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Zero
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# Copyright (c) Meta Platforms, Inc. and affiliates.
import re
from bytelatent.tokenizers.abstract_tokenizer import Tokenizer
from bytelatent.tokenizers.constants import (
BOE_ID,
BOS_ID,
BPE_ID,
BYTE_UNITS,
EOS_ID,
OFFSET,
PAD_ID,
)
from bytelatent.tokenizers.sentence_piece_tokenizer import SentencePieceTokenizer
def convert_to_bytes(s):
# check if the output is a bytes like object of the format <0x00>
if re.match(r"<0x[0-9a-fA-F]+>", s):
return bytes.fromhex(s[3:-1])
else:
return bytes(s, "utf-8", errors="ignore")
def text2bytes_bpe_delims(
text: str,
*,
bpe_tokenizer,
bpe_id: int,
offsetting_special_char: int,
add_bos: bool,
add_eos: bool,
):
cur_bpe = bpe_tokenizer.encode(text, add_bos=add_bos, add_eos=add_eos)
# merge the leading space tokens
leading_space_tokens = []
other_bpe_tokens = []
leading = True
for token in cur_bpe:
bpe_str = bpe_tokenizer.sp_model.id_to_piece(token)
if leading and all(c == "▁" for c in bpe_str):
leading_space_tokens.append(bpe_str)
else:
leading = False
other_bpe_tokens.append(bpe_str)
cur_bpe_strs = ["".join(leading_space_tokens)] + other_bpe_tokens
# Remove the '▁' characters
bpe_strs = []
for i, bpe_str in enumerate(cur_bpe_strs):
if (
len(bpe_strs) <= 1
and all([c == " " for s in bpe_strs for c in s])
and not all(c == "▁" for c in bpe_str)
):
# Remove leading space for first non space token.
bpe_str = bpe_str.replace("▁", "")
elif i == 0 and all(c == "▁" for c in bpe_str):
bpe_str = " " * (len(text) - len(text.lstrip(" ")))
else:
bpe_str = bpe_str.replace("▁", " ")
if len(bpe_str) > 0:
bpe_strs.append(bpe_str)
ex_seq = []
# Convert bpe tokens to bytes
for s in bpe_strs:
byte_chunk = convert_to_bytes(s)
proc_chunk = [int(unit) for unit in byte_chunk]
ex_seq.extend([bpe_id - offsetting_special_char] + proc_chunk)
return ex_seq
class BltTokenizer(Tokenizer):
def __init__(
self,
*,
vocab_size_unit_1: int = BYTE_UNITS,
bpe_delim: bool = False,
bpe_tokenizer_path="/home/artidoro/tokenizers/llama_v2.tokenizer.model",
add_bos: bool = True,
add_eos: bool = True,
):
self.add_bos = add_bos
self.add_eos = add_eos
self.vocab_size_unit_1 = vocab_size_unit_1
self.boe_id = BOE_ID
self.bos_id = BOS_ID
self.eos_id = EOS_ID
self.pad_id = PAD_ID
self.bpe_id = BPE_ID
self.bpe_tokenizer_path = bpe_tokenizer_path
if bpe_delim:
self.bpe_tokenizer = SentencePieceTokenizer(
model_path=self.bpe_tokenizer_path
)
else:
self.bpe_tokenizer = None
self.bpe_delim = bpe_delim
self.offsetting_special_char = OFFSET
self.vocab_size_unit_1 = vocab_size_unit_1
self.n_words = vocab_size_unit_1 + self.offsetting_special_char
def get_vocab_size(self) -> int:
return self.n_words
def encode(
self, text: str, add_bos: bool | None = None, add_eos: bool | None = None
):
if add_bos is None:
add_bos = self.add_bos
if add_eos is None:
add_eos = self.add_eos
if self.bpe_delim:
tokens = text2bytes_bpe_delims(
text,
bpe_tokenizer=self.bpe_tokenizer,
bpe_id=self.bpe_id,
offsetting_special_char=self.offsetting_special_char,
add_bos=False,
add_eos=False,
)
else:
tokens = bytes(text, encoding="utf-8", errors="ignore")
# Offsetting
tokens = [int(unit) + self.offsetting_special_char for unit in tokens]
if add_bos:
tokens.insert(0, self.bos_id)
if add_eos:
tokens.append(self.eos_id)
return tokens
def decode(self, tokens: list[int], cut_at_eos: bool = False):
if cut_at_eos:
for k, t in enumerate(tokens):
if t == self.eos_id:
tokens = tokens[: k + 1]
break
return bytes(
[
tok - self.offsetting_special_char
for tok in tokens
if tok - self.offsetting_special_char >= 0
]
).decode("utf-8", errors="ignore")
def get_token_offsets(self, text: str, tokens: list[int] | None = None):
# TODO: Figure out what this does
raise NotImplementedError()
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