# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Base classes common to both the slow and the fast tokenization classes: PreTrainedTokenizerBase (host all the user fronting encoding methods) Special token mixing (host the special tokens logic) and BatchEncoding (wrap the dictionary of output with special method for the Fast tokenizers) """ import copy import json import os import re import warnings from collections import OrderedDict, UserDict from collections.abc import Mapping, Sized from contextlib import contextmanager from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union import numpy as np from packaging import version from transformers import __version__ from transformers.dynamic_module_utils import custom_object_save from transformers.utils import ( ExplicitEnum, PaddingStrategy, PushToHubMixin, TensorType, add_end_docstrings, add_model_info_to_auto_map, cached_file, copy_func, download_url, extract_commit_hash, is_flax_available, is_jax_tensor, is_numpy_array, is_offline_mode, is_remote_url, is_tf_available, is_tf_tensor, is_tokenizers_available, is_torch_available, is_torch_device, is_torch_tensor, logging, requires_backends, to_py_obj, ) if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp # noqa: F401 if is_tokenizers_available(): from tokenizers import AddedToken from tokenizers import Encoding as EncodingFast else: @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__ @dataclass class EncodingFast: """This is dummy class because without the `tokenizers` library we don't have these objects anyway""" pass logger = logging.get_logger(__name__) 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" # Fast tokenizers (provided by HuggingFace tokenizer's library) can be saved in a single file FULL_TOKENIZER_FILE = "tokenizer.json" _re_tokenizer_file = re.compile(r"tokenizer\.(.*)\.json") 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" 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 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 class BatchEncoding(UserDict): """ Holds the output of the [`~tokenization_utils_base.PreTrainedTokenizerBase.__call__`], [`~tokenization_utils_base.PreTrainedTokenizerBase.encode_plus`] and [`~tokenization_utils_base.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_plus`/`batch_encode_plus` methods ('input_ids', 'attention_mask', etc.). encoding (`tokenizers.Encoding` or `Sequence[tokenizers.Encoding]`, *optional*): If the tokenizer is a fast tokenizer which outputs additional information like mapping from word/character space to token space the `tokenizers.Encoding` instance or list of instance (for batches) hold this information. tensor_type (`Union[None, str, TensorType]`, *optional*): You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/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). n_sequences (`Optional[int]`, *optional*): You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at initialization. """ def __init__( self, data: Optional[Dict[str, Any]] = None, encoding: Optional[Union[EncodingFast, Sequence[EncodingFast]]] = None, tensor_type: Union[None, str, TensorType] = None, prepend_batch_axis: bool = False, n_sequences: Optional[int] = None, ): super().__init__(data) if isinstance(encoding, EncodingFast): 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 [`PreTrainedTokenizerFast`] or not. """ return self._encodings is not None def __getitem__(self, item: Union[int, str]) -> Union[Any, EncodingFast]: """ 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 `tokenizers.Encoding` for batch item with index `key`. If the key is a slice, returns the value of the dict associated to `key` ('input_ids', 'attention_mask', etc.) with the constraint of slice. """ if isinstance(item, str): return self.data[item] elif self._encodings is not None: return self._encodings[item] elif isinstance(item, slice): return {key: self.data[key][item] for key in self.data.keys()} else: raise KeyError( "Invalid key. Only three types of key are available: " "(1) string, (2) integers for backend Encoding, and (3) slices for data subsetting." ) 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"] def keys(self): return self.data.keys() def values(self): return self.data.values() def items(self): return self.data.items() # After this point: # Extended properties and methods only available for fast (Rust-based) tokenizers # provided by HuggingFace tokenizers library. @property def encodings(self) -> Optional[List[EncodingFast]]: """ `Optional[List[tokenizers.Encoding]]`: 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 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 non-fast tokenizers (e.g. instance of a `XxxTokenizerFast`" " class)." ) return self._encodings[batch_index].tokens 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 non-fast tokenizers (e.g. instance of a `XxxTokenizerFast`" " class)." ) return self._encodings[batch_index].sequence_ids 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 non-fast tokenizers (e.g. instance of a `XxxTokenizerFast`" " class)." ) 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) 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 non-fast tokenizers (e.g. instance of a `XxxTokenizerFast`" " class)." ) return self._encodings[batch_index].word_ids 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) 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) 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 [`~tokenization_utils_base.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: ([`~tokenization_utils_base.TokenSpan`], *optional*): Span of tokens in the encoded sequence. Returns `None` if no tokens correspond to the word. This can happen especially when the token is a special token that has been used to format the tokenization. For example when we add a class token at the very beginning of the tokenization. """ 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 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 [`~tokenization_utils_base.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: [`~tokenization_utils_base.CharSpan`]: Span of characters in the original string, or None, if the token (e.g. , ) doesn't correspond to any chars in the origin 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 span_indices = self._encodings[batch_index].token_to_chars(token_index) return CharSpan(*span_indices) if span_indices is not None else None 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) 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))) 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) 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 [`~utils.TensorType`], *optional*): The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.