You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1006 lines
48 KiB
1006 lines
48 KiB
# Copyright 2022 The HuggingFace Inc. team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
|
|
from tokenizers import Tokenizer, decoders, pre_tokenizers, processors
|
|
from tokenizers.models import BPE
|
|
|
|
from ...tokenization_utils_base import (
|
|
ENCODE_KWARGS_DOCSTRING,
|
|
AddedToken,
|
|
BatchEncoding,
|
|
EncodedInput,
|
|
PaddingStrategy,
|
|
PreTokenizedInput,
|
|
TensorType,
|
|
TextInput,
|
|
TextInputPair,
|
|
TruncationStrategy,
|
|
)
|
|
from ...tokenization_utils_tokenizers import TokenizersBackend
|
|
from ...utils import add_end_docstrings, logging
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
|
|
|
|
|
|
MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
|
|
add_special_tokens (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to encode the sequences with the special tokens relative to their model.
|
|
padding (`bool`, `str` or [`~tokenization_utils_base.PaddingStrategy`], *optional*, defaults to `False`):
|
|
Activates and controls padding. Accepts the following values:
|
|
|
|
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
|
|
sequence if provided).
|
|
- `'max_length'`: Pad 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.
|
|
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
|
|
lengths).
|
|
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
|
|
Activates and controls truncation. Accepts the following values:
|
|
|
|
- `True` or `'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.
|
|
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
|
|
greater than the model maximum admissible input size).
|
|
max_length (`int`, *optional*):
|
|
Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to
|
|
`None`, this will use the predefined model maximum length if a maximum length is required by one of the
|
|
truncation/padding parameters. If the model has no specific maximum input length (like XLNet)
|
|
truncation/padding to a maximum length will be deactivated.
|
|
stride (`int`, *optional*, defaults to 0):
|
|
If set to a number along with `max_length`, the overflowing tokens returned when
|
|
`return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
|
|
returned to provide some overlap between truncated and overflowing sequences. The value of this
|
|
argument defines the number of overlapping tokens.
|
|
is_split_into_words (`bool`, *optional*, defaults to `False`):
|
|
Whether or not the input is already pretokenized (e.g. split into words). Set this to `True` if you are
|
|
passing pretokenized inputs to avoid additional tokenization.
|
|
pad_to_multiple_of (`int`, *optional*):
|
|
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
|
|
the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
|
|
return_tensors (`str` or [`~tokenization_utils_base.TensorType`], *optional*):
|
|
If set, will return tensors instead of list of python integers. Acceptable values are:
|
|
|
|
- `'pt'`: Return PyTorch `torch.Tensor` objects.
|
|
- `'np'`: Return Numpy `np.ndarray` objects.
|
|
"""
|
|
|
|
|
|
class MarkupLMTokenizer(TokenizersBackend):
|
|
r"""
|
|
Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
|
|
|
|
[`MarkupLMTokenizer`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`,
|
|
`token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`TokenizersBackend`] which
|
|
contains most of the main methods and ensures a `tokenizers` backend is always instantiated.
|
|
|
|
Users should refer to this superclass for more information regarding those methods.
|
|
|
|
Args:
|
|
vocab (`str` or `dict[str, int]`, *optional*):
|
|
Custom vocabulary dictionary. If not provided, the vocabulary is loaded from `vocab_file`.
|
|
merges (`str` or `list[str]`, *optional*):
|
|
Custom merges list. If not provided, merges are loaded from `merges_file`.
|
|
errors (`str`, *optional*, defaults to `"replace"`):
|
|
Paradigm to follow when decoding bytes to UTF-8. See
|
|
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
|
|
bos_token (`str`, *optional*, defaults to `"<s>"`):
|
|
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
|
|
|
|
<Tip>
|
|
|
|
When building a sequence using special tokens, this is not the token that is used for the beginning of
|
|
sequence. The token used is the `cls_token`.
|
|
|
|
</Tip>
|
|
|
|
eos_token (`str`, *optional*, defaults to `"</s>"`):
|
|
The end of sequence token.
