# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# 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.
import os
import sentencepiece as spm
import unicodedata
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union
from ..tokenizer_utils_base import TensorType, PaddingStrategy, TruncationStrategy
from .. import PretrainedTokenizer
__all__ = ["ErnieMTokenizer"]
SPIECE_UNDERLINE = "▁"
[文档]class ErnieMTokenizer(PretrainedTokenizer):
r"""
Constructs a ErnieM tokenizer. It uses the `sentencepiece` tools to cut the words to sub-words.
Args:
vocab_file (str):
The file path of the vocabulary.
sentencepiece_model_file (str):
The file path of sentencepiece model.
do_lower_case (str, optional):
Whether or not to lowercase the input when tokenizing.
Defaults to`True`.
unk_token (str, optional):
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]".
sep_token (str, optional):
A special token separating two different sentences in the same input.
Defaults to "[SEP]".
pad_token (str, optional):
A special token used to make arrays of tokens the same size for batching purposes.
Defaults to "[PAD]".
cls_token (str, optional):
A special token used for sequence classification. It is the last token
of the sequence when built with special tokens. Defaults to "[CLS]".
mask_token (str, optional):
A special token representing a masked token. This is the token used
in the masked language modeling task which the model tries to predict the original unmasked ones.
Defaults to "[MASK]".
"""
resource_files_names = {
"sentencepiece_model_file": "sentencepiece.bpe.model",
"vocab_file": "vocab.txt",
} # for save_pretrained
pretrained_resource_files_map = {
"vocab_file": {
"ernie-m-base": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_m/ernie_m.vocab.txt",
"ernie-m-large": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_m/ernie_m.vocab.txt",
},
"sentencepiece_model_file": {
"ernie-m-base": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_m/ernie_m.sentencepiece.bpe.model",
"ernie-m-large": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_m/ernie_m.sentencepiece.bpe.model",
},
}
pretrained_init_configuration = {
"ernie-m-base": {"do_lower_case": False},
"ernie-m-large": {"do_lower_case": False},
}
max_model_input_sizes = {"ernie-m-base": 514, "ernie-m-large": 514}
# Ernie-M model doesn't have token_type embedding.
model_input_names: List[str] = ["input_ids"]
def __init__(
self,
vocab_file,
sentencepiece_model_file,
do_lower_case=False,
encoding="utf8",
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
**kwargs
):
self.sp_model = spm.SentencePieceProcessor()
self.do_lower_case = do_lower_case
self.encoding = encoding
if not os.path.isfile(vocab_file):
raise ValueError("Can't find a vocabulary file at path '{}'.".format(vocab_file))
self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token)
self.vocab_file = vocab_file
self.sentencepiece_model_file = sentencepiece_model_file
if os.path.isfile(sentencepiece_model_file):
self.sp_model.Load(sentencepiece_model_file)
self.SP_CHAR_MAPPING = {}
for ch in range(65281, 65375):
if ch in [ord("～")]:
self.SP_CHAR_MAPPING[chr(ch)] = chr(ch)
continue
self.SP_CHAR_MAPPING[chr(ch)] = chr(ch - 65248)
[文档] def get_offset_mapping(self, text):
if text is None:
return None
split_tokens = self.tokenize(text)
normalized_text, char_mapping = "", []
for i, ch in enumerate(text):
if ch in self.SP_CHAR_MAPPING:
ch = self.SP_CHAR_MAPPING.get(ch)
else:
ch = unicodedata.normalize("NFKC", ch)
if self.is_whitespace(ch):
continue
normalized_text += ch
char_mapping.extend([i] * len(ch))
text, token_mapping, offset = normalized_text, [], 0
if self.do_lower_case:
text = text.lower()
for token in split_tokens:
if token[:1] == "▁":
token = token[1:]
start = text[offset:].index(token) + offset
end = start + len(token)
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
offset = end
return token_mapping
@property
def vocab_size(self):
r"""
Return the size of vocabulary.
Returns:
int: The size of vocabulary.
"""
return self.sp_model.vocab_size()
[文档] def get_vocab(self):
return dict(self.vocab.token_to_idx, **self.added_tokens_encoder)
[文档] def clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
return "".join((self.SP_CHAR_MAPPING.get(c, c) for c in text))
def _tokenize(self, text, sample=False):
"""Tokenize a string."""
if not sample:
pieces = self.sp_model.EncodeAsPieces(text)
else:
pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1)
new_pieces = []
for piece in pieces:
if piece == SPIECE_UNDERLINE:
continue
lst_i = 0
for i, c in enumerate(piece):
if c == SPIECE_UNDERLINE:
continue
if self.is_ch_char(c) or self.is_punct(c):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i])
new_pieces.append(c)
lst_i = i + 1
elif c.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i])
lst_i = i
elif not c.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i])
lst_i = i
if len(piece) > lst_i:
new_pieces.append(piece[lst_i:])
return new_pieces
[文档] def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
return out_string
[文档] def convert_ids_to_string(self, ids):
"""
Converts a sequence of tokens (strings for sub-words) in a single string.
"""
tokens = self.convert_ids_to_tokens(ids)
out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
return out_string
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
r"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
An ERNIE-M offset_mapping has the following format:
- single sequence: ``(0,0) X (0,0)``
- pair of sequences: ``(0,0) A (0,0) (0,0) B (0,0)``
Args:
offset_mapping_ids_0 (List[tuple]):
List of char offsets to which the special tokens will be added.
offset_mapping_ids_1 (List[tuple], optional):
Optional second list of wordpiece offsets for offset mapping pairs.
Defaults to `None`.
Returns:
List[tuple]: List of wordpiece offsets with the appropriate offsets of special tokens.
"""
if offset_mapping_1 is None:
return [(0, 0)] + offset_mapping_0 + [(0, 0)]
return [(0, 0)] + offset_mapping_0 + [(0, 0), (0, 0)] + offset_mapping_1 + [(0, 0)]
[文档] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
r"""
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], optinal):
Optional second list of IDs for sequence pairs.
Defaults to `None`.
already_has_special_tokens (str, optional):
Whether or not the token list is already formatted with special tokens for the model.
Defaults to `False`.
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:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
[文档] def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create the token type IDs corresponding to the sequences passed. [What are token type
IDs?](../glossary#token-type-ids)
Should be overridden in a subclass if the model has a special way of building those.
Args:
token_ids_0 (`List[int]`): The first tokenized sequence.
token_ids_1 (`List[int]`, *optional*): The second tokenized sequence.
Returns:
`List[int]`: The token type ids.
"""
# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
if token_ids_1 is None:
# [CLS] X [SEP]
return (len(token_ids_0) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(token_ids_0) + 1) + [1] * (len(token_ids_1) + 3)
[文档] def is_ch_char(self, char):
"""
is_ch_char
"""
if "\u4e00" <= char <= "\u9fff":
return True
return False
[文档] def is_alpha(self, char):
"""
is_alpha
"""
if "a" <= char <= "z":
return True
if "A" <= char <= "Z":
return True
return False
[文档] def is_punct(self, char):
"""
is_punct
"""
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
[文档] def is_whitespace(self, char):
"""
is whitespace
"""
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(char) == 1:
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False