paddlenlp.transformers.tokenizer_utils_base 源代码

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import copy
import io
import json
import os
import re
import warnings
from collections import OrderedDict, UserDict
from dataclasses import dataclass, field
from enum import Enum
from shutil import copyfile
from typing import (TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional,
                    Sequence, Tuple, Union)

import numpy as np
import paddle
from huggingface_hub import hf_hub_download

from paddlenlp import __version__
from paddlenlp.utils.downloader import (COMMUNITY_MODEL_PREFIX,
                                        get_path_from_url_with_filelock)
from paddlenlp.utils.env import MODEL_HOME
from paddlenlp.utils.log import logger


[文档]@dataclass(frozen=True, eq=True) class AddedToken: """ AddedToken represents a token to be added to a Tokenizer An AddedToken can have special options defining the way it should behave. """ content: str = field(default_factory=str) single_word: bool = False lstrip: bool = False rstrip: bool = False normalized: bool = True def __getstate__(self): return self.__dict__ def __str__(self): return self.content
[文档]@dataclass class FastEncoding: """This is dummy class reserved for fast tokenizer""" pass
[文档]class ExplicitEnum(Enum): """ Enum with more explicit error message for missing values. """ @classmethod def _missing_(cls, value): raise ValueError( f"{value} is not a valid {cls.__name__}, please select one of {list(cls._value2member_map_.keys())}" )
[文档]class PaddingStrategy(ExplicitEnum): """ Possible values for the `padding` argument in [`PretrainedTokenizerBase.__call__`]. Useful for tab-completion in an IDE. """ LONGEST = "longest" MAX_LENGTH = "max_length" DO_NOT_PAD = "do_not_pad"
[文档]class TensorType(ExplicitEnum): """ Possible values for the `return_tensors` argument in [`PretrainedTokenizerBase.__call__`]. Useful for tab-completion in an IDE. """ PADDLE = "pd" NUMPY = "np"
VERY_LARGE_INTEGER = int( 1e30 ) # This is used to set the max input length for a model with infinite size input LARGE_INTEGER = int( 1e20 ) # This is used when we need something big but slightly smaller than VERY_LARGE_INTEGER # Define type aliases and NamedTuples TextInput = str PreTokenizedInput = List[str] EncodedInput = List[int] TextInputPair = Tuple[str, str] PreTokenizedInputPair = Tuple[List[str], List[str]] EncodedInputPair = Tuple[List[int], List[int]] # Slow tokenizers used to be saved in three separated files SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json" ADDED_TOKENS_FILE = "added_tokens.json" TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
[文档]def to_py_obj(obj): """ Convert a Paddle tensor, Numpy array or python list to a python list. """ if isinstance(obj, (dict, UserDict)): return {k: to_py_obj(v) for k, v in obj.items()} elif isinstance(obj, (list, tuple)): return [to_py_obj(o) for o in obj] elif isinstance(obj, paddle.Tensor): return obj.numpy().tolist() elif isinstance( obj, (np.ndarray, np.number)): # tolist also works on 0d np arrays return obj.tolist() else: return obj
def _is_numpy(x): return isinstance(x, np.ndarray)
[文档]class TruncationStrategy(ExplicitEnum): """ Possible values for the `truncation` argument in [`PretrainedTokenizerBase.__call__`]. Useful for tab-completion in an IDE. """ ONLY_FIRST = "only_first" ONLY_SECOND = "only_second" LONGEST_FIRST = "longest_first" DO_NOT_TRUNCATE = "do_not_truncate"
[文档]class CharSpan(NamedTuple): """ Character span in the original string. Args: start (`int`): Index of the first character in the original string. end (`int`): Index of the character following the last character in the original string. """ start: int end: int
[文档]class TokenSpan(NamedTuple): """ Token span in an encoded string (list of tokens). Args: start (`int`): Index of the first token in the span. end (`int`): Index of the token following the last token in the span. """ start: int end: int
[文档]class BatchEncoding(UserDict): """ Holds the output of the [`PretrainedTokenizerBase.__call__`], [`PretrainedTokenizerBase.encode_plus`] and [`PretrainedTokenizerBase.batch_encode_plus`] methods (tokens, attention_masks, etc). This class is derived from a python dictionary and can be used as a dictionary. In addition, this class exposes utility methods to map from word/character space to token space. Args: data (`dict`): Dictionary of lists/arrays/tensors returned by the `__call__`/`encode`/`batch_encode` methods ('input_ids', 'attention_mask', etc.). tensor_type (`Union[None, str, TensorType]`, *optional*): You can give a tensor_type here to convert the lists of integers in Paddle/Numpy Tensors at initialization. prepend_batch_axis (`bool`, *optional*, defaults to `False`): Whether or not to add a batch axis when converting to tensors (see `tensor_type` above). """ def __init__( self, data: Optional[Dict[str, Any]] = None, encoding: Optional[Union[FastEncoding, Sequence[FastEncoding]]] = None, tensor_type: Union[None, str] = None, prepend_batch_axis: bool = False, n_sequences: Optional[int] = None, ): super().__init__(data) if isinstance(encoding, FastEncoding): encoding = [encoding] self._encodings = encoding if n_sequences is None and encoding is not None and len(encoding): n_sequences = encoding[0].n_sequences self._n_sequences = n_sequences self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis) @property def n_sequences(self) -> Optional[int]: """ `Optional[int]`: The number of sequences used to generate each sample from the batch encoded in this [`BatchEncoding`]. Currently can be one of `None` (unknown), `1` (a single sentence) or `2` (a pair of sentences) """ return self._n_sequences @property def is_fast(self) -> bool: """ `bool`: Indicate whether this [`BatchEncoding`] was generated from the result of a [`PretrainedFastTokenizer`] or not. """ return self._encodings is not None def __getitem__(self, item: Union[int, str]) -> Union[Any, FastEncoding]: """ If the key is a string, returns the value of the dict associated to `key` ('input_ids', 'attention_mask', etc.). If the key is an integer, get the `Encoding` for batch item with index `key`. """ if isinstance(item, str): return self.data[item] elif self._encodings is not None: return self._encodings[item] else: raise KeyError( "Indexing with integers is not available when using tokenizer.__call__()" " with return_dict=True. Please set return_dict to False to use integer indexing." ) def __getattr__(self, item: str): try: return self.data[item] except KeyError: raise AttributeError def __getstate__(self): return {"data": self.data, "encodings": self._encodings} def __setstate__(self, state): if "data" in state: self.data = state["data"] if "encodings" in state: self._encodings = state["encodings"]
[文档] def keys(self): return self.data.keys()
[文档] def values(self): return self.data.values()
[文档] def items(self): return self.data.items()
# After this point: # Extended properties and methods only available for fast tokenizers # not yet supported @property def encodings(self) -> Optional[List[FastEncoding]]: """ `Optional[List[FastEncoding]]`: The list all encodings from the tokenization process. Returns `None` if the input was tokenized through Python (i.e., not a fast) tokenizer. """ return self._encodings
[文档] def tokens(self, batch_index: int = 0) -> List[str]: """ Return the list of tokens (sub-parts of the input strings after word/subword splitting and before conversion to integer indices) at a given batch index (only works for the output of a fast tokenizer). Args: batch_index (`int`, *optional*, defaults to 0): The index to access in the batch. Returns: `List[str]`: The list of tokens at that index. """ if not self._encodings: raise ValueError( "tokens() is not available when using Python-based tokenizers") return self._encodings[batch_index].tokens
[文档] def sequence_ids(self, batch_index: int = 0) -> List[Optional[int]]: """ Return a list mapping the tokens to the id of their original sentences: - `None` for special tokens added around or between sequences, - `0` for tokens corresponding to words in the first sequence, - `1` for tokens corresponding to words in the second sequence when a pair of sequences was jointly encoded. Args: batch_index (`int`, *optional*, defaults to 0): The index to access in the batch. Returns: `List[Optional[int]]`: A list indicating the sequence id corresponding to each token. Special tokens added by the tokenizer are mapped to `None` and other tokens are mapped to the index of their corresponding sequence. """ if not self._encodings: raise ValueError( "sequence_ids() is not available when using Python-based tokenizers" ) return self._encodings[batch_index].sequence_ids
[文档] def words(self, batch_index: int = 0) -> List[Optional[int]]: """ Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer. Args: batch_index (`int`, *optional*, defaults to 0): The index to access in the batch. Returns: `List[Optional[int]]`: A list indicating the word corresponding to each token. Special tokens added by the tokenizer are mapped to `None` and other tokens are mapped to the index of their corresponding word (several tokens will be mapped to the same word index if they are parts of that word). """ if not self._encodings: raise ValueError( "words() is not available when using Python-based tokenizers") warnings.warn( "`BatchEncoding.words()` property is deprecated and should be replaced with the identical, " "but more self-explanatory `BatchEncoding.word_ids()` property.", FutureWarning, ) return self.word_ids(batch_index)
[文档] def word_ids(self, batch_index: int = 0) -> List[Optional[int]]: """ Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer. Args: batch_index (`int`, *optional*, defaults to 0): The index to access in the batch. Returns: `List[Optional[int]]`: A list indicating the word corresponding to each token. Special tokens added by the tokenizer are mapped to `None` and other tokens are mapped to the index of their corresponding word (several tokens will be mapped to the same word index if they are parts of that word). """ if not self._encodings: raise ValueError( "word_ids() is not available when using Python-based tokenizers" ) return self._encodings[batch_index].word_ids
[文档] def token_to_sequence(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int: """ Get the index of the sequence represented by the given token. In the general use case, this method returns `0` for a single sequence or the first sequence of a pair, and `1` for the second sequence of a pair Can be called as: - `self.token_to_sequence(token_index)` if batch size is 1 - `self.token_to_sequence(batch_index, token_index)` if batch size is greater than 1 This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e., words are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized words. Args: batch_or_token_index (`int`): Index of the sequence in the batch. If the batch only comprises one sequence, this can be the index of the token in the sequence. token_index (`int`, *optional*): If a batch index is provided in *batch_or_token_index*, this can be the index of the token in the sequence. Returns: `int`: Index of the word in the input sequence. """ if not self._encodings: raise ValueError( "token_to_sequence() is not available when using Python based tokenizers" ) if token_index is not None: batch_index = batch_or_token_index else: batch_index = 0 token_index = batch_or_token_index if batch_index < 0: batch_index = self._batch_size + batch_index if token_index < 0: token_index = self._seq_len + token_index return self._encodings[batch_index].token_to_sequence(token_index)
[文档] def token_to_word(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int: """ Get the index of the word corresponding (i.e. comprising) to an encoded token in a sequence of the batch. Can be called as: - `self.token_to_word(token_index)` if batch size is 1 - `self.token_to_word(batch_index, token_index)` if batch size is greater than 1 This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e., words are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized words. Args: batch_or_token_index (`int`): Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of the token in the sequence. token_index (`int`, *optional*): If a batch index is provided in *batch_or_token_index*, this can be the index of the token in the sequence. Returns: `int`: Index of the word in the input sequence. """ if not self._encodings: raise ValueError( "token_to_word() is not available when using Python based tokenizers" ) if token_index is not None: batch_index = batch_or_token_index else: batch_index = 0 token_index = batch_or_token_index if batch_index < 0: batch_index = self._batch_size + batch_index if token_index < 0: token_index = self._seq_len + token_index return self._encodings[batch_index].token_to_word(token_index)
