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# -*- coding: utf-8 -*-
# file: omnigenbench_wrapper.py
# time: 00:57 27/04/2024
# author: YANG, HENG <hy345@exeter.ac.uk> (杨恒)
# github: https://github.com/yangheng95
# huggingface: https://huggingface.co/yangheng
# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en
# Copyright (C) 2019-2024. All Rights Reserved.
import warnings
from omnigenbench import OmniTokenizer
class Tokenizer(OmniTokenizer):
def __init__(self, base_tokenizer=None, u2t=True, add_whitespace=False, **kwargs):
super(Tokenizer, self).__init__(
base_tokenizer, u2t=u2t, add_whitespace=add_whitespace, **kwargs
)
self.metadata["tokenizer_name"] = self.__class__.__name__
def __call__(self, sequence, **kwargs):
if self.u2t:
sequence = "".join([seq.replace("U", "T").upper() for seq in sequence])
if self.t2u:
sequence = "".join([seq.replace("T", "U").upper() for seq in sequence])
if self.add_whitespace:
sequence = " ".join(list(sequence))
sequence_tokens = self.tokenize(sequence)[
: kwargs.get("max_length", self.max_length) - 2
]
tokenized_inputs = {
"input_ids": [],
"attention_mask": [],
}
bos_id = (
self.base_tokenizer.bos_token_id
if self.base_tokenizer.bos_token_id is not None
else self.base_tokenizer.cls_token_id
)
eos_id = (
self.base_tokenizer.eos_token_id
if self.base_tokenizer.eos_token_id is not None
else self.base_tokenizer.sep_token_id
)
for tokens in sequence_tokens:
tokenized_inputs["input_ids"].append(
[bos_id] + self.base_tokenizer.convert_tokens_to_ids(tokens) + [eos_id]
)
tokenized_inputs["attention_mask"].append(
[1] * len(tokenized_inputs["input_ids"][-1])
)
for i, ids in enumerate(tokenized_inputs["input_ids"]):
if ids.count(self.base_tokenizer.unk_token_id) / len(ids) > 0.1:
warnings.warn(
f"Unknown tokens are more than "
f"{ids.count(self.base_tokenizer.unk_token_id) / len(ids)}% in the {i}-th sequence, "
f"please check the tokenization process."
)
max_length = max(len(ids) for ids in tokenized_inputs["input_ids"])
tokenized_inputs = self.base_tokenizer.pad(
tokenized_inputs,
padding=kwargs.get("padding", "max_length"),
max_length=min(max_length, kwargs.get("max_length", 512)),
return_attention_mask=kwargs.get("return_attention_mask", True),
return_tensors="pt",
)
return tokenized_inputs
def tokenize(self, sequence, **kwargs):
if isinstance(sequence, str):
sequences = [sequence]
else:
sequences = sequence
sequence_tokens = []
for i in range(len(sequences)):
sequence_tokens.append(list(sequences[i]))
return sequence_tokens
def encode(self, sequence, **kwargs):
return self.base_tokenizer.encode(sequence, **kwargs)
def decode(self, sequence, **kwargs):
return self.base_tokenizer.decode(sequence, **kwargs)
def encode_plus(self, sequence, **kwargs):
return self.base_tokenizer.encode_plus(sequence, **kwargs)
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