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from __future__ import annotations
import logging
import math
import re
import shutil
import unicodedata
from pathlib import Path
from typing import Any
import sentencepiece as spm
from transformers import PreTrainedTokenizer
CHINESE_RE = re.compile(r"[\u3400-\u4dbf\u4e00-\u9fff]")
CHINESE_SPAN_RE = re.compile(r"[\u3400-\u4dbf\u4e00-\u9fff]+")
PINYIN_CODE_TOKEN_RE = re.compile(
r"(?<![A-Za-z0-9])[A-Za-z]\d(?:[A-Za-z]\d)*(?![A-Za-z0-9])"
)
SPECIAL_MARKER_RE = re.compile(r"<[A-Z_]+>")
PUNCTUATION = set(
"\u3002\uff0c\u3001\uff1f\uff01\uff1a\uff1b.,?!:;()[]{}<>\u300a\u300b"
"\u3010\u3011\u201c\u201d\"'\u2018\u2019\u300c\u300d\u300e\u300f"
"\u2014-~\u2026/\\"
)
LATIN_LETTER = (
r"A-Za-z\u00c0-\u00d6\u00d8-\u00f6\u00f8-\u00ff"
r"\u0100-\u017f\u0180-\u024f\u0250-\u02af"
)
LATIN_ALNUM_PATTERN = (
rf"(?:[{LATIN_LETTER}][{LATIN_LETTER}0-9]*"
rf"(?:[-_][{LATIN_LETTER}0-9]+)*|"
rf"[0-9]+[{LATIN_LETTER}][{LATIN_LETTER}0-9]*"
rf"(?:[-_][{LATIN_LETTER}0-9]+)*)"
)
LATIN_ALNUM_RE = re.compile(LATIN_ALNUM_PATTERN)
URL_RE = re.compile(r"\b(?:https?://\S*|www\.\S+)", flags=re.I)
DISCARDED_UNICODE_CATEGORIES = {"Cc", "Cf", "Co", "Cs", "Cn"}
TOKEN_RE = re.compile(
r"<[A-Z_]+>|"
r"[\u3400-\u4dbf\u4e00-\u9fff]+|"
rf"{LATIN_ALNUM_PATTERN}|"
r"\S"
)
LABELS = {
"\u9898\u5e72": "<QUESTION>",
"\u9009\u9879": "<OPTIONS>",
"\u7b54\u6848": "<ANSWER>",
"\u89e3\u6790": "<EXPLANATION>",
}
PINYIN_FORMAT_ALIASES = {
"code": "pinyin-code",
"codes": "pinyin-code",
"pinyin-code": "pinyin-code",
"initial": "pinyin-initial",
"initials": "pinyin-initial",
"pinyin-initial": "pinyin-initial",
"hanzi": "hanzi",
}
def latin_token_to_model_token(token: str) -> str:
upper = token.upper()
return upper if upper in {"A", "B", "C", "D"} else token.lower()
def should_preserve_fallback_token(token: str) -> bool:
if token == "\ufffd":
return False
for char in token:
category = unicodedata.category(char)
if category in DISCARDED_UNICODE_CATEGORIES:
return False
if category[0] not in {"L", "P", "S"}:
return False
return True
class PinyinCodeTokenizer(PreTrainedTokenizer):
"""Slow tokenizer that preserves the existing SentencePiece model."""