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.TENSORFLOW: if not is_tf_available(): raise ImportError( "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed." ) import tensorflow as tf as_tensor = tf.constant is_tensor = tf.is_tensor elif tensor_type == TensorType.PYTORCH: if not is_torch_available(): raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.") import torch as_tensor = torch.tensor is_tensor = torch.is_tensor elif tensor_type == TensorType.JAX: if not is_flax_available(): raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed.") import jax.numpy as jnp # noqa: F811 as_tensor = jnp.array is_tensor = is_jax_tensor else: def as_tensor(value, dtype=None): if isinstance(value, (list, tuple)) and isinstance(value[0], (list, tuple, np.ndarray)): value_lens = [len(val) for val in value] if len(set(value_lens)) > 1 and dtype is None: # we have a ragged list so handle explicitly value = as_tensor([np.asarray(val) for val in value], dtype=object) return np.asarray(value, dtype=dtype) is_tensor = is_numpy_array # 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) # Removing this for now in favor of controlling the shape with `prepend_batch_axis` # # at-least2d # if tensor.ndim > 2: # tensor = tensor.squeeze(0) # elif tensor.ndim < 2: # tensor = tensor[None, :] self[key] = tensor except Exception as e: 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." ) from e 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. Perhaps your" f" features (`{key}` in this case) have excessive nesting (inputs type `list` where type `int` is" " expected)." ) from e return self def to(self, device: Union[str, "torch.device"]) -> "BatchEncoding": """ Send all values to device by calling `v.to(device)` (PyTorch only). Args: device (`str` or `torch.device`): The device to put the tensors on. Returns: [`BatchEncoding`]: The same instance after modification. """ requires_backends(self, ["torch"]) # This check catches things like APEX blindly calling "to" on all inputs to a module # Otherwise it passes the casts down and casts the LongTensor containing the token idxs # into a HalfTensor if isinstance(device, str) or is_torch_device(device) or isinstance(device, int): self.data = {k: v.to(device=device) for k, v in self.data.items()} else: logger.warning(f"Attempting to cast a BatchEncoding to type {str(device)}. This is not supported.") return self class SpecialTokensMixin: """ A mixin derived by [`PreTrainedTokenizer`] and [`PreTrainedTokenizerFast`] 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 `tokenizers.AddedToken`, *optional*): A special token representing the beginning of a sentence. eos_token (`str` or `tokenizers.AddedToken`, *optional*): A special token representing the end of a sentence. unk_token (`str` or `tokenizers.AddedToken`, *optional*): A special token representing an out-of-vocabulary token. sep_token (`str` or `tokenizers.AddedToken`, *optional*): A special token separating two different sentences in the same input (used by BERT for instance). pad_token (`str` or `tokenizers.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 `tokenizers.AddedToken`, *optional*): A special token representing the class of the input (used by BERT for instance). mask_token (`str` or `tokenizers.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 `tokenizers.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): self._bos_token = None self._eos_token = None self._unk_token = None self._sep_token = None self._pad_token = None self._cls_token = None self._mask_token = None self._pad_token_type_id = 0 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)}") 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) def add_special_tokens( self, special_tokens_dict: Dict[str, Union[str, AddedToken]], replace_additional_special_tokens=True ) -> 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 `''`). Args: special_tokens_dict (dictionary *str* to *str* or `tokenizers.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). replace_additional_special_tokens (`bool`, *optional*,, defaults to `True`): If `True`, the existing list of additional special tokens will be replaced by the one specified in `special_tokens_dict`. Otherwise, `self._additional_special_tokens` is updated. In the former case, the tokens will NOT be removed from the tokenizer's full vocabulary - they are only being flagged as non-special tokens. 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": ""} 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 == "" ```""" 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") 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" if replace_additional_special_tokens: setattr(self, key, value) else: # This is a copy of `self._additional_special_tokens` additional_special_tokens = getattr(self, key) additional_special_tokens_set = set(additional_special_tokens) to_add = [] for token in value: if str(token) not in additional_special_tokens_set and str(token) not in to_add: to_add.append(token) # update the property additional_special_tokens.extend(to_add) self.additional_special_tokens = additional_special_tokens 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" setattr(self, key, value) added_tokens += self.add_tokens([value], special_tokens=True) return added_tokens 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 and and will be isolated before the tokenization algorithm is applied. Added tokens and tokens from the vocabulary of the tokenization algorithm are therefore not treated in the same way. Note, 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`, `tokenizers.AddedToken` or a list of *str* or `tokenizers.AddedToken`): Tokens are only added if they are not already in the vocabulary. `tokenizers.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). See details for `tokenizers.AddedToken` in HuggingFace tokenizers library. 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 = BertTokenizerFast.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: if 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: if 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: if 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: if 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: if 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: if 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: if 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: if 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 (`''`, `''`, etc.). Convert potential tokens of `tokenizers.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, tokenizers.AddedToken, List[Union[str, tokenizers.AddedToken]]]]`: A dictionary mapping special token class attributes (`cls_token`, `unk_token`, etc.) to their values (`''`, `''`, etc.). Don't convert tokens of `tokenizers.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 (`''`, `''`, etc.) mapped to class attributes. Convert tokens of `tokenizers.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, tokenizers.AddedToken]]`: All the special tokens (`''`, `''`, etc.) mapped to class attributes. Don't convert tokens of `tokenizers.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(`''`, `''`, etc.) mapped to class attributes. """ all_toks = self.all_special_tokens all_ids = self.convert_tokens_to_ids(all_toks) return all_ids ENCODE_KWARGS_DOCSTRING = r""" add_special_tokens (`bool`, *optional*, defaults to `True`): Whether or not to encode the sequences with the special tokens relative to their model. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): 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). truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): 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). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. stride (`int`, *optional*, defaults to 0): If set to a number along with `max_length`, the overflowing tokens returned when `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens. is_split_into_words (`bool`, *optional*, defaults to `False`): Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. Requires `padding` to be activated. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. """ ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r""" return_token_type_ids (`bool`, *optional*): Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are token type IDs?](../glossary#token-type-ids) 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_overflowing_tokens (`bool`, *optional*, defaults to `False`): Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead of returning overflowing tokens. return_special_tokens_mask (`bool`, *optional*, defaults to `False`): Whether or not to return special tokens mask information. return_offsets_mapping (`bool`, *optional*, defaults to `False`): Whether or not to return `(char_start, char_end)` for each token. This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using Python's tokenizer, this method will raise `NotImplementedError`. return_length (`bool`, *optional*, defaults to `False`): Whether or not to return the lengths of the encoded inputs. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. **kwargs: passed to the `self.tokenize()` method Return: [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. [What are input IDs?](../glossary#input-ids) - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or if *"token_type_ids"* is in `self.model_input_names`). [What are token type IDs?](../glossary#token-type-ids) - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`). [What are attention masks?](../glossary#attention-mask) - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and `return_overflowing_tokens=True`). - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and `return_overflowing_tokens=True`). - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`). - **length** -- The length of the inputs (when `return_length=True`) """ INIT_TOKENIZER_DOCSTRING = r""" Class attributes (overridden by derived classes) - **vocab_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_vocab_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 [`~tokenization_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 [`~tokenization_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 `tokenizers.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 `tokenizers.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 `tokenizers.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 `tokenizers.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 `tokenizers.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 `tokenizers.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 `tokenizers.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 `tokenizers.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`. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not the model should cleanup the spaces that were added when splitting the input text during the tokenization process. """ @add_end_docstrings(INIT_TOKENIZER_DOCSTRING) class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin): """ Base class for [`PreTrainedTokenizer`] and [`PreTrainedTokenizerFast`]. Handles shared (mostly boiler plate) methods for those two classes. """ vocab_files_names: Dict[str, str] = {} pretrained_vocab_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 # 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", "attention_mask"] 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 = 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) # By default, cleaning tokenization spaces for both fast and slow tokenizers self.clean_up_tokenization_spaces = kwargs.pop("clean_up_tokenization_spaces", False) self.deprecation_warnings = ( {} ) # Use to store when we have already noticed a deprecation warning (avoid overlogging). self._in_target_context_manager = False 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): logger.warning( "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): logger.warning( "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"{self.__class__.__name__}(name_or_path='{self.name_or_path}'," f" vocab_size={self.vocab_size}, model_max_length={self.model_max_length}, is_fast={self.is_fast}," f" padding_side='{self.padding_side}', truncation_side='{self.truncation_side}'," f" special_tokens={self.special_tokens_map_extended}, clean_up_tokenization_spaces={self.clean_up_tokenization_spaces})" ) def __len__(self) -> int: raise NotImplementedError() 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() @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Union[str, os.PathLike], *init_inputs, cache_dir: Optional[Union[str, os.PathLike]] = None, force_download: bool = False, local_files_only: bool = False, token: Optional[Union[str, bool]] = None, revision: str = "main", **kwargs, ): r""" Instantiate a [`~tokenization_utils_base.PreTrainedTokenizerBase`] (or a derived class) from a predefined tokenizer. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): Can be either: - A string, the *model id* of a predefined tokenizer hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing vocabulary files required by the tokenizer, for instance saved using the [`~tokenization_utils_base.PreTrainedTokenizerBase.save_pretrained`] method, e.g., `./my_model_directory/`. - (**Deprecated**, not applicable to all derived classes) A path or url to a single saved vocabulary file (if and only if the tokenizer only requires a single vocabulary file like Bert or XLNet), e.g., `./my_model_directory/vocab.txt`. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download the vocabulary files and override the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). local_files_only (`bool`, *optional*, defaults to `False`): Whether or not to only rely on local files and not to attempt to download any files. revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. subfolder (`str`, *optional*): In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for facebook/rag-token-base), specify it here. inputs (additional positional arguments, *optional*): Will be passed along to the Tokenizer `__init__` method. kwargs (additional keyword arguments, *optional*): Will be passed to the Tokenizer `__init__` method. Can be used to set special tokens like `bos_token`, `eos_token`, `unk_token`, `sep_token`, `pad_token`, `cls_token`, `mask_token`, `additional_special_tokens`. See parameters in the `__init__` for more details. Passing `use_auth_token=True` is required when you want to use a private model. Examples: ```python # We can't instantiate directly the base class *PreTrainedTokenizerBase* so let's show our examples on a derived class: BertTokenizer # Download vocabulary from huggingface.co and cache. tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") # Download vocabulary from huggingface.co (user-uploaded) and cache. tokenizer = BertTokenizer.from_pretrained("dbmdz/bert-base-german-cased") # If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*) tokenizer = BertTokenizer.from_pretrained("./test/saved_model/") # If the tokenizer uses a single vocabulary file, you can point directly to this file tokenizer = BertTokenizer.from_pretrained("./test/saved_model/my_vocab.txt") # You can link tokens to special vocabulary when instantiating tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", unk_token="") # You should be sure '' is in the vocabulary when doing that. # Otherwise use tokenizer.add_special_tokens({'unk_token': ''}) instead) assert tokenizer.unk_token == "" ```""" resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) use_auth_token = kwargs.pop("use_auth_token", None) subfolder = kwargs.pop("subfolder", None) from_pipeline = kwargs.pop("_from_pipeline", None) from_auto_class = kwargs.pop("_from_auto", False) commit_hash = kwargs.pop("_commit_hash", None) if use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.", FutureWarning ) if token is not None: raise ValueError( "`token` and `use_auth_token` are both specified. Please set only the argument `token`." ) token = use_auth_token user_agent = {"file_type": "tokenizer", "from_auto_class": from_auto_class, "is_fast": "Fast" in cls.__name__} if from_pipeline is not None: user_agent["using_pipeline"] = from_pipeline if is_offline_mode() and not local_files_only: logger.info("Offline mode: forcing local_files_only=True") local_files_only = True pretrained_model_name_or_path = str(pretrained_model_name_or_path) vocab_files = {} init_configuration = {} is_local = os.path.isdir(pretrained_model_name_or_path) single_file_id = None if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path): if len(cls.vocab_files_names) > 1: raise ValueError( f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is not " "supported for this tokenizer. Use a model identifier or the path to a directory instead." ) warnings.warn( f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is deprecated and " "won't be possible anymore in v5. Use a model identifier or the path to a directory instead.", FutureWarning, ) file_id = list(cls.vocab_files_names.keys())[0] vocab_files[file_id] = pretrained_model_name_or_path single_file_id = file_id else: # At this point pretrained_model_name_or_path is either a directory or a model identifier name 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 = {**cls.vocab_files_names, **additional_files_names} if "tokenizer_file" in vocab_files: # Try to get the tokenizer config to see if there are versioned tokenizer files. fast_tokenizer_file = FULL_TOKENIZER_FILE resolved_config_file = cached_file( pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, use_auth_token=token, revision=revision, local_files_only=local_files_only, subfolder=subfolder, user_agent=user_agent, _raise_exceptions_for_missing_entries=False, _raise_exceptions_for_connection_errors=False, _commit_hash=commit_hash, ) commit_hash = extract_commit_hash(resolved_config_file, commit_hash) if resolved_config_file is not None: with open(resolved_config_file, encoding="utf-8") as reader: tokenizer_config = json.load(reader) if "fast_tokenizer_files" in tokenizer_config: fast_tokenizer_file = get_fast_tokenizer_file(tokenizer_config["fast_tokenizer_files"]) vocab_files["tokenizer_file"] = fast_tokenizer_file # Get files from url, cache, or disk depending on the case resolved_vocab_files = {} unresolved_files = [] for file_id, file_path in vocab_files.items(): if file_path is None: resolved_vocab_files[file_id] = None elif single_file_id == file_id: if os.path.isfile(file_path): resolved_vocab_files[file_id] = file_path elif is_remote_url(file_path): resolved_vocab_files[file_id] = download_url(file_path, proxies=proxies) else: resolved_vocab_files[file_id] = cached_file( pretrained_model_name_or_path, file_path, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=token, user_agent=user_agent, revision=revision, subfolder=subfolder, _raise_exceptions_for_missing_entries=False, _raise_exceptions_for_connection_errors=False, _commit_hash=commit_hash, ) commit_hash = extract_commit_hash(resolved_vocab_files[file_id], commit_hash) if len(unresolved_files) > 0: logger.info( f"Can't load following files from cache: {unresolved_files} and cannot check if these " "files are necessary for the tokenizer to operate." ) if all(full_file_name is None for full_file_name in resolved_vocab_files.values()): raise EnvironmentError( f"Can't load tokenizer for '{pretrained_model_name_or_path}'. If you were trying to load it from " "'https://huggingface.co/models', make sure you don't have a local directory with the same name. " f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory " f"containing all relevant files for a {cls.__name__} tokenizer." ) for file_id, file_path in vocab_files.items(): if file_id not in resolved_vocab_files: continue if is_local: logger.info(f"loading file {file_path}") else: logger.info(f"loading file {file_path} from cache at {resolved_vocab_files[file_id]}") return cls._from_pretrained( resolved_vocab_files, pretrained_model_name_or_path, init_configuration, *init_inputs, use_auth_token=token, cache_dir=cache_dir, local_files_only=local_files_only, _commit_hash=commit_hash, _is_local=is_local, **kwargs, ) @classmethod def _from_pretrained( cls, resolved_vocab_files, pretrained_model_name_or_path, init_configuration, *init_inputs, use_auth_token=None, cache_dir=None, local_files_only=False, _commit_hash=None, _is_local=False, **kwargs, ): # We instantiate fast tokenizers based on a slow tokenizer if we don't have access to the tokenizer.json # file or if `from_slow` is set to True. from_slow = kwargs.get("from_slow", False) has_tokenizer_file = resolved_vocab_files.get("tokenizer_file", None) is not None if (from_slow or not has_tokenizer_file) and cls.slow_tokenizer_class is not None: slow_tokenizer = (cls.slow_tokenizer_class)._from_pretrained( copy.deepcopy(resolved_vocab_files), pretrained_model_name_or_path, copy.deepcopy(init_configuration), *init_inputs, use_auth_token=use_auth_token, cache_dir=cache_dir, local_files_only=local_files_only, _commit_hash=_commit_hash, **(copy.deepcopy(kwargs)), ) else: slow_tokenizer = None # Prepare tokenizer initialization kwargs # Did we saved some inputs and kwargs to reload ? tokenizer_config_file = resolved_vocab_files.pop("tokenizer_config_file", None) if tokenizer_config_file is not None: with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle: init_kwargs = json.load(tokenizer_config_handle) # First attempt. We get tokenizer_class from tokenizer_config to check mismatch between tokenizers. config_tokenizer_class = init_kwargs.get("tokenizer_class") init_kwargs.pop("tokenizer_class", None) saved_init_inputs = init_kwargs.pop("init_inputs", ()) if not init_inputs: init_inputs = saved_init_inputs else: config_tokenizer_class = None