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# coding=utf-8
# Modified from rinna
# https://github.com/rinnakk/japanese-clip/blob/master/src/japanese_clip/tokenizer.py
# ################################## COPIED ##################################
# Copyright 2022 rinna Co., Ltd.
#
# 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.
# ################################## COPIED ##################################
from typing import Union
import torch
from transformers import AutoTokenizer, T5Tokenizer
class CustomCLIPTokenizer(T5Tokenizer):
model_input_names = ["input_ids", "attention_mask", "position_ids"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.do_lower_case = True # due to some bug of tokenizer config loading
def __call__(
self,
texts: Union[str, list[str]],
tokenizer: T5Tokenizer = None,
max_seq_len: int = 77,
device: Union[str, torch.device] = (
"cuda" if torch.cuda.is_available() else "cpu"
),
**kwargs,
):
if isinstance(texts, str):
texts = [texts]
if tokenizer is None:
tokenizer = self
tokenizer_call = super().__call__
else:
tokenizer_call = tokenizer
inputs = tokenizer_call(
texts,
max_length=max_seq_len - 1,
padding="max_length",
truncation=True,
add_special_tokens=False,
)
# add cls token at first place
input_ids = [[tokenizer.cls_token_id] + ids for ids in inputs["input_ids"]]
attention_mask = [[1] + am for am in inputs["attention_mask"]]
position_ids = [list(range(0, len(input_ids[0])))] * len(texts)
input_ids = torch.tensor(input_ids, dtype=torch.long)
attention_mask = torch.tensor(attention_mask, dtype=torch.long)
position_ids = torch.tensor(position_ids, dtype=torch.long)
return {
"input_ids": input_ids.to(device),
"attention_mask": attention_mask.to(device),
"position_ids": position_ids.to(device),
}
AutoTokenizer.register("CustomCLIPTokenizer", CustomCLIPTokenizer)
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