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import torch
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from transformers import BertTokenizerFast
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from colbert.modeling.tokenization.utils import _split_into_batches, _sort_by_length
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class DocTokenizer():
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def __init__(self, doc_maxlen):
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self.tok = BertTokenizerFast.from_pretrained('bert-base-multilingual-uncased')
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self.doc_maxlen = doc_maxlen
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self.D_marker_token, self.D_marker_token_id = '[D]', self.tok.convert_tokens_to_ids('[unused1]')
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self.cls_token, self.cls_token_id = self.tok.cls_token, self.tok.cls_token_id
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self.sep_token, self.sep_token_id = self.tok.sep_token, self.tok.sep_token_id
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assert self.D_marker_token_id == 1
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def tokenize(self, batch_text, add_special_tokens=False):
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assert type(batch_text) in [list, tuple], (type(batch_text))
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tokens = [self.tok.tokenize(x, add_special_tokens=False) for x in batch_text]
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if not add_special_tokens:
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return tokens
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prefix, suffix = [self.cls_token, self.D_marker_token], [self.sep_token]
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tokens = [prefix + lst + suffix for lst in tokens]
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return tokens
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def encode(self, batch_text, add_special_tokens=False):
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assert type(batch_text) in [list, tuple], (type(batch_text))
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ids = self.tok(batch_text, add_special_tokens=False)['input_ids']
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if not add_special_tokens:
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return ids
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prefix, suffix = [self.cls_token_id, self.D_marker_token_id], [self.sep_token_id]
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ids = [prefix + lst + suffix for lst in ids]
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return ids
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def tensorize(self, batch_text, bsize=None):
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assert type(batch_text) in [list, tuple], (type(batch_text))
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batch_text = ['. ' + x for x in batch_text]
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obj = self.tok(batch_text, padding='longest', truncation='longest_first',
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return_tensors='pt', max_length=self.doc_maxlen)
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ids, mask = obj['input_ids'], obj['attention_mask']
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ids[:, 1] = self.D_marker_token_id
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if bsize:
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ids, mask, reverse_indices = _sort_by_length(ids, mask, bsize)
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batches = _split_into_batches(ids, mask, bsize)
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return batches, reverse_indices
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return ids, mask
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