vocab_files_names = {"vocab_file": "tokenizer.model"}
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file: str,
add_bos_token: bool = False,
add_eos_token: bool = False,
transliteration: str = "pinyin-code",
pinyin_format: str | None = None,
use_jieba: bool = True,
jieba: bool | None = None,
**kwargs,
) -> None:
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(model_file=vocab_file)
self.add_bos_token = add_bos_token
self.add_eos_token = add_eos_token
self.transliteration = self._normalize_transliteration(
pinyin_format or transliteration
)
self.use_jieba = use_jieba if jieba is None else jieba
kwargs.setdefault("unk_token", self._piece_or_none(self.sp_model.unk_id()))
kwargs.setdefault("bos_token", self._piece_or_none(self.sp_model.bos_id()))
kwargs.setdefault("eos_token", self._piece_or_none(self.sp_model.eos_id()))
kwargs.setdefault("pad_token", self._piece_or_none(self.sp_model.pad_id()))
cls_token_id = self._piece_to_id_or_none("[CLS]")
sep_token_id = self._piece_to_id_or_none("[SEP]")
mask_token_id = self._piece_to_id_or_none("[MASK]")
if cls_token_id is not None:
kwargs.setdefault("cls_token", self.sp_model.id_to_piece(cls_token_id))
if sep_token_id is not None:
kwargs.setdefault("sep_token", self.sp_model.id_to_piece(sep_token_id))
if mask_token_id is not None:
kwargs.setdefault("mask_token", self.sp_model.id_to_piece(mask_token_id))
kwargs.setdefault("transliteration", self.transliteration)
kwargs.setdefault("pinyin_format", self.transliteration)
kwargs.setdefault("use_jieba", self.use_jieba)
kwargs.setdefault("jieba", self.use_jieba)
super().__init__(**kwargs)
def _normalize_transliteration(self, value: str) -> str:
normalized = PINYIN_FORMAT_ALIASES.get(value.lower())
if normalized is None:
allowed = ", ".join(sorted(set(PINYIN_FORMAT_ALIASES.values())))
raise ValueError(f"Unsupported transliteration {value!r}; choose from {allowed}")
return normalized
def _piece_or_none(self, token_id: int) -> str | None:
if token_id is None or token_id < 0:
return None
return self.sp_model.id_to_piece(token_id)
def _piece_to_id_or_none(self, piece: str) -> int | None:
token_id = int(self.sp_model.piece_to_id(piece))
if token_id < 0 or self.sp_model.id_to_piece(token_id) != piece:
return None
return token_id
def _looks_preprocessed(self, text: str) -> bool:
if SPECIAL_MARKER_RE.search(text):
return True
if self.transliteration == "pinyin-code" and PINYIN_CODE_TOKEN_RE.search(text):
return True
return False
def _preprocess_raw_text(self, text: str) -> str:
if not CHINESE_RE.search(text) and self._looks_preprocessed(text):
return text
try:
from preprocessing.preprocess import (
hanzi_to_encoded,
process_text,
require_dependencies,
)
except ImportError:
return self._fallback_process_text(text)
require_dependencies()
if self.transliteration == "pinyin-code":
return hanzi_to_encoded(text, self.use_jieba)
return process_text(text, self.transliteration, self.use_jieba)
def _fallback_process_text(self, text: str) -> str:
if self.use_jieba:
try:
import jieba
except ImportError as exc:
raise ImportError(
"Tokenizing raw Mandarin benchmark text with jieba segmentation "
"requires jieba. Install the model dependencies before running "
"lm_eval."
) from exc
jieba.setLogLevel(logging.WARNING)
else:
jieba = None
if self.transliteration != "hanzi":
try:
from pypinyin import Style, pinyin
except ImportError as exc:
raise ImportError(
"Tokenizing raw Mandarin benchmark text as pinyin requires pypinyin. "
"Install the model dependencies before running lm_eval."
) from exc
def normalize_text(value: str) -> str:
value = unicodedata.normalize("NFKC", value)
value = URL_RE.sub(" <URL> ", value)
value = re.sub(r"\$\$.*?\$\$", " <MATH> ", value, flags=re.DOTALL)
value = re.sub(r"[\uff08(]\s*[\uff09)]", " <BLANK> ", value)
for label, marker in LABELS.items():
value = re.sub(rf"{label}\s*[:\uff1a]", f" {marker} ", value)
value = re.sub(
rf"(?<![{LATIN_LETTER}])yes(?![{LATIN_LETTER}])",
" <YES> ",
value,
flags=re.I,
)
value = re.sub(
rf"(?<![{LATIN_LETTER}])no(?![{LATIN_LETTER}])",
" <NO> ",
value,
flags=re.I,
)
value = re.sub(
rf"(?<![{LATIN_LETTER}])[ABCD](?=\s*[:\uff1a.\uff0e\u3001\)])",
r" \g<0> ",
value,
)
value = re.sub(
rf"(?<![{LATIN_LETTER}0-9])[-+]?\d+(?:[.,]\d+)*(?:%|\uff05)?"
