# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2021 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 typing import Optional, Union
from paddlenlp.transformers.tokenizer_utils_base import (
PaddingStrategy,
TensorType,
TruncationStrategy,
)
from ...utils.log import logger
from .. import BertTokenizer
from ..tokenizer_utils_base import BatchEncoding
__all__ = ["MobileBertTokenizer"]
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"mobilebert-uncased": 512}
[docs]
class MobileBertTokenizer(BertTokenizer):
r"""
Construct a MobileBERT tokenizer.
:class:`~paddlenlp.transformers.MobileBertTokenizer is identical to :class:`~paddlenlp.transformers.BertTokenizer` and runs end-to-end
tokenization: punctuation splitting and wordpiece.
Refer to superclass :class:`~~paddlenlp.transformers.BertTokenizer` for usage examples and documentation concerning
parameters.
"""
resource_files_names = {"vocab_file": "vocab.txt"}
pretrained_resource_files_map = {
"vocab_file": {
"mobilebert-uncased": "https://bj.bcebos.com/paddlenlp/models/transformers/mobilebert/mobilebert-uncased/vocab.txt"
}
}
pretrained_init_configuration = {"mobilebert-uncased": {"do_lower_case": True}}
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
[docs]
def batch_encode(
self,
batch_text_or_text_pairs,
max_length: int = 512,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
stride=0,
is_split_into_words=False,
return_position_ids=False,
return_token_type_ids=True,
return_attention_mask=False,
return_length=False,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
return_dict=True,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
verbose: bool = True,
**kwargs
):
"""
Performs tokenization and uses the tokenized tokens to prepare model
inputs. It supports batch inputs of sequence or sequence pair.
Args:
batch_text_or_text_pairs (list):
The element of list can be sequence or sequence pair, and the
sequence is a string or a list of strings depending on whether
it has been pretokenized. If each sequence is provided as a list
of strings (pretokenized), you must set `is_split_into_words` as
`True` to disambiguate with a sequence pair.
max_length (int, optional):
If set to a number, will limit the total sequence returned so
that it has a maximum length. If there are overflowing tokens,
those overflowing tokens will be added to the returned dictionary
when `return_overflowing_tokens` is `True`. Defaults to `None`.
stride (int, optional):
Only available for batch input of sequence pair and mainly for
question answering usage. When for QA, `text` represents questions
and `text_pair` represents contexts. If `stride` is set to a
positive number, the context will be split into multiple spans
where `stride` defines the number of (tokenized) tokens to skip
from the start of one span to get the next span, thus will produce
a bigger batch than inputs to include all spans. Moreover, 'overflow_to_sample'
and 'offset_mapping' preserving the original example and position
information will be added to the returned dictionary. Defaults to 0.
padding (bool, optional):
If set to `True`, the returned sequences would be padded up to
`max_length` specified length according to padding side
(`self.padding_side`) and padding token id. Defaults to `False`.
truncation_strategy (str, optional):
String selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence
until the input is under `max_length` starting from the longest
one at each token (when there is a pair of input sequences).
- 'only_first': Only truncate the first sequence.
- 'only_second': Only truncate the second sequence.
- 'do_not_truncate': Do not truncate (raise an error if the input
sequence is longer than `max_length`).
Defaults to 'longest_first'.
return_position_ids (bool, optional):
Whether to include tokens position ids in the returned dictionary.
Defaults to `False`.
return_token_type_ids (bool, optional):
Whether to include token type ids in the returned dictionary.
Defaults to `True`.
return_attention_mask (bool, optional):
Whether to include the attention mask in the returned dictionary.
Defaults to `False`.
return_length (bool, optional):
Whether to include the length of each encoded inputs in the
returned dictionary. Defaults to `False`.
return_overflowing_tokens (bool, optional):
Whether to include overflowing token information in the returned
dictionary. Defaults to `False`.
return_special_tokens_mask (bool, optional):
Whether to include special tokens mask information in the returned
dictionary. Defaults to `False`.
Returns:
dict:
The dict has the following optional items:
- **input_ids** (list[int]): List of token ids to be fed to a model.
- **position_ids** (list[int], optional): List of token position ids to be
fed to a model. Included when `return_position_ids` is `True`
- **token_type_ids** (list[int], optional): List of token type ids to be
fed to a model. Included when `return_token_type_ids` is `True`.
- **attention_mask** (list[int], optional): List of integers valued 0 or 1,
where 0 specifies paddings and should not be attended to by the
model. Included when `return_attention_mask` is `True`.
- **seq_len** (int, optional): The input_ids length. Included when `return_length`
is `True`.
- **overflowing_tokens** (list[int], optional): List of overflowing tokens.
Included when if `max_length` is specified and `return_overflowing_tokens`
is True.
- **num_truncated_tokens** (int, optional): The number of overflowing tokens.
Included when if `max_length` is specified and `return_overflowing_tokens`
is True.
- **special_tokens_mask** (list[int], optional): List of integers valued 0 or 1,
with 0 specifying special added tokens and 1 specifying sequence tokens.
Included when `return_special_tokens_mask` is `True`.
