1446 lines
67 KiB
Python
1446 lines
67 KiB
Python
# coding=utf-8
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# Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization class for MarkupLM."""
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import json
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import os
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from functools import lru_cache
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from typing import Dict, List, Optional, Tuple, Union
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import regex as re
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from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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from ...tokenization_utils_base import (
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ENCODE_KWARGS_DOCSTRING,
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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 logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
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MARKUPLM_ENCODE_PLUS_ADDITIONAL_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. If left unset or set to
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`None`, this will use the predefined model maximum length if a maximum length is required by one of the
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truncation/padding parameters. If the model has no specific maximum input length (like XLNet)
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truncation/padding to a maximum length will be deactivated.
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stride (`int`, *optional*, defaults to 0):
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If set to a number along with `max_length`, the overflowing tokens returned when
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`return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
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returned to provide some overlap between truncated and overflowing sequences. The value of this
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argument defines the number of overlapping tokens.
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pad_to_multiple_of (`int`, *optional*):
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If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
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the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
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return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
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If set, will return tensors instead of list of python integers. Acceptable values are:
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- `'tf'`: Return TensorFlow `tf.constant` objects.
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- `'pt'`: Return PyTorch `torch.Tensor` objects.
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- `'np'`: Return Numpy `np.ndarray` objects.
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"""
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@lru_cache()
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
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characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large #
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of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset
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you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe
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vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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"""
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bs = (
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list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
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)
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8 + n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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def get_pairs(word):
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"""
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Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length
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strings).
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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return pairs
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class MarkupLMTokenizer(PreTrainedTokenizer):
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r"""
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Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). [`MarkupLMTokenizer`] can be used to
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turn HTML strings into to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and
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`xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods.
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Users should 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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Path to the vocabulary file.
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merges_file (`str`):
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Path to the merges file.
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errors (`str`, *optional*, defaults to `"replace"`):
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Paradigm to follow when decoding bytes to UTF-8. See
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[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
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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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add_prefix_space (`bool`, *optional*, defaults to `False`):
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Whether or not to add an initial space to the input. This allows to treat the leading word just as any
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other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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def __init__(
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self,
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vocab_file,
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merges_file,
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tags_dict,
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errors="replace",
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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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add_prefix_space=False,
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max_depth=50,
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max_width=1000,
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pad_width=1001,
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pad_token_label=-100,
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only_label_first_subword=True,
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**kwargs,
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):
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bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
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eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
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sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
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cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
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unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
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pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
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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, rstrip=False) if isinstance(mask_token, str) else mask_token
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with open(vocab_file, encoding="utf-8") as vocab_handle:
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self.encoder = json.load(vocab_handle)
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self.tags_dict = tags_dict
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self.decoder = {v: k for k, v in self.encoder.items()}
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self.errors = errors # how to handle errors in decoding
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self.byte_encoder = bytes_to_unicode()
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self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
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with open(merges_file, encoding="utf-8") as merges_handle:
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bpe_merges = merges_handle.read().split("\n")[1:-1]
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bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
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self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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self.cache = {}
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self.add_prefix_space = add_prefix_space
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# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
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self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
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# additional properties
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self.max_depth = max_depth
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self.max_width = max_width
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self.pad_width = pad_width
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self.unk_tag_id = len(self.tags_dict)
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self.pad_tag_id = self.unk_tag_id + 1
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self.pad_xpath_tags_seq = [self.pad_tag_id] * self.max_depth
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self.pad_xpath_subs_seq = [self.pad_width] * self.max_depth
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super().__init__(
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vocab_file=vocab_file,
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merges_file=merges_file,
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tags_dict=tags_dict,
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errors=errors,
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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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add_prefix_space=add_prefix_space,
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max_depth=max_depth,
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max_width=max_width,
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pad_width=pad_width,
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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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**kwargs,
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)
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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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def get_xpath_seq(self, xpath):
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"""
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Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of
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tag IDs and corresponding subscripts, taking into account max depth.