rf"(?![{LATIN_LETTER}0-9])",
" <NUM> ",
value,
)
value = value.replace("\uff08", "(").replace("\uff09", ")")
return re.sub(r"\s+", " ", value).strip()
def split_tone3_syllable(syllable: str) -> tuple[str, int]:
match = re.fullmatch(r"([a-z\u00fcv]+)([1-5]?)", syllable.lower())
if not match:
return syllable, 5
plain, tone = match.groups()
return plain, int(tone or "5")
def length_digit_offset(syllable: str) -> int:
return min(max(len(syllable), 1), 5) - 1
def syllable_to_initial_code(syllable: str) -> str:
plain, tone = split_tone3_syllable(syllable)
if not plain:
return ""
tone_offset = 5 if tone in {3, 4, 5} else 0
digit = tone_offset + length_digit_offset(plain)
initial = plain[0].upper() if tone in {1, 3, 5} else plain[0].lower()
return f"{initial}{digit}"
def syllable_to_initial_letter(syllable: str) -> str:
plain, _ = split_tone3_syllable(syllable)
return plain[:1].lower()
def convert_word(word: str) -> str:
if self.transliteration == "hanzi":
return word
syllables = pinyin(word, style=Style.TONE3, heteronym=False, errors="ignore")
if self.transliteration == "pinyin-code":
codes = [
syllable_to_initial_code(item[0])
for item in syllables
if item and item[0]
]
return "".join(code for code in codes if code)
initials = [
syllable_to_initial_letter(item[0])
for item in syllables
if item and item[0]
]
return "".join(initial for initial in initials if initial)
def tokenize_chinese_span(value: str) -> list[str]:
tokens = []
words = jieba.cut(value, cut_all=False) if self.use_jieba else value
for word in words:
word = word.strip()
if word and CHINESE_SPAN_RE.search(word):
token = convert_word(word)
if token:
tokens.append(token)
return tokens
tokens = []
for part in TOKEN_RE.findall(normalize_text(text)):
if part.startswith("<") and part.endswith(">"):
tokens.append(part)
elif CHINESE_SPAN_RE.fullmatch(part):
tokens.extend(tokenize_chinese_span(part))
elif part in PUNCTUATION:
tokens.append(part)
elif LATIN_ALNUM_RE.fullmatch(part):
tokens.append(latin_token_to_model_token(part))
elif part.isdigit():
tokens.append("<NUM>")
elif should_preserve_fallback_token(part):
tokens.append(part.lower())
return " ".join(tokens)
def _preprocess_tokenizer_input(self, value: Any) -> Any:
if value is None:
return None
if isinstance(value, str):
return self._preprocess_raw_text(value)
if isinstance(value, tuple):
return tuple(self._preprocess_tokenizer_input(item) for item in value)
if isinstance(value, list):
return [self._preprocess_tokenizer_input(item) for item in value]
return value
def _non_content_token_ids(self) -> set[int]:
return {
token_id
for token_id in (
self.pad_token_id,
self.bos_token_id,
self.eos_token_id,
self.cls_token_id,
self.sep_token_id,
self.mask_token_id,
)
if token_id is not None
}
def _offset_source_text(self, value: Any, is_split_into_words: bool = False) -> str:
if value is None:
return ""
if isinstance(value, str):
return value
if isinstance(value, tuple):
return " ".join(self._offset_source_text(item) for item in value)
if isinstance(value, list):
separator = " " if is_split_into_words else ""
return separator.join(self._offset_source_text(item) for item in value)
return str(value)
def _synthetic_offset_mapping(self, text: Any, input_ids: Any, is_split_into_words: bool = False) -> list[tuple[int, int]]:
"""Return slow-tokenizer-compatible offsets for evaluators that require them.
