51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
import logging
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import torch
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from tokenizers import Tokenizer
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# Special tokens
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SOT = "[START]"
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EOT = "[STOP]"
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UNK = "[UNK]"
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SPACE = "[SPACE]"
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SPECIAL_TOKENS = [SOT, EOT, UNK, SPACE, "[PAD]", "[SEP]", "[CLS]", "[MASK]"]
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logger = logging.getLogger(__name__)
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class EnTokenizer:
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def __init__(self, vocab_file_path):
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self.tokenizer: Tokenizer = Tokenizer.from_file(vocab_file_path)
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self.check_vocabset_sot_eot()
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def check_vocabset_sot_eot(self):
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voc = self.tokenizer.get_vocab()
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assert SOT in voc
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assert EOT in voc
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def text_to_tokens(self, text: str):
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text_tokens = self.encode(text)
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text_tokens = torch.IntTensor(text_tokens).unsqueeze(0)
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return text_tokens
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def encode( self, txt: str, verbose=False):
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"""
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clean_text > (append `lang_id`) > replace SPACE > encode text using Tokenizer
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"""
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txt = txt.replace(' ', SPACE)
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code = self.tokenizer.encode(txt)
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ids = code.ids
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return ids
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def decode(self, seq):
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if isinstance(seq, torch.Tensor):
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seq = seq.cpu().numpy()
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txt: str = self.tokenizer.decode(seq,
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skip_special_tokens=False)
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txt = txt.replace(' ', '')
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txt = txt.replace(SPACE, ' ')
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txt = txt.replace(EOT, '')
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txt = txt.replace(UNK, '')
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return txt
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