847 lines
37 KiB
Python
847 lines
37 KiB
Python
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# coding=utf-8
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# Copyright 2020 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Tokenization classes for fast tokenizers (provided by HuggingFace's tokenizers library). For slow (python) tokenizers
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see tokenization_utils.py
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"""
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import copy
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import json
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import os
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from collections import defaultdict
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from typing import Any, Dict, List, Optional, Tuple, Union
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import tokenizers.pre_tokenizers as pre_tokenizers_fast
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from tokenizers import Encoding as EncodingFast
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from tokenizers import Tokenizer as TokenizerFast
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from tokenizers.decoders import Decoder as DecoderFast
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from tokenizers.trainers import BpeTrainer, UnigramTrainer, WordLevelTrainer, WordPieceTrainer
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from .convert_slow_tokenizer import convert_slow_tokenizer
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from .tokenization_utils import PreTrainedTokenizer
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from .tokenization_utils_base import (
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INIT_TOKENIZER_DOCSTRING,
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AddedToken,
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BatchEncoding,
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PreTokenizedInput,
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PreTokenizedInputPair,
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PreTrainedTokenizerBase,
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SpecialTokensMixin,
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TextInput,
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TextInputPair,
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TruncationStrategy,
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)
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from .utils import PaddingStrategy, add_end_docstrings, logging
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logger = logging.get_logger(__name__)
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# Fast tokenizers (provided by HuggingFace tokenizer's library) can be saved in a single file
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TOKENIZER_FILE = "tokenizer.json"
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SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
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TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
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# Slow tokenizers have an additional added tokens files
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ADDED_TOKENS_FILE = "added_tokens.json"
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INIT_TOKENIZER_DOCSTRING += """
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tokenizer_object ([`tokenizers.Tokenizer`]):
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A [`tokenizers.Tokenizer`] object from 🤗 tokenizers to instantiate from. See [Using tokenizers from 🤗
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tokenizers](../fast_tokenizers) for more information.
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tokenizer_file ([`str`]):
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A path to a local JSON file representing a previously serialized [`tokenizers.Tokenizer`] object from 🤗
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tokenizers.
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"""
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MODEL_TO_TRAINER_MAPPING = {
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"BPE": BpeTrainer,
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"Unigram": UnigramTrainer,
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"WordLevel": WordLevelTrainer,
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"WordPiece": WordPieceTrainer,
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}
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VOCAB_FILES_NAMES = {"tokenizer_file": TOKENIZER_FILE}
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@add_end_docstrings(INIT_TOKENIZER_DOCSTRING)
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class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
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"""
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Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).
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Inherits from [`~tokenization_utils_base.PreTrainedTokenizerBase`].
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Handles all the shared methods for tokenization and special tokens, as well as methods for
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downloading/caching/loading pretrained tokenizers, as well as adding tokens to the vocabulary.
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This class also contains the added tokens in a unified way on top of all tokenizers so we don't have to handle the
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specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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slow_tokenizer_class: PreTrainedTokenizer = None
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def __init__(self, *args, **kwargs):
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tokenizer_object = kwargs.pop("tokenizer_object", None)
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slow_tokenizer = kwargs.pop("__slow_tokenizer", None)
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fast_tokenizer_file = kwargs.pop("tokenizer_file", None)
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from_slow = kwargs.pop("from_slow", False)
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added_tokens_decoder = kwargs.pop("added_tokens_decoder", {})
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if from_slow and slow_tokenizer is None and self.slow_tokenizer_class is None:
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raise ValueError(
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"Cannot instantiate this tokenizer from a slow version. If it's based on sentencepiece, make sure you "
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"have sentencepiece installed."
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)
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if tokenizer_object is not None:
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fast_tokenizer = copy.deepcopy(tokenizer_object)
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elif fast_tokenizer_file is not None and not from_slow:
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# We have a serialization from tokenizers which let us directly build the backend
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fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
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elif slow_tokenizer is not None:
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# We need to convert a slow tokenizer to build the backend
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fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)
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elif self.slow_tokenizer_class is not None:
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# We need to create and convert a slow tokenizer to build the backend
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slow_tokenizer = self.slow_tokenizer_class(*args, **kwargs)
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fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)
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else:
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raise ValueError(
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"Couldn't instantiate the backend tokenizer from one of: \n"
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"(1) a `tokenizers` library serialization file, \n"
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"(2) a slow tokenizer instance to convert or \n"
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"(3) an equivalent slow tokenizer class to instantiate and convert. \n"
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"You need to have sentencepiece installed to convert a slow tokenizer to a fast one."
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)
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self._tokenizer = fast_tokenizer
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if slow_tokenizer is not None:
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kwargs.update(slow_tokenizer.init_kwargs)
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self._decode_use_source_tokenizer = False
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_truncation = self._tokenizer.truncation
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if _truncation is not None:
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self._tokenizer.enable_truncation(**_truncation)
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kwargs.setdefault("max_length", _truncation["max_length"])
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kwargs.setdefault("truncation_side", _truncation["direction"])
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kwargs.setdefault("stride", _truncation["stride"])
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kwargs.setdefault("truncation_strategy", _truncation["strategy"])
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else:
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self._tokenizer.no_truncation()
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_padding = self._tokenizer.padding
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if _padding is not None:
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self._tokenizer.enable_padding(**_padding)
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kwargs.setdefault("pad_token", _padding["pad_token"])
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kwargs.setdefault("pad_token_type_id", _padding["pad_type_id"])
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kwargs.setdefault("padding_side", _padding["direction"])
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kwargs.setdefault("max_length", _padding["length"])
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kwargs.setdefault("pad_to_multiple_of", _padding["pad_to_multiple_of"])
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# We call this after having initialized the backend tokenizer because we update it.