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"""
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xpath_tags_list = []
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xpath_subs_list = []
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xpath_units = xpath.split("/")
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for unit in xpath_units:
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if not unit.strip():
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continue
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name_subs = unit.strip().split("[")
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tag_name = name_subs[0]
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sub = 0 if len(name_subs) == 1 else int(name_subs[1][:-1])
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xpath_tags_list.append(self.tags_dict.get(tag_name, self.unk_tag_id))
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xpath_subs_list.append(min(self.max_width, sub))
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xpath_tags_list = xpath_tags_list[: self.max_depth]
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xpath_subs_list = xpath_subs_list[: self.max_depth]
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xpath_tags_list += [self.pad_tag_id] * (self.max_depth - len(xpath_tags_list))
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xpath_subs_list += [self.pad_width] * (self.max_depth - len(xpath_subs_list))
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return xpath_tags_list, xpath_subs_list
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@property
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def vocab_size(self):
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return len(self.encoder)
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def get_vocab(self):
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vocab = self.encoder.copy()
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vocab.update(self.added_tokens_encoder)
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return vocab
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def bpe(self, token):
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if token in self.cache:
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return self.cache[token]
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word = tuple(token)
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pairs = get_pairs(word)
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if not pairs:
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return token
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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except ValueError:
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new_word.extend(word[i:])
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break
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else:
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new_word.extend(word[i:j])
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i = j
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
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new_word.append(first + second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = " ".join(word)
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self.cache[token] = word
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return word
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def _tokenize(self, text):
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"""Tokenize a string."""
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bpe_tokens = []
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for token in re.findall(self.pat, text):
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token = "".join(
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self.byte_encoder[b] for b in token.encode("utf-8")
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) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
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bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
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return bpe_tokens
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.encoder.get(token, self.encoder.get(self.unk_token))
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.decoder.get(index)
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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logger.warning(
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"MarkupLM now does not support generative tasks, decoding is experimental and subject to change."
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)
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text = "".join(tokens)
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text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
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return text
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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merge_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
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)
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# save vocab_file
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with open(vocab_file, "w", encoding="utf-8") as f:
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f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
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# save merge_file
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index = 0
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with open(merge_file, "w", encoding="utf-8") as writer:
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writer.write("#version: 0.2\n")
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for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning(
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f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
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" Please check that the tokenizer is not corrupted!"
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)
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index = token_index
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writer.write(" ".join(bpe_tokens) + "\n")
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index += 1
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return vocab_file, merge_file
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def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
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add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
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if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
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text = " " + text
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return (text, kwargs)
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. A RoBERTa sequence has the following format:
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- single sequence: `<s> X </s>`
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- pair of sequences: `<s> A </s></s> B </s>`
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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if token_ids_1 is None:
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return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
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cls = [self.cls_token_id]
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sep = [self.sep_token_id]
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return cls + token_ids_0 + sep + token_ids_1 + sep
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def build_xpath_tags_with_special_tokens(
|
|
self, xpath_tags_0: List[int], xpath_tags_1: Optional[List[int]] = None
|
|
) -> List[int]:
|
|
pad = [self.pad_xpath_tags_seq]
|
|
if len(xpath_tags_1) == 0:
|
|
return pad + xpath_tags_0 + pad
|
|
return pad + xpath_tags_0 + pad + xpath_tags_1 + pad
|
|
|
|
def build_xpath_subs_with_special_tokens(
|
|
self, xpath_subs_0: List[int], xpath_subs_1: Optional[List[int]] = None
|
|
) -> List[int]:
|
|
pad = [self.pad_xpath_subs_seq]
|
|
if len(xpath_subs_1) == 0:
|
|
return pad + xpath_subs_0 + pad
|
|
return pad + xpath_subs_0 + pad + xpath_subs_1 + pad
|
|
|
|
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]:
|
|
"""
|
|
Args:
|
|
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.
|
|
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. 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 + token_ids_1 + sep) * [0]
|
|
|
|
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def __call__(
|
|
self,
|
|
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
|
|
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
|
|
xpaths: Union[List[List[int]], List[List[List[int]]]] = None,
|
|
node_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 node-level xpaths 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
|
|
(nodes of a single example or questions of a batch of examples) or a list of list of strings (batch of
|
|
nodes).
|
|
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).
|
|
xpaths (`List[List[int]]`, `List[List[List[int]]]`):
|
|
Node-level xpaths.
|
|
node_labels (`List[int]`, `List[List[int]]`, *optional*):
|
|
Node-level integer labels (for token classification tasks).
|
|
"""
|
|
|
|
# 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 = nodes
|
|
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(
|
|
"Nodes must be of type `List[str]` (single pretokenized example), "
|
|
"or `List[List[str]]` (batch of pretokenized examples)."
