345 lines
15 KiB
Python
345 lines
15 KiB
Python
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes."""
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import os
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import unicodedata
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from shutil import copyfile
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from typing import Any, Dict, List, Optional, Tuple
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import sentencepiece as spm
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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from ...utils import SPIECE_UNDERLINE, logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
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class CpmTokenizer(PreTrainedTokenizer):
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"""Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models."""
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vocab_files_names = VOCAB_FILES_NAMES
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def __init__(
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self,
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vocab_file,
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do_lower_case=False,
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remove_space=True,
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keep_accents=False,
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bos_token="<s>",
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eos_token="</s>",
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unk_token="<unk>",
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sep_token="<sep>",
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pad_token="<pad>",
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cls_token="<cls>",
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mask_token="<mask>",
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additional_special_tokens=["<eop>", "<eod>"],
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs,
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) -> None:
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"""
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Construct a CPM tokenizer. Based on [Jieba](https://pypi.org/project/jieba/) and
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[SentencePiece](https://github.com/google/sentencepiece).
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should
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refer to this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
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contains the vocabulary necessary to instantiate a tokenizer.
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do_lower_case (`bool`, *optional*, defaults to `True`):
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Whether to lowercase the input when tokenizing.
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remove_space (`bool`, *optional*, defaults to `True`):
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Whether to strip the text when tokenizing (removing excess spaces before and after the string).
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keep_accents (`bool`, *optional*, defaults to `False`):
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Whether to keep accents when tokenizing.
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier
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token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the beginning of
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sequence. The token used is the `cls_token`.
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</Tip>
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of
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sequence. The token used is the `sep_token`.
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</Tip>
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be
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this token instead.
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sep_token (`str`, *optional*, defaults to `"<sep>"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences
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for sequence classification or for a text and a question for question answering. It is also used as the
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last token of a sequence built with special tokens.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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cls_token (`str`, *optional*, defaults to `"<cls>"`):
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The classifier token which is used when doing sequence classification (classification of the whole
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sequence instead of per-token classification). It is the first token of the sequence when built with
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special tokens.
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mask_token (`str`, *optional*, defaults to `"<mask>"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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additional_special_tokens (`List[str]`, *optional*, defaults to `["<eop>", "<eod>"]`):
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Additional special tokens used by the tokenizer.
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Attributes:
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sp_model (`SentencePieceProcessor`):
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The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
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"""
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# Mask token behave like a normal word, i.e. include the space before it
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mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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self.do_lower_case = do_lower_case
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self.remove_space = remove_space
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self.keep_accents = keep_accents
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self.vocab_file = vocab_file
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(vocab_file)
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try:
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import jieba
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except ModuleNotFoundError as error:
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raise error.__class__(
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"You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
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"See https://pypi.org/project/jieba/ for installation."
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)
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self.jieba = jieba
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self.translator = str.maketrans(" \n", "\u2582\u2583")
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super().__init__(
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do_lower_case=do_lower_case,
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remove_space=remove_space,
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keep_accents=keep_accents,
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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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sep_token=sep_token,
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pad_token=pad_token,
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cls_token=cls_token,
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mask_token=mask_token,
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additional_special_tokens=additional_special_tokens,
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sp_model_kwargs=self.sp_model_kwargs,
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**kwargs,
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)
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self._pad_token_type_id = 3
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@property
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
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def vocab_size(self):
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return len(self.sp_model)
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.get_vocab
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def get_vocab(self):
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.__getstate__
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def __getstate__(self):
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state = self.__dict__.copy()
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state["sp_model"] = None
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return state
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.__setstate__
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def __setstate__(self, d):
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self.__dict__ = d
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# for backward compatibility
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if not hasattr(self, "sp_model_kwargs"):
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self.sp_model_kwargs = {}
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(self.vocab_file)
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.preprocess_text
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def preprocess_text(self, inputs):
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if self.remove_space:
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outputs = " ".join(inputs.strip().split())
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else:
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outputs = inputs
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outputs = outputs.replace("``", '"').replace("''", '"')
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if not self.keep_accents:
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outputs = unicodedata.normalize("NFKD", outputs)
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outputs = "".join([c for c in outputs if not unicodedata.combining(c)])
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if self.do_lower_case:
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outputs = outputs.lower()
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return outputs
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer._tokenize
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def _tokenize(self, text: str) -> List[str]:
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"""Tokenize a string."""
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text = self.preprocess_text(text)
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pieces = self.sp_model.encode(text, out_type=str)
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new_pieces = []
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for piece in pieces:
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if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
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cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, ""))
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if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
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if len(cur_pieces[0]) == 1:
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cur_pieces = cur_pieces[1:]
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else:
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cur_pieces[0] = cur_pieces[0][1:]
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cur_pieces.append(piece[-1])
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new_pieces.extend(cur_pieces)
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else:
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new_pieces.append(piece)
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return new_pieces
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer._convert_token_to_id
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.sp_model.PieceToId(token)
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer._convert_id_to_token
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.sp_model.IdToPiece(index)
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.convert_tokens_to_string
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (strings for sub-words) in a single string."""
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out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
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return out_string
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.build_inputs_with_special_tokens
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. An XLNet sequence has the following format:
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- single sequence: `X <sep> <cls>`
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- pair of sequences: `A <sep> B <sep> <cls>`
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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sep = [self.sep_token_id]
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cls = [self.cls_token_id]
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if token_ids_1 is None:
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return token_ids_0 + sep + cls
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return token_ids_0 + sep + token_ids_1 + sep + cls
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.get_special_tokens_mask
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` method.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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if token_ids_1 is not None:
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return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1]
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return ([0] * len(token_ids_0)) + [1, 1]
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.create_token_type_ids_from_sequences
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def create_token_type_ids_from_sequences(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLNet
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sequence pair mask has the following format:
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```
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
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| first sequence | second sequence |
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```
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If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
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"""
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sep = [self.sep_token_id]
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cls_segment_id = [2]
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if token_ids_1 is None:
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return len(token_ids_0 + sep) * [0] + cls_segment_id
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return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id
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# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.save_vocabulary
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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out_vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
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copyfile(self.vocab_file, out_vocab_file)
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elif not os.path.isfile(self.vocab_file):
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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,)
|
||
|
|
|
||
|
|
def _decode(self, *args, **kwargs):
|
||
|
|
text = super()._decode(*args, **kwargs)
|
||
|
|
text = text.replace(" ", "").replace("\u2582", " ").replace("\u2583", "\n")
|
||
|
|
return text
|