[文档] def word_to_tokens(self, batch_or_word_index: int, word_index: Optional[int] = None, sequence_index: int = 0) -> Optional[TokenSpan]: """ Get the encoded token span corresponding to a word in a sequence of the batch. Token spans are returned as a [`TokenSpan`] with: - **start** -- Index of the first token. - **end** -- Index of the token following the last token. Can be called as: - `self.word_to_tokens(word_index, sequence_index: int = 0)` if batch size is 1 - `self.word_to_tokens(batch_index, word_index, sequence_index: int = 0)` if batch size is greater or equal to 1 This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized words. Args: batch_or_word_index (`int`): Index of the sequence in the batch. If the batch only comprises one sequence, this can be the index of the word in the sequence. word_index (`int`, *optional*): If a batch index is provided in *batch_or_token_index*, this can be the index of the word in the sequence. sequence_index (`int`, *optional*, defaults to 0): If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0 or 1) the provided word index belongs to. Returns: Optional [`TokenSpan`] Span of tokens in the encoded sequence. Returns `None` if no tokens correspond to the word. """ if not self._encodings: raise ValueError( "word_to_tokens() is not available when using Python based tokenizers" ) if word_index is not None: batch_index = batch_or_word_index else: batch_index = 0 word_index = batch_or_word_index if batch_index < 0: batch_index = self._batch_size + batch_index if word_index < 0: word_index = self._seq_len + word_index span = self._encodings[batch_index].word_to_tokens( word_index, sequence_index) return TokenSpan(*span) if span is not None else None
[文档] def token_to_chars(self, batch_or_token_index: int, token_index: Optional[int] = None) -> CharSpan: """ Get the character span corresponding to an encoded token in a sequence of the batch. Character spans are returned as a [`CharSpan`] with: - **start** -- Index of the first character in the original string associated to the token. - **end** -- Index of the character following the last character in the original string associated to the token. Can be called as: - `self.token_to_chars(token_index)` if batch size is 1 - `self.token_to_chars(batch_index, token_index)` if batch size is greater or equal to 1 Args: batch_or_token_index (`int`): Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of the token in the sequence. token_index (`int`, *optional*): If a batch index is provided in *batch_or_token_index*, this can be the index of the token or tokens in the sequence. Returns: [`CharSpan`]: Span of characters in the original string. """ if not self._encodings: raise ValueError( "token_to_chars() is not available when using Python based tokenizers" ) if token_index is not None: batch_index = batch_or_token_index else: batch_index = 0 token_index = batch_or_token_index return CharSpan( *(self._encodings[batch_index].token_to_chars(token_index)))
[文档] def char_to_token(self, batch_or_char_index: int, char_index: Optional[int] = None, sequence_index: int = 0) -> int: """ Get the index of the token in the encoded output comprising a character in the original string for a sequence of the batch. Can be called as: - `self.char_to_token(char_index)` if batch size is 1 - `self.char_to_token(batch_index, char_index)` if batch size is greater or equal to 1 This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized words. Args: batch_or_char_index (`int`): Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of the word in the sequence char_index (`int`, *optional*): If a batch index is provided in *batch_or_token_index*, this can be the index of the word in the sequence. sequence_index (`int`, *optional*, defaults to 0): If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0 or 1) the provided character index belongs to. Returns: `int`: Index of the token. """ if not self._encodings: raise ValueError( "char_to_token() is not available when using Python based tokenizers" ) if char_index is not None: batch_index = batch_or_char_index else: batch_index = 0 char_index = batch_or_char_index return self._encodings[batch_index].char_to_token( char_index, sequence_index)
[文档] def word_to_chars(self, batch_or_word_index: int, word_index: Optional[int] = None, sequence_index: int = 0) -> CharSpan: """ Get the character span in the original string corresponding to given word in a sequence of the batch. Character spans are returned as a CharSpan NamedTuple with: - start: index of the first character in the original string - end: index of the character following the last character in the original string Can be called as: - `self.word_to_chars(word_index)` if batch size is 1 - `self.word_to_chars(batch_index, word_index)` if batch size is greater or equal to 1 Args: batch_or_word_index (`int`): Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of the word in the sequence word_index (`int`, *optional*): If a batch index is provided in *batch_or_token_index*, this can be the index of the word in the sequence. sequence_index (`int`, *optional*, defaults to 0): If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0 or 1) the provided word index belongs to. Returns: `CharSpan` or `List[CharSpan]`: Span(s) of the associated character or characters in the string. CharSpan are NamedTuple with: - start: index of the first character associated to the token in the original string - end: index of the character following the last character associated to the token in the original string """ if not self._encodings: raise ValueError( "word_to_chars() is not available when using Python based tokenizers" ) if word_index is not None: batch_index = batch_or_word_index else: batch_index = 0 word_index = batch_or_word_index return CharSpan(*(self._encodings[batch_index].word_to_chars( word_index, sequence_index)))
[文档] def char_to_word(self, batch_or_char_index: int, char_index: Optional[int] = None, sequence_index: int = 0) -> int: """ Get the word in the original string corresponding to a character in the original string of a sequence of the batch. Can be called as: - `self.char_to_word(char_index)` if batch size is 1 - `self.char_to_word(batch_index, char_index)` if batch size is greater than 1 This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized words. Args: batch_or_char_index (`int`): Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of the character in the original string. char_index (`int`, *optional*): If a batch index is provided in *batch_or_token_index*, this can be the index of the character in the original string. sequence_index (`int`, *optional*, defaults to 0): If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0 or 1) the provided character index belongs to. Returns: `int` or `List[int]`: Index or indices of the associated encoded token(s). """ if not self._encodings: raise ValueError( "char_to_word() is not available when using Python based tokenizers" ) if char_index is not None: batch_index = batch_or_char_index else: batch_index = 0 char_index = batch_or_char_index return self._encodings[batch_index].char_to_word( char_index, sequence_index)
[文档] def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None, prepend_batch_axis: bool = False): """ Convert the inner content to tensors. Args: tensor_type (`str` or [`TensorType`], *optional*): The type of tensors to use. If `str`, should be one of the values of the enum [`TensorType`]. If `None`, no modification is done. prepend_batch_axis (`int`, *optional*, defaults to `False`): Whether or not to add the batch dimension during the conversion. """ if tensor_type is None: return self # Convert to TensorType if not isinstance(tensor_type, TensorType): tensor_type = TensorType(tensor_type) # Get a function reference for the correct framework if tensor_type == TensorType.PADDLE: as_tensor = paddle.to_tensor is_tensor = paddle.is_tensor else: as_tensor = np.asarray is_tensor = _is_numpy # Do the tensor conversion in batch for key, value in self.items(): try: if prepend_batch_axis: value = [value] if not is_tensor(value): tensor = as_tensor(value) self[key] = tensor except: # noqa E722 if key == "overflowing_tokens": raise ValueError( "Unable to create tensor returning overflowing tokens of different lengths. " "Please see if a fast version of this tokenizer is available to have this feature available." ) raise ValueError( "Unable to create tensor, you should probably activate truncation and/or padding " "with 'padding=True' 'truncation=True' to have batched tensors with the same length." ) return self
[文档]class SpecialTokensMixin: """ A mixin derived by [`PretrainedTokenizer`] to handle specific behaviors related to special tokens. In particular, this class hold the attributes which can be used to directly access these special tokens in a model-independent manner and allow to set and update the special tokens. Args: bos_token (`str` or `AddedToken`, *optional*): A special token representing the beginning of a sentence. eos_token (`str` or `AddedToken`, *optional*): A special token representing the end of a sentence. unk_token (`str` or `AddedToken`, *optional*): A special token representing an out-of-vocabulary token. sep_token (`str` or `AddedToken`, *optional*): A special token separating two different sentences in the same input (used by BERT for instance). pad_token (`str` or `AddedToken`, *optional*): A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation. cls_token (`str` or `AddedToken`, *optional*): A special token representing the class of the input (used by BERT for instance). mask_token (`str` or `AddedToken`, *optional*): A special token representing a masked token (used by masked-language modeling pretraining objectives, like BERT). additional_special_tokens (tuple or list of `str` or `AddedToken`, *optional*): A tuple or a list of additional special tokens. """ SPECIAL_TOKENS_ATTRIBUTES = [ "bos_token", "eos_token", "unk_token", "sep_token", "pad_token", "cls_token", "mask_token", "additional_special_tokens", ] def __init__(self, verbose=True, **kwargs): # note(guosheng): Since `__init__` might be called multiple times which # is hooked before `PretrainedTokenizer` init, we do not set to None as # HF to avoid unintentional overriding. self._bos_token = getattr(self, "_bos_token", None) self._eos_token = getattr(self, "_eos_token", None) self._unk_token = getattr(self, "_unk_token", None) self._sep_token = getattr(self, "_sep_token", None) self._pad_token = getattr(self, "_pad_token", None) self._cls_token = getattr(self, "_cls_token", None) self._mask_token = getattr(self, "_mask_token", None) self._pad_token_type_id = getattr(self, "_pad_token_type_id", 0) self._additional_special_tokens = getattr(self, "_additional_special_tokens", []) self.verbose = verbose # We directly set the hidden value to allow initialization with special tokens # which are not yet in the vocabulary. Necessary for serialization/de-serialization # TODO clean this up at some point (probably by switching to fast tokenizers) for key, value in kwargs.items(): if value is None: continue if key in self.SPECIAL_TOKENS_ATTRIBUTES: if key == "additional_special_tokens": assert isinstance( value, (list, tuple)), f"Value {value} is not a list or tuple" assert all( isinstance(t, (str, AddedToken)) for t in value ), "One of the tokens is not a string or an AddedToken" setattr(self, key, value) elif isinstance(value, (str, AddedToken)): setattr(self, key, value) else: raise TypeError( f"special token {key} has to be either str or AddedToken but got: {type(value)}" )
[文档] def sanitize_special_tokens(self) -> int: """ Make sure that all the special tokens attributes of the tokenizer (`tokenizer.mask_token`, `tokenizer.cls_token`, etc.) are in the vocabulary. Add the missing ones to the vocabulary if needed. Return: `int`: The number of tokens added in the vocabulary during the operation. """ return self.add_tokens(self.all_special_tokens_extended, special_tokens=True)