|
|
)
|
|
else:
|
|
# in case only text is provided => must be nodes
|
|
if not isinstance(text, (list, tuple)):
|
|
raise ValueError(
|
|
"Nodes must be of type `List[str]` (single pretokenized example), "
|
|
"or `List[List[str]]` (batch of pretokenized examples)."
|
|
)
|
|
|
|
if text_pair is not None:
|
|
is_batched = isinstance(text, (list, tuple))
|
|
else:
|
|
is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
|
|
|
|
nodes = text if text_pair is None else text_pair
|
|
assert xpaths is not None, "You must provide corresponding xpaths"
|
|
if is_batched:
|
|
assert len(nodes) == len(xpaths), "You must provide nodes and xpaths for an equal amount of examples"
|
|
for nodes_example, xpaths_example in zip(nodes, xpaths):
|
|
assert len(nodes_example) == len(xpaths_example), "You must provide as many nodes as there are xpaths"
|
|
else:
|
|
assert len(nodes) == len(xpaths), "You must provide as many nodes as there are xpaths"
|
|
|
|
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,
|
|
xpaths=xpaths,
|
|
node_labels=node_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,
|
|
xpaths=xpaths,
|
|
node_labels=node_labels,
|
|
add_special_tokens=add_special_tokens,
|
|
padding=padding,
|
|
truncation=truncation,
|
|
max_length=max_length,
|
|
stride=stride,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
return_tensors=return_tensors,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_attention_mask=return_attention_mask,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_offsets_mapping=return_offsets_mapping,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def batch_encode_plus(
|
|
self,
|
|
batch_text_or_text_pairs: Union[
|
|
List[TextInput],
|
|
List[TextInputPair],
|
|
List[PreTokenizedInput],
|
|
],
|
|
is_pair: bool = None,
|
|
xpaths: Optional[List[List[List[int]]]] = None,
|
|
node_labels: Optional[Union[List[int], List[List[int]]]] = None,
|
|
add_special_tokens: bool = True,
|
|
padding: Union[bool, str, PaddingStrategy] = False,
|
|
truncation: Union[bool, str, TruncationStrategy] = None,
|
|
max_length: Optional[int] = None,
|
|
stride: int = 0,
|
|
pad_to_multiple_of: Optional[int] = None,
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
|
return_token_type_ids: Optional[bool] = None,
|
|
return_attention_mask: Optional[bool] = None,
|
|
return_overflowing_tokens: bool = False,
|
|
return_special_tokens_mask: bool = False,
|
|
return_offsets_mapping: bool = False,
|
|
return_length: bool = False,
|
|
verbose: bool = True,
|
|
**kwargs,
|
|
) -> BatchEncoding:
|
|
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
|
|
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
|
|
padding=padding,
|
|
truncation=truncation,
|
|
max_length=max_length,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
return self._batch_encode_plus(
|
|
batch_text_or_text_pairs=batch_text_or_text_pairs,
|
|
is_pair=is_pair,
|
|
xpaths=xpaths,
|
|
node_labels=node_labels,
|
|
add_special_tokens=add_special_tokens,
|
|
padding_strategy=padding_strategy,
|
|
truncation_strategy=truncation_strategy,
|
|
max_length=max_length,
|
|
stride=stride,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
return_tensors=return_tensors,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_attention_mask=return_attention_mask,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_offsets_mapping=return_offsets_mapping,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
def _batch_encode_plus(
|
|
self,
|
|
batch_text_or_text_pairs: Union[
|
|
List[TextInput],
|
|
List[TextInputPair],
|
|
List[PreTokenizedInput],
|
|
],
|
|
is_pair: bool = None,
|
|
xpaths: Optional[List[List[List[int]]]] = None,
|
|
node_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,
|
|
xpaths=xpaths,
|
|
node_labels=node_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(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def _batch_prepare_for_model(
