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83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import html
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import string
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import ftfy
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import regex as re
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from transformers import AutoTokenizer
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__all__ = ['HuggingfaceTokenizer']
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def basic_clean(text):
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text = ftfy.fix_text(text)
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text = html.unescape(html.unescape(text))
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return text.strip()
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def whitespace_clean(text):
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text = re.sub(r'\s+', ' ', text)
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text = text.strip()
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return text
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def canonicalize(text, keep_punctuation_exact_string=None):
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text = text.replace('_', ' ')
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if keep_punctuation_exact_string:
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text = keep_punctuation_exact_string.join(
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part.translate(str.maketrans('', '', string.punctuation))
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for part in text.split(keep_punctuation_exact_string))
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else:
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text = text.translate(str.maketrans('', '', string.punctuation))
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text = text.lower()
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text = re.sub(r'\s+', ' ', text)
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return text.strip()
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class HuggingfaceTokenizer:
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def __init__(self, name, seq_len=None, clean=None, **kwargs):
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assert clean in (None, 'whitespace', 'lower', 'canonicalize')
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self.name = name
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self.seq_len = seq_len
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self.clean = clean
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# init tokenizer
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self.tokenizer = AutoTokenizer.from_pretrained(name, **kwargs)
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self.vocab_size = self.tokenizer.vocab_size
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def __call__(self, sequence, **kwargs):
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return_mask = kwargs.pop('return_mask', False)
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# arguments
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_kwargs = {'return_tensors': 'pt'}
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if self.seq_len is not None:
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_kwargs.update({
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'padding': 'max_length',
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'truncation': True,
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'max_length': self.seq_len
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})
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_kwargs.update(**kwargs)
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# tokenization
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if isinstance(sequence, str):
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sequence = [sequence]
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if self.clean:
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sequence = [self._clean(u) for u in sequence]
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ids = self.tokenizer(sequence, **_kwargs)
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# output
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if return_mask:
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return ids.input_ids, ids.attention_mask
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else:
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return ids.input_ids
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def _clean(self, text):
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if self.clean == 'whitespace':
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text = whitespace_clean(basic_clean(text))
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elif self.clean == 'lower':
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text = whitespace_clean(basic_clean(text)).lower()
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elif self.clean == 'canonicalize':
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text = canonicalize(basic_clean(text))
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return text
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