|
|
|
|
<Tip>
|
|
|
|
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
|
|
The token used is the `sep_token`.
|
|
|
|
</Tip>
|
|
|
|
sep_token (`str`, *optional*, defaults to `"</s>"`):
|
|
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
|
|
sequence classification or for a text and a question for question answering. It is also used as the last
|
|
token of a sequence built with special tokens.
|
|
cls_token (`str`, *optional*, defaults to `"<s>"`):
|
|
The classifier token which is used when doing sequence classification (classification of the whole sequence
|
|
instead of per-token classification). It is the first token of the sequence when built with special tokens.
|
|
unk_token (`str`, *optional*, defaults to `"<unk>"`):
|
|
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
|
|
token instead.
|
|
pad_token (`str`, *optional*, defaults to `"<pad>"`):
|
|
The token used for padding, for example when batching sequences of different lengths.
|
|
mask_token (`str`, *optional*, defaults to `"<mask>"`):
|
|
The token used for masking values. This is the token used when training this model with masked language
|
|
modeling. This is the token which the model will try to predict.
|
|
add_prefix_space (`bool`, *optional*, defaults to `False`):
|
|
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
|
|
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
|
|
"""
|
|
|
|
vocab_files_names = VOCAB_FILES_NAMES
|
|
model_input_names = ["input_ids", "token_type_ids", "attention_mask"]
|
|
model = BPE
|
|
|
|
def __init__(
|
|
self,
|
|
tags_dict,
|
|
vocab: str | dict[str, int] | list[tuple[str, float]] | None = None,
|
|
merges: str | list[str] | None = None,
|
|
errors="replace",
|
|
bos_token="<s>",
|
|
eos_token="</s>",
|
|
sep_token="</s>",
|
|
cls_token="<s>",
|
|
unk_token="<unk>",
|
|
pad_token="<pad>",
|
|
mask_token="<mask>",
|
|
add_prefix_space=False,
|
|
max_depth=50,
|
|
max_width=1000,
|
|
pad_width=1001,
|
|
pad_token_label=-100,
|
|
only_label_first_subword=True,
|
|
trim_offsets=False,
|
|
**kwargs,
|
|
):
|
|
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
|
|
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
|
|
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
|
|
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
|
|
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
|
|
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
|
|
# Mask token behave like a normal word, i.e. include the space before it
|
|
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
|
|
|
|
if vocab is None:
|
|
vocab = {
|
|
str(pad_token): 0,
|
|
str(unk_token): 1,
|
|
str(cls_token): 2,
|
|
str(sep_token): 3,
|
|
str(mask_token): 4,
|
|
}
|
|
merges = merges or []
|
|
tokenizer = Tokenizer(
|
|
BPE(
|
|
vocab=vocab,
|
|
merges=merges,
|
|
dropout=None,
|
|
continuing_subword_prefix="",
|
|
end_of_word_suffix="",
|
|
fuse_unk=False,
|
|
)
|
|
)
|
|
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=add_prefix_space)
|
|
tokenizer.decoder = decoders.ByteLevel()
|
|
self._vocab = vocab
|
|
self._merges = merges
|
|
self._tokenizer = tokenizer
|
|
super().__init__(
|
|
tags_dict=tags_dict,
|
|
errors=errors,
|
|
bos_token=bos_token,
|
|
eos_token=eos_token,
|
|
unk_token=unk_token,
|
|
sep_token=sep_token,
|
|
cls_token=cls_token,
|
|
pad_token=pad_token,
|
|
mask_token=mask_token,
|
|