SentencePiece offsets are not available for this Python tokenizer because
raw Mandarin text is preprocessed into pinyin-code before encoding. These
spans conservatively distribute non-special tokens across the original
text so suffix/completion masking code can run without requiring a fast
tokenizer.
"""
ids = input_ids.tolist() if hasattr(input_ids, "tolist") else list(input_ids)
source = self._offset_source_text(text, is_split_into_words=is_split_into_words)
source_length = len(source)
if not ids:
return []
if source_length == 0:
return [(0, 0) for _ in ids]
non_content_ids = self._non_content_token_ids()
content_positions = [
index for index, token_id in enumerate(ids) if int(token_id) not in non_content_ids
]
if not content_positions:
return [(0, 0) for _ in ids]
offsets = [(0, 0) for _ in ids]
count = len(content_positions)
for ordinal, position in enumerate(content_positions):
start = math.floor(ordinal * source_length / count)
end = math.ceil((ordinal + 1) * source_length / count)
if end <= start:
end = min(source_length, start + 1)
offsets[position] = (start, end)
return offsets
def _with_optional_offsets(
self,
encoding,
original_text: Any,
return_offsets_mapping: bool,
is_split_into_words: bool = False,
return_tensors: str | None = None,
):
if not return_offsets_mapping:
return encoding
input_ids = encoding["input_ids"]
tensor_input = hasattr(input_ids, "ndim")
input_ids_list = input_ids.tolist() if tensor_input else input_ids
is_batched = False
if tensor_input:
is_batched = input_ids.ndim > 1
elif input_ids_list and isinstance(input_ids_list[0], list):
is_batched = True
if is_batched:
if isinstance(original_text, list) and not is_split_into_words:
texts = original_text
else:
texts = [original_text] * len(input_ids_list)
offsets = [
self._synthetic_offset_mapping(text, ids, is_split_into_words=is_split_into_words)
for text, ids in zip(texts, input_ids_list)
]
else:
offsets = self._synthetic_offset_mapping(
original_text,
input_ids_list,
is_split_into_words=is_split_into_words,
)
if return_tensors == "pt" or tensor_input:
try:
import torch
offsets = torch.tensor(offsets, dtype=torch.long)
except ImportError:
pass
encoding["offset_mapping"] = offsets
return encoding
def __call__(self, text=None, text_pair=None, *args, **kwargs):
original_text = text
return_offsets_mapping = bool(kwargs.pop("return_offsets_mapping", False))
is_split_into_words = bool(kwargs.get("is_split_into_words", False))
return_tensors = kwargs.get("return_tensors")
if "text_target" in kwargs:
kwargs["text_target"] = self._preprocess_tokenizer_input(kwargs["text_target"])
if "text_pair_target" in kwargs:
kwargs["text_pair_target"] = self._preprocess_tokenizer_input(
kwargs["text_pair_target"]
)
text = self._preprocess_tokenizer_input(text)
text_pair = self._preprocess_tokenizer_input(text_pair)
if text_pair is None:
encoding = super().__call__(text, *args, **kwargs)
else:
encoding = super().__call__(text, text_pair, *args, **kwargs)
return self._with_optional_offsets(
encoding,
original_text,
return_offsets_mapping,
is_split_into_words=is_split_into_words,
return_tensors=return_tensors,
)
def encode(self, text, text_pair=None, add_special_tokens=True, *args, **kwargs):
kwargs["add_special_tokens"] = add_special_tokens
text = self._preprocess_tokenizer_input(text)
text_pair = self._preprocess_tokenizer_input(text_pair)
if text_pair is None:
return super().encode(text, *args, **kwargs)
return super().encode(text, text_pair, *args, **kwargs)
def encode_plus(self, text, text_pair=None, *args, **kwargs):
original_text = text