[文档] def add_special_tokens( self, special_tokens_dict: Dict[str, Union[str, AddedToken]]) -> int: """ Add a dictionary of special tokens (eos, pad, cls, etc.) to the encoder and link them to class attributes. If special tokens are NOT in the vocabulary, they are added to it (indexed starting from the last index of the current vocabulary). Note,None When adding new tokens to the vocabulary, you should make sure to also resize the token embedding matrix of the model so that its embedding matrix matches the tokenizer. In order to do that, please use the [`~PreTrainedModel.resize_token_embeddings`] method. Using `add_special_tokens` will ensure your special tokens can be used in several ways: - Special tokens are carefully handled by the tokenizer (they are never split). - You can easily refer to special tokens using tokenizer class attributes like `tokenizer.cls_token`. This makes it easy to develop model-agnostic training and fine-tuning scripts. When possible, special tokens are already registered for provided pretrained models (for instance [`BertTokenizer`] `cls_token` is already registered to be :obj*'[CLS]'* and XLM's one is also registered to be `'</s>'`). Args: special_tokens_dict (dictionary *str* to *str* or `AddedToken`): Keys should be in the list of predefined special attributes: [`bos_token`, `eos_token`, `unk_token`, `sep_token`, `pad_token`, `cls_token`, `mask_token`, `additional_special_tokens`]. Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the `unk_token` to them). Returns: `int`: Number of tokens added to the vocabulary. Examples: ```python # Let's see how to add a new classification token to GPT-2 tokenizer = GPT2Tokenizer.from_pretrained("gpt2") model = GPT2Model.from_pretrained("gpt2") special_tokens_dict = {"cls_token": "<CLS>"} num_added_toks = tokenizer.add_special_tokens(special_tokens_dict) print("We have added", num_added_toks, "tokens") # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer. model.resize_token_embeddings(len(tokenizer)) assert tokenizer.cls_token == "<CLS>" ```""" if not special_tokens_dict: return 0 added_tokens = 0 for key, value in special_tokens_dict.items(): assert key in self.SPECIAL_TOKENS_ATTRIBUTES, f"Key {key} is not a special token" if self.verbose: logger.info( f"Assigning {value} to the {key} key of the tokenizer") setattr(self, key, value) if key == "additional_special_tokens": assert isinstance(value, (list, tuple)) and all( isinstance(t, (str, AddedToken)) for t in value ), f"Tokens {value} for key {key} should all be str or AddedToken instances" added_tokens += self.add_tokens(value, special_tokens=True) else: assert isinstance( value, (str, AddedToken) ), f"Token {value} for key {key} should be a str or an AddedToken instance" added_tokens += self.add_tokens([value], special_tokens=True) return added_tokens
[文档] def add_tokens(self, new_tokens: Union[str, AddedToken, List[Union[str, AddedToken]]], special_tokens: bool = False) -> int: """ Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to it with indices starting from length of the current vocabulary. Note,None When adding new tokens to the vocabulary, you should make sure to also resize the token embedding matrix of the model so that its embedding matrix matches the tokenizer. In order to do that, please use the [`~PreTrainedModel.resize_token_embeddings`] method. Args: new_tokens (`str`, `AddedToken` or a list of *str* or `AddedToken`): Tokens are only added if they are not already in the vocabulary. `AddedToken` wraps a string token to let you personalize its behavior: whether this token should only match against a single word, whether this token should strip all potential whitespaces on the left side, whether this token should strip all potential whitespaces on the right side, etc. special_tokens (`bool`, *optional*, defaults to `False`): Can be used to specify if the token is a special token. This mostly change the normalization behavior (special tokens like CLS or [MASK] are usually not lower-cased for instance). Returns: `int`: Number of tokens added to the vocabulary. Examples: ```python # Let's see how to increase the vocabulary of Bert model and tokenizer tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") model = BertModel.from_pretrained("bert-base-uncased") num_added_toks = tokenizer.add_tokens(["new_tok1", "my_new-tok2"]) print("We have added", num_added_toks, "tokens") # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer. model.resize_token_embeddings(len(tokenizer)) ```""" if not new_tokens: return 0 if not isinstance(new_tokens, (list, tuple)): new_tokens = [new_tokens] return self._add_tokens(new_tokens, special_tokens=special_tokens)
def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int: raise NotImplementedError @property def bos_token(self) -> str: """ `str`: Beginning of sentence token. Log an error if used while not having been set. """ if self._bos_token is None and self.verbose: logger.error("Using bos_token, but it is not set yet.") return None return str(self._bos_token) @property def eos_token(self) -> str: """ `str`: End of sentence token. Log an error if used while not having been set. """ if self._eos_token is None and self.verbose: logger.error("Using eos_token, but it is not set yet.") return None return str(self._eos_token) @property def unk_token(self) -> str: """ `str`: Unknown token. Log an error if used while not having been set. """ if self._unk_token is None and self.verbose: logger.error("Using unk_token, but it is not set yet.") return None return str(self._unk_token) @property def sep_token(self) -> str: """ `str`: Separation token, to separate context and query in an input sequence. Log an error if used while not having been set. """ if self._sep_token is None and self.verbose: logger.error("Using sep_token, but it is not set yet.") return None return str(self._sep_token) @property def pad_token(self) -> str: """ `str`: Padding token. Log an error if used while not having been set. """ if self._pad_token is None and self.verbose: logger.error("Using pad_token, but it is not set yet.") return None return str(self._pad_token) @property def cls_token(self) -> str: """ `str`: Classification token, to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """ if self._cls_token is None and self.verbose: logger.error("Using cls_token, but it is not set yet.") return None return str(self._cls_token) @property def mask_token(self) -> str: """ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not having been set. """ if self._mask_token is None and self.verbose: logger.error("Using mask_token, but it is not set yet.") return None return str(self._mask_token) @property def additional_special_tokens(self) -> List[str]: """ `List[str]`: All the additional special tokens you may want to use. Log an error if used while not having been set. """ if self._additional_special_tokens is None and self.verbose: logger.error( "Using additional_special_tokens, but it is not set yet.") return None return [str(tok) for tok in self._additional_special_tokens] @bos_token.setter def bos_token(self, value): self._bos_token = value @eos_token.setter def eos_token(self, value): self._eos_token = value @unk_token.setter def unk_token(self, value): self._unk_token = value @sep_token.setter def sep_token(self, value): self._sep_token = value @pad_token.setter def pad_token(self, value): self._pad_token = value @cls_token.setter def cls_token(self, value): self._cls_token = value @mask_token.setter def mask_token(self, value): self._mask_token = value @additional_special_tokens.setter def additional_special_tokens(self, value): self._additional_special_tokens = value @property def bos_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the beginning of sentence token in the vocabulary. Returns `None` if the token has not been set. """ if self._bos_token is None: return None return self.convert_tokens_to_ids(self.bos_token) @property def eos_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been set. """ if self._eos_token is None: return None return self.convert_tokens_to_ids(self.eos_token) @property def unk_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the unknown token in the vocabulary. Returns `None` if the token has not been set. """ if self._unk_token is None: return None return self.convert_tokens_to_ids(self.unk_token) @property def sep_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the separation token in the vocabulary, to separate context and query in an input sequence. Returns `None` if the token has not been set. """ if self._sep_token is None: return None return self.convert_tokens_to_ids(self.sep_token) @property def pad_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the padding token in the vocabulary. Returns `None` if the token has not been set. """ if self._pad_token is None: return None return self.convert_tokens_to_ids(self.pad_token) @property def pad_token_type_id(self) -> int: """ `int`: Id of the padding token type in the vocabulary. """ return self._pad_token_type_id @property def cls_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the classification token in the vocabulary, to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Returns `None` if the token has not been set. """ if self._cls_token is None: return None return self.convert_tokens_to_ids(self.cls_token) @property def mask_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the mask token in the vocabulary, used when training a model with masked-language modeling. Returns `None` if the token has not been set. """ if self._mask_token is None: return None return self.convert_tokens_to_ids(self.mask_token) @property def additional_special_tokens_ids(self) -> List[int]: """ `List[int]`: Ids of all the additional special tokens in the vocabulary. Log an error if used while not having been set. """ return self.convert_tokens_to_ids(self.additional_special_tokens) @bos_token_id.setter def bos_token_id(self, value): self._bos_token = self.convert_ids_to_tokens( value) if value is not None else None @eos_token_id.setter def eos_token_id(self, value): self._eos_token = self.convert_ids_to_tokens( value) if value is not None else None @unk_token_id.setter def unk_token_id(self, value): self._unk_token = self.convert_ids_to_tokens( value) if value is not None else None @sep_token_id.setter def sep_token_id(self, value): self._sep_token = self.convert_ids_to_tokens( value) if value is not None else None @pad_token_id.setter def pad_token_id(self, value): self._pad_token = self.convert_ids_to_tokens( value) if value is not None else None @cls_token_id.setter def cls_token_id(self, value): self._cls_token = self.convert_ids_to_tokens( value) if value is not None else None @mask_token_id.setter def mask_token_id(self, value): self._mask_token = self.convert_ids_to_tokens( value) if value is not None else None @additional_special_tokens_ids.setter def additional_special_tokens_ids(self, values): self._additional_special_tokens = [ self.convert_ids_to_tokens(value) for value in values ] @property def special_tokens_map(self) -> Dict[str, Union[str, List[str]]]: """ `Dict[str, Union[str, List[str]]]`: A dictionary mapping special token class attributes (`cls_token`, `unk_token`, etc.) to their values (`'<unk>'`, `'<cls>'`, etc.). Convert potential tokens of `AddedToken` type to string. """ set_attr = {} for attr in self.SPECIAL_TOKENS_ATTRIBUTES: attr_value = getattr(self, "_" + attr) if attr_value: set_attr[attr] = (type(attr_value)( str(attr_value_sub) for attr_value_sub in attr_value) if isinstance( attr_value, (list, tuple)) else str(attr_value)) return set_attr @property def special_tokens_map_extended( self ) -> Dict[str, Union[str, AddedToken, List[Union[str, AddedToken]]]]: """ `Dict[str, Union[str, AddedToken, List[Union[str, AddedToken]]]]`: A dictionary mapping special token class attributes (`cls_token`, `unk_token`, etc.) to their values (`'<unk>'`, `'<cls>'`, etc.). Don't convert tokens of `AddedToken` type to string so they can be used to control more finely how special tokens are tokenized. """ set_attr = {} for attr in self.SPECIAL_TOKENS_ATTRIBUTES: attr_value = getattr(self, "_" + attr) if attr_value: set_attr[attr] = attr_value return set_attr @property def all_special_tokens(self) -> List[str]: """ `List[str]`: All the special tokens (`'<unk>'`, `'<cls>'`, etc.) mapped to class attributes. Convert tokens of `AddedToken` type to string. """ all_toks = [str(s) for s in self.all_special_tokens_extended] return all_toks @property def all_special_tokens_extended(self) -> List[Union[str, AddedToken]]: """ `List[Union[str, AddedToken]]`: All the special tokens (`'<unk>'`, `'<cls>'`, etc.) mapped to class attributes. Don't convert tokens of `AddedToken` type to string so they can be used to control more finely how special tokens are tokenized. """ all_toks = [] set_attr = self.special_tokens_map_extended for attr_value in set_attr.values(): all_toks = all_toks + (list(attr_value) if isinstance( attr_value, (list, tuple)) else [attr_value]) all_toks = list(OrderedDict.fromkeys(all_toks)) return all_toks @property def all_special_ids(self) -> List[int]: """ `List[int]`: List the ids of the special tokens(`'<unk>'`, `'<cls>'`, etc.) mapped to class attributes. """ all_toks = self.all_special_tokens all_ids = self.convert_tokens_to_ids(all_toks) return all_ids