add_prefix_space=add_prefix_space,
|
|
trim_offsets=trim_offsets,
|
|
max_depth=max_depth,
|
|
max_width=max_width,
|
|
pad_width=pad_width,
|
|
pad_token_label=pad_token_label,
|
|
only_label_first_subword=only_label_first_subword,
|
|
**kwargs,
|
|
)
|
|
sep_token_str = str(sep_token)
|
|
cls_token_str = str(cls_token)
|
|
cls_token_id = self.cls_token_id
|
|
sep_token_id = self.sep_token_id
|
|
self._tokenizer.post_processor = processors.TemplateProcessing(
|
|
single=f"{cls_token_str} $A {sep_token_str}",
|
|
pair=f"{cls_token_str} $A {sep_token_str} $B {sep_token_str}",
|
|
special_tokens=[
|
|
(cls_token_str, cls_token_id),
|
|
(sep_token_str, sep_token_id),
|
|
],
|
|
)
|
|
|
|
self.tags_dict = tags_dict
|
|
|
|
# additional properties
|
|
self.max_depth = max_depth
|
|
self.max_width = max_width
|
|
self.pad_width = pad_width
|
|
self.unk_tag_id = len(self.tags_dict)
|
|
self.pad_tag_id = self.unk_tag_id + 1
|
|
self.pad_xpath_tags_seq = [self.pad_tag_id] * self.max_depth
|
|
self.pad_xpath_subs_seq = [self.pad_width] * self.max_depth
|
|
self.pad_token_label = pad_token_label
|
|
self.only_label_first_subword = only_label_first_subword
|
|
|
|
def get_xpath_seq(self, xpath):
|
|
"""
|
|
Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of
|
|
tag IDs and corresponding subscripts, taking into account max depth.
|
|
"""
|
|
xpath_tags_list = []
|
|
xpath_subs_list = []
|
|
|
|
xpath_units = xpath.split("/")
|
|
for unit in xpath_units:
|
|
if not unit.strip():
|
|
continue
|
|
name_subs = unit.strip().split("[")
|
|
tag_name = name_subs[0]
|
|
sub = 0 if len(name_subs) == 1 else int(name_subs[1][:-1])
|
|
xpath_tags_list.append(self.tags_dict.get(tag_name, self.unk_tag_id))
|
|
xpath_subs_list.append(min(self.max_width, sub))
|
|
|
|
xpath_tags_list = xpath_tags_list[: self.max_depth]
|
|
xpath_subs_list = xpath_subs_list[: self.max_depth]
|
|
xpath_tags_list += [self.pad_tag_id] * (self.max_depth - len(xpath_tags_list))
|
|
xpath_subs_list += [self.pad_width] * (self.max_depth - len(xpath_subs_list))
|
|
|
|
return xpath_tags_list, xpath_subs_list
|
|
|
|
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def __call__(
|
|
self,
|
|
text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput],
|
|
text_pair: PreTokenizedInput | list[PreTokenizedInput] | None = None,
|
|
xpaths: list[list[int]] | list[list[list[int]]] | None = None,
|
|
node_labels: list[int] | list[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,
|
|
is_split_into_words: bool = False,
|
|
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:
|
|
"""
|
|
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
|
|
sequences with nodes, 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
|
|
(words of a single example or questions of a batch of examples) or a list of list of strings (batch of
|
|
words).
|
|
text_pair (`list[str]`, `list[list[str]]`):
|
|
The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
|
|
(pretokenized string).
|
|
xpaths (`list[list[int]]`, `list[list[list[int]]]`):
|
|
Node-level xpaths. Each bounding box should be normalized to be on a 0-1000 scale.
|
|
node_labels (`list[int]`, `list[list[int]]`, *optional*):
|
|
Node-level integer labels (for token classification tasks).
|
|
is_split_into_words (`bool`, *optional*):
|
|
Set to `True` if the inputs are already provided as pretokenized word lists.