init_kwargs = init_configuration if "auto_map" in init_kwargs and not _is_local: # For backward compatibility with odl format. if isinstance(init_kwargs["auto_map"], (tuple, list)): init_kwargs["auto_map"] = {"AutoTokenizer": init_kwargs["auto_map"]} init_kwargs["auto_map"] = add_model_info_to_auto_map( init_kwargs["auto_map"], pretrained_model_name_or_path ) if config_tokenizer_class is None: from .models.auto.configuration_auto import AutoConfig # tests_ignore # Second attempt. If we have not yet found tokenizer_class, let's try to use the config. try: config = AutoConfig.from_pretrained( pretrained_model_name_or_path, use_auth_token=use_auth_token, cache_dir=cache_dir, local_files_only=local_files_only, _commit_hash=_commit_hash, ) config_tokenizer_class = config.tokenizer_class except (OSError, ValueError, KeyError): # skip if an error occurred. config = None if config_tokenizer_class is None: # Third attempt. If we have not yet found the original type of the tokenizer, # we are loading we see if we can infer it from the type of the configuration file from .models.auto.tokenization_auto import TOKENIZER_MAPPING_NAMES # tests_ignore if hasattr(config, "model_type"): model_type = config.model_type else: # Fallback: use pattern matching on the string. model_type = None for pattern in TOKENIZER_MAPPING_NAMES.keys(): if pattern in str(pretrained_model_name_or_path): model_type = pattern break if model_type is not None: config_tokenizer_class, config_tokenizer_class_fast = TOKENIZER_MAPPING_NAMES.get( model_type, (None, None) ) if config_tokenizer_class is None: config_tokenizer_class = config_tokenizer_class_fast if config_tokenizer_class is not None: if cls.__name__.replace("Fast", "") != config_tokenizer_class.replace("Fast", ""): logger.warning( "The tokenizer class you load from this checkpoint is not the same type as the class this" " function is called from. It may result in unexpected tokenization. \nThe tokenizer class you" f" load from this checkpoint is '{config_tokenizer_class}'. \nThe class this function is called" f" from is '{cls.__name__}'." ) # Update with newly provided kwargs init_kwargs.update(kwargs) # Convert AddedTokens serialized as dict to class instances def convert_added_tokens(obj: Union[AddedToken, Any]): if isinstance(obj, dict) and "__type" in obj and obj["__type"] == "AddedToken": obj.pop("__type") return AddedToken(**obj) elif isinstance(obj, (list, tuple)): return [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)): model_max_length = min(init_kwargs.get("model_max_length", int(1e30)), model_max_length) # TODO(PVP) - uncomment following line in Transformers v5 # init_kwargs["model_max_length"] = model_max_length # TODO(PVP) - remove in Transformers v5 # --- init_kwargs["model_max_length"] = cls._eventually_correct_t5_max_length( pretrained_model_name_or_path, model_max_length, init_kwargs.get("model_max_length") ) # --- # Merge resolved_vocab_files arguments in init_kwargs. added_tokens_file = resolved_vocab_files.pop("added_tokens_file", None) for args_name, file_path in resolved_vocab_files.items(): if args_name not in init_kwargs: init_kwargs[args_name] = file_path if slow_tokenizer is not None: init_kwargs["__slow_tokenizer"] = slow_tokenizer init_kwargs["name_or_path"] = pretrained_model_name_or_path # Instantiate tokenizer. try: tokenizer = cls(*init_inputs, **init_kwargs) except OSError: raise OSError( "Unable to load vocabulary from file. " "Please check that the provided vocabulary is accessible and not corrupted." ) # Save inputs and kwargs for saving and re-loading with ``save_pretrained`` # Removed: Now done at the base class level # tokenizer.init_inputs = init_inputs # tokenizer.init_kwargs = init_kwargs # If there is a complementary special token map, load it 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 = sorted(added_tok_encoder.items(), key=lambda x: x[1]) # Accumulate added tokens into batches of special/non-special tokens, because calling add_tokens() for # individual tokens would repeatedly rebuild a trie, which can be slow. is_last_special = None tokens = [] for token, index in added_tok_encoder_sorted: current_index = len(tokenizer) + len(tokens) if has_tokenizer_file and index != current_index and tokenizer.convert_tokens_to_ids(token) != index: # Tokenizer fast: added token needs to either be in the vocabulary with the proper index or the # 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 != current_index: # 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 {current_index} but has index {index} in saved vocabulary." ) is_special = bool(token in special_tokens) if is_last_special is None or is_last_special == is_special: tokens.append(token) else: tokenizer.add_tokens(tokens, special_tokens=is_last_special) tokens = [token] is_last_special = is_special if tokens: tokenizer.add_tokens(tokens, special_tokens=is_last_special) # 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.warning_advice( "Special tokens have been added in the vocabulary, make sure the associated word embeddings are" " fine-tuned or trained." ) return tokenizer @staticmethod def _eventually_correct_t5_max_length(pretrained_model_name_or_path, max_model_length, init_max_model_length): # This method should be deleted in Transformers v5 # Its only purpose is to potentially throw a warning # that incorrectly defined max lengths of T5's tokenizer are used # which we will correct in Transformers v5. return max_model_length def save_pretrained( self, save_directory: Union[str, os.PathLike], legacy_format: Optional[bool] = None, filename_prefix: Optional[str] = None, push_to_hub: bool = False, **kwargs, ) -> Tuple[str]: """ Save the full tokenizer state. This method make sure the full tokenizer can then be re-loaded using the [`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`] class method.. Warning,None This won't save modifications you may have applied to the tokenizer after the instantiation (for instance, modifying `tokenizer.do_lower_case` after creation). Args: save_directory (`str` or `os.PathLike`): The path to a directory where the tokenizer will be saved. legacy_format (`bool`, *optional*): Only applicable for a fast tokenizer. If unset (default), will save the tokenizer in the unified JSON format as well as in legacy format if it exists, i.e. with tokenizer specific vocabulary and a separate added_tokens files. If `False`, will only save the tokenizer in the unified JSON format. This format is incompatible with "slow" tokenizers (not powered by the *tokenizers* library), so the tokenizer will not be able to be loaded in the corresponding "slow" tokenizer. If `True`, will save the tokenizer in legacy format. If the "slow" tokenizer doesn't exits, a value error is raised. filename_prefix (`str`, *optional*): A prefix to add to the names of the files saved by the tokenizer. push_to_hub (`bool`, *optional*, defaults to `False`): Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace). kwargs (`Dict[str, Any]`, *optional*): Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method. Returns: A tuple of `str`: The files saved. """ if