=return_tensors,
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return_token_type_ids=return_token_type_ids,
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return_attention_mask=return_attention_mask,
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return_overflowing_tokens=return_overflowing_tokens,
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return_special_tokens_mask=return_special_tokens_mask,
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return_offsets_mapping=return_offsets_mapping,
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return_length=return_length,
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verbose=verbose,
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**kwargs,
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)
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else:
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return self.encode_plus(
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text=text,
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text_pair=text_pair,
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boxes=boxes,
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word_labels=word_labels,
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add_special_tokens=add_special_tokens,
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padding=padding,
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truncation=truncation,
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max_length=max_length,
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stride=stride,
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pad_to_multiple_of=pad_to_multiple_of,
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padding_side=padding_side,
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return_tensors=return_tensors,
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return_token_type_ids=return_token_type_ids,
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return_attention_mask=return_attention_mask,
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return_overflowing_tokens=return_overflowing_tokens,
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return_special_tokens_mask=return_special_tokens_mask,
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return_offsets_mapping=return_offsets_mapping,
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return_length=return_length,
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verbose=verbose,
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**kwargs,
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)
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@add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
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def batch_encode_plus(
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self,
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batch_text_or_text_pairs: list[TextInput] | list[TextInputPair] | list[PreTokenizedInput],
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is_pair: bool | None = None,
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boxes: list[list[list[int]]] | None = None,
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word_labels: list[int] | list[list[int]] | None = None,
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add_special_tokens: bool = True,
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padding: bool | str | PaddingStrategy = False,
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truncation: bool | str | TruncationStrategy = None,
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max_length: int | None = None,
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stride: int = 0,
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pad_to_multiple_of: int | None = None,
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padding_side: str | None = None,
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return_tensors: str | TensorType | None = None,
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return_token_type_ids: bool | None = None,
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return_attention_mask: bool | None = None,
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return_overflowing_tokens: bool = False,
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return_special_tokens_mask: bool = False,
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return_offsets_mapping: bool = False,
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return_length: bool = False,
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verbose: bool = True,
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**kwargs,
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) -> BatchEncoding:
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# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
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padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
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padding=padding,
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truncation=truncation,
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max_length=max_length,