|
|
self,
|
|
batch_text_or_text_pairs,
|
|
is_pair: bool = None,
|
|
xpaths: Optional[List[List[int]]] = None,
|
|
node_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, xpaths)):
|
|
batch_text_or_text_pair, xpaths_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,
|
|
xpaths_example,
|
|
node_labels=node_labels[idx] if node_labels is not None else None,
|
|
add_special_tokens=add_special_tokens,
|
|
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
|
|
truncation=truncation_strategy.value,
|
|
max_length=max_length,
|
|
stride=stride,
|
|
pad_to_multiple_of=None, # we pad in batch afterward
|
|
return_attention_mask=False, # we pad in batch afterward
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_length=return_length,
|
|
return_tensors=None, # We convert the whole batch to tensors at the end
|
|
prepend_batch_axis=False,
|
|
verbose=verbose,
|
|
)
|
|
|
|
for key, value in outputs.items():
|
|
if key not in batch_outputs:
|
|
batch_outputs[key] = []
|
|
batch_outputs[key].append(value)
|
|
|
|
batch_outputs = self.pad(
|
|
batch_outputs,
|
|
padding=padding_strategy.value,
|
|
max_length=max_length,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
return_attention_mask=return_attention_mask,
|
|
)
|
|
|
|
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
|
|
|
|
return batch_outputs
|
|
|
|
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING)
|
|
def encode(
|
|
self,
|
|
text: Union[TextInput, PreTokenizedInput],
|
|
text_pair: Optional[PreTokenizedInput] = None,
|
|
xpaths: Optional[List[List[int]]] = None,
|
|
node_labels: Optional[List[int]] = None,
|
|
add_special_tokens: bool = True,
|
|
padding: Union[bool, str, PaddingStrategy] = False,
|
|
truncation: Union[bool, str, TruncationStrategy] = None,
|
|
max_length: Optional[int] = None,
|
|
stride: int = 0,
|
|
pad_to_multiple_of: Optional[int] = None,
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
|
return_token_type_ids: Optional[bool] = None,
|
|
return_attention_mask: Optional[bool] = None,
|
|
return_overflowing_tokens: bool = False,
|
|
return_special_tokens_mask: bool = False,
|
|
return_offsets_mapping: bool = False,
|
|
return_length: bool = False,
|
|
verbose: bool = True,
|
|
**kwargs,
|
|
) -> List[int]:
|
|
encoded_inputs = self.encode_plus(
|
|
text=text,
|
|
text_pair=text_pair,
|
|
xpaths=xpaths,
|
|
node_labels=node_labels,
|
|
add_special_tokens=add_special_tokens,
|
|
padding=padding,
|
|
truncation=truncation,
|
|
max_length=max_length,
|
|
stride=stride,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
return_tensors=return_tensors,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_attention_mask=return_attention_mask,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_offsets_mapping=return_offsets_mapping,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
return encoded_inputs["input_ids"]
|
|
|
|
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def encode_plus(
|
|
self,
|
|
text: Union[TextInput, PreTokenizedInput],
|
|
text_pair: Optional[PreTokenizedInput] = None,
|
|
xpaths: Optional[List[List[int]]] = None,
|
|
node_labels: Optional[List[int]] = None,
|
|
add_special_tokens: bool = True,
|
|
padding: Union[bool, str, PaddingStrategy] = False,
|
|
truncation: Union[bool, str, TruncationStrategy] = None,
|
|
max_length: Optional[int] = None,
|
|
stride: int = 0,
|
|
pad_to_multiple_of: Optional[int] = None,
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
|
return_token_type_ids: Optional[bool] = None,
|
|
return_attention_mask: Optional[bool] = None,
|
|
return_overflowing_tokens: bool = False,
|
|
return_special_tokens_mask: bool = False,
|
|
return_offsets_mapping: bool = False,
|
|
return_length: bool = False,
|
|
verbose: bool = True,
|
|
**kwargs,
|
|
) -> BatchEncoding:
|
|
"""
|
|
Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
|
|
`__call__` should be used instead.
