Make bert_japanese and cpm independent of their inherited modules (#19431)

* Make cpm tokenization independent of xlnet

* Make bert japanese tokenization independent of bert

src/transformers/models/bert_japanese/tokenization_bert_japanese.py

@@ -19,10 +19,10 @@ import collections
 import copy
 import os
 import unicodedata
-from typing import Optional
+from typing import List, Optional, Tuple
 
+from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
 from ...utils import logging
-from ..bert.tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer, load_vocab
 
 
 logger = logging.get_logger(__name__)
@@ -75,10 +75,35 @@ PRETRAINED_INIT_CONFIGURATION = {
 }
 
 
-class BertJapaneseTokenizer(BertTokenizer):
+# Copied from transformers.models.bert.tokenization_bert.load_vocab
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    vocab = collections.OrderedDict()
+    with open(vocab_file, "r", encoding="utf-8") as reader:
+        tokens = reader.readlines()
+    for index, token in enumerate(tokens):
+        token = token.rstrip("\n")
+        vocab[token] = index
+    return vocab
+
+
+# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a piece of text."""
+    text = text.strip()
+    if not text:
+        return []
+    tokens = text.split()
+    return tokens
+
+
+class BertJapaneseTokenizer(PreTrainedTokenizer):
     r"""
     Construct a BERT tokenizer for Japanese text.
 
+    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`):
             Path to a one-wordpiece-per-line vocabulary file.
@@ -124,7 +149,7 @@ class BertJapaneseTokenizer(BertTokenizer):
         jumanpp_kwargs=None,
         **kwargs
     ):
-        super(BertTokenizer, self).__init__(
+        super().__init__(
             unk_token=unk_token,
             sep_token=sep_token,
             pad_token=pad_token,
@@ -141,7 +166,6 @@ class BertJapaneseTokenizer(BertTokenizer):
             jumanpp_kwargs=jumanpp_kwargs,
             **kwargs,
         )
-        # ^^ We call the grandparent's init, not the parent's.
 
         if not os.path.isfile(vocab_file):
             raise ValueError(
@@ -226,6 +250,137 @@ class BertJapaneseTokenizer(BertTokenizer):
 
         return split_tokens
 
+    @property
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.vocab_size
+    def vocab_size(self):
+        return len(self.vocab)
+
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.get_vocab
+    def get_vocab(self):
+        return dict(self.vocab, **self.added_tokens_encoder)
+
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer._convert_token_to_id
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer._convert_id_to_token
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.ids_to_tokens.get(index, self.unk_token)
+
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.convert_tokens_to_string
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        out_string = " ".join(tokens).replace(" ##", "").strip()
+        return out_string
+
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.build_inputs_with_special_tokens
+    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. A BERT 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.
+        """
+        if token_ids_1 is None:
+            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+        cls = [self.cls_token_id]
+        sep = [self.sep_token_id]
+        return cls + token_ids_0 + sep + token_ids_1 + sep
+
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.get_special_tokens_mask
+    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]
+
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.create_token_type_ids_from_sequences
+    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. A BERT 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]
+
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.save_vocabulary
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        index = 0
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+            )
+        else:
+            vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+                        " Please check that the vocabulary is not corrupted!"
+                    )
+                    index = token_index
+                writer.write(token + "\n")
+                index += 1
+        return (vocab_file,)
+
 
 class MecabTokenizer:
     """Runs basic tokenization with MeCab morphological parser."""
@@ -530,3 +685,211 @@ class CharacterTokenizer:
             output_tokens.append(char)
 