[文档]class PretrainedTokenizerBase(SpecialTokensMixin): """ Base class for [`PretrainedTokenizer`]. Class attributes (overridden by derived classes) - **resource_files_names** (`Dict[str, str]`) -- A dictionary with, as keys, the `__init__` keyword name of each vocabulary file required by the model, and as associated values, the filename for saving the associated file (string). - **pretrained_resource_files_map** (`Dict[str, Dict[str, str]]`) -- A dictionary of dictionaries, with the high-level keys being the `__init__` keyword name of each vocabulary file required by the model, the low-level being the `short-cut-names` of the pretrained models with, as associated values, the `url` to the associated pretrained vocabulary file. - **max_model_input_sizes** (`Dict[str, Optional[int]]`) -- A dictionary with, as keys, the `short-cut-names` of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model, or `None` if the model has no maximum input size. - **pretrained_init_configuration** (`Dict[str, Dict[str, Any]]`) -- A dictionary with, as keys, the `short-cut-names` of the pretrained models, and as associated values, a dictionary of specific arguments to pass to the `__init__` method of the tokenizer class for this pretrained model when loading the tokenizer with the [`~tokenizer_utils_base.PretrainedTokenizerBase.from_pretrained`] method. - **model_input_names** (`List[str]`) -- A list of inputs expected in the forward pass of the model. - **padding_side** (`str`) -- The default value for the side on which the model should have padding applied. Should be `'right'` or `'left'`. - **truncation_side** (`str`) -- The default value for the side on which the model should have truncation applied. Should be `'right'` or `'left'`. Args: model_max_length (`int`, *optional*): The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is loaded with [`~tokenizer_utils_base.PretrainedTokenizerBase.from_pretrained`], this will be set to the value stored for the associated model in `max_model_input_sizes` (see above). If no value is provided, will default to VERY_LARGE_INTEGER (`int(1e30)`). padding_side (`str`, *optional*): The side on which the model should have padding applied. Should be selected between ['right', 'left']. Default value is picked from the class attribute of the same name. truncation_side (`str`, *optional*): The side on which the model should have truncation applied. Should be selected between ['right', 'left']. Default value is picked from the class attribute of the same name. model_input_names (`List[string]`, *optional*): The list of inputs accepted by the forward pass of the model (like `"token_type_ids"` or `"attention_mask"`). Default value is picked from the class attribute of the same name. bos_token (`str` or `AddedToken`, *optional*): A special token representing the beginning of a sentence. Will be associated to `self.bos_token` and `self.bos_token_id`. eos_token (`str` or `AddedToken`, *optional*): A special token representing the end of a sentence. Will be associated to `self.eos_token` and `self.eos_token_id`. unk_token (`str` or `AddedToken`, *optional*): A special token representing an out-of-vocabulary token. Will be associated to `self.unk_token` and `self.unk_token_id`. sep_token (`str` or `AddedToken`, *optional*): A special token separating two different sentences in the same input (used by BERT for instance). Will be associated to `self.sep_token` and `self.sep_token_id`. pad_token (`str` or `AddedToken`, *optional*): A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation. Will be associated to `self.pad_token` and `self.pad_token_id`. cls_token (`str` or `AddedToken`, *optional*): A special token representing the class of the input (used by BERT for instance). Will be associated to `self.cls_token` and `self.cls_token_id`. mask_token (`str` or `AddedToken`, *optional*): A special token representing a masked token (used by masked-language modeling pretraining objectives, like BERT). Will be associated to `self.mask_token` and `self.mask_token_id`. additional_special_tokens (tuple or list of `str` or `AddedToken`, *optional*): A tuple or a list of additional special tokens. Add them here to ensure they won't be split by the tokenization process. Will be associated to `self.additional_special_tokens` and `self.additional_special_tokens_ids`. """ resource_files_names: Dict[str, str] = {} pretrained_resource_files_map: Dict[str, Dict[str, str]] = {} pretrained_init_configuration: Dict[str, Dict[str, Any]] = {} max_model_input_sizes: Dict[str, Optional[int]] = {} _auto_class: Optional[str] = None tokenizer_config_file = "tokenizer_config.json" # first name has to correspond to main model input name # to make sure `tokenizer.pad(...)` works correctly model_input_names: List[str] = ["input_ids", "token_type_ids"] padding_side: str = "right" truncation_side: str = "right" slow_tokenizer_class = None def __init__(self, **kwargs): # inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``) self.init_inputs = () self.init_kwargs = getattr(self, "init_kwargs", None) or copy.deepcopy(kwargs) self.name_or_path = kwargs.pop("name_or_path", "") self._processor_class = kwargs.pop("processor_class", None) # For backward compatibility we fallback to set model_max_length from max_len if provided model_max_length = kwargs.pop("model_max_length", kwargs.pop("max_len", None)) self.model_max_length = model_max_length if model_max_length is not None else VERY_LARGE_INTEGER # Padding and truncation side are right by default and overridden in subclasses. If specified in the kwargs, it # is changed. self.padding_side = kwargs.pop("padding_side", self.padding_side) if self.padding_side not in ["right", "left"]: raise ValueError( f"Padding side should be selected between 'right' and 'left', current value: {self.padding_side}" ) self.truncation_side = kwargs.pop("truncation_side", self.truncation_side) if self.truncation_side not in ["right", "left"]: raise ValueError( f"Padding side should be selected between 'right' and 'left', current value: {self.truncation_side}" ) self.model_input_names = kwargs.pop("model_input_names", self.model_input_names) self.deprecation_warnings = ( {} ) # Use to store when we have already noticed a deprecation warning (avoid overlogging). super().__init__(**kwargs) @property def max_len_single_sentence(self) -> int: """ `int`: The maximum length of a sentence that can be fed to the model. """ return self.model_max_length - self.num_special_tokens_to_add( pair=False) @property def max_len_sentences_pair(self) -> int: """ `int`: The maximum combined length of a pair of sentences that can be fed to the model. """ return self.model_max_length - self.num_special_tokens_to_add(pair=True) @max_len_single_sentence.setter def max_len_single_sentence(self, value) -> int: # For backward compatibility, allow to try to setup 'max_len_single_sentence'. if value == self.model_max_length - self.num_special_tokens_to_add( pair=False) and self.verbose: if not self.deprecation_warnings.get("max_len_single_sentence", False): warnings.warn( "Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up.") self.deprecation_warnings["max_len_single_sentence"] = True else: raise ValueError( "Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up.") @max_len_sentences_pair.setter def max_len_sentences_pair(self, value) -> int: # For backward compatibility, allow to try to setup 'max_len_sentences_pair'. if value == self.model_max_length - self.num_special_tokens_to_add( pair=True) and self.verbose: if not self.deprecation_warnings.get("max_len_sentences_pair", False): warnings.warn( "Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up.") self.deprecation_warnings["max_len_sentences_pair"] = True else: raise ValueError( "Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up.") def _set_processor_class(self, processor_class: str): """Sets processor class as an attribute.""" self._processor_class = processor_class def __repr__(self) -> str: return ( f"{'PretrainedTokenizer'}(name_or_path='{self.name_or_path}', " f"vocab_size={self.vocab_size}, model_max_len={self.model_max_length}, " f"padding_side='{self.padding_side}', truncation_side='{self.truncation_side}', special_tokens={self.special_tokens_map_extended})" )
[文档] def get_vocab(self) -> Dict[str, int]: """ Returns the vocabulary as a dictionary of token to index. `tokenizer.get_vocab()[token]` is equivalent to `tokenizer.convert_tokens_to_ids(token)` when `token` is in the vocab. Returns: `Dict[str, int]`: The vocabulary. """ raise NotImplementedError()
[文档] @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *args, from_hf_hub=False, **kwargs): """ Creates an instance of `PretrainedTokenizer`. Related resources are loaded by specifying name of a built-in pretrained model, or a community-contributed pretrained model, or a local file directory path. Args: pretrained_model_name_or_path (str): Name of pretrained model or dir path to load from. The string can be: - Name of built-in pretrained model - Name of a community-contributed pretrained model. - Local directory path which contains tokenizer related resources and tokenizer config file ("tokenizer_config.json"). *args (tuple): position arguments for model `__init__`. If provided, use these as position argument values for tokenizer initialization. **kwargs (dict): keyword arguments for model `__init__`. If provided, use these to update pre-defined keyword argument values for tokenizer initialization. Returns: PretrainedTokenizer: An instance of `PretrainedTokenizer`. Example: .. code-block:: from paddlenlp.transformers import BertTokenizer # Name of built-in pretrained model tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') # Name of community-contributed pretrained model tokenizer = BertTokenizer.from_pretrained('yingyibiao/bert-base-uncased-sst-2-finetuned') # Load from local directory path tokenizer = BertTokenizer.from_pretrained('./my_bert/') """ pretrained_model_name_or_path = str(pretrained_model_name_or_path) vocab_files = {} init_configuration = {} additional_files_names = { "added_tokens_file": ADDED_TOKENS_FILE, "special_tokens_map_file": SPECIAL_TOKENS_MAP_FILE, "tokenizer_config_file": TOKENIZER_CONFIG_FILE, } vocab_files_target = { **cls.resource_files_names, **additional_files_names } # From HF Hub if from_hf_hub: # Only include the necessary resource files specified by the tokenizer cls # Deep copy to avoid modifiying the class attributes vocab_files = copy.deepcopy(cls.resource_files_names) vocab_files["tokenizer_config_file"] = cls.tokenizer_config_file # From built-in pretrained models elif pretrained_model_name_or_path in cls.pretrained_init_configuration: for file_id, map_list in cls.pretrained_resource_files_map.items(): vocab_files[file_id] = map_list[pretrained_model_name_or_path] init_configuration = copy.deepcopy( cls.pretrained_init_configuration[pretrained_model_name_or_path] ) # From local dir path elif os.path.isdir(pretrained_model_name_or_path): vocab_files_target[ "tokenizer_config_file"] = cls.tokenizer_config_file for file_id, file_name in vocab_files_target.items(): full_file_name = os.path.join(pretrained_model_name_or_path, file_name) if os.path.isfile(full_file_name): vocab_files[file_id] = full_file_name else: # Assuming from community-contributed pretrained models for file_id, file_name in vocab_files_target.items(): full_file_name = "/".join([ COMMUNITY_MODEL_PREFIX, pretrained_model_name_or_path, file_name ]) vocab_files[file_id] = full_file_name vocab_files["tokenizer_config_file"] = "/".join([ COMMUNITY_MODEL_PREFIX, pretrained_model_name_or_path, cls.tokenizer_config_file ]) default_root = os.path.join(MODEL_HOME, pretrained_model_name_or_path) resolved_vocab_files = {} for file_id, file_path in vocab_files.items(): if file_path is None or os.path.isfile(file_path): resolved_vocab_files[file_id] = file_path continue if from_hf_hub: resolved_vocab_files[file_id] = hf_hub_download( repo_id=pretrained_model_name_or_path, filename=file_path, cache_dir=MODEL_HOME, library_name="PaddleNLP", library_version=__version__) else: path = os.path.join(default_root, file_path.split('/')[-1]) if os.path.exists(path): logger.info("Already cached %s" % path) resolved_vocab_files[file_id] = path else: logger.info("Downloading %s and saved to %s" % (file_path, default_root)) try: resolved_vocab_files[ file_id] = get_path_from_url_with_filelock( file_path, default_root) except RuntimeError as err: if file_id not in cls.resource_files_names: resolved_vocab_files[file_id] = None else: logger.error(err) raise RuntimeError( f"Can't load tokenizer for '{pretrained_model_name_or_path}'.