paddlenlp.transformers.layoutxlm.tokenizer 源代码

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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" Tokenization classes for LayoutXLM model."""

import itertools
from dataclasses import dataclass, field
from collections import OrderedDict
from typing import List, Optional

import sentencepiece as spm

from .. import PretrainedTokenizer, AddedToken
from ..tokenizer_utils import _is_punctuation, _is_control, _is_whitespace

SPIECE_UNDERLINE = "▁"

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "layoutxlm-base-uncased": 514,

    # FIXME(wj-Mcat): why this model-name not in the init-configuration
    # "layoutxlm-wo-backbone-base-uncased": 514
}


def _is_end_of_word(text):
    """Checks whether the last character in text is one of a punctuation, control or whitespace character."""
    last_char = text[-1]
    return bool(
        _is_control(last_char) | _is_punctuation(last_char)
        | _is_whitespace(last_char))


def _is_start_of_word(text):
    """Checks whether the first character in text is one of a punctuation, control or whitespace character."""
    first_char = text[0]
    return bool(
        _is_control(first_char) | _is_punctuation(first_char)
        | _is_whitespace(first_char))


[文档]class LayoutXLMTokenizer(PretrainedTokenizer): resource_files_names = {"vocab_file": "sentencepiece.bpe.model"} pretrained_resource_files_map = { "vocab_file": { "layoutxlm-base-uncased": "https://bj.bcebos.com/paddlenlp/models/transformers/layoutxlm_base/sentencepiece.bpe.model", } } pretrained_init_configuration = { "layoutxlm-base-uncased": { "do_lower_case": False }, } max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] SPECIAL_TOKENS_ATTRIBUTES = [ "bos_token", "eos_token", "unk_token", "sep_token", "pad_token", "cls_token", "mask_token", "additional_special_tokens", ] def __init__(self, vocab_file, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", **kwargs): mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance( mask_token, str) else mask_token self._bos_token = bos_token self._eos_token = eos_token self._sep_token = sep_token self._cls_token = cls_token self._unk_token = unk_token self._pad_token = pad_token self._mask_token = mask_token self.sp_model = spm.SentencePieceProcessor() self.sp_model.Load(vocab_file) self.vocab_file = vocab_file self.tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3} # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab self.offset = 1 self.tokens_to_ids["<mask>"] = len(self.sp_model) + self.offset self.ids_to_tokens = {v: k for k, v in self.tokens_to_ids.items()}
[文档] def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]: if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + sep + token_ids_1 + sep
[文档] def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False) -> List[int]: if already_has_special_tokens: if token_ids_1 is not None: raise ValueError( "You should not supply a second sequence if the provided sequence of " "ids is already formatted with special tokens for the model." ) return list( map( lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ( [0] * len(token_ids_1)) + [1]
[文档] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]: sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
@property def vocab_size(self): return len(self.sp_model) + self.offset + 1 # Add the <mask> token
[文档] def get_vocab(self): vocab = { self.convert_ids_to_tokens(i): i for i in range(self.vocab_size) } vocab.update(self.added_tokens_encoder) return vocab
def _tokenize(self, text): return self.sp_model.EncodeAsPieces(text) def _convert_token_to_id(self, token): """ Converts a token (str) in an id using the vocab. """ if token in self.tokens_to_ids: return self.tokens_to_ids[token] spm_id = self.sp_model.PieceToId(token) # Need to return unknown token if the SP model returned 0 return spm_id + self.offset if spm_id else self.unk_token_id def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index in self.ids_to_tokens: return self.ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.offset)
[文档] def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (strings for sub-words) in a single string.""" out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string
[文档] def num_special_tokens_to_add(self, pair=False): token_ids_0 = [] token_ids_1 = [] return len( self.build_inputs_with_special_tokens( token_ids_0, token_ids_1 if pair else None))