File size: 4,056 Bytes
3652445 | 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 | """Slow SentencePiece tokenizer for Needle."""
from __future__ import annotations
import os
import shutil
from typing import Any
import sentencepiece as spm
from transformers import PreTrainedTokenizer
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
class NeedleTokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file: str,
unk_token: str = "<unk>",
bos_token: str = "<s>",
eos_token: str = "</s>",
pad_token: str = "<pad>",
tool_call_token: str = "<tool_call>",
tools_token: str = "<tools>",
**kwargs: Any,
) -> None:
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(vocab_file)
self.sp = self.sp_model
self.tool_call_token = tool_call_token
self.tools_token = tools_token
additional = list(kwargs.pop("additional_special_tokens", []) or [])
for token in (tool_call_token, tools_token):
if token not in additional:
additional.append(token)
super().__init__(
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
additional_special_tokens=additional,
**kwargs,
)
@property
def vocab_size(self) -> int:
return int(self.sp_model.GetPieceSize())
@property
def tool_call_token_id(self) -> int:
return int(self.sp_model.PieceToId(self.tool_call_token))
@property
def tools_token_id(self) -> int:
return int(self.sp_model.PieceToId(self.tools_token))
def get_vocab(self) -> dict[str, int]:
vocab = {self.sp_model.IdToPiece(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text: str) -> list[str]:
return list(self.sp_model.EncodeAsPieces(text))
def _convert_token_to_id(self, token: str) -> int:
return int(self.sp_model.PieceToId(token))
def _convert_id_to_token(self, index: int) -> str:
return str(self.sp_model.IdToPiece(int(index)))
def convert_tokens_to_string(self, tokens: list[str]) -> str:
return self.sp_model.DecodePieces(tokens)
def build_inputs_with_special_tokens(
self,
token_ids_0: list[int],
token_ids_1: list[int] | None = None,
) -> list[int]:
if token_ids_1 is None:
return list(token_ids_0)
return list(token_ids_0) + list(token_ids_1)
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:
all_ids = list(token_ids_0)
else:
all_ids = self.build_inputs_with_special_tokens(token_ids_0, token_ids_1)
special = {
self.pad_token_id,
self.eos_token_id,
self.bos_token_id,
self.unk_token_id,
self.tool_call_token_id,
self.tools_token_id,
}
return [1 if token_id in special else 0 for token_id in all_ids]
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]:
os.makedirs(save_directory, exist_ok=True)
out_name = "tokenizer.model" if filename_prefix is None else f"{filename_prefix}-tokenizer.model"
out_path = os.path.join(save_directory, out_name)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_path):
shutil.copyfile(self.vocab_file, out_path)
return (out_path,)
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