\n" f"Please make sure that '{pretrained_model_name_or_path}' is:\n" "- a correct model-identifier of built-in pretrained models,\n" "- or a correct model-identifier of community-contributed pretrained models,\n" "- or the correct path to a directory containing relevant tokenizer files.\n" ) # Prepare tokenizer initialization kwargs # Did we saved some inputs and kwargs to reload ? has_tokenizer_file = resolved_vocab_files.get("tokenizer_file", None) is not None tokenizer_config_file = resolved_vocab_files.pop( "tokenizer_config_file", None) if tokenizer_config_file is not None: with io.open(tokenizer_config_file, encoding="utf-8") as f: init_kwargs = json.load(f) else: init_kwargs = init_configuration # position args are stored in kwargs, maybe better not include init_args = init_kwargs.pop("init_args", ()) init_kwargs.pop("init_class", None) # Update with newly provided args and kwargs init_args = init_args if not args else args init_kwargs.update(kwargs) def convert_added_tokens(obj): if isinstance( obj, dict) and "__type" in obj and obj["__type"] == "AddedToken": obj.pop("__type") return AddedToken(**obj) elif isinstance(obj, (list, tuple)): return list(convert_added_tokens(o) for o in obj) elif isinstance(obj, dict): return {k: convert_added_tokens(v) for k, v in obj.items()} return obj init_kwargs = convert_added_tokens(init_kwargs) # Set max length if needed if pretrained_model_name_or_path in cls.max_model_input_sizes: # if we're using a pretrained model, ensure the tokenizer # wont index sequences longer than the number of positional embeddings model_max_length = cls.max_model_input_sizes[ pretrained_model_name_or_path] if model_max_length is not None and isinstance( model_max_length, (int, float)): init_kwargs["model_max_length"] = min( init_kwargs.get("model_max_length", int(1e30)), model_max_length) added_tokens_file = resolved_vocab_files.pop("added_tokens_file", None) # Merge resolved_vocab_files arguments in init_kwargs if not including. # Maybe need more ways to load resources. for args_name, file_path in resolved_vocab_files.items(): # when `pretrained_model_name_or_path` is a pretrained model name, # use pretrained_init_configuration as `init_kwargs` to init which # does not include the vocab file in it, thus add vocab file into # args. if args_name not in init_kwargs: init_kwargs[args_name] = file_path # when `pretrained_model_name_or_path` is a pretrained model dir, # use tokenizer_config_file.json as `init_kwargs` to init which # does include a vocab file path in it. However, if the vocab file # path included in json does not exist, such as was deleted, to make # it still work, use the vocab file under this dir. elif not os.path.isfile(init_kwargs[args_name] or '') and os.path.isfile(file_path): init_kwargs[args_name] = file_path # TODO(guosheng): avoid reduplication of position args and key word args tokenizer = cls(*init_args, **init_kwargs) special_tokens_map_file = resolved_vocab_files.pop( "special_tokens_map_file", None) if special_tokens_map_file is not None: with open(special_tokens_map_file, encoding="utf-8") as special_tokens_map_handle: special_tokens_map = json.load(special_tokens_map_handle) for key, value in special_tokens_map.items(): if key in kwargs and kwargs[key]: # This value has already been redefined by the kwargs # We keep this new value and ignore the one stored in the special_tokens_map_file continue if isinstance(value, dict): value = AddedToken(**value) elif isinstance(value, list): value = [ AddedToken( **token) if isinstance(token, dict) else token for token in value ] setattr(tokenizer, key, value) # Add supplementary tokens. special_tokens = tokenizer.all_special_tokens if added_tokens_file is not None: with open(added_tokens_file, encoding="utf-8") as added_tokens_handle: added_tok_encoder = json.load(added_tokens_handle) # Sort added tokens by index added_tok_encoder_sorted = list( sorted(added_tok_encoder.items(), key=lambda x: x[1])) for token, index in added_tok_encoder_sorted: if has_tokenizer_file and index != len( tokenizer) and tokenizer.convert_tokens_to_ids( token) != index: # index is the current length of the tokenizer (not in vocabulary) raise ValueError( f"Wrong index found for {token}: should be {tokenizer.convert_tokens_to_ids(token)} but found " f"{index}.") elif not has_tokenizer_file and index != len(tokenizer): # Tokenizer slow: added token cannot already be in the vocabulary so its index needs to be the # current length of the tokenizer. raise ValueError( f"Non-consecutive added token '{token}' found. " f"Should have index {len(tokenizer)} but has index {index} in saved vocabulary." ) tokenizer.add_tokens( token, special_tokens=bool(token in special_tokens)) # Check all our special tokens are registered as "no split" token (we don't cut them) and are in the vocab added_tokens = tokenizer.sanitize_special_tokens() if added_tokens: logger.info( "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained." ) # save all of related things into default root dir if pretrained_model_name_or_path in cls.pretrained_init_configuration: tokenizer.save_pretrained(default_root) return tokenizer
[文档] def save_pretrained(self, save_directory, filename_prefix: Optional[str] = None, **kwargs): """ Save tokenizer configuration and related resources to files under `save_directory`. The tokenizer configuration would be saved into `tokenizer_config_file` indicating file (thus `tokenizer_config.json`), and resources would be saved into `resource_files_names` indicating files by using `self.save_resources(save_directory)`. The `save_directory` can be used in `from_pretrained` as argument value of `pretrained_model_name_or_path` to re-load the tokenizer. Args: save_directory (str): Directory to save files into. filename_prefix: (str, optional): A prefix to add to the names of the files saved by the tokenizer. Example: .. code-block:: from paddlenlp.transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') tokenizer.save_pretrained('trained_model') # reload from save_directory tokenizer = BertTokenizer.from_pretrained('trained_model') """ assert not os.path.isfile( save_directory ), "Saving directory ({}) should be a directory, not a file".format( save_directory) os.makedirs(save_directory, exist_ok=True) special_tokens_map_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + SPECIAL_TOKENS_MAP_FILE) tokenizer_config_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + self.tokenizer_config_file) tokenizer_config = copy.deepcopy(self.init_kwargs) if len(self.init_inputs) > 0: tokenizer_config["init_inputs"] = copy.deepcopy(self.init_inputs) for file_id in self.resource_files_names.keys(): tokenizer_config.pop(file_id, None) # Sanitize AddedTokens def convert_added_tokens(obj: Union[AddedToken, Any], add_type_field=True): if isinstance(obj, AddedToken): out = obj.__getstate__() if add_type_field: out["__type"] = "AddedToken" return out elif isinstance(obj, (list, tuple)): return list( convert_added_tokens(o, add_type_field=add_type_field) for o in obj) elif isinstance(obj, dict): return { k: convert_added_tokens(v, add_type_field=add_type_field) for k, v in obj.items() } return obj # add_type_field=True to allow dicts in the kwargs / differentiate from AddedToken serialization tokenizer_config = convert_added_tokens(tokenizer_config, add_type_field=True) # Add tokenizer class to the tokenizer config to be able to reload it with from_pretrained tokenizer_class = self.__class__.__name__ tokenizer_config["tokenizer_class"] = tokenizer_class with io.open(tokenizer_config_file, "w", encoding="utf-8") as f: f.write(json.dumps(tokenizer_config, ensure_ascii=False)) logger.info(f"tokenizer config file saved in {tokenizer_config_file}") # Sanitize AddedTokens in special_tokens_map write_dict = convert_added_tokens(self.special_tokens_map_extended, add_type_field=False) with open(special_tokens_map_file, "w", encoding="utf-8") as f: f.write(json.dumps(write_dict, ensure_ascii=False)) logger.info(f"Special tokens file saved in {special_tokens_map_file}") file_names = (tokenizer_config_file, special_tokens_map_file) save_files = self._save_pretrained( save_directory=save_directory, file_names=file_names, filename_prefix=filename_prefix, ) return save_files
def _save_pretrained(self, save_directory: Union[str, os.PathLike], file_names: Tuple[str], filename_prefix: Optional[str] = None) -> Tuple[str]: """ Save a tokenizer using the tokenizer format: vocabulary + added tokens. """ save_directory = str(save_directory) added_tokens_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + ADDED_TOKENS_FILE) added_vocab = self.get_added_vocab() if added_vocab: with open(added_tokens_file, "w", encoding="utf-8") as f: out_str = json.dumps(added_vocab, ensure_ascii=False) f.write(out_str) logger.info(f"added tokens file saved in {added_tokens_file}") self.save_resources(save_directory) return file_names + (added_tokens_file, )
[文档] def save_resources(self, save_directory): """ Save tokenizer related resources to `resource_files_names` indicating files under `save_directory` by copying directly. Override it if necessary. Args: save_directory (str): Directory to save files into. """ for name, file_name in self.resource_files_names.items(): src_path = self.init_kwargs[name] dst_path = os.path.join(save_directory, file_name) if os.path.abspath(src_path) != os.path.abspath(dst_path): copyfile(src_path, dst_path)
[文档] def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]: """ Converts a string in a sequence of tokens, replacing unknown tokens with the `unk_token`. Args: text (`str`): The sequence to be encoded. pair (`str`, *optional*): A second sequence to be encoded with the first. add_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to add the special tokens associated with the corresponding model. kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific encode method. See details in [`~PretrainedTokenizerBase.__call__`] Returns: `List[str]`: The list of tokens. """ raise NotImplementedError
def num_special_tokens_to_add(self, pair: bool = False) -> int: raise NotImplementedError def _get_padding_truncation_strategies(self, padding=False, truncation=False, max_length=None, pad_to_multiple_of=None, verbose=True, **kwargs): """ Find the correct padding/truncation strategy with backward compatibility for old arguments (truncation_strategy and pad_to_max_length) and behaviors. """ old_truncation_strategy = kwargs.pop("truncation_strategy", "do_not_truncate") old_pad_to_max_length = kwargs.pop("pad_to_max_seq_len", False) # Backward compatibility for previous behavior, maybe we should deprecate it: # If you only set max_length, it activates truncation for max_length if max_length is not None and padding is False and truncation is False: if verbose: if not self.deprecation_warnings.get( "Truncation-not-explicitly-activated", False): warnings.warn( "Truncation was not explicitly activated but `max_length` is provided a specific value, " "please use `truncation=True` to explicitly truncate examples to max length. " "Defaulting to 'longest_first' truncation strategy. " "If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy " "more precisely by providing a specific strategy to `truncation`." ) self.deprecation_warnings[ "Truncation-not-explicitly-activated"] = True truncation = "longest_first" # Get padding strategy if padding is False and old_pad_to_max_length: if verbose: warnings.warn( "The `pad_to_max_length` argument is deprecated and will be removed in a future version, " "use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or " "use `padding='max_length'` to pad to a max length. In this case, you can give a specific " "length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the " "maximal input size of the model (e.g. 512 for Bert).", FutureWarning, ) if max_length is None: padding_strategy = PaddingStrategy.LONGEST else: padding_strategy = PaddingStrategy.MAX_LENGTH elif padding is not False: if padding is True: if verbose: if max_length is not None and (truncation is False or truncation == "do_not_truncate"): warnings.warn( "`max_length` is ignored when `padding`=`True` and there is no truncation strategy. " "To pad to max