|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""Tokenization classes for ESM.""" |
|
import os |
|
from typing import List, Optional, Union |
|
|
|
from transformers.tokenization_utils import PreTrainedTokenizer |
|
from transformers.tokenization_utils_base import AddedToken |
|
from transformers.utils import logging |
|
|
|
|
|
logger = logging.get_logger(__name__) |
|
|
|
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} |
|
|
|
PRETRAINED_VOCAB_FILES_MAP = { |
|
"vocab_file": { |
|
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt", |
|
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt", |
|
}, |
|
} |
|
|
|
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { |
|
"facebook/esm2_t6_8M_UR50D": 1024, |
|
"facebook/esm2_t12_35M_UR50D": 1024, |
|
} |
|
|
|
|
|
def load_vocab_file(vocab_file): |
|
with open(vocab_file, "r") as f: |
|
lines = f.read().splitlines() |
|
return [l.strip() for l in lines] |
|
|
|
|
|
class EsmTokenizer(PreTrainedTokenizer): |
|
""" |
|
Constructs an ESM tokenizer. |
|
""" |
|
|
|
vocab_files_names = VOCAB_FILES_NAMES |
|
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP |
|
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES |
|
model_input_names = ["input_ids", "attention_mask"] |
|
|
|
def __init__(self, vocab_file, **kwargs): |
|
super().__init__(**kwargs) |
|
self.all_tokens = load_vocab_file(vocab_file) |
|
self._id_to_token = {ind: tok for ind, tok in enumerate(self.all_tokens)} |
|
self._token_to_id = {tok: ind for ind, tok in enumerate(self.all_tokens)} |
|
self.unk_token = "<unk>" |
|
self.cls_token = "<cls>" |
|
self.pad_token = "<pad>" |
|
self.mask_token = "<mask>" |
|
self.eos_token = "<eos>" |
|
self.unique_no_split_tokens = self.all_tokens |
|
self._create_trie(self.unique_no_split_tokens) |
|
|
|
def _convert_id_to_token(self, index: int) -> str: |
|
return self._id_to_token.get(index, self.unk_token) |
|
|
|
def _convert_token_to_id(self, token: str) -> int: |
|
return self._token_to_id.get(token, self._token_to_id.get(self.unk_token)) |
|
|
|
def _tokenize(self, text, **kwargs): |
|
return text.split() |
|
|
|
def get_vocab_size(self, with_added_tokens=False): |
|
return len(self._id_to_token) |
|
|
|
def get_vocab(self): |
|
return {token: i for i, token in enumerate(self.all_tokens)} |
|
|
|
def token_to_id(self, token: str) -> int: |
|
return self._token_to_id.get(token, self._token_to_id.get(self.unk_token)) |
|
|
|
def id_to_token(self, index: int) -> str: |
|
return self._id_to_token.get(index, self.unk_token) |
|
|
|
def build_inputs_with_special_tokens( |
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
|
) -> List[int]: |
|
if token_ids_1 is None: |
|
return [self.cls_token_id] + token_ids_0 + [self.eos_token_id] |
|
cls = [self.cls_token_id] |
|
sep = [self.eos_token_id] |
|
return cls + token_ids_0 + sep + token_ids_1 + sep |
|
|
|
def get_special_tokens_mask( |
|
self, token_ids_0: List, token_ids_1: Optional[List] = 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 of the first sequence. |
|
token_ids_1 (`List[int]`, *optional*): |
|
List of ids of the second sequence. |
|
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: |
|
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. |
|
""" |
|
if already_has_special_tokens: |
|
if token_ids_1 is not None: |
|
raise ValueError( |
|
"You should not supply a second sequence if the provided sequence of " |
|
"ids is already formatted with special tokens for the model." |
|
) |
|
|
|
return [1 if token in self.all_special_ids else 0 for token in token_ids_0] |
|
mask = [1] + ([0] * len(token_ids_0)) + [1] |
|
if token_ids_1 is not None: |
|
mask += [0] * len(token_ids_1) + [1] |
|
return mask |
|
|
|
def save_vocabulary(self, save_directory, filename_prefix): |
|
vocab_file = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + "vocab.txt") |
|
with open(vocab_file, "w") as f: |
|
f.write("\n".join(self.all_tokens)) |
|
return (vocab_file,) |
|
|
|
@property |
|
def vocab_size(self) -> int: |
|
return self.get_vocab_size(with_added_tokens=False) |
|
|
|
def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int: |
|
return super()._add_tokens(new_tokens, special_tokens=True) |
|
|