os.path.isfile(save_directory): logger.error(f"Provided path ({save_directory}) should be a directory, not a file") return os.makedirs(save_directory, exist_ok=True) if push_to_hub: commit_message = kwargs.pop("commit_message", None) repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1]) repo_id = self._create_repo(repo_id, **kwargs) files_timestamps = self._get_files_timestamps(save_directory) 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 "") + TOKENIZER_CONFIG_FILE ) tokenizer_config = copy.deepcopy(self.init_kwargs) # TODO: Ensure the modified attributes (those are also in the __init__ kwargs) will give identical tokenizers # target_keys = self.init_kwargs.keys() target_keys = ["model_max_length", "clean_up_tokenization_spaces"] for k in target_keys: if hasattr(self, k): tokenizer_config[k] = getattr(self, k) if len(self.init_inputs) > 0: tokenizer_config["init_inputs"] = copy.deepcopy(self.init_inputs) for file_id in self.vocab_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 [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__ # Remove the Fast at the end unless we have a special `PreTrainedTokenizerFast` if tokenizer_class.endswith("Fast") and tokenizer_class != "PreTrainedTokenizerFast": tokenizer_class = tokenizer_class[:-4] tokenizer_config["tokenizer_class"] = tokenizer_class if getattr(self, "_auto_map", None) is not None: tokenizer_config["auto_map"] = self._auto_map if getattr(self, "_processor_class", None) is not None: tokenizer_config["processor_class"] = self._processor_class # If we have a custom model, we copy the file defining it in the folder and set the attributes so it can be # loaded from the Hub. if self._auto_class is not None: custom_object_save(self, save_directory, config=tokenizer_config) # remove private information if "name_or_path" in tokenizer_config: tokenizer_config.pop("name_or_path") tokenizer_config.pop("special_tokens_map_file", None) with open(tokenizer_config_file, "w", encoding="utf-8") as f: out_str = json.dumps(tokenizer_config, indent=2, sort_keys=True, ensure_ascii=False) + "\n" f.write(out_str) 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: out_str = json.dumps(write_dict, indent=2, sort_keys=True, ensure_ascii=False) + "\n" f.write(out_str) 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, legacy_format=legacy_format, filename_prefix=filename_prefix, ) if push_to_hub: self._upload_modified_files( save_directory, repo_id, files_timestamps, commit_message=commit_message, token=kwargs.get("use_auth_token"), ) return save_files def _save_pretrained( self, save_directory: Union[str, os.PathLike], file_names: Tuple[str], legacy_format: Optional[bool] = None, filename_prefix: Optional[str] = None, ) -> Tuple[str]: """ Save a tokenizer using the slow-tokenizer/legacy format: vocabulary + added tokens. Fast tokenizers can also be saved in a unique JSON file containing {config + vocab + added-tokens} using the specific [`~tokenization_utils_fast.PreTrainedTokenizerFast._save_pretrained`] """ if legacy_format is False: raise ValueError( "Only fast tokenizers (instances of PreTrainedTokenizerFast) can be saved in non legacy format." ) 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, indent=2, sort_keys=True, ensure_ascii=False) + "\n" f.write(out_str) logger.info(f"added tokens file saved in {added_tokens_file}") vocab_files = self.save_vocabulary(save_directory, filename_prefix=filename_prefix) return file_names + vocab_files + (added_tokens_file,) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: """ Save only the vocabulary of the tokenizer (vocabulary + added tokens). This method won't save the configuration and special token mappings of the tokenizer. Use [`~PreTrainedTokenizerFast._save_pretrained`] to save the whole state of the tokenizer. Args: save_directory (`str`): The directory in which to save the vocabulary. filename_prefix (`str`, *optional*): An optional prefix to add to the named of the saved files. Returns: `Tuple(str)`: Paths to the files saved. """ raise NotImplementedError 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 @add_end_docstrings( ENCODE_KWARGS_DOCSTRING, """ **kwargs: Passed along to the `.tokenize()` method. """, """ Returns: `List[int]`, `torch.Tensor`, `tf.Tensor` or `np.ndarray`: The tokenized ids of the text. """, ) def encode( self, text: Union[TextInput, PreTokenizedInput, EncodedInput], text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs, ) -> List[int]: """ Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary. Same as doing `self.convert_tokens_to_ids(self.tokenize(text))`. 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). """ encoded_inputs = self.encode_plus( text, text_pair=text_pair, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, return_tensors=return_tensors, **kwargs, ) return encoded_inputs["input_ids"] def num_special_tokens_to_add(self, pair: bool = False) -> int: raise NotImplementedError def _get_padding_truncation_strategies( self, padding=False, truncation=None, 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_length", 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 None: if verbose: if not self.deprecation_warnings.get("Truncation-not-explicitly-activated", False): logger.warning( "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 None or 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`.") padding_strategy = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch 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 None 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 and truncation is not None: 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): logger.warning( "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): logger.warning( "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( "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 @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def __call__( self, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None, text_target: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, text_pair_target: Optional[ Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] ] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, 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, **kwargs, ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences. Args: text (`str`, `List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). text_pair (`str`, `List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). text_target (`str`, `List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). text_pair_target (`str`, `List[str]`, `List[List[str]]`, *optional*): The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). """ # To avoid duplicating all_kwargs = { "add_special_tokens": add_special_tokens, "padding": padding, "truncation": truncation, "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_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, } all_kwargs.update(kwargs) if text is None and text_target is None: raise ValueError("You need to specify either `text` or `text_target`.") if text is not None: # The context manager will send the inputs as normal texts