return_offsets_mapping = bool(kwargs.pop("return_offsets_mapping", False))
is_split_into_words = bool(kwargs.get("is_split_into_words", False))
return_tensors = kwargs.get("return_tensors")
text = self._preprocess_tokenizer_input(text)
text_pair = self._preprocess_tokenizer_input(text_pair)
if text_pair is None:
encoding = super().encode_plus(text, *args, **kwargs)
else:
encoding = super().encode_plus(text, text_pair, *args, **kwargs)
return self._with_optional_offsets(
encoding,
original_text,
return_offsets_mapping,
is_split_into_words=is_split_into_words,
return_tensors=return_tensors,
)
def batch_encode_plus(self, batch_text_or_text_pairs, *args, **kwargs):
original_batch = batch_text_or_text_pairs
return_offsets_mapping = bool(kwargs.pop("return_offsets_mapping", False))
is_split_into_words = bool(kwargs.get("is_split_into_words", False))
return_tensors = kwargs.get("return_tensors")
batch_text_or_text_pairs = self._preprocess_tokenizer_input(
batch_text_or_text_pairs
)
encoding = super().batch_encode_plus(batch_text_or_text_pairs, *args, **kwargs)
return self._with_optional_offsets(
encoding,
original_batch,
return_offsets_mapping,
is_split_into_words=is_split_into_words,
return_tensors=return_tensors,
)
@property
def vocab_size(self) -> int:
return self.sp_model.get_piece_size()
def get_vocab(self) -> dict[str, int]:
vocab = {self.sp_model.id_to_piece(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text: str) -> list[str]:
text = self._preprocess_raw_text(text)
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token: str) -> int:
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, index: int) -> str:
return self.sp_model.id_to_piece(index)
def convert_tokens_to_string(self, tokens: list[str]) -> str:
return self.sp_model.decode(tokens)
def build_inputs_with_special_tokens(
self,
token_ids_0: list[int],
token_ids_1: list[int] | None = None,
) -> list[int]:
output = list(token_ids_0)
if self.add_bos_token and self.bos_token_id is not None:
output = [self.bos_token_id] + output
if self.add_eos_token and self.eos_token_id is not None:
output = output + [self.eos_token_id]
if token_ids_1 is not None:
output += list(token_ids_1)
if self.add_eos_token and self.eos_token_id is not None:
output.append(self.eos_token_id)
return output
def get_special_tokens_mask(
self,
token_ids_0: list[int],
token_ids_1: list[int] | None = None,
already_has_special_tokens: bool = False,
) -> list[int]:
if already_has_special_tokens:
special_ids = set(self.all_special_ids)
return [1 if token_id in special_ids else 0 for token_id in token_ids_0]
mask = [0] * len(token_ids_0)
if self.add_bos_token and self.bos_token_id is not None:
mask = [1] + mask
if self.add_eos_token and self.eos_token_id is not None:
mask = mask + [1]
if token_ids_1 is not None:
mask += [0] * len(token_ids_1)
if self.add_eos_token and self.eos_token_id is not None:
mask.append(1)
return mask
def create_token_type_ids_from_sequences(
self,
token_ids_0: list[int],
token_ids_1: list[int] | None = None,
) -> list[int]:
return [0] * len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1))
def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]:
output_name = "tokenizer.model"
if filename_prefix:
output_name = f"{filename_prefix}-{output_name}"
output_path = Path(save_directory) / output_name
if Path(self.vocab_file).resolve() != output_path.resolve():
shutil.copyfile(self.vocab_file, output_path)
return (str(output_path),)
class EncodedMandarinTokenizer(PinyinCodeTokenizer):
"""Tokenizer wrapper that hides Hanzi-to-encoded-Mandarin preprocessing."""
|