length, use `padding='max_length'`.") if old_pad_to_max_length is not False: warnings.warn( "Though `pad_to_max_length` = `True`, it is ignored because `padding`=`True`." ) # Default to pad to the longest sequence in the batch padding_strategy = PaddingStrategy.LONGEST elif not isinstance(padding, PaddingStrategy): padding_strategy = PaddingStrategy(padding) elif isinstance(padding, PaddingStrategy): padding_strategy = padding else: padding_strategy = PaddingStrategy.DO_NOT_PAD # Get truncation strategy if truncation is False and old_truncation_strategy != "do_not_truncate": if verbose: warnings.warn( "The `truncation_strategy` argument is deprecated and will be removed in a future version, " "use `truncation=True` to truncate examples to a max length. You can give a specific " "length with `max_length` (e.g. `max_length=45`) or leave max_length to None to truncate to the " "maximal input size of the model (e.g. 512 for Bert). " " If you have pairs of inputs, you can give a specific truncation strategy selected among " "`truncation='only_first'` (will only truncate the first sentence in the pairs) " "`truncation='only_second'` (will only truncate the second sentence in the pairs) " "or `truncation='longest_first'` (will iteratively remove tokens from the longest sentence in the pairs).", FutureWarning, ) truncation_strategy = TruncationStrategy(old_truncation_strategy) elif truncation is not False: if truncation is True: truncation_strategy = ( TruncationStrategy.LONGEST_FIRST ) # Default to truncate the longest sequences in pairs of inputs elif not isinstance(truncation, TruncationStrategy): truncation_strategy = TruncationStrategy(truncation) elif isinstance(truncation, TruncationStrategy): truncation_strategy = truncation else: truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE # Set max length if needed if max_length is None: if padding_strategy == PaddingStrategy.MAX_LENGTH: if self.model_max_length > LARGE_INTEGER: if verbose: if not self.deprecation_warnings.get( "Asking-to-pad-to-max_length", False): warnings.warn( "Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. " "Default to no padding.") self.deprecation_warnings[ "Asking-to-pad-to-max_length"] = True padding_strategy = PaddingStrategy.DO_NOT_PAD else: max_length = self.model_max_length if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE: if self.model_max_length > LARGE_INTEGER: if verbose: if not self.deprecation_warnings.get( "Asking-to-truncate-to-max_length", False): warnings.warn( "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. " "Default to no truncation.") self.deprecation_warnings[ "Asking-to-truncate-to-max_length"] = True truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE else: max_length = self.model_max_length # Test if we have a padding token if padding_strategy != PaddingStrategy.DO_NOT_PAD and ( not self.pad_token or self.pad_token_id < 0): raise ValueError( "Asking to pad but the tokenizer does not have a padding token. " "Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` " "or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`." ) # Check that we will truncate to a multiple of pad_to_multiple_of if both are provided if (truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and padding_strategy != PaddingStrategy.DO_NOT_PAD and pad_to_multiple_of is not None and max_length is not None and (max_length % pad_to_multiple_of != 0)): raise ValueError( f"Truncation and padding are both activated but " f"truncation length ({max_length}) is not a multiple of pad_to_multiple_of ({pad_to_multiple_of})." ) return padding_strategy, truncation_strategy, max_length, kwargs
[文档] def __call__(self, text: Union[str, List[str], List[List[str]]], text_pair: Optional[Union[str, List[str], List[List[str]]]] = None, max_length: Optional[int] = None, stride: int = 0, is_split_into_words: Union[bool, str] = False, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = False, return_position_ids: bool = False, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_length: bool = False, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_dict: bool = True, return_offsets_mapping: bool = False, add_special_tokens: bool = True, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, verbose: bool = True, **kwargs): """ Performs tokenization and uses the tokenized tokens to prepare model inputs. It supports sequence or sequence pair as input, and batch input is allowed. `self.encode()` or `self.batch_encode()` would be called separately for single or batch input depending on input format and `is_split_into_words` argument. Args: text (str, List[str] or List[List[str]]): The sequence or batch of sequences to be processed. One sequence is a string or a list of strings depending on whether it has been pretokenized. If each sequence is provided as a list of strings (pretokenized), you must set `is_split_into_words` as `True` to disambiguate with a batch of sequences. text_pair (str, List[str] or List[List[str]], optional): Same as `text` argument, while it represents for the latter sequence of the sequence pair. max_length (int, optional): If set to a number, will limit the total sequence returned so that it has a maximum length. If there are overflowing tokens, those overflowing tokens will be added to the returned dictionary when `return_overflowing_tokens` is `True`. Defaults to `None`. stride (int, optional): Only available for batch input of sequence pair and mainly for question answering usage. When for QA, `text` represents questions and `text_pair` represents contexts. If `stride` is set to a positive number, the context will be split into multiple spans where `stride` defines the number of (tokenized) tokens to skip from the start of one span to get the next span, thus will produce a bigger batch than inputs to include all spans. Moreover, 'overflow_to_sample' and 'offset_mapping' preserving the original example and position information will be added to the returned dictionary. Defaults to 0. is_split_into_words (Union[bool, str], optional): when the text is words or tokens, `is_split_into_words` should be True or `token`. `True`: means that the text should be words which should be tokenized. `token`: means that the text should be tokens which already be tokenized, so it should not be tokenized again. padding (bool, str or [PaddingStrategy], optional): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). Defaults to `False`. truncation (bool, str or [TruncationStrategy], optional): Activates and controls truncation. Accepts the following values: - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). Defaults to `False`. return_position_ids (bool, optional): Whether to include tokens position ids in the returned dictionary. Defaults to `False`. return_token_type_ids (bool, optional): Whether to include token type ids in the returned dictionary. Defaults to `True`. return_attention_mask (bool, optional): Whether to include the attention mask in the returned dictionary. Defaults to `False`. return_length (bool, optional): Whether to include the length of each encoded inputs in the returned dictionary. Defaults to `False`. return_overflowing_tokens (bool, optional): Whether to include overflowing token information in the returned dictionary. Defaults to `False`. return_special_tokens_mask (bool, optional): Whether to include special tokens mask information in the returned dictionary. Defaults to `False`. return_dict (bool, optional): Decide the format for returned encoded batch inputs. Only works when input is a batch of data. :: - If True, encoded inputs would be a dictionary like: {'input_ids': [[1, 4444, 4385, 1545, 6712],[1, 4444, 4385]], 'token_type_ids': [[0, 0, 0, 0, 0], [0, 0, 0]]} - If False, encoded inputs would be a list like: [{'input_ids': [1, 4444, 4385, 1545, 6712], 'token_type_ids': [0, 0, 0, 0, 0]}, {'input_ids': [1, 4444, 4385], 'token_type_ids': [0, 0, 0]}] Defaults to `True`. return_offsets_mapping (bool, optional): Whether to include the list of pair preserving the index of start and end char in original input for each token in the returned dictionary. Would be automatically set to `True` when `stride` > 0. Defaults to `False`. add_special_tokens (bool, optional): Whether to add the special tokens associated with the corresponding model to the encoded inputs. Defaults to `True` pad_to_multiple_of (int, optional): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). Defaults to `None`. return_tensors (str or [TensorType], optional): If set, will return tensors instead of list of python integers. Acceptable values are: - `'pd'`: Return Paddle `paddle.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. Defaults to `None`. verbose (bool, optional): Whether or not to print more information and warnings. Defaults to True. Returns: dict or list[dict] (for batch input): The dict has the following optional items: - **input_ids** (list[int] or list[list[int]]): List of token ids to be fed to a model. - **position_ids** (list[int] or list[list[int]], optional): List of token position ids to be fed to a model. Included when `return_position_ids` is `True` - **token_type_ids** (list[int] or list[list[int]], optional): List of token type ids to be fed to a model. Included when `return_token_type_ids` is `True`. - **attention_mask** (list[int] or list[list[int]], optional): List of integers valued 0 or 1, where 0 specifies paddings and should not be attended to by the model. Included when `return_attention_mask` is `True`. - **seq_len** (int or list[int], optional): The input_ids length. Included when `return_length` is `True`. - **overflowing_tokens** (list[int] or list[list[int]], optional): List of overflowing tokens. Included when if `max_length` is specified and `return_overflowing_tokens` is True. - **num_truncated_tokens** (int or list[int], optional): The number of overflowing tokens. Included when if `max_length` is specified and `return_overflowing_tokens` is True. - **special_tokens_mask** (list[int] or list[list[int]], optional): List of integers valued 0 or 1, with 0 specifying special added tokens and 1 specifying sequence tokens. Included when `return_special_tokens_mask` is `True`. - **offset_mapping** (list[int], optional): list of pair preserving the index of start and end char in original input for each token. For a sqecial token, the index pair is `(0, 0)`. Included when `return_overflowing_tokens` is True or `stride` > 0. - **overflow_to_sample** (int or list[int], optional): Index of example from which this feature is generated. Included when `stride` works. """ # Input type checking for clearer error def _is_valid_text_input(t): if isinstance(t, str): # Strings are fine return True elif isinstance(t, (list, tuple)): # List are fine as long as they are... if len(t) == 0: # ... empty return True elif isinstance(t[0], str): # ... list of strings return True elif isinstance(t[0], (list, tuple)): # ... list with an empty list or with a list of strings return len(t[0]) == 0 or isinstance(t[0][0], str) else: return False else: return False if not _is_valid_text_input(text): raise ValueError( "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) " "or `List[List[str]]` (batch of pretokenized examples).") if text_pair is not None and not _is_valid_text_input(text_pair): raise ValueError( "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) " "or `List[List[str]]` (batch of pretokenized examples).") # check `split_into_words` value if isinstance(is_split_into_words, str) and is_split_into_words != 'token': raise ValueError( "the value of `is_split_into_words` should be one of: {True, False, 'token'} but receive: <%s>", is_split_into_words) if is_split_into_words: is_batched = isinstance(text, (list, tuple)) and text and isinstance( text[0], (list, tuple)) else: is_batched = isinstance(text, (list, tuple)) if is_batched: if isinstance(text_pair, str): raise TypeError( "when tokenizing batches of text, `text_pair` must be a list or tuple with the same length as `text`." ) if text_pair is not None and len(text) != len(text_pair): raise ValueError( f"batch length of `text`: {len(text)} does not match batch length of `text_pair`: {len(text_pair)}." ) batch_text_or_text_pairs = list(zip( text, text_pair)) if text_pair is not None else text return self.batch_encode( batch_text_or_text_pairs=batch_text_or_text_pairs, max_length=max_length, stride=stride, is_split_into_words=is_split_into_words, padding=padding, truncation=truncation, return_position_ids=return_position_ids, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_length=return_length, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_dict=return_dict, return_offsets_mapping=return_offsets_mapping, add_special_tokens=add_special_tokens, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, verbose=verbose, **kwargs) else: return self.encode( text=text, text_pair=text_pair, max_length=max_length, stride=stride, is_split_into_words=is_split_into_words, padding=padding, truncation=truncation, return_position_ids=return_position_ids, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_length=return_length, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, add_special_tokens=add_special_tokens, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, verbose=verbose, **kwargs)