|
|
|
|
Args:
|
|
text (`str`, `List[str]`, `List[List[str]]`):
|
|
The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
|
|
text_pair (`List[str]` or `List[int]`, *optional*):
|
|
Optional second sequence to be encoded. This can be a list of strings (nodes of a single example) or a
|
|
list of list of strings (nodes 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,
|
|
xpaths=xpaths,
|
|
text_pair=text_pair,
|
|
node_labels=node_labels,
|
|
add_special_tokens=add_special_tokens,
|
|
padding_strategy=padding_strategy,
|
|
truncation_strategy=truncation_strategy,
|
|
max_length=max_length,
|
|
stride=stride,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
return_tensors=return_tensors,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_attention_mask=return_attention_mask,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_offsets_mapping=return_offsets_mapping,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
def _encode_plus(
|
|
self,
|
|
text: Union[TextInput, PreTokenizedInput],
|
|
text_pair: Optional[PreTokenizedInput] = None,
|
|
xpaths: Optional[List[List[int]]] = None,
|
|
node_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,
|
|
xpaths=xpaths,
|
|
node_labels=node_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(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def prepare_for_model(
|
|
self,
|
|
text: Union[TextInput, PreTokenizedInput],
|
|
text_pair: Optional[PreTokenizedInput] = None,
|
|
xpaths: Optional[List[List[int]]] = None,
|
|
node_labels: Optional[List[int]] = None,
|
|
add_special_tokens: bool = True,
|
|
padding: Union[bool, str, PaddingStrategy] = False,
|
|
truncation: Union[bool, str, TruncationStrategy] = None,
|
|
max_length: Optional[int] = None,
|
|
stride: int = 0,
|
|
pad_to_multiple_of: Optional[int] = None,
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
|
return_token_type_ids: Optional[bool] = None,
|
|
return_attention_mask: Optional[bool] = None,
|
|
return_overflowing_tokens: bool = False,
|
|
return_special_tokens_mask: bool = False,
|
|
return_offsets_mapping: bool = False,
|
|
return_length: bool = False,
|
|
verbose: bool = True,
|
|
prepend_batch_axis: bool = False,
|
|
**kwargs,
|
|
) -> BatchEncoding:
|
|
"""
|
|
Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
|
|
truncates sequences if overflowing while taking into account the special tokens and manages a moving window
|
|
(with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and
|
|
*truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a
|
|
combination of arguments will raise an error.
|
|
|
|
Node-level `xpaths` are turned into token-level `xpath_tags_seq` and `xpath_subs_seq`. If provided, node-level
|
|
`node_labels` are turned into token-level `labels`. The node label is used for the first token of the node,
|
|
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 (nodes of a single example) or a
|
|
list of list of strings (nodes 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 = []
|
|
xpath_tags_seq = []
|
|
xpath_subs_seq = []
|
|
pair_xpath_tags_seq = []
|
|
pair_xpath_subs_seq = []
|
|
labels = []
|
|
|
|
if text_pair is None:
|
|
if node_labels is None:
|
|
# CASE 1: web page classification (training + inference) + CASE 2: token classification (inference)
|
|
for word, xpath in zip(text, xpaths):
|
|
if len(word) < 1: # skip empty nodes
|
|
continue
|
|
word_tokens = self.tokenize(word)
|
|
tokens.extend(word_tokens)
|
|
xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath)
|
|
xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens))
|
|
xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens))
|
|
else:
|
|
# CASE 2: token classification (training)
|
|
for word, xpath, label in zip(text, xpaths, node_labels):
|
|
if len(word) < 1: # skip empty nodes
|
|
continue
|
|
word_tokens = self.tokenize(word)
|
|
tokens.extend(word_tokens)
|
|
xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath)
|
|
xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens))
|
|
xpath_subs_seq.extend([xpath_subs_list] * 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: web page question answering (inference)
|
|
# text = question
|
|
# text_pair = nodes
|
|
tokens = self.tokenize(text)
|
|
xpath_tags_seq = [self.pad_xpath_tags_seq for _ in range(len(tokens))]
|
|
xpath_subs_seq = [self.pad_xpath_subs_seq for _ in range(len(tokens))]
|
|
|
|
for word, xpath in zip(text_pair, xpaths):
|
|
if len(word) < 1: # skip empty nodes
|
|
continue
|
|
word_tokens = self.tokenize(word)
|
|
pair_tokens.extend(word_tokens)
|
|
xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath)
|
|
pair_xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens))
|
|
pair_xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens))
|
|
|
|
# Create ids + pair_ids
|
|
ids = self.convert_tokens_to_ids(tokens)
|
|
pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None
|
|
|
|
if (
|
|
return_overflowing_tokens
|
|
and truncation_strategy == TruncationStrategy.LONGEST_FIRST
|
|
and pair_ids is not None
|
|
):
|
|
raise ValueError(
|
|
"Not possible to return overflowing tokens for pair of sequences with the "
|
|
"`longest_first`. Please select another truncation strategy than `longest_first`, "
|
|
"for instance `only_second` or `only_first`."