         return output_tokens
+
+
+# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
+class BasicTokenizer(object):
+    """
+    Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
+
+    Args:
+        do_lower_case (`bool`, *optional*, defaults to `True`):
+            Whether or not to lowercase the input when tokenizing.
+        never_split (`Iterable`, *optional*):
+            Collection of tokens which will never be split during tokenization. Only has an effect when
+            `do_basic_tokenize=True`
+        tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+            Whether or not to tokenize Chinese characters.
+
+            This should likely be deactivated for Japanese (see this
+            [issue](https://github.com/huggingface/transformers/issues/328)).
+        strip_accents (`bool`, *optional*):
+            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+            value for `lowercase` (as in the original BERT).
+    """
+
+    def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None):
+        if never_split is None:
+            never_split = []
+        self.do_lower_case = do_lower_case
+        self.never_split = set(never_split)
+        self.tokenize_chinese_chars = tokenize_chinese_chars
+        self.strip_accents = strip_accents
+
+    def tokenize(self, text, never_split=None):
+        """
+        Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see
+        WordPieceTokenizer.
+
+        Args:
+            never_split (`List[str]`, *optional*)
+                Kept for backward compatibility purposes. Now implemented directly at the base class level (see
+                [`PreTrainedTokenizer.tokenize`]) List of token not to split.
+        """
+        # union() returns a new set by concatenating the two sets.
+        never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
+        text = self._clean_text(text)
+
+        # This was added on November 1st, 2018 for the multilingual and Chinese
+        # models. This is also applied to the English models now, but it doesn't
+        # matter since the English models were not trained on any Chinese data
+        # and generally don't have any Chinese data in them (there are Chinese
+        # characters in the vocabulary because Wikipedia does have some Chinese
+        # words in the English Wikipedia.).
+        if self.tokenize_chinese_chars:
+            text = self._tokenize_chinese_chars(text)
+        orig_tokens = whitespace_tokenize(text)
+        split_tokens = []
+        for token in orig_tokens:
+            if token not in never_split:
+                if self.do_lower_case:
+                    token = token.lower()
+                    if self.strip_accents is not False:
+                        token = self._run_strip_accents(token)
+                elif self.strip_accents:
+                    token = self._run_strip_accents(token)
+            split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+        output_tokens = whitespace_tokenize(" ".join(split_tokens))
+        return output_tokens
+
+    def _run_strip_accents(self, text):
+        """Strips accents from a piece of text."""
+        text = unicodedata.normalize("NFD", text)
+        output = []
+        for char in text:
+            cat = unicodedata.category(char)
+            if cat == "Mn":
+                continue
+            output.append(char)
+        return "".join(output)
+
+    def _run_split_on_punc(self, text, never_split=None):
+        """Splits punctuation on a piece of text."""
+        if never_split is not None and text in never_split:
+            return [text]
+        chars = list(text)
+        i = 0
+        start_new_word = True
+        output = []
+        while i < len(chars):
+            char = chars[i]
+            if _is_punctuation(char):
+                output.append([char])
+                start_new_word = True
+            else:
+                if start_new_word:
+                    output.append([])
+                start_new_word = False
+                output[-1].append(char)
+            i += 1
+
+        return ["".join(x) for x in output]
+
+    def _tokenize_chinese_chars(self, text):
+        """Adds whitespace around any CJK character."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if self._is_chinese_char(cp):
+                output.append(" ")
+                output.append(char)
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+    def _is_chinese_char(self, cp):
+        """Checks whether CP is the codepoint of a CJK character."""
+        # This defines a "chinese character" as anything in the CJK Unicode block:
+        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+        #
+        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+        # despite its name. The modern Korean Hangul alphabet is a different block,
+        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+        # space-separated words, so they are not treated specially and handled
+        # like the all of the other languages.
+        if (
+            (cp >= 0x4E00 and cp <= 0x9FFF)
+            or (cp >= 0x3400 and cp <= 0x4DBF)  #
+            or (cp >= 0x20000 and cp <= 0x2A6DF)  #
+            or (cp >= 0x2A700 and cp <= 0x2B73F)  #
+            or (cp >= 0x2B740 and cp <= 0x2B81F)  #
+            or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
+            or (cp >= 0xF900 and cp <= 0xFAFF)
+            or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
+        ):  #
+            return True
+
+        return False
+
+    def _clean_text(self, text):
+        """Performs invalid character removal and whitespace cleanup on text."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if cp == 0 or cp == 0xFFFD or _is_control(char):
+                continue
+            if _is_whitespace(char):
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+
+# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
+class WordpieceTokenizer(object):
+    """Runs WordPiece tokenization."""
+
+    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+        self.vocab = vocab
+        self.unk_token = unk_token
+        self.max_input_chars_per_word = max_input_chars_per_word
+
+    def tokenize(self, text):
+        """
+        Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
+        tokenization using the given vocabulary.
+
+        For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
+
+        Args:
+            text: A single token or whitespace separated tokens. This should have
+                already been passed through *BasicTokenizer*.
+
+        Returns:
+            A list of wordpiece tokens.
+        """
+
+        output_tokens = []
+        for token in whitespace_tokenize(text):
+            chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
+            is_bad = False
+            start = 0
+            sub_tokens = []
+            while start < len(chars):
+                end = len(chars)
+                cur_substr = None
+                while start < end:
+                    substr = "".join(chars[start:end])
+                    if start > 0:
+                        substr = "##" + substr
+                    if substr in self.vocab:
+                        cur_substr = substr
+                        break
+                    end -= 1
+                if cur_substr is None:
+                    is_bad = True
+                    break
+                sub_tokens.append(cur_substr)
+                start = end
+
+            if is_bad:
+                output_tokens.append(self.unk_token)
+            else:
+                output_tokens.extend(sub_tokens)
+        return output_tokens

