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# coding=utf-8 | |
# Copyright 2018 Google AI, Google Brain and 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. | |
""" Tokenization classes for ALBERT model.""" | |
import os | |
import unicodedata | |
from shutil import copyfile | |
from typing import Any, Dict, List, Optional, Tuple | |
import sentencepiece as spm | |
from ...tokenization_utils import AddedToken, PreTrainedTokenizer | |
from ...utils import logging | |
logger = logging.get_logger(__name__) | |
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} | |
PRETRAINED_VOCAB_FILES_MAP = { | |
"vocab_file": { | |
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model", | |
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model", | |
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model", | |
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model", | |
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model", | |
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model", | |
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model", | |
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model", | |
} | |
} | |
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { | |
"albert-base-v1": 512, | |
"albert-large-v1": 512, | |
"albert-xlarge-v1": 512, | |
"albert-xxlarge-v1": 512, | |
"albert-base-v2": 512, | |
"albert-large-v2": 512, | |
"albert-xlarge-v2": 512, | |
"albert-xxlarge-v2": 512, | |
} | |
SPIECE_UNDERLINE = "▁" | |
class AlbertTokenizer(PreTrainedTokenizer): | |
""" | |
Construct an ALBERT tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). | |
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to | |
this superclass for more information regarding those methods. | |
Args: | |
vocab_file (`str`): | |
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that | |
contains the vocabulary necessary to instantiate a tokenizer. | |
do_lower_case (`bool`, *optional*, defaults to `True`): | |
Whether or not to lowercase the input when tokenizing. | |
remove_space (`bool`, *optional*, defaults to `True`): | |
Whether or not to strip the text when tokenizing (removing excess spaces before and after the string). | |
keep_accents (`bool`, *optional*, defaults to `False`): | |
Whether or not to keep accents when tokenizing. | |
bos_token (`str`, *optional*, defaults to `"[CLS]"`): | |
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 `"[SEP]"`): | |
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> | |
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. | |
sep_token (`str`, *optional*, defaults to `"[SEP]"`): | |
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. | |
pad_token (`str`, *optional*, defaults to `"<pad>"`): | |
The token used for padding, for example when batching sequences of different lengths. | |
cls_token (`str`, *optional*, defaults to `"[CLS]"`): | |
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. | |
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. | |
sp_model_kwargs (`dict`, *optional*): | |
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for | |
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, | |
to set: | |
- `enable_sampling`: Enable subword regularization. | |
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. | |
- `nbest_size = {0,1}`: No sampling is performed. | |
- `nbest_size > 1`: samples from the nbest_size results. | |
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) | |
using forward-filtering-and-backward-sampling algorithm. | |
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for | |
BPE-dropout. | |
Attributes: | |
sp_model (`SentencePieceProcessor`): | |
The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). | |
""" | |
vocab_files_names = VOCAB_FILES_NAMES | |
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP | |
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES | |
def __init__( | |
self, | |
vocab_file, | |
do_lower_case=True, | |
remove_space=True, | |
keep_accents=False, | |
bos_token="[CLS]", | |
eos_token="[SEP]", | |
unk_token="<unk>", | |
sep_token="[SEP]", | |
pad_token="<pad>", | |
cls_token="[CLS]", | |
mask_token="[MASK]", | |
sp_model_kwargs: Optional[Dict[str, Any]] = None, | |
**kwargs, | |
) -> None: | |
# Mask token behave like a normal word, i.e. include the space before it and | |
# is included in the raw text, there should be a match in a non-normalized sentence. | |
mask_token = ( | |
AddedToken(mask_token, lstrip=True, rstrip=False, normalized=False) | |
if isinstance(mask_token, str) | |
else mask_token | |
) | |
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs | |
self.do_lower_case = do_lower_case | |
self.remove_space = remove_space | |
self.keep_accents = keep_accents | |
self.vocab_file = vocab_file | |
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) | |
self.sp_model.Load(vocab_file) | |
super().__init__( | |
do_lower_case=do_lower_case, | |
remove_space=remove_space, | |
keep_accents=keep_accents, | |
bos_token=bos_token, | |