|
|
)
|
|
|
|
# Compute the total size of the returned encodings
|
|
pair = bool(pair_ids is not None)
|
|
len_ids = len(ids)
|
|
len_pair_ids = len(pair_ids) if pair else 0
|
|
total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
|
|
|
|
# Truncation: Handle max sequence length
|
|
overflowing_tokens = []
|
|
overflowing_xpath_tags_seq = []
|
|
overflowing_xpath_subs_seq = []
|
|
overflowing_labels = []
|
|
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
|
|
(
|
|
ids,
|
|
xpath_tags_seq,
|
|
xpath_subs_seq,
|
|
pair_ids,
|
|
pair_xpath_tags_seq,
|
|
pair_xpath_subs_seq,
|
|
labels,
|
|
overflowing_tokens,
|
|
overflowing_xpath_tags_seq,
|
|
overflowing_xpath_subs_seq,
|
|
overflowing_labels,
|
|
) = self.truncate_sequences(
|
|
ids,
|
|
xpath_tags_seq=xpath_tags_seq,
|
|
xpath_subs_seq=xpath_subs_seq,
|
|
pair_ids=pair_ids,
|
|
pair_xpath_tags_seq=pair_xpath_tags_seq,
|
|
pair_xpath_subs_seq=pair_xpath_subs_seq,
|
|
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_xpath_tags_seq"] = overflowing_xpath_tags_seq
|
|
encoded_inputs["overflowing_xpath_subs_seq"] = overflowing_xpath_subs_seq
|
|
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)
|
|
xpath_tags_ids = self.build_xpath_tags_with_special_tokens(xpath_tags_seq, pair_xpath_tags_seq)
|
|
xpath_subs_ids = self.build_xpath_subs_with_special_tokens(xpath_subs_seq, pair_xpath_subs_seq)
|
|
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 [])
|
|
xpath_tags_ids = xpath_tags_seq + pair_xpath_tags_seq if pair else xpath_tags_seq
|
|
xpath_subs_ids = xpath_subs_seq + pair_xpath_subs_seq if pair else xpath_subs_seq
|
|
|
|
# Build output dictionary
|
|
encoded_inputs["input_ids"] = sequence
|
|
encoded_inputs["xpath_tags_seq"] = xpath_tags_ids
|
|
encoded_inputs["xpath_subs_seq"] = xpath_subs_ids
|
|
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],
|
|
xpath_tags_seq: List[List[int]],
|
|
xpath_subs_seq: List[List[int]],
|
|
pair_ids: Optional[List[int]] = None,
|
|
pair_xpath_tags_seq: Optional[List[List[int]]] = None,
|
|
pair_xpath_subs_seq: 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]]:
|
|
"""
|
|
Args:
|
|
Truncates a sequence pair in-place following the strategy.
|
|
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.
|
|
xpath_tags_seq (`List[List[int]]`):
|
|
XPath tag IDs of the first sequence.
|
|
xpath_subs_seq (`List[List[int]]`):
|
|
XPath sub IDs 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_xpath_tags_seq (`List[List[int]]`, *optional*):
|
|
XPath tag IDs of the second sequence.
|
|
pair_xpath_subs_seq (`List[List[int]]`, *optional*):
|
|
XPath sub IDs of the second sequence.
|
|
num_tokens_to_remove (`int`, *optional*, defaults to 0):
|
|
Number of tokens to remove using the truncation strategy.
|
|
truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to
|
|
`False`):
|
|
The strategy to follow for truncation. Can be:
|
|
- `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
|
|
maximum acceptable input length for the model if that argument is not provided. This will truncate
|
|
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
|
|
batch of pairs) is provided.
|
|
- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
|
|
maximum acceptable input length for the model if that argument is not provided. This will only
|
|
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
|
|
- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
|
|
maximum acceptable input length for the model if that argument is not provided. This will only
|
|
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
|
|
- `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
|
|
than the model maximum admissible input size).
|
|
stride (`int`, *optional*, defaults to 0):
|
|
If set to a positive number, the overflowing tokens returned will contain some tokens from the main
|
|
sequence returned. The value of this argument defines the number of additional tokens.
|
|
Returns:
|
|
`Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
|
|
overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair
|
|
of sequences (or a batch of pairs) is provided.
