144 lines
5.2 KiB
Python
144 lines
5.2 KiB
Python
# coding=utf-8
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# Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes for ESM."""
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import os
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from typing import List, Optional
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from ...tokenization_utils import PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
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def load_vocab_file(vocab_file):
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with open(vocab_file, "r") as f:
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lines = f.read().splitlines()
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return [l.strip() for l in lines]
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class EsmTokenizer(PreTrainedTokenizer):
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"""
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Constructs an ESM tokenizer.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(
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self,
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vocab_file,
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unk_token="<unk>",
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cls_token="<cls>",
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pad_token="<pad>",
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mask_token="<mask>",
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eos_token="<eos>",
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**kwargs,
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):
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self.all_tokens = load_vocab_file(vocab_file)
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self._id_to_token = dict(enumerate(self.all_tokens))
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self._token_to_id = {tok: ind for ind, tok in enumerate(self.all_tokens)}
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super().__init__(
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unk_token=unk_token,
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cls_token=cls_token,
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pad_token=pad_token,
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mask_token=mask_token,
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eos_token=eos_token,
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**kwargs,
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)
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# TODO, all the tokens are added? But they are also part of the vocab... bit strange.
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# none of them are special, but they all need special splitting.
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self.unique_no_split_tokens = self.all_tokens
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self._update_trie(self.unique_no_split_tokens)
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def _convert_id_to_token(self, index: int) -> str:
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return self._id_to_token.get(index, self.unk_token)
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def _convert_token_to_id(self, token: str) -> int:
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return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))
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def _tokenize(self, text, **kwargs):
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return text.split()
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def get_vocab(self):
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base_vocab = self._token_to_id.copy()
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base_vocab.update(self.added_tokens_encoder)
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return base_vocab
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def token_to_id(self, token: str) -> int:
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return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))
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def id_to_token(self, index: int) -> str:
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return self._id_to_token.get(index, self.unk_token)
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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cls = [self.cls_token_id]
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sep = [self.eos_token_id] # No sep token in ESM vocabulary
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if token_ids_1 is None:
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if self.eos_token_id is None:
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return cls + token_ids_0
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else:
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return cls + token_ids_0 + sep
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elif self.eos_token_id is None:
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raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!")
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return cls + token_ids_0 + sep + token_ids_1 + sep # Multiple inputs always have an EOS token
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def get_special_tokens_mask(
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self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
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Args:
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token_ids_0 (`List[int]`):
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List of ids of the first sequence.
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token_ids_1 (`List[int]`, *optional*):
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List of ids of the second sequence.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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if token_ids_1 is not None:
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raise ValueError(
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"You should not supply a second sequence if the provided sequence of "
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"ids is already formatted with special tokens for the model."
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)
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return [1 if token in self.all_special_ids else 0 for token in token_ids_0]
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mask = [1] + ([0] * len(token_ids_0)) + [1]
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if token_ids_1 is not None:
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mask += [0] * len(token_ids_1) + [1]
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return mask
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def save_vocabulary(self, save_directory, filename_prefix):
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vocab_file = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + "vocab.txt")
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with open(vocab_file, "w") as f:
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f.write("\n".join(self.all_tokens))
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return (vocab_file,)
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@property
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def vocab_size(self) -> int:
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return len(self.all_tokens)
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