src/transformers/models/cpm/tokenization_cpm.py

@@ -13,8 +13,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Tokenization classes."""
-from ...utils import logging
-from ..xlnet.tokenization_xlnet import XLNetTokenizer
+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 SPIECE_UNDERLINE, logging
 
 
 logger = logging.get_logger(__name__)
@@ -28,10 +35,26 @@ PRETRAINED_VOCAB_FILES_MAP = {
 }
 
 
-class CpmTokenizer(XLNetTokenizer):
+class CpmTokenizer(PreTrainedTokenizer):
     """Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models."""
 
-    def __init__(self, *args, **kwargs):
+    def __init__(
+        self,
+        vocab_file,
+        do_lower_case=False,
+        remove_space=True,
+        keep_accents=False,
+        bos_token="<s>",
+        eos_token="</s>",
+        unk_token="<unk>",
+        sep_token="<sep>",
+        pad_token="<pad>",
+        cls_token="<cls>",
+        mask_token="<mask>",
+        additional_special_tokens=["<eop>", "<eod>"],
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ) -> None:
         """
         Construct a CPM tokenizer. Based on [Jieba](https://pypi.org/project/jieba/) and
         [SentencePiece](https://github.com/google/sentencepiece).
@@ -93,7 +116,37 @@ class CpmTokenizer(XLNetTokenizer):
             sp_model (`SentencePieceProcessor`):
                 The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
         """
-        super().__init__(*args, **kwargs)
+        # Mask token behave like a normal word, i.e. include the space before it
+        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+        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,
+            additional_special_tokens=additional_special_tokens,
+            sp_model_kwargs=self.sp_model_kwargs,
+            **kwargs,
+        )
+
+        self._pad_token_type_id = 3
+
+        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)
+
         try:
             import jieba
         except ModuleNotFoundError as error:
@@ -104,10 +157,190 @@ class CpmTokenizer(XLNetTokenizer):
         self.jieba = jieba
         self.translator = str.maketrans(" \n", "\u2582\u2583")
 
-    def _tokenize(self, text, *args, **kwargs):
-        text = [x.translate(self.translator) for x in self.jieba.cut(text, cut_all=False)]
-        text = " ".join(text)
-        return super()._tokenize(text, *args, **kwargs)
+    @property
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
+    def vocab_size(self):
+        return len(self.sp_model)
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.get_vocab
+    def get_vocab(self):
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.__getstate__
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.__setstate__
+    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)
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.preprocess_text
+    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
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer._tokenize
+    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():
+                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
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer._convert_token_to_id
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.PieceToId(token)
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer._convert_id_to_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)
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.convert_tokens_to_string
+    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
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.build_inputs_with_special_tokens
+    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 XLNet sequence has the following format:
+
+        - single sequence: `X <sep> <cls>`
+        - pair of sequences: `A <sep> B <sep> <cls>`
+
+        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 token_ids_0 + sep + cls
+        return token_ids_0 + sep + token_ids_1 + sep + cls
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.get_special_tokens_mask
+    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 ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1]
+        return ([0] * len(token_ids_0)) + [1, 1]
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.create_token_type_ids_from_sequences
+    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 XLNet
+        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_segment_id = [2]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + sep) * [0] + cls_segment_id
+        return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.save_vocabulary
+    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,)
 
     def _decode(self, *args, **kwargs):
         text = super()._decode(*args, **kwargs)

src/transformers/models/cpm/tokenization_cpm_fast.py

@@ -13,8 +13,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Tokenization classes."""
+import os
+from shutil import copyfile
+from typing import List, Optional, Tuple
+
+from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
 from ...utils import logging
-from ..xlnet.tokenization_xlnet_fast import XLNetTokenizerFast
 
 
 logger = logging.get_logger(__name__)
@@ -31,10 +35,26 @@ PRETRAINED_VOCAB_FILES_MAP = {
 }
 