[文档] def encode(self, text, text_pair=None, add_special_tokens=True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = False, max_length: Optional[int] = None, stride: int = 0, is_split_into_words: bool = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, return_position_ids=False, **kwargs) -> BatchEncoding: """ Tokenize and prepare for the model a sequence or a pair of sequences. Args: text (`str`, `List[str]` or `List[int]`): The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` method). text_pair (`str`, `List[str]` or `List[int]`, *optional*): Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` method). """ # Backward compatibility for 'max_seq_len' old_max_seq_len = kwargs.get('max_seq_len', None) if max_length is None and old_max_seq_len: if verbose: warnings.warn( "The `max_seq_len` argument is deprecated and will be removed in a future version, " "please use `max_length` instead.", FutureWarning, ) max_length = old_max_seq_len # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._encode_plus( text=text, text_pair=text_pair, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_position_ids=return_position_ids, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, )
def _encode_plus( self, text: Union[TextInput, PreTokenizedInput, EncodedInput], text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy. DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, is_split_into_words: bool = False, pad_to_multiple_of: Optional[int] = None, return_position_ids: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs) -> BatchEncoding: raise NotImplementedError
[文档] def batch_encode( self, batch_text_or_text_pairs: Union[List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair], List[EncodedInput], List[EncodedInputPair], ], max_length=None, stride: int = 0, is_split_into_words: bool = False, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = False, return_position_ids=False, # TODO(wj-mcat): keep align with `encode` method return_token_type_ids=None, return_attention_mask=None, return_length=False, return_overflowing_tokens=False, return_special_tokens_mask=False, return_dict=True, return_offsets_mapping=False, add_special_tokens=True, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, verbose: bool = True, **kwargs) -> BatchEncoding: """ Performs tokenization and uses the tokenized tokens to prepare model inputs. It supports batch inputs of sequence or sequence pair. Args: batch_text_or_text_pairs (list): The element of list can be sequence or sequence pair, and the sequence is a string or a list of strings depending on whether it has been pretokenized. If each sequence is provided as a list of strings (pretokenized), you must set `is_split_into_words` as `True` to disambiguate with a sequence pair. Returns: dict or list[dict]: The dict has the following optional items: """ # Backward compatibility for 'max_seq_len' old_max_seq_len = kwargs.get('max_seq_len', None) if max_length is None and old_max_seq_len: if verbose: warnings.warn( "The `max_seq_len` argument is deprecated and will be removed in a future version, " "please use `max_length` instead.", FutureWarning, ) max_length = old_max_seq_len # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_position_ids=return_position_ids, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_dict=return_dict, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, )
def _batch_encode_plus( self, batch_text_or_text_pairs: Union[List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair], List[EncodedInput], List[EncodedInputPair], ], add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy. DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, is_split_into_words: bool = False, pad_to_multiple_of: Optional[int] = None, return_position_ids: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_dict: bool = True, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs) -> BatchEncoding: raise NotImplementedError
[文档] def pad( self, encoded_inputs: Union[BatchEncoding, List[BatchEncoding], Dict[str, EncodedInput], Dict[str, List[EncodedInput]], List[Dict[str, EncodedInput]], ], padding: Union[bool, str, PaddingStrategy] = True, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, verbose: bool = True, ) -> BatchEncoding: """ Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length in the batch. Padding side (left/right) padding token ids are defined at the tokenizer level (with `self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`) <Tip> If the `encoded_inputs` passed are dictionary of numpy arrays, Paddle tensors, the result will use the same type unless you provide a different tensor type with `return_tensors`. </Tip> Args: encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `Dict[str, List[int]]`, `Dict[str, List[List[int]]` or `List[Dict[str, List[int]]]`): Tokenized inputs. Can represent one input ([`BatchEncoding`] or `Dict[str, List[int]]`) or a batch of tokenized inputs (list of [`BatchEncoding`], *Dict[str, List[List[int]]]* or *List[Dict[str, List[int]]]*) so you can use this method during preprocessing as well as in a Paddle Dataloader collate function. Instead of `List[int]` you can have tensors (numpy arrays, Paddle tensors), see the note above for the return type. padding (`bool`, `str` or [`PaddingStrategy`], *optional*, defaults to `True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Maximum length of the returned list and optionally padding length (see above). pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_attention_mask (`bool`, *optional*): Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) return_tensors (`str` or [`TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'pd'`: Return Paddle `paddle.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. """ # If we have a list of dicts, let's convert it in a dict of lists if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], (dict, BatchEncoding)): encoded_inputs = { key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys() } # The model's main input name, usually `input_ids`, has be passed for padding if self.model_input_names[0] not in encoded_inputs: raise ValueError( "You should supply an encoding or a list of encodings to this method " f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}" ) required_input = encoded_inputs[self.model_input_names[0]] if not required_input: if return_attention_mask: encoded_inputs["attention_mask"] = [] return encoded_inputs # If we have Paddle/NumPy tensors/arrays as inputs, we cast them as python objects # and rebuild them afterwards if no return_tensors is specified first_element = required_input[0] if isinstance(first_element, (list, tuple)): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. for item in required_input: if len(item) != 0: first_element = item[0] break # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do. if not isinstance(first_element, (int, list, tuple)): if isinstance(first_element, paddle.Tensor): return_tensors = "pd" if return_tensors is None else return_tensors else: raise ValueError( f"type of {first_element} unknown: {type(first_element)}. " f"Should be either python or paddle object.") for key, value in encoded_inputs.items(): encoded_inputs[key] = to_py_obj(value) # Convert padding_strategy in PaddingStrategy padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies( padding=padding, max_length=max_length, verbose=verbose) required_input = encoded_inputs[self.model_input_names[0]] if required_input and not isinstance(required_input[0], (list, tuple)): encoded_inputs = self._pad( encoded_inputs, max_length=max_length, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask) return BatchEncoding(encoded_inputs, tensor_type=return_tensors) batch_size = len(required_input) assert all( len(v) == batch_size for v in encoded_inputs.values() ), "Some items in the output dictionary have a different batch size than others." if padding_strategy == PaddingStrategy.LONGEST: max_length = max(len(inputs) for inputs in required_input) padding_strategy = PaddingStrategy.MAX_LENGTH batch_outputs = {} for i in range(batch_size): inputs = dict((k, v[i]) for k, v in encoded_inputs.items()) outputs = self._pad( inputs, max_length=max_length, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) return BatchEncoding(batch_outputs, tensor_type=return_tensors)
[文档] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]: """ Create the token type IDs corresponding to the sequences passed. [What are token type IDs?](../glossary#token-type-ids) Should be overridden in a subclass if the model has a special way of building those. Args: token_ids_0 (`List[int]`): The first tokenized sequence. token_ids_1 (`List[int]`, *optional*): The second tokenized sequence. Returns: `List[int]`: The token type ids. """ if token_ids_1 is None: return len(token_ids_0) * [0] return [0] * len(token_ids_0) + [1] * len(token_ids_1)
[文档] def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. This implementation does not add special tokens and this method should be overridden in a subclass. Args: token_ids_0 (`List[int]`): The first tokenized sequence. token_ids_1 (`List[int]`, *optional*): The second tokenized sequence. Returns: `List[int]`: The model input with special tokens. """ if token_ids_1 is None: return token_ids_0 return token_ids_0 + token_ids_1
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): """ Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. Should be overridden in a subclass if the model has a special way of building those. Args: offset_mapping_0 (List[tuple]): List of char offsets to which the special tokens will be added. offset_mapping_1 (List[tuple], optional): Optional second list of char offsets for offset mapping pairs. Returns: List[tuple]: List of char offsets with the appropriate offsets of special tokens. """ if offset_mapping_1 is None: return offset_mapping_0 return offset_mapping_0 + offset_mapping_1
[文档] def prepare_for_model(self, ids, pair_ids=None, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = False, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_position_ids=False, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_length=False, return_overflowing_tokens=False, return_special_tokens_mask=False, return_offsets_mapping=False, add_special_tokens=True, verbose: bool = True, prepend_batch_axis: bool = False, **kwargs): """ Performs tokenization and uses the tokenized tokens to prepare model inputs. It supports sequence or sequence pair as input, and batch input is not allowed. """ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) pair = bool(pair_ids is not None) len_ids = len(ids) len_pair_ids = len(pair_ids) if pair else 0 if return_token_type_ids and not add_special_tokens: raise ValueError( "Asking to return token_type_ids while setting add_special_tokens to False " "results in an undefined behavior. Please set add_special_tokens to True or " "set return_token_type_ids to None.") if (return_overflowing_tokens and truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is not None): raise ValueError( "Not possible to return overflowing tokens for pair of sequences with the " "`longest_first`. Please select another truncation strategy than `longest_first`, " "for instance `only_second` or `only_first`.") # Load from model defaults if return_token_type_ids is None: return_token_type_ids = "token_type_ids" in self.model_input_names if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names encoded_inputs = {} # Truncation: Handle max sequence length total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add( pair=pair) if add_special_tokens else 0) overflowing_tokens = [] if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length: ids, pair_ids, overflowing_tokens = self.truncate_sequences( ids, pair_ids=pair_ids, num_tokens_to_remove=total_len - max_length, truncation_strategy=truncation_strategy, stride=stride, ) if return_overflowing_tokens: encoded_inputs["overflowing_tokens"] = overflowing_tokens encoded_inputs["num_truncated_tokens"] = total_len - max_length # Add special tokens if add_special_tokens: sequence = self.build_inputs_with_special_tokens(ids, pair_ids) token_type_ids = self.create_token_type_ids_from_sequences( ids, pair_ids) else: sequence = ids + pair_ids if pair else ids token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else []) # Build output dictionnary encoded_inputs["input_ids"] = sequence if return_token_type_ids: encoded_inputs["token_type_ids"] = token_type_ids if return_special_tokens_mask: if add_special_tokens: encoded_inputs[ "special_tokens_mask"] = self.get_special_tokens_mask( ids, pair_ids) else: encoded_inputs["special_tokens_mask"] = [0] * len(sequence) if return_offsets_mapping and 'text' in kwargs and 'text_pair' in kwargs: text = kwargs.pop('text') text_pair = kwargs.pop('text_pair') token_offset_mapping = self.get_offset_mapping(text) token_pair_offset_mapping = self.get_offset_mapping( text_pair) if text_pair is not None else None if max_length and total_len > max_length: token_offset_mapping, token_pair_offset_mapping, _ = self.truncate_sequences( token_offset_mapping, pair_ids=token_pair_offset_mapping, num_tokens_to_remove=total_len - max_length, truncation_strategy=truncation_strategy, stride=stride) if add_special_tokens: offset_mapping = self.build_offset_mapping_with_special_tokens( token_offset_mapping, token_pair_offset_mapping) else: offset_mapping = token_offset_mapping + \ token_pair_offset_mapping if token_pair_offset_mapping else token_offset_mapping encoded_inputs['offset_mapping'] = offset_mapping # Check lengths self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose) if return_position_ids: encoded_inputs["position_ids"] = list( range(len(encoded_inputs["input_ids"]))) if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask: encoded_inputs = self.pad( encoded_inputs, max_length=max_length, padding=padding_strategy.value, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) if return_length: encoded_inputs["length"] = len(encoded_inputs["input_ids"]) # for compatibility encoded_inputs["seq_len"] = encoded_inputs["length"] batch_outputs = BatchEncoding(encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis) return batch_outputs
[文档] def truncate_sequences( self, ids: List[int], pair_ids: Optional[List[int]] = None, num_tokens_to_remove: int = 0, truncation_strategy: Union[str, TruncationStrategy] = "longest_first", stride: int = 0, ) -> Tuple[List[int], List[int], List[int]]: """ Truncates a sequence pair in-place following the strategy. Args: ids (`List[int]`): Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. pair_ids (`List[int]`, *optional*): Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. num_tokens_to_remove (`int`, *optional*, defaults to 0): Number of tokens to remove using the truncation strategy. truncation_strategy (`str` or [`TruncationStrategy`], *optional*, defaults to `False`): The strategy to follow for truncation. Can be: - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). stride (`int`, *optional*, defaults to 0): If set to a positive number, the overflowing tokens returned will contain some tokens from the main sequence returned. The value of this argument defines the number of additional tokens. Returns: `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair of sequences (or a batch of pairs) is provided. """ if num_tokens_to_remove <= 0: return ids, pair_ids, [] if not isinstance(truncation_strategy, TruncationStrategy): truncation_strategy = TruncationStrategy(truncation_strategy) overflowing_tokens = [] if truncation_strategy == TruncationStrategy.ONLY_FIRST or ( truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None): if len(ids) > num_tokens_to_remove: window_len = min(len(ids), stride + num_tokens_to_remove) if self.truncation_side == "left": overflowing_tokens = ids[:window_len] ids = ids[num_tokens_to_remove:] elif self.truncation_side == "right": overflowing_tokens = ids[-window_len:] ids = ids[:-num_tokens_to_remove] else: raise ValueError( f"invalid truncation strategy: {self.truncation_side}, use 'left' or 'right'." ) else: error_msg = ( f"We need to remove {num_tokens_to_remove} to truncate the input " f"but the first sequence has a length {len(ids)}. ") if truncation_strategy == TruncationStrategy.ONLY_FIRST: error_msg = ( error_msg + "Please select another truncation strategy than " f"{truncation_strategy}, for instance 'longest_first' or 'only_second'." ) logger.error(error_msg) elif truncation_strategy == TruncationStrategy.LONGEST_FIRST: warnings.warn( f"Be aware, overflowing tokens are not returned for the setting you have chosen," f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' " f"truncation strategy. So the returned list will always be empty even if some " f"tokens have been removed.") for _ in range(num_tokens_to_remove): if pair_ids is None or len(ids) > len(pair_ids): if self.truncation_side == "right": ids = ids[:-1] elif self.truncation_side == "left": ids = ids[1:] else: raise ValueError("invalid truncation strategy:" + str(self.truncation_side)) else: if self.truncation_side == "right": pair_ids = pair_ids[:-1] elif self.truncation_side == "left": pair_ids = pair_ids[1:] else: raise ValueError("invalid truncation strategy:" + str(self.truncation_side)) elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None: if len(pair_ids) > num_tokens_to_remove: window_len = min(len(pair_ids), stride + num_tokens_to_remove) if self.truncation_side == "right": overflowing_tokens = pair_ids[-window_len:] pair_ids = pair_ids[:-num_tokens_to_remove] elif self.truncation_side == "left": overflowing_tokens = pair_ids[:window_len] pair_ids = pair_ids[num_tokens_to_remove:] else: raise ValueError("invalid truncation strategy:" + str(self.truncation_side)) else: logger.error( f"We need to remove {num_tokens_to_remove} to truncate the input " f"but the second sequence has a length {len(pair_ids)}. " f"Please select another truncation strategy than {truncation_strategy}, " f"for instance 'longest_first' or 'only_first'.") return (ids, pair_ids, overflowing_tokens)
def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, ) -> dict: """ Pad encoded inputs (on left/right and up to predefined length or max length in the batch) Args: encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in self.padding_side: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability >= 7.5 (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) """ # Load from model defaults if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names or "attention_mask" in encoded_inputs required_input = encoded_inputs[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: max_length = len(required_input) if max_length is not None and pad_to_multiple_of is not None and ( max_length % pad_to_multiple_of != 0): max_length = ( (max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len( required_input) != max_length # Initialize attention mask if not present. if return_attention_mask and "attention_mask" not in encoded_inputs: encoded_inputs["attention_mask"] = [1] * len(required_input) if needs_to_be_padded: difference = max_length - len(required_input) if self.padding_side == "right": if return_attention_mask: encoded_inputs["attention_mask"] = encoded_inputs[ "attention_mask"] + [0] * difference if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = ( encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference) if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = encoded_inputs[ "special_tokens_mask"] + [1] * difference if "offset_mapping" in encoded_inputs: encoded_inputs["offset_mapping"] = encoded_inputs[ "offset_mapping"] + [(0, 0)] * difference if "position_ids" in encoded_inputs: encoded_inputs["position_ids"] = encoded_inputs[ "position_ids"] + [0] * difference encoded_inputs[self.model_input_names[ 0]] = required_input + [self.pad_token_id] * difference elif self.padding_side == "left": if return_attention_mask: encoded_inputs["attention_mask"] = [ 0 ] * difference + encoded_inputs["attention_mask"] if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = [ self.pad_token_type_id ] * difference + encoded_inputs["token_type_ids"] if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = [ 1 ] * difference + encoded_inputs["special_tokens_mask"] if "offset_mapping" in encoded_inputs: encoded_inputs["offset_mapping"] = [ (0, 0) ] * difference + encoded_inputs["offset_mapping"] if "position_ids" in encoded_inputs: encoded_inputs["position_ids"] = [ 0 ] * difference + encoded_inputs["position_ids"] encoded_inputs[self.model_input_names[ 0]] = [self.pad_token_id] * difference + required_input else: raise ValueError("Invalid padding strategy:" + str(self.padding_side)) return encoded_inputs
[文档] def convert_tokens_to_string(self, tokens: List[str]) -> str: """ Converts a sequence of tokens in a single string. The most simple way to do it is `" ".join(tokens)` but we often want to remove sub-word tokenization artifacts at the same time. Args: tokens (`List[str]`): The token to join in a string. Returns: `str`: The joined tokens. """ raise NotImplementedError
[文档] def batch_decode(self, sequences: Union[List[int], List[List[int]], "np.ndarray", "paddle.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, **kwargs) -> List[str]: """ Convert a list of lists of token ids into a list of strings by calling decode. Args: sequences (`Union[List[int], List[List[int]], np.ndarray, paddle.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `List[str]`: The list of decoded sentences. """ return [ self.decode( seq, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, ) for seq in sequences ]
[文档] def decode(self, token_ids: Union[int, List[int], "np.ndarray", "paddle.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, **kwargs) -> str: """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, paddle.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str`: The decoded sentence. """ # Convert inputs to python lists token_ids = to_py_obj(token_ids) return self._decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, )
def _decode(self, token_ids: Union[int, List[int]], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, **kwargs) -> str: raise NotImplementedError
[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]: """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods. Args: token_ids_0 (`List[int]`): List of ids of the first sequence. token_ids_1 (`List[int]`, *optional*): List of ids of the second sequence. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ assert already_has_special_tokens and token_ids_1 is None, ( "You cannot use ``already_has_special_tokens=False`` with this tokenizer. " "Please use a slow (full python) tokenizer to activate this argument. " "Or set `return_special_tokens_mask=True` when calling the encoding method " "to get the special tokens mask in any tokenizer. ") all_special_ids = self.all_special_ids # cache the property special_tokens_mask = [ 1 if token in all_special_ids else 0 for token in token_ids_0 ] return special_tokens_mask
[文档] @staticmethod def clean_up_tokenization(out_string: str) -> str: """ Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms. Args: out_string (`str`): The text to clean up. Returns: `str`: The cleaned-up string. """ out_string = (out_string.replace(" .", ".").replace(" ?", "?").replace( " !", "!").replace(" ,", ",").replace(" ' ", "'").replace( " n't", "n't").replace(" 'm", "'m").replace(" 's", "'s").replace( " 've", "'ve").replace(" 're", "'re")) return out_string
def _eventual_warn_about_too_long_sequence(self, ids: List[int], max_length: Optional[int], verbose: bool): """ Depending on the input and internal state we might trigger a warning about a sequence that is too long for its corresponding model Args: ids (`List[str]`): The ids produced by the tokenization max_length (`int`, *optional*): The max_length desired (does not trigger a warning if it is set) verbose (`bool`): Whether or not to print more information and warnings. """ if max_length is None and len(ids) > self.model_max_length and verbose: if not self.deprecation_warnings.get( "sequence-length-is-longer-than-the-specified-maximum", False): logger.warning( "Token indices sequence length is longer than the specified maximum sequence length " f"for this model ({len(ids)} > {self.model_max_length}). Running this sequence through the model " "will result in indexing errors") self.deprecation_warnings[ "sequence-length-is-longer-than-the-specified-maximum"] = True