|
|
"""
|
|
|
|
placeholder_xpath = "/document/node"
|
|
|
|
if isinstance(text, tuple):
|
|
text = list(text)
|
|
if text_pair is not None and isinstance(text_pair, tuple):
|
|
text_pair = list(text_pair)
|
|
|
|
if xpaths is None and not is_split_into_words:
|
|
nodes_source = text if text_pair is None else text_pair
|
|
if isinstance(nodes_source, tuple):
|
|
nodes_source = list(nodes_source)
|
|
processed_nodes = nodes_source
|
|
|
|
if isinstance(nodes_source, str):
|
|
processed_nodes = nodes_source.split()
|
|
elif isinstance(nodes_source, list):
|
|
if nodes_source and isinstance(nodes_source[0], str):
|
|
requires_split = any(" " in entry for entry in nodes_source)
|
|
if requires_split:
|
|
processed_nodes = [entry.split() for entry in nodes_source]
|
|
else:
|
|
processed_nodes = nodes_source
|
|
elif nodes_source and isinstance(nodes_source[0], tuple):
|
|
processed_nodes = [list(sample) for sample in nodes_source]
|
|
|
|
if text_pair is None:
|
|
text = processed_nodes
|
|
else:
|
|
text_pair = processed_nodes
|
|
|
|
if isinstance(processed_nodes, list) and processed_nodes and isinstance(processed_nodes[0], (list, tuple)):
|
|
xpaths = [[placeholder_xpath] * len(sample) for sample in processed_nodes]
|
|
else:
|
|
length = len(processed_nodes) if hasattr(processed_nodes, "__len__") else 0
|
|
xpaths = [placeholder_xpath] * length
|
|
|
|
def _is_valid_text_input(t):
|
|
if isinstance(t, str):
|
|
return True
|
|
if isinstance(t, (list, tuple)):
|
|
if len(t) == 0:
|
|
return True
|
|
if isinstance(t[0], str):
|
|
return True
|
|
if isinstance(t[0], (list, tuple)):
|
|
return len(t[0]) == 0 or isinstance(t[0][0], str)
|
|
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)."
|
|
)
|
|
is_batched = isinstance(text, (list, tuple))
|
|
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)."
|
|
)
|
|
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,
|
|
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,
|
|
)
|
|
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,
|
|
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,
|
|
)
|
|
|
|
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def batch_encode_plus(
|
|
self,
|
|
batch_text_or_text_pairs: list[TextInput] | list[TextInputPair] | list[PreTokenizedInput],
|
|
is_pair: bool | None = None,
|
|
xpaths: list[list[list[int]]] | None = None,
|
|
node_labels: list[int] | list[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:
|
|
# 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,
|
|
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
|
|
|
|
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def encode_plus(
|
|
self,
|
|
text: TextInput | PreTokenizedInput,
|
|
text_pair: PreTokenizedInput | None = None,
|
|
xpaths: list[list[int]] | None = None,
|
|
node_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,
|
|
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,
|
|
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,
|
|
xpaths: list[list[list[int]]] | None = None,
|
|
node_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:
|
|
processed_inputs = []
|
|
for text, text_pair in batch_text_or_text_pairs:
|
|
if isinstance(text, tuple):
|
|
text = list(text)
|
|
if isinstance(text, str):
|
|
text = [text]
|
|
if isinstance(text_pair, tuple):
|
|
text_pair = list(text_pair)
|
|
if isinstance(text_pair, str):
|
|
text_pair = [text_pair]
|
|
processed_inputs.append((text, text_pair))
|
|
batch_text_or_text_pairs = processed_inputs
|
|
else:
|
|
processed_inputs = []
|
|
for text in batch_text_or_text_pairs:
|
|
if isinstance(text, tuple):
|
|
text = list(text)
|
|
if isinstance(text, str):
|
|
text = [text]
|
|
processed_inputs.append(text)
|
|
batch_text_or_text_pairs = processed_inputs
|
|
|
|
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 MarkupLM always expects pretokenized inputs
|
|
)
|
|
|
|
# Convert encoding to dict
|
|
# `Tokens` is a tuple of (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 node_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-level xpaths tags and subscripts
|
|
xpath_tags_seq = []
|
|
xpath_subs_seq = []
