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# Copyright (c) 2025 Baidu, Inc. 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
from shutil import copyfile
from typing import Dict, List, Optional, Tuple, Union
import torch
import numpy as np
import sentencepiece as spm
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import (
PaddingStrategy,
)
from transformers.utils import logging
logger = logging.get_logger(__name__)
class Ernie4_5_Tokenizer(PreTrainedTokenizer):
vocab_files_names = {
"vocab_file": "tokenizer.model",
}
# Model input names expected by the tokenizer
model_input_names = ["input_ids", "position_ids", "attention_mask", "labels"]
# Padding side (where to add padding tokens)
padding_side = "right"
def __init__(
self,
vocab_file,
bos_token="<s>",
cls_token="<cls>",
eos_token="</s>",
mask_token="<mask:0>",
pad_token="<pad>",
sep_token="<sep>",
unk_token="<unk>",
additional_special_tokens=None,
verbose=False,
**kwargs,
):
"""
Initialize the ERNIE tokenizer.
Args:
vocab_file (str): Path to the SentencePiece model file.
bos_token (str, optional): Beginning of sentence token. Defaults to "<s>".
cls_token (str, optional): Classification token. Defaults to "<cls>".
eos_token (str, optional): End of sentence token. Defaults to "</s>".
mask_token (str, optional): Mask token. Defaults to "<mask:0>".
pad_token (str, optional): Padding token. Defaults to "<pad>".
sep_token (str, optional): Separator token. Defaults to "<sep>".
unk_token (str, optional): Unknown token. Defaults to "<unk>".
additional_special_tokens (List[str], optional): Additional special tokens.
Defaults to ["<mask:1>", "<mask:7>"].
verbose (bool, optional): Whether to print detailed logs or progress information during execution.
**kwargs: Additional keyword arguments passed to the parent class.
"""
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(vocab_file)
if additional_special_tokens is None:
additional_special_tokens = ["<mask:1>", "<mask:7>"]
super().__init__(
bos_token=bos_token,
cls_token=cls_token,
eos_token=eos_token,
mask_token=mask_token,
pad_token=pad_token,
sep_token=sep_token,
unk_token=unk_token,
additional_special_tokens=additional_special_tokens,
verbose=verbose,
**kwargs,
)
@property
def vocab_size(self):
"""Returns the size of the vocabulary.
Returns:
int: The number of tokens in the vocabulary.
"""
return self.sp_model.vocab_size()
def get_vocab(self):
"""Get the vocabulary as a dictionary mapping tokens to their IDs.
Returns:
dict: A dictionary mapping tokens to their corresponding IDs.
"""
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text):
"""Tokenize text using SentencePiece.
Args:
text (str): The text to tokenize.
Returns:
list: A list of tokens.
"""
return self.sp_model.encode_as_pieces(text)
def _convert_token_to_id(self, token):
"""Convert a token (str) to an ID using the vocabulary.
Args:
token (str): The token to convert.
Returns:
int: The corresponding token ID.
"""
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, id):
"""Convert an ID to a token (str) using the vocabulary.
Args:
id (int): The token ID to convert.
Returns:
str: The corresponding token.
"""
if id >= self.vocab_size:
return self.unk_token
else:
return self.sp_model.id_to_piece(id)
def convert_tokens_to_string(self, tokens):
"""Convert a sequence of tokens back to a single string.
Args:
tokens (List[str]): A list of tokens to convert.
Returns:
str: The reconstructed 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(current_sub_tokens) + token
current_sub_tokens = []
else:
current_sub_tokens.append(token)
out_string += self.sp_model.decode(current_sub_tokens)
return out_string
def prepare_for_model(self, *args, **kwargs):
if "add_special_tokens" in kwargs:
kwargs.pop("add_special_tokens")
return super().prepare_for_model(*args, **kwargs)
def save_vocabulary(
self, save_directory, filename_prefix: Optional[str] = None
) -> Tuple[str]:
"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (str): The directory in which to save the vocabulary.
filename_prefix (Optional[str]): Optional prefix for the saved filename.
Returns:
Tuple[str]: Paths to the files saved.
Raises:
ValueError: If the save_directory is not a valid directory.
"""
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 "")
+ self.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):
kwargs.pop("clean_up_tokenization_spaces", None)
kwargs.pop("spaces_between_special_tokens", None)
return super()._decode(
*args,
**kwargs,
clean_up_tokenization_spaces=False,
spaces_between_special_tokens=False,
)
def _pad(
self,
encoded_inputs: Union[Dict],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Pad encoded inputs according to specified strategy.
Args:
encoded_inputs (Union[Dict]): Dictionary of encoded inputs.
max_length (Optional[int]): Maximum length to pad to.
padding_strategy (PaddingStrategy): Strategy for padding.
pad_to_multiple_of (Optional[int]): Pad to a multiple of this value.
return_attention_mask (Optional[bool]): Whether to return attention mask.
Returns:
dict: Dictionary with padded inputs and optional attention mask.
Raises:
ValueError: If attention_mask has unexpected type or invalid padding strategy.
"""
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
if return_attention_mask:
required_input = encoded_inputs[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if (
max_length is not None
and pad_to_multiple_of is not None
and (max_length % pad_to_multiple_of != 0)
):
max_length = (
(max_length // pad_to_multiple_of) + 1
) * pad_to_multiple_of
needs_to_be_padded = (
padding_strategy != PaddingStrategy.DO_NOT_PAD
and len(required_input) != max_length
)
if (
"attention_mask" in encoded_inputs
and encoded_inputs["attention_mask"] is not None
):
attention_mask = encoded_inputs.pop("attention_mask")
if isinstance(attention_mask, torch.Tensor):
attention_mask = attention_mask.numpy()
elif isinstance(attention_mask, list):
attention_mask = np.array(attention_mask)
elif not isinstance(attention_mask, np.ndarray):
raise ValueError(
f"Unexpected type {type(attention_mask)} of attention_mask, "
)
else:
# Create default attention mask if none provided
attention_mask = np.tril(
np.ones((len(required_input), len(required_input)), dtype=np.int64)
)
attention_mask = np.expand_dims(attention_mask, axis=0)
if needs_to_be_padded:
difference = max_length - len(required_input)
if self.padding_side == "right":
if attention_mask.ndim == 1:
pad_width = [(0, difference)]
else:
pad_width = [(0, 0), (0, difference), (0, difference)]
elif self.padding_side == "left":
if attention_mask.ndim == 1:
pad_width = [(difference, 0)]
else:
pad_width = [(0, 0), (difference, 0), (difference, 0)]
else:
raise ValueError(
"Invalid padding strategy:" + str(self.padding_side)
)
attention_mask = np.pad(
attention_mask,
pad_width=pad_width,
mode="constant",
constant_values=0,
)
encoded_inputs = super()._pad(
encoded_inputs,
max_length,
padding_strategy=padding_strategy,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=False,
)
if return_attention_mask:
encoded_inputs["attention_mask"] = attention_mask.tolist()
return encoded_inputs
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