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pad_to_multiple_of=pad_to_multiple_of,
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verbose=verbose,
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**kwargs,
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)
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|
|
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,
|
|
padding_side=padding_side,
|
|
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 tokenize(self, text: str, pair: str | None = None, add_special_tokens: bool = False, **kwargs) -> list[str]:
|
|
batched_input = [(text, pair)] if pair else [text]
|
|
encodings = self._tokenizer.encode_batch(
|
|
batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
|
|
)
|
|
|
|
return encodings[0].tokens if encodings else []
|
|
|
|
@add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def encode_plus(
|
|
self,
|
|
text: TextInput | PreTokenizedInput,
|
|
text_pair: PreTokenizedInput | None = None,
|
|
boxes: list[list[int]] | None = None,
|
|
word_labels: list[int] | None = None,
|
|
add_special_tokens: bool = True,
|
|
padding: bool | str | PaddingStrategy = False,
|
|
truncation: bool | str | TruncationStrategy = None,
|
|
max_length: int | None = None,
|
|
stride: int = 0,
|
|
pad_to_multiple_of: int | None = None,
|
|
padding_side: str | None = None,
|
|
return_tensors: str | TensorType | None = None,
|
|
return_token_type_ids: bool | None = None,
|
|
return_attention_mask: bool | None = 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,
|
|
padding_side=padding_side,
|
|
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: list[TextInput] | list[TextInputPair] | list[PreTokenizedInput],
|
|
is_pair: bool | None = None,
|
|
boxes: list[list[list[int]]] | None = None,
|
|
word_labels: list[list[int]] | None = None,
|
|
add_special_tokens: bool = True,
|
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
|
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
|
|
max_length: int | None = None,
|
|
stride: int = 0,
|
|
pad_to_multiple_of: int | None = None,
|
|
padding_side: str | None = None,
|
|
return_tensors: str | None = None,
|
|
return_token_type_ids: bool | None = None,
|
|
return_attention_mask: bool | None = None,
|
|
return_overflowing_tokens: bool = False,
|
|
return_special_tokens_mask: bool = False,
|
|
return_offsets_mapping: bool = False,
|
|
return_length: bool = False,
|
|
verbose: bool = True,
|
|
) -> BatchEncoding:
|
|
if not isinstance(batch_text_or_text_pairs, list):
|
|
raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})")
|
|
|
|
# Set the truncation and padding strategy and restore the initial configuration
|
|
self.set_truncation_and_padding(
|
|
padding_strategy=padding_strategy,
|
|
truncation_strategy=truncation_strategy,
|
|
max_length=max_length,
|
|
stride=stride,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
)
|
|
|
|
if is_pair:
|
|
batch_text_or_text_pairs = [(text.split(), text_pair) for text, text_pair in batch_text_or_text_pairs]
|
|
|
|
encodings = self._tokenizer.encode_batch(
|
|
batch_text_or_text_pairs,
|
|
add_special_tokens=add_special_tokens,
|
|
is_pretokenized=True, # we set this to True as LayoutLMv2 always expects pretokenized inputs
|
|
)
|
|
|
|
# Convert encoding to dict
|
|
# `Tokens` has type: Tuple[
|
|
# List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
|
|
# List[EncodingFast]
|
|
# ]
|
|
# with nested dimensions corresponding to batch, overflows, sequence length
|
|
tokens_and_encodings = [
|
|
self._convert_encoding(
|
|
encoding=encoding,
|
|
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=True
|
|
if word_labels is not None
|
|
else return_offsets_mapping, # we use offsets to create the labels
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
)
|
|
for encoding in encodings
|
|
]
|
|
|
|
# Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
|
|
# From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
|
|
# (we say ~ because the number of overflow varies with the example in the batch)
|
|
#
|
|
# To match each overflowing sample with the original sample in the batch
|
|
# we add an overflow_to_sample_mapping array (see below)
|
|
sanitized_tokens = {}
|
|
for key in tokens_and_encodings[0][0]:
|
|
stack = [e for item, _ in tokens_and_encodings for e in item[key]]
|
|
sanitized_tokens[key] = stack
|
|
sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
|
|
|
|
# If returning overflowing tokens, we need to return a mapping
|
|
# from the batch idx to the original sample
|
|
if return_overflowing_tokens:
|
|
overflow_to_sample_mapping = []
|
|
for i, (toks, _) in enumerate(tokens_and_encodings):
|
|
overflow_to_sample_mapping += [i] * len(toks["input_ids"])
|
|
sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
|
|
|
|
for input_ids in sanitized_tokens["input_ids"]:
|
|
self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
|
|
|
|
# create the token boxes
|
|
token_boxes = []
|
|
for batch_index in range(len(sanitized_tokens["input_ids"])):
|
|
if return_overflowing_tokens:
|
|
original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
|
|
else:
|
|
original_index = batch_index
|
|
token_boxes_example = []
|
|
for id, sequence_id, word_id in zip(
|
|
sanitized_tokens["input_ids"][batch_index],
