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# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# 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 RWKV5."""
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
from transformers.utils import logging
if TYPE_CHECKING:
pass
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.txt",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"ArthurZ/rwkv-5-utf": "https://huggingface.co/ArthurZ/rwkv-5-utf/blob/main/vocab.txt",
},
}
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text.
The separators are kept
"""
text = text.strip()
if not text:
return []
tokens = re.split(b"(?= )", text)
return tokens
class WordpieceTokenizer(object):
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token):
self.vocab = vocab
self.unk_token = unk_token
def tokenize(self, text):
"""
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
tokenization using the given vocabulary.
For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through *BasicTokenizer*.
Returns:
A list of wordpiece tokens.
"""
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = bytes(chars[start:end])
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
try:
cur_substr = cur_substr.decode()
except UnicodeDecodeError:
cur_substr = str(cur_substr)
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
class Rwkv5Tokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = {"ArthurZ/rwkv-5-utf": 2048}
model_input_names = ["input_ids", "attention_mask"]
def __init__(self, vocab_file, bos_token="<s>", eos_token="<s>", unk_token="<s>", **kwargs):
if not os.path.isfile(vocab_file):
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
" model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
with open(vocab_file, "r") as reader:
tokens = reader.readlines()
vocab = {}
for index, token in enumerate(tokens):
token = eval(token.rstrip("\n"))
vocab[token] = index
self.add_bos_token = True
self.encoder = vocab
self.decoder = {v: k for k, v in vocab.items()}
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.encoder, unk_token=str(unk_token))
self._added_tokens_decoder = {0: AddedToken(str(bos_token))}
super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
@property
def vocab_size(self):
return len(self.encoder)
def get_vocab(self):
vocab = {str(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, split_special_tokens=False):
return self.wordpiece_tokenizer.tokenize(text.encode("utf-8"))
def _convert_token_to_id(self, token):
"""Converts a token (byte) to an id using the vocab."""
if token.startswith("b'\\"):
token = eval(token)
elif not isinstance(token, bytes):
token = token.encode("utf-8", errors="replace")
return self.encoder.get(token, self.unk_token_id)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (byte) using the vocab."""
token = self.decoder.get(index, self.unk_token)
if isinstance(token, (bytes)):
token = token.decode("utf-8", errors="replace")
return token
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (bytes) in a single string. Additional tokens are encoded to bytes"""
out_string = b"".join([k.encode(errors="replace") if isinstance(k, str) else k for k in tokens]).decode(
"utf-8"
)
return out_string
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
else:
vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(vocab_file, "w") as writer:
for token, token_index in sorted(self.encoder.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!"
)
index = token_index
writer.write(str(token) + "\n")
index += 1
return (vocab_file,)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
if self.add_bos_token:
bos_token_ids = [self.bos_token_id]
else:
bos_token_ids = []
output = bos_token_ids + token_ids_0
if token_ids_1 is None:
return output
return output + bos_token_ids + token_ids_1
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if not self.add_bos_token:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0))
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
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