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963fde0 4204a26 963fde0 e3827cb c5d242d 42aa86f e3827cb bdf2e42 138e620 963fde0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | # Copyright 2026 The HuggingFace Inc. team. 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.
"""Tokenization classes for the Nandi family."""
from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers
from tokenizers.models import BPE
from transformers.tokenization_utils_tokenizers import TokenizersBackend
from transformers.utils import logging
logger = logging.get_logger(__name__)
PRETOKENIZE_REGEX = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?(?:\p{L}\p{M}*)+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
class NandiTokenizer(TokenizersBackend):
model_input_names = ["input_ids", "attention_mask"]
model = BPE
def __init__(
self,
vocab: str | dict[str, int] | None = None,
merges: str | list[str] | None = None,
vocab_file=None,
merges_file=None,
unk_token: str = "<|endoftext|>",
bos_token: str = "<|im_start|>",
eos_token: str = "<|endoftext|>",
pad_token: str = "<|pad|>",
add_prefix_space: bool | None = None,
**kwargs,
):
self._vocab = (
vocab
if vocab is not None
else {
"<|endoftext|>": 0,
}
)
self._merges = merges or []
self._tokenizer = Tokenizer(
BPE(
vocab=self._vocab,
merges=self._merges,
dropout=None,
unk_token=None,
continuing_subword_prefix="",
end_of_word_suffix="",
fuse_unk=False,
byte_fallback=False,
)
)
self._tokenizer.decoder = decoders.ByteLevel()
self._tokenizer.normalizer = normalizers.NFC()
self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
[
pre_tokenizers.Split(
Regex(PRETOKENIZE_REGEX),
behavior="isolated",
invert=False,
),
pre_tokenizers.ByteLevel(
add_prefix_space=False,
trim_offsets=True,
use_regex=False
),
]
)
super().__init__(
vocab_file=vocab_file,
merges_file=merges_file,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
add_prefix_space=add_prefix_space,
**kwargs,
)
def __call__(self, text, *args, **kwargs):
add_special_tokens = kwargs.get("add_special_tokens", False)
def add_prefix(t):
if isinstance(t, str):
return "<|im_start|> " + t
return t
# Only inject when special tokens are disabled
if not add_special_tokens:
if isinstance(text, list):
text = [add_prefix(t) for t in text]
else:
text = add_prefix(text)
return super().__call__(text, *args, **kwargs)
def encode(
self,
text,
text_pair=None,
add_special_tokens: bool = True,
padding=False,
truncation=None,
max_length=None,
stride: int = 0,
padding_side=None,
return_tensors=None,
**kwargs,
):
if isinstance(text, str):
# This is a temporary fix to match the behaviour of the training pipeline
text = "<|im_start|>" + " " + text
return super().encode(
text,
text_pair=text_pair,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
padding_side=padding_side,
return_tensors=return_tensors,
**kwargs,
)
__all__ = ["NandiTokenizer"]
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