|
|
"""
|
|
if num_tokens_to_remove <= 0:
|
|
return ids, xpath_tags_seq, xpath_subs_seq, pair_ids, pair_xpath_tags_seq, pair_xpath_subs_seq, [], [], []
|
|
|
|
if not isinstance(truncation_strategy, TruncationStrategy):
|
|
truncation_strategy = TruncationStrategy(truncation_strategy)
|
|
|
|
overflowing_tokens = []
|
|
overflowing_xpath_tags_seq = []
|
|
overflowing_xpath_subs_seq = []
|
|
overflowing_labels = []
|
|
if truncation_strategy == TruncationStrategy.ONLY_FIRST or (
|
|
truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None
|
|
):
|
|
if len(ids) > num_tokens_to_remove:
|
|
window_len = min(len(ids), stride + num_tokens_to_remove)
|
|
overflowing_tokens = ids[-window_len:]
|
|
overflowing_xpath_tags_seq = xpath_tags_seq[-window_len:]
|
|
overflowing_xpath_subs_seq = xpath_subs_seq[-window_len:]
|
|
ids = ids[:-num_tokens_to_remove]
|
|
xpath_tags_seq = xpath_tags_seq[:-num_tokens_to_remove]
|
|
xpath_subs_seq = xpath_subs_seq[:-num_tokens_to_remove]
|
|
labels = labels[:-num_tokens_to_remove]
|
|
else:
|
|
error_msg = (
|
|
f"We need to remove {num_tokens_to_remove} to truncate the input "
|
|
f"but the first sequence has a length {len(ids)}. "
|
|
)
|
|
if truncation_strategy == TruncationStrategy.ONLY_FIRST:
|
|
error_msg = (
|
|
error_msg + "Please select another truncation strategy than "
|
|
f"{truncation_strategy}, for instance 'longest_first' or 'only_second'."
|
|
)
|
|
logger.error(error_msg)
|
|
elif truncation_strategy == TruncationStrategy.LONGEST_FIRST:
|
|
logger.warning(
|
|
"Be aware, overflowing tokens are not returned for the setting you have chosen,"
|
|
f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
|
|
"truncation strategy. So the returned list will always be empty even if some "
|
|
"tokens have been removed."
|
|
)
|
|
for _ in range(num_tokens_to_remove):
|
|
if pair_ids is None or len(ids) > len(pair_ids):
|
|
ids = ids[:-1]
|
|
xpath_tags_seq = xpath_tags_seq[:-1]
|
|
xpath_subs_seq = xpath_subs_seq[:-1]
|
|
labels = labels[:-1]
|
|
else:
|
|
pair_ids = pair_ids[:-1]
|
|
pair_xpath_tags_seq = pair_xpath_tags_seq[:-1]
|
|
pair_xpath_subs_seq = pair_xpath_subs_seq[:-1]
|
|
elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
|
|
if len(pair_ids) > num_tokens_to_remove:
|
|
window_len = min(len(pair_ids), stride + num_tokens_to_remove)
|
|
overflowing_tokens = pair_ids[-window_len:]
|
|
overflowing_xpath_tags_seq = pair_xpath_tags_seq[-window_len:]
|
|
overflowing_xpath_subs_seq = pair_xpath_subs_seq[-window_len:]
|
|
pair_ids = pair_ids[:-num_tokens_to_remove]
|
|
pair_xpath_tags_seq = pair_xpath_tags_seq[:-num_tokens_to_remove]
|
|
pair_xpath_subs_seq = pair_xpath_subs_seq[:-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,
|
|
xpath_tags_seq,
|
|
xpath_subs_seq,
|
|
pair_ids,
|
|
pair_xpath_tags_seq,
|
|
pair_xpath_subs_seq,
|
|
labels,
|
|
overflowing_tokens,
|
|
overflowing_xpath_tags_seq,
|
|
overflowing_xpath_subs_seq,
|
|
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:
|
|
"""
|
|
Args:
|
|
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
|
|
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 "xpath_tags_seq" in encoded_inputs:
|
|
encoded_inputs["xpath_tags_seq"] = (
|
|
encoded_inputs["xpath_tags_seq"] + [self.pad_xpath_tags_seq] * difference
|
|
)
|
|
if "xpath_subs_seq" in encoded_inputs:
|
|
encoded_inputs["xpath_subs_seq"] = (
|
|
encoded_inputs["xpath_subs_seq"] + [self.pad_xpath_subs_seq] * 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 "xpath_tags_seq" in encoded_inputs:
|
|
encoded_inputs["xpath_tags_seq"] = [self.pad_xpath_tags_seq] * difference + encoded_inputs[
|
|
"xpath_tags_seq"
|
|
]
|
|
if "xpath_subs_seq" in encoded_inputs:
|
|
encoded_inputs["xpath_subs_seq"] = [self.pad_xpath_subs_seq] * difference + encoded_inputs[
|
|
"xpath_subs_seq"
|
|
]
|
|
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
|