eos_token=eos_token, | |
unk_token=unk_token, | |
sep_token=sep_token, | |
pad_token=pad_token, | |
cls_token=cls_token, | |
mask_token=mask_token, | |
sp_model_kwargs=self.sp_model_kwargs, | |
**kwargs, | |
) | |
def vocab_size(self) -> int: | |
return len(self.sp_model) | |
def get_vocab(self) -> Dict[str, int]: | |
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} | |
vocab.update(self.added_tokens_encoder) | |
return vocab | |
def __getstate__(self): | |
state = self.__dict__.copy() | |
state["sp_model"] = None | |
return state | |
def __setstate__(self, d): | |
self.__dict__ = d | |
# for backward compatibility | |
if not hasattr(self, "sp_model_kwargs"): | |
self.sp_model_kwargs = {} | |
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) | |
self.sp_model.Load(self.vocab_file) | |
def preprocess_text(self, inputs): | |
if self.remove_space: | |
outputs = " ".join(inputs.strip().split()) | |
else: | |
outputs = inputs | |
outputs = outputs.replace("``", '"').replace("''", '"') | |
if not self.keep_accents: | |
outputs = unicodedata.normalize("NFKD", outputs) | |
outputs = "".join([c for c in outputs if not unicodedata.combining(c)]) | |
if self.do_lower_case: | |
outputs = outputs.lower() | |
return outputs | |
def _tokenize(self, text: str) -> List[str]: | |
"""Tokenize a string.""" | |
text = self.preprocess_text(text) | |
pieces = self.sp_model.encode(text, out_type=str) | |
new_pieces = [] | |
for piece in pieces: | |
if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit(): | |
# Logic to handle special cases see https://github.com/google-research/bert/blob/master/README.md#tokenization | |
# `9,9` -> ['▁9', ',', '9'] instead of [`_9,`, '9'] | |
cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, "")) | |
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: | |
if len(cur_pieces[0]) == 1: | |
cur_pieces = cur_pieces[1:] | |
else: | |
cur_pieces[0] = cur_pieces[0][1:] | |
cur_pieces.append(piece[-1]) | |
new_pieces.extend(cur_pieces) | |
else: | |
new_pieces.append(piece) | |
return new_pieces | |
def _convert_token_to_id(self, token): | |
"""Converts a token (str) in an id using the vocab.""" | |
return self.sp_model.PieceToId(token) | |
def _convert_id_to_token(self, index): | |
"""Converts an index (integer) in a token (str) using the vocab.""" | |
return self.sp_model.IdToPiece(index) | |
def convert_tokens_to_string(self, tokens): | |
"""Converts a sequence of tokens (string) in a single string.""" | |
current_sub_tokens = [] | |
out_string = "" | |
prev_is_special = False | |
for token in tokens: | |
# make sure that special tokens are not decoded using sentencepiece model | |
if token in self.all_special_tokens: | |
if not prev_is_special: | |
out_string += " " | |
out_string += self.sp_model.decode(current_sub_tokens) + token | |
prev_is_special = True | |
current_sub_tokens = [] | |
else: | |
current_sub_tokens.append(token) | |
prev_is_special = False | |
out_string += self.sp_model.decode(current_sub_tokens) | |
return out_string.strip() | |
def build_inputs_with_special_tokens( | |
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None | |
) -> List[int]: | |
""" | |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and | |
adding special tokens. An ALBERT sequence has the following format: | |
- single sequence: `[CLS] X [SEP]` | |
- pair of sequences: `[CLS] A [SEP] B [SEP]` | |
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. | |
""" | |
sep = [self.sep_token_id] | |
cls = [self.cls_token_id] | |
if token_ids_1 is None: | |
return cls + token_ids_0 + sep | |
return cls + token_ids_0 + sep + token_ids_1 + sep | |
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]: | |
""" | |
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. | |
Args: | |
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 not None: | |
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] | |
return [1] + ([0] * len(token_ids_0)) + [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. An ALBERT | |
sequence pair mask has the following format: | |
``` | |
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | |
| first sequence | second sequence | | |
``` | |
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 IDs](../glossary#token-type-ids) according to the given sequence(s). | |
""" | |
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) * [0] + len(token_ids_1 + sep) * [1] | |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: | |
if not os.path.isdir(save_directory): | |
logger.error(f"Vocabulary path ({save_directory}) should be a directory") | |
return | |
out_vocab_file = os.path.join( | |
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] | |
) | |
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): | |
copyfile(self.vocab_file, out_vocab_file) | |
elif not os.path.isfile(self.vocab_file): | |
with open(out_vocab_file, "wb") as fi: | |
content_spiece_model = self.sp_model.serialized_model_proto() | |
fi.write(content_spiece_model) | |
return (out_vocab_file,) | |