and not text_target, but we shouldn't change the # input mode in this case. if not self._in_target_context_manager: self._switch_to_input_mode() encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs) if text_target is not None: self._switch_to_target_mode() target_encodings = self._call_one(text=text_target, text_pair=text_pair_target, **all_kwargs) # Leave back tokenizer in input mode self._switch_to_input_mode() if text_target is None: return encodings elif text is None: return target_encodings else: encodings["labels"] = target_encodings["input_ids"] return encodings def _call_one( self, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, 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, **kwargs, ) -> BatchEncoding: # 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)." ) 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`:" f" {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_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, 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_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, ) else: return self.encode_plus( text=text, text_pair=text_pair, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, 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_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, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def encode_plus( self, text: Union[TextInput, PreTokenizedInput, EncodedInput], text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, 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, **kwargs, ) -> BatchEncoding: """ Tokenize and prepare for the model a sequence or a pair of sequences. This method is deprecated, `__call__` should be used instead. Args: text (`str`, `List[str]` or `List[int]` (the latter only for not-fast tokenizers)): 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 '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_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_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 @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) 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: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, 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, **kwargs, ) -> BatchEncoding: """ Tokenize and prepare for the model a list of sequences or a list of pairs of sequences. This method is deprecated, `__call__` should be used instead. Args: batch_text_or_text_pairs (`List[str]`, `List[Tuple[str, str]]`, `List[List[str]]`, `List[Tuple[List[str], List[str]]]`, and for not-fast tokenizers, also `List[List[int]]`, `List[Tuple[List[int], List[int]]]`): Batch of sequences or pair of sequences to be encoded. This can be a list of string/string-sequences/int-sequences or a list of pair of string/string-sequences/int-sequence (see details in `encode_plus`). """ # 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_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 _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_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 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`). Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. If the `encoded_inputs` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the specific device of your tensors however. 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 PyTorch Dataloader collate function. Instead of `List[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see the note above for the return type. padding (`bool`, `str` or [`~utils.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 [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. """ if self.__class__.__name__.endswith("Fast"): if not self.deprecation_warnings.get("Asking-to-pad-a-fast-tokenizer", False): logger.warning_advice( f"You're using a {self.__class__.__name__} tokenizer. Please note that with a fast tokenizer," " using the `__call__` method is faster than using a method to encode the text followed by a call" " to the `pad` method to get a padded encoding." ) self.deprecation_warnings["Asking-to-pad-a-fast-tokenizer"] = True # If we have a list of dicts, let's convert it in a dict of lists # We do this to allow using this method as a collate_fn function in PyTorch Dataloader if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], Mapping): 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 required_input is None or (isinstance(required_input, Sized) and len(required_input) == 0): if return_attention_mask: encoded_inputs["attention_mask"] = [] return encoded_inputs # If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects # and rebuild them afterwards if no return_tensors is specified # Note that we lose the specific device the tensor may be on for PyTorch 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 is_tf_tensor(first_element): return_tensors = "tf" if return_tensors is None else return_tensors elif is_torch_tensor(first_element): return_tensors = "pt" if return_tensors is None else return_tensors elif isinstance(first_element, np.ndarray): return_tensors = "np" if return_tensors is None else return_tensors else: raise ValueError( f"type of {first_element} unknown: {type(first_element)}. " "Should be one of a python, numpy, pytorch or tensorflow 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 = {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) 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) 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 @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def prepare_for_model( self, ids: List[int], pair_ids: Optional[List[int]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, 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, prepend_batch_axis: bool = False, **kwargs, ) -> BatchEncoding: """ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Please Note, for *pair_ids* different than `None` and *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an error. 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. """ # 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, ) 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 = {} # Compute the total size of the returned encodings total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0) # Truncation: Handle max sequence length 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 dictionary 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) # Check lengths self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose) # Padding 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"]) batch_outputs = BatchEncoding( encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis ) return batch_outputs 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 [`~tokenization_utils_base.