 
-class CpmTokenizerFast(XLNetTokenizerFast):
+class CpmTokenizerFast(PreTrainedTokenizerFast):
     """Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models."""
 
-    def __init__(self, *args, **kwargs):
+    def __init__(
+        self,
+        vocab_file=None,
+        tokenizer_file=None,
+        do_lower_case=False,
+        remove_space=True,
+        keep_accents=False,
+        bos_token="<s>",
+        eos_token="</s>",
+        unk_token="<unk>",
+        sep_token="<sep>",
+        pad_token="<pad>",
+        cls_token="<cls>",
+        mask_token="<mask>",
+        additional_special_tokens=["<eop>", "<eod>"],
+        **kwargs
+    ):
         """
         Construct a CPM tokenizer. Based on [Jieba](https://pypi.org/project/jieba/) and
         [SentencePiece](https://github.com/google/sentencepiece).
@@ -96,7 +116,33 @@ class CpmTokenizerFast(XLNetTokenizerFast):
             sp_model (`SentencePieceProcessor`):
                 The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
         """
-        super().__init__(*args, **kwargs)
+        # Mask token behave like a normal word, i.e. include the space before it
+        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+        super().__init__(
+            vocab_file=vocab_file,
+            tokenizer_file=tokenizer_file,
+            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,
+            additional_special_tokens=additional_special_tokens,
+            **kwargs,
+        )
+
+        self._pad_token_type_id = 3
+        self.do_lower_case = do_lower_case
+        self.remove_space = remove_space
+        self.keep_accents = keep_accents
+        self.vocab_file = vocab_file
+        self.can_save_slow_tokenizer = False if not self.vocab_file else True
+
         try:
             import jieba
         except ModuleNotFoundError as error:
@@ -107,6 +153,83 @@ class CpmTokenizerFast(XLNetTokenizerFast):
         self.jieba = jieba
         self.translator = str.maketrans(" \n", "\u2582\u2583")
 
+    # Copied from transformers.models.xlnet.tokenization_xlnet_fast.XLNetTokenizerFast.build_inputs_with_special_tokens
+    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 XLNet sequence has the following format:
+
+        - single sequence: `X <sep> <cls>`
+        - pair of sequences: `A <sep> B <sep> <cls>`
+
+        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 token_ids_0 + sep + cls
+        return token_ids_0 + sep + token_ids_1 + sep + cls
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet_fast.XLNetTokenizerFast.create_token_type_ids_from_sequences
+    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 XLNet
+        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_segment_id = [2]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + sep) * [0] + cls_segment_id
+        return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id
+
+    # Copied from transformers.models.xlnet.tokenization_xlnet_fast.XLNetTokenizerFast.save_vocabulary
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not self.can_save_slow_tokenizer:
+            raise ValueError(
+                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
+                "tokenizer."
+            )
+
+        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):
+            copyfile(self.vocab_file, out_vocab_file)
+
+        return (out_vocab_file,)
+
     def _batch_encode_plus(self, batch_text_or_text_pairs, *args, **kwargs):
         batch_text_or_text_pairs = [
             " ".join([x.translate(self.translator) for x in self.jieba.cut(text, cut_all=False)])

tests/models/bert_japanese/test_tokenization_bert_japanese.py

@@ -19,10 +19,10 @@ import pickle
 import unittest
 
 from transformers import AutoTokenizer
+from transformers.models.bert.tokenization_bert import BertTokenizer
 from transformers.models.bert_japanese.tokenization_bert_japanese import (
     VOCAB_FILES_NAMES,
     BertJapaneseTokenizer,
-    BertTokenizer,
     CharacterTokenizer,
     JumanppTokenizer,
     MecabTokenizer,