- **offset_mapping** (list[int], optional): list of pair preserving the
index of start and end char in original input for each token.
For a sqecial token, the index pair is `(0, 0)`. Included when
`stride` works.
- **overflow_to_sample** (int, optional): Index of example from which this
feature is generated. Included when `stride` works.
"""
# Backward compatibility for 'max_seq_len'
old_max_seq_len = kwargs.get("max_seq_len", None)
if max_length is None and old_max_seq_len:
if verbose:
logger.warnings(
"The `max_seq_len` argument is deprecated and will be removed in a future version, "
"please use `max_length` instead.",
FutureWarning,
)
max_length = old_max_seq_len
padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
padding=padding, max_length=max_length, verbose=verbose
)
def get_input_ids(text):
if isinstance(text, str):
tokens = self._tokenize(text)
return self.convert_tokens_to_ids(tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
return self.convert_tokens_to_ids(text)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
else:
raise ValueError(
"Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
)
batch_encode_inputs = []
for example_id, tokens_or_pair_tokens in enumerate(batch_text_or_text_pairs):
if not isinstance(tokens_or_pair_tokens, (list, tuple)):
text, text_pair = tokens_or_pair_tokens, None
elif is_split_into_words and not isinstance(tokens_or_pair_tokens[0], (list, tuple)):
text, text_pair = tokens_or_pair_tokens, None
else:
text, text_pair = tokens_or_pair_tokens
first_ids = get_input_ids(text)
second_ids = get_input_ids(text_pair) if text_pair is not None else None
if stride > 0 and second_ids is not None:
max_len_for_pair = (
max_length - len(first_ids) - self.num_special_tokens_to_add(pair=True)
) # need -4 <sep> A </sep> </sep> B <sep>
token_offset_mapping = self.get_offset_mapping(text)
token_pair_offset_mapping = self.get_offset_mapping(text_pair)
while True:
encoded_inputs = {}
ids = first_ids
mapping = token_offset_mapping
if len(second_ids) <= max_len_for_pair:
pair_ids = second_ids
pair_mapping = token_pair_offset_mapping
else:
pair_ids = second_ids[:max_len_for_pair]
pair_mapping = token_pair_offset_mapping[:max_len_for_pair]
offset_mapping = self.build_offset_mapping_with_special_tokens(mapping, pair_mapping)
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
# Build output dictionnary
encoded_inputs["input_ids"] = sequence
if return_token_type_ids:
encoded_inputs["token_type_ids"] = token_type_ids
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
if return_length:
encoded_inputs["seq_len"] = len(encoded_inputs["input_ids"])
# Check lengths
assert max_length is None or len(encoded_inputs["input_ids"]) <= max_length
# Padding
needs_to_be_padded = padding and max_length and len(encoded_inputs["input_ids"]) < max_length
encoded_inputs["offset_mapping"] = offset_mapping
if needs_to_be_padded:
difference = max_length - len(encoded_inputs["input_ids"])
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"]) + [
0
] * difference
if return_token_type_ids:
# 0 for padding token mask
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
)
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = (
encoded_inputs["special_tokens_mask"] + [1] * difference
)
encoded_inputs["input_ids"] = (
encoded_inputs["input_ids"] + [self.pad_token_id] * difference
)
encoded_inputs["offset_mapping"] = encoded_inputs["offset_mapping"] + [(0, 0)] * difference
elif self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + [1] * len(
encoded_inputs["input_ids"]
)
if return_token_type_ids:
# 0 for padding token mask
encoded_inputs["token_type_ids"] = [
self.pad_token_type_id
] * difference + encoded_inputs["token_type_ids"]
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs[
"special_tokens_mask"
]
encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs[
"input_ids"
]
encoded_inputs["offset_mapping"] = [(0, 0)] * difference + encoded_inputs["offset_mapping"]
else:
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
if return_position_ids:
encoded_inputs["position_ids"] = list(range(len(encoded_inputs["input_ids"])))
encoded_inputs["overflow_to_sample"] = example_id
batch_encode_inputs.append(encoded_inputs)
if len(second_ids) <= max_len_for_pair:
break
else:
second_ids = second_ids[max_len_for_pair - stride :]
token_pair_offset_mapping = token_pair_offset_mapping[max_len_for_pair - stride :]
else:
batch_encode_inputs.append(
self.encode(
first_ids,
second_ids,
max_length=max_length,
padding=padding,
truncation=truncation,
return_position_ids=return_position_ids,
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,
)
)
batch_encode_inputs = {k: [output[k] for output in batch_encode_inputs] for k in batch_encode_inputs[0].keys()}
batch_encode_inputs = self.pad(
batch_encode_inputs,
padding=padding_strategy.value,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
if return_dict:
batch_outputs = BatchEncoding(batch_encode_inputs, tensor_type=return_tensors)
return batch_outputs
else:
batch_outputs_list = []
for k, v in batch_encode_inputs.items():
for i in range(len(v)):
if i >= len(batch_outputs_list):
batch_outputs_list.append({k: v[i]})
else:
batch_outputs_list[i][k] = v[i]
return batch_outputs_list
