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from typing import Dict, Iterator, List, Optional, Tuple, Union
from tokenizers import AddedToken, Tokenizer, decoders, pre_tokenizers, processors, trainers
from tokenizers.models import BPE
from tokenizers.normalizers import Lowercase, Sequence, unicode_normalizer_from_str
from .base_tokenizer import BaseTokenizer
class ByteLevelBPETokenizer(BaseTokenizer):
"""ByteLevelBPETokenizer
Represents a Byte-level BPE as introduced by OpenAI with their GPT-2 model
"""
def __init__(
self,
vocab: Optional[Union[str, Dict[str, int]]] = None,
merges: Optional[Union[str, Dict[Tuple[int, int], Tuple[int, int]]]] = None,
add_prefix_space: bool = False,
lowercase: bool = False,
dropout: Optional[float] = None,
unicode_normalizer: Optional[str] = None,
continuing_subword_prefix: Optional[str] = None,
end_of_word_suffix: Optional[str] = None,
trim_offsets: bool = False,
):
if vocab is not None and merges is not None:
tokenizer = Tokenizer(
BPE(
vocab,
merges,
dropout=dropout,
continuing_subword_prefix=continuing_subword_prefix or "",
end_of_word_suffix=end_of_word_suffix or "",
)
)
else:
tokenizer = Tokenizer(BPE())
# Check for Unicode normalization first (before everything else)
normalizers = []
if unicode_normalizer:
normalizers += [unicode_normalizer_from_str(unicode_normalizer)]
if lowercase:
normalizers += [Lowercase()]
# Create the normalizer structure
if len(normalizers) > 0:
if len(normalizers) > 1:
tokenizer.normalizer = Sequence(normalizers)
else:
tokenizer.normalizer = normalizers[0]
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=add_prefix_space)
tokenizer.decoder = decoders.ByteLevel()
tokenizer.post_processor = processors.ByteLevel(trim_offsets=trim_offsets)
parameters = {
"model": "ByteLevelBPE",
"add_prefix_space": add_prefix_space,
"lowercase": lowercase,
"dropout": dropout,
"unicode_normalizer": unicode_normalizer,
"continuing_subword_prefix": continuing_subword_prefix,
"end_of_word_suffix": end_of_word_suffix,
"trim_offsets": trim_offsets,
}
super().__init__(tokenizer, parameters)
@staticmethod
def from_file(vocab_filename: str, merges_filename: str, **kwargs):
vocab, merges = BPE.read_file(vocab_filename, merges_filename)
return ByteLevelBPETokenizer(vocab, merges, **kwargs)
def train(
self,
files: Union[str, List[str]],
vocab_size: int = 30000,
min_frequency: int = 2,
show_progress: bool = True,
special_tokens: List[Union[str, AddedToken]] = [],
):
"""Train the model using the given files"""
trainer = trainers.BpeTrainer(
vocab_size=vocab_size,
min_frequency=min_frequency,
show_progress=show_progress,
special_tokens=special_tokens,
initial_alphabet=pre_tokenizers.ByteLevel.alphabet(),
)
if isinstance(files, str):
files = [files]
self._tokenizer.train(files, trainer=trainer)
def train_from_iterator(
self,
iterator: Union[Iterator[str], Iterator[Iterator[str]]],
vocab_size: int = 30000,
min_frequency: int = 2,
show_progress: bool = True,
special_tokens: List[Union[str, AddedToken]] = [],
length: Optional[int] = None,
):
"""Train the model using the given iterator"""
trainer = trainers.BpeTrainer(
vocab_size=vocab_size,
min_frequency=min_frequency,
show_progress=show_progress,
special_tokens=special_tokens,
initial_alphabet=pre_tokenizers.ByteLevel.alphabet(),
)
self._tokenizer.train_from_iterator(
iterator,
trainer=trainer,
length=length,
)
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