|
|
sanitized_encodings[batch_index].sequence_ids,
|
|
sanitized_encodings[batch_index].word_ids,
|
|
):
|
|
if word_id is not None:
|
|
if is_pair and sequence_id == 0:
|
|
token_boxes_example.append(self.pad_token_box)
|
|
else:
|
|
token_boxes_example.append(boxes[original_index][word_id])
|
|
else:
|
|
if id == self.cls_token_id:
|
|
token_boxes_example.append(self.cls_token_box)
|
|
elif id == self.sep_token_id:
|
|
token_boxes_example.append(self.sep_token_box)
|
|
elif id == self.pad_token_id:
|
|
token_boxes_example.append(self.pad_token_box)
|
|
else:
|
|
raise ValueError("Id not recognized")
|
|
token_boxes.append(token_boxes_example)
|
|
|
|
sanitized_tokens["bbox"] = token_boxes
|
|
|
|
# optionally, create the labels
|
|
if word_labels is not None:
|
|
labels = []
|
|
for batch_index in range(len(sanitized_tokens["input_ids"])):
|
|
if return_overflowing_tokens:
|
|
original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
|
|
else:
|
|
original_index = batch_index
|
|
labels_example = []
|
|
for id, offset, word_id in zip(
|
|
sanitized_tokens["input_ids"][batch_index],
|
|
sanitized_tokens["offset_mapping"][batch_index],
|
|
sanitized_encodings[batch_index].word_ids,
|
|
):
|
|
if word_id is not None:
|
|
if self.only_label_first_subword:
|
|
if offset[0] == 0:
|
|
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
|
|
labels_example.append(word_labels[original_index][word_id])
|
|
else:
|
|
labels_example.append(self.pad_token_label)
|
|
else:
|
|
labels_example.append(word_labels[original_index][word_id])
|
|
else:
|
|
labels_example.append(self.pad_token_label)
|
|
labels.append(labels_example)
|
|
|
|
sanitized_tokens["labels"] = labels
|
|
# finally, remove offsets if the user didn't want them
|
|
if not return_offsets_mapping:
|
|
del sanitized_tokens["offset_mapping"]
|
|
|
|
return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
|
|
|
|
def _encode_plus(
|
|
self,
|
|
text: TextInput | PreTokenizedInput,
|
|
text_pair: PreTokenizedInput | None = None,
|
|
boxes: list[list[int]] | None = None,
|
|
word_labels: list[int] | None = None,
|
|
add_special_tokens: bool = True,
|
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
|
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
|
|
max_length: int | None = None,
|
|
stride: int = 0,
|
|
pad_to_multiple_of: int | None = None,
|
|
padding_side: str | None = None,
|
|
return_tensors: bool | None = None,
|
|
return_token_type_ids: bool | None = None,
|
|
return_attention_mask: bool | None = 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:
|
|
# make it a batched input
|
|
# 2 options:
|
|
# 1) only text, in case text must be a list of str
|
|
# 2) text + text_pair, in which case text = str and text_pair a list of str
|
|
batched_input = [(text, text_pair)] if text_pair else [text]
|
|
batched_boxes = [boxes]
|
|
batched_word_labels = [word_labels] if word_labels is not None else None
|
|
batched_output = self._batch_encode_plus(
|
|
batched_input,
|
|
is_pair=bool(text_pair is not None),
|
|
boxes=batched_boxes,
|
|
word_labels=batched_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,
|
|
padding_side=padding_side,
|
|
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 tensor is None, then we can remove the leading batch axis
|
|
# Overflowing tokens are returned as a batch of output so we keep them in this case
|
|
if return_tensors is None and not return_overflowing_tokens:
|
|
batched_output = BatchEncoding(
|
|
{
|
|
key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
|
|
for key, value in batched_output.items()
|
|
},
|
|
batched_output.encodings,
|
|
)
|
|
|
|
self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
|
|
|
|
return batched_output
|
|
|
|
def _pad(
|
|
self,
|
|
encoded_inputs: dict[str, EncodedInput] | BatchEncoding,
|
|
max_length: int | None = None,
|
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
|
pad_to_multiple_of: int | None = None,
|
|
padding_side: str | None = None,
|
|
return_attention_mask: bool | None = 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).
|
|
padding_side:
|
|
The side on which the model should have padding applied. Should be selected between ['right', 'left'].
|
|
Default value is picked from the class attribute of the same name.
|
|
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)
|
|
padding_side = padding_side if padding_side is not None else self.padding_side
|
|
if 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 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(padding_side))
|
|
|
|
return encoded_inputs
|
|
|
|
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
|
"""
|
|
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
|
adding special tokens. A BERT sequence has the following format:
|
|
|
|
- single sequence: `[CLS] X [SEP]`
|
|
- pair of sequences: `[CLS] A [SEP] B [SEP]`
|
|
|
|
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.
|
|
"""
|
|
output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
|
|
|
|
if token_ids_1:
|
|
output += token_ids_1 + [self.sep_token_id]
|
|
|
|
return output
|
|
|
|
|
|
__all__ = ["LayoutLMv2Tokenizer"]
|
|
|
|
# Backward alias
|
|
LayoutLMv2TokenizerFast = LayoutLMv2Tokenizer
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