|
|
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
|
|
xpath_tags_seq_example = []
|
|
xpath_subs_seq_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:
|
|
xpath_tags_seq_example.append(self.pad_xpath_tags_seq)
|
|
xpath_subs_seq_example.append(self.pad_xpath_subs_seq)
|
|
else:
|
|
xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpaths[original_index][word_id])
|
|
xpath_tags_seq_example.extend([xpath_tags_list])
|
|
xpath_subs_seq_example.extend([xpath_subs_list])
|
|
else:
|
|
if id in [self.cls_token_id, self.sep_token_id, self.pad_token_id]:
|
|
xpath_tags_seq_example.append(self.pad_xpath_tags_seq)
|
|
xpath_subs_seq_example.append(self.pad_xpath_subs_seq)
|
|
else:
|
|
raise ValueError("Id not recognized")
|
|
xpath_tags_seq.append(xpath_tags_seq_example)
|
|
xpath_subs_seq.append(xpath_subs_seq_example)
|
|
|
|
sanitized_tokens["xpath_tags_seq"] = xpath_tags_seq
|
|
sanitized_tokens["xpath_subs_seq"] = xpath_subs_seq
|
|
|
|
# optionally, create the labels
|
|
if node_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(node_labels[original_index][word_id])
|
|
else:
|
|
labels_example.append(self.pad_token_label)
|
|
else:
|
|
labels_example.append(node_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,
|
|
xpaths: list[list[int]] | None = None,
|
|
node_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:
|
|
placeholder_xpath = "/document/node"
|
|
|
|
if isinstance(text, tuple):
|
|
text = list(text)
|
|
if text_pair is not None and isinstance(text_pair, tuple):
|
|
text_pair = list(text_pair)
|
|
|
|
nodes_single = text if text_pair is None else text_pair
|
|
processed_nodes = nodes_single
|
|
|
|
if isinstance(nodes_single, str):
|
|
processed_nodes = nodes_single.split()
|
|
elif isinstance(nodes_single, list) and nodes_single and isinstance(nodes_single[0], str):
|
|
processed_nodes = nodes_single
|
|
|
|
if text_pair is None:
|
|
text = processed_nodes
|
|
else:
|
|
text_pair = processed_nodes
|
|
|
|
if xpaths is None:
|
|
length = len(processed_nodes) if hasattr(processed_nodes, "__len__") else 0
|
|
xpaths = [placeholder_xpath] * length
|
|
|
|
# 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_xpaths = [xpaths]
|
|
batched_node_labels = [node_labels] if node_labels is not None else None
|
|
batched_output = self._batch_encode_plus(
|
|
batched_input,
|
|
is_pair=bool(text_pair is not None),
|
|
xpaths=batched_xpaths,
|
|
node_labels=batched_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,
|
|
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:
|
|
"""
|
|
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).
|
|
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 "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 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(padding_side))
|
|
|
|
return encoded_inputs
|
|
|
|
def build_inputs_with_special_tokens(
|
|
self, token_ids_0: list[int], token_ids_1: list[int] | None = None
|
|
) -> list[int]:
|
|
"""
|
|
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
|
adding special tokens. A RoBERTa sequence has the following format:
|
|
- single sequence: `<s> X </s>`
|
|
- pair of sequences: `<s> A </s></s> B </s>`
|
|
|
|
Args:
|
|
token_ids_0 (`list[int]`):
|
|
List of IDs to which the special tokens will be added.
|
|
token_ids_1 (`list[int]`, *optional*):
|
|
Optional second list of IDs for sequence pairs.
|
|
Returns:
|
|
`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
|
"""
|
|
if token_ids_1 is None:
|
|
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
|
|
cls = [self.cls_token_id]
|
|
sep = [self.sep_token_id]
|
|
return cls + token_ids_0 + sep + token_ids_1 + sep
|
|
|
|
def create_token_type_ids_from_sequences(
|
|
self, token_ids_0: list[int], token_ids_1: list[int] | None = 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]
|
|
|
|
def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]:
|
|
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
|
|
return tuple(files)
|
|
|
|
|
|
MarkupLMTokenizerFast = MarkupLMTokenizer
|
|
|
|
|
|
__all__ = ["MarkupLMTokenizer", "MarkupLMTokenizerFast"]
|