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: logger.warning( "Be aware, overflowing tokens are not returned for the setting you have chosen," f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' " "truncation strategy. So the returned list will always be empty even if some " "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}, " "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 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 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"] 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 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 def batch_decode( self, sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = None, **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, torch.Tensor, tf.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*): Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_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 ] def decode( self, token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = None, **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, torch.Tensor, tf.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*): Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_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 = None, **kwargs, ) -> str: raise NotImplementedError 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 @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 def _switch_to_input_mode(self): """ Private method to put the tokenizer in input mode (when it has different modes for input/outputs) """ pass def _switch_to_target_mode(self): """ Private method to put the tokenizer in target mode (when it has different modes for input/outputs) """ pass @contextmanager def as_target_tokenizer(self): """ Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to sequence-to-sequence models that need a slightly different processing for the labels. """ warnings.warn( "`as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your " "labels by using the argument `text_target` of the regular `__call__` method (either in the same call as " "your input texts if you use the same keyword arguments, or in a separate call." ) self._switch_to_target_mode() self._in_target_context_manager = True yield self._in_target_context_manager = False self._switch_to_input_mode() @classmethod def register_for_auto_class(cls, auto_class="AutoTokenizer"): """ Register this class with a given auto class. This should only be used for custom tokenizers as the ones in the library are already mapped with `AutoTokenizer`. This API is experimental and may have some slight breaking changes in the next releases. Args: auto_class (`str` or `type`, *optional*, defaults to `"AutoTokenizer"`): The auto class to register this new tokenizer with. """ if not isinstance(auto_class, str): auto_class = auto_class.__name__ import transformers.models.auto as auto_module if not hasattr(auto_module, auto_class): raise ValueError(f"{auto_class} is not a valid auto class.") cls._auto_class = auto_class def prepare_seq2seq_batch( self, src_texts: List[str], tgt_texts: Optional[List[str]] = None, max_length: Optional[int] = None, max_target_length: Optional[int] = None, padding: str = "longest", return_tensors: str = None, truncation: bool = True, **kwargs, ) -> BatchEncoding: """ Prepare model inputs for translation. For best performance, translate one sentence at a time. Arguments: src_texts (`List[str]`): List of documents to summarize or source language texts. tgt_texts (`list`, *optional*): List of summaries or target language texts. max_length (`int`, *optional*): Controls the maximum length for encoder inputs (documents to summarize or source language texts) If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. max_target_length (`int`, *optional*): Controls the maximum length of decoder inputs (target language texts or summaries) If left unset or set to `None`, this will use the max_length value. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): 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). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `True`): 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). **kwargs: Additional keyword arguments passed along to `self.__call__`. Return: [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: - **input_ids** -- List of token ids to be fed to the encoder. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model. - **labels** -- List of token ids for tgt_texts. The full set of keys `[input_ids, attention_mask, labels]`, will only be returned if tgt_texts is passed. Otherwise, input_ids, attention_mask will be the only keys. """ # docstyle-ignore formatted_warning = """ `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of HuggingFace Transformers. Use the regular `__call__` method to prepare your inputs and targets. Here is a short example: model_inputs = tokenizer(src_texts, text_target=tgt_texts, ...) If you either need to use different keyword arguments for the source and target texts, you should do two calls like this: model_inputs = tokenizer(src_texts, ...) labels = tokenizer(text_target=tgt_texts, ...) model_inputs["labels"] = labels["input_ids"] See the documentation of your specific tokenizer for more details on the specific arguments to the tokenizer of choice. For a more complete example, see the implementation of `prepare_seq2seq_batch`. """ warnings.warn(formatted_warning, FutureWarning) # mBART-specific kwargs that should be ignored by other models. kwargs.pop("src_lang", None) kwargs.pop("tgt_lang", None) if max_length is None: max_length = self.model_max_length model_inputs = self( src_texts, add_special_tokens=True, return_tensors=return_tensors, max_length=max_length, padding=padding, truncation=truncation, **kwargs, ) if tgt_texts is None: return model_inputs # Process tgt_texts if max_target_length is None: max_target_length = max_length with self.as_target_tokenizer(): labels = self( tgt_texts, add_special_tokens=True, return_tensors=return_tensors, padding=padding, max_length=max_target_length, truncation=truncation, **kwargs, ) model_inputs["labels"] = labels["input_ids"] return model_inputs def get_fast_tokenizer_file(tokenization_files: List[str]) -> str: """ Get the tokenization file to use for this version of transformers. Args: tokenization_files (`List[str]`): The list of available configuration files. Returns: `str`: The tokenization file to use. """ tokenizer_files_map = {} for file_name in tokenization_files: search = _re_tokenizer_file.search(file_name) if search is not None: v = search.groups()[0] tokenizer_files_map[v] = file_name available_versions = sorted(tokenizer_files_map.keys()) # Defaults to FULL_TOKENIZER_FILE and then try to look at some newer versions. tokenizer_file = FULL_TOKENIZER_FILE transformers_version = version.parse(__version__) for v in available_versions: if version.parse(v) <= transformers_version: tokenizer_file = tokenizer_files_map[v] else: # No point going further since the versions are sorted. break return tokenizer_file # To update the docstring, we need to copy the method, otherwise we change the original docstring. PreTrainedTokenizerBase.push_to_hub = copy_func(PreTrainedTokenizerBase.push_to_hub) if PreTrainedTokenizerBase.push_to_hub.__doc__ is not None: PreTrainedTokenizerBase.push_to_hub.__doc__ = PreTrainedTokenizerBase.push_to_hub.__doc__.format( object="tokenizer", object_class="AutoTokenizer", object_files="tokenizer files" )