paddlenlp.transformers.reformer.tokenizer 源代码
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import re
import warnings
import sentencepiece as spm
from ..albert.tokenizer import AlbertEnglishTokenizer
__all__ = ["ReformerTokenizer"]
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"reformer-enwik8": 65536, "reformer-crime-and-punishment": 524288}
[文档]class ReformerTokenizer(AlbertEnglishTokenizer):
"""
Constructs a Reformer tokenizer based on SentencePiece .
This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer`
which contains most of the main methods. For more information regarding those methods,
please refer to this superclass.
Args:
sentencepiece_model_file (str):
The vocabulary file (ends with '.spm') required to instantiate
a `SentencePiece <https://github.com/google/sentencepiece>`__ tokenizer.
do_lower_case (bool):
Whether or not to lowercase the input when tokenizing. Defaults to `False`.
remove_space (bool):
Whether or note to remove space when tokenizing. Defaults to `True`.
keep_accents (bool):
Whether or note to keep accents when tokenizing. Defaults to `False`.
eos_token (str):
A special token representing the *eos (end-of-sentence)* token.
Defaults to "</s>".
unk_token (str):
A special token representing the *unknown (out-of-vocabulary)* token.
An unknown token is set to be `unk_token` inorder to be converted to an ID.
Defaults to "<unk>".
pad_token (str):
A special token used to make arrays of tokens the same size for batching purposes.
Defaults to "<unk>".
"""
resource_files_names = {
"sentencepiece_model_file": "spiece.model",
}
pretrained_resource_files_map = {
"sentencepiece_model_file": {
"reformer-crime-and-punishment": "http://paddlenlp.bj.bcebos.com/models/transformers/reformer/reformer-crime-and-punishment/spiece.model",
},
}
pretrained_init_configuration = {
"reformer-crime-and-punishment": {"do_lower_case": False},
}
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
sentencepiece_model_file,
do_lower_case=False,
remove_space=True,
keep_accents=True,
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
extra_ids=100,
additional_special_tokens=[],
sp_model_kwargs=None,
**kwargs
):
# Add extra_ids to the special token list
if extra_ids > 0 and len(additional_special_tokens) == 0:
self._additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
elif extra_ids > 0 and len(additional_special_tokens) != 0:
# Check that we have the right number of extra_id special tokens
extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
if extra_tokens != extra_ids:
raise ValueError(
f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are provided to ReformerTokenizer. "
"In this case the additional_special_tokens must include the extra_ids tokens"
)
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.extra_ids = extra_ids
self.sentencepiece_model_file = sentencepiece_model_file
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(sentencepiece_model_file)
def __call__(
self,
text,
text_pair=None,
max_length=None,
stride=0,
is_split_into_words=False,
padding=None,
truncation="longest_first",
return_position_ids=False,
return_token_type_ids=False,
return_attention_mask=True,
return_length=False,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
**kwargs
):
if "pad_to_max_seq_len" in kwargs and padding is None:
pad_to_max_seq_len = kwargs.pop("pad_to_max_seq_len")
padding = "max_length" if pad_to_max_seq_len else False
elif padding is None:
padding = False
if "max_seq_len" in kwargs and max_length is None:
max_length = kwargs["max_seq_len"]
if "truncation_strategy" in kwargs and kwargs["truncation_strategy"] != "longest_first":
truncation = kwargs["truncation_strategy"]
return super(ReformerTokenizer, self).__call__(
text=text,
text_pair=text_pair,
max_length=max_length,
stride=stride,
is_split_into_words=is_split_into_words,
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_length=return_length,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
**kwargs,
)
@property
def vocab_size(self):
return len(self.sp_model) + self.extra_ids
def _add_eos_if_not_present(self, token_ids):
"""Do not add eos again if user already added it."""
if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added."
)
return token_ids
else:
return token_ids + [self.eos_token_id]
[文档] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1):
"""
Build model inputs from a sequence or a pair of sequence.
An Reformer sequence has the following format:
- single sequence: ``X </s>``
- pair of sequences: ``A </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. Defaults to None.
Returns:
List[int]: List of input_id with the appropriate special tokens.
"""
token_ids_0 = self._add_eos_if_not_present(token_ids_0)
if token_ids_1 is None:
return token_ids_0
else:
token_ids_1 = self._add_eos_if_not_present(token_ids_1)
return token_ids_0 + token_ids_1
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
Should be overridden in a subclass if the model has a special way of building those.
Args:
offset_mapping_0 (List[tuple]):
List of char offsets to which the special tokens will be added.
offset_mapping_1 (List[tuple], optional):
Optional second list of char offsets for offset mapping pairs.
Returns:
List[tuple]: List of char offsets with the appropriate offsets of special tokens.
"""
if offset_mapping_1 is None:
return offset_mapping_0 + [(0, 0)]
return offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)]
[文档] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
"""
Create a mask from the two sequences.
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
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 token_type_id according to the given sequence(s).
"""
eos = [self.eos_token_id]
if token_ids_1 is None:
return len(token_ids_0 + eos) * [0]
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
[文档] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``encode`` methods.
Args:
token_ids_0 (List[int]): List of ids of the first sequence.
token_ids_1 (List[int], optional): List of ids of the second sequence.
already_has_special_tokens (bool, optional): Whether or not the token list is already
formatted with special tokens for the model. Defaults to None.
Returns:
List[int]: The 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,
)
# normal case: some special tokens
if token_ids_1 is None:
return ([0] * len(token_ids_0)) + [1]
return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
[文档] def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode_pieces(current_sub_tokens) + token + " "
current_sub_tokens = []
else:
current_sub_tokens.append(token)
out_string += self.sp_model.decode_pieces(current_sub_tokens)
return out_string.strip()
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if token.startswith("<extra_id_"):
match = re.match(r"<extra_id_(\d+)>", token)
num = int(match.group(1))
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
if index < self.sp_model.get_piece_size():
token = self.sp_model.IdToPiece(index)
else:
token = f"<extra_id_{self.vocab_size - 1 - index}>"
return token