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super().__init__(**kwargs)
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# The following logic will be replace with a single add_tokens once a fix is pushed to tokenizers
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# allows converting a slow -> fast, non-legacy: if the `tokenizer.json` does not have all the added tokens
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# uses the information stored in `added_tokens_decoder`.
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# this is costly for fast tokenizers as we re-compute the regex again. But not all tokens are added tokens
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tokens_to_add = [
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token
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for index, token in sorted(added_tokens_decoder.items(), key=lambda x: x[0])
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if token not in self.added_tokens_decoder
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]
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encoder = list(self.added_tokens_encoder.keys()) + [str(token) for token in tokens_to_add]
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# if some of the special tokens are strings, we check if we don't already have a token
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tokens_to_add += [
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token for token in self.all_special_tokens_extended if token not in encoder and token not in tokens_to_add
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]
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if len(tokens_to_add) > 0:
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# super hack: if a token.special is set, tokenizer ignores it for now so FIXME @ArthurZ
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# Accumulate added tokens into batches of special/non-special tokens, because calling add_tokens() for
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# individual tokens would repeatedly rebuild a trie, which can be slow.
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is_last_special = None
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tokens = []
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special_tokens = self.all_special_tokens
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for token in tokens_to_add:
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is_special = (
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(token.special or str(token) in special_tokens)
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if isinstance(token, AddedToken)
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else str(token) in special_tokens
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)
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if is_last_special is None or is_last_special == is_special:
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tokens.append(token)
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else:
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self._add_tokens(tokens, special_tokens=is_last_special)
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tokens = [token]
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is_last_special = is_special
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if tokens:
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self._add_tokens(tokens, special_tokens=is_last_special)
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@property
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def is_fast(self) -> bool:
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return True
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@property
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def can_save_slow_tokenizer(self) -> bool:
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"""
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`bool`: Whether or not the slow tokenizer can be saved. Usually for sentencepiece based slow tokenizer, this
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can only be `True` if the original `"sentencepiece.model"` was not deleted.
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"""
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return True
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@property
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def vocab_size(self) -> int:
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"""
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`int`: Size of the base vocabulary (without the added tokens).
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"""
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return self._tokenizer.get_vocab_size(with_added_tokens=False)
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def get_vocab(self) -> Dict[str, int]:
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return self._tokenizer.get_vocab(with_added_tokens=True)
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@property
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def vocab(self) -> Dict[str, int]:
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return self.get_vocab()
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@property
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def added_tokens_encoder(self) -> Dict[str, int]:
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"""
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Returns the sorted mapping from string to index. The added tokens encoder is cached for performance
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optimisation in `self._added_tokens_encoder` for the slow tokenizers.
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"""
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return {k.content: v for v, k in sorted(self.added_tokens_decoder.items(), key=lambda item: item[0])}
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@property
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def added_tokens_decoder(self) -> Dict[int, AddedToken]:
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"""
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Returns the added tokens in the vocabulary as a dictionary of index to AddedToken.
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Returns:
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`Dict[str, int]`: The added tokens.
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"""
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return self._tokenizer.get_added_tokens_decoder()
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def get_added_vocab(self) -> Dict[str, int]:
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"""
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Returns the added tokens in the vocabulary as a dictionary of token to index.
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Returns:
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`Dict[str, int]`: The added tokens.
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"""
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return {k.content: v for v, k in sorted(self.added_tokens_decoder.items(), key=lambda item: item[0])}
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def __len__(self) -> int:
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"""
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Size of the full vocabulary with the added tokens.
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"""
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return self._tokenizer.get_vocab_size(with_added_tokens=True)
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@property
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def backend_tokenizer(self) -> TokenizerFast:
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"""
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`tokenizers.implementations.BaseTokenizer`: The Rust tokenizer used as a backend.
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"""
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return self._tokenizer
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@property
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def decoder(self) -> DecoderFast:
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"""
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`tokenizers.decoders.Decoder`: The Rust decoder for this tokenizer.
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"""
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return self._tokenizer.decoder
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def _convert_encoding(
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self,
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encoding: EncodingFast,
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return_token_type_ids: Optional[bool] = None,
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return_attention_mask: Optional[bool] = None,
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return_overflowing_tokens: bool = False,
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return_special_tokens_mask: bool = False,
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return_offsets_mapping: bool = False,
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return_length: bool = False,
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verbose: bool = True,
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) -> Tuple[Dict[str, Any], List[EncodingFast]]:
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"""
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Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict and a list
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of encodings, take care of building a batch from overflowing tokens.
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Overflowing tokens are converted to additional examples (like batches) so the output values of the dict are
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lists (overflows) of lists (tokens).
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Output shape: (overflows, sequence length)
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"""
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if return_token_type_ids is None:
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return_token_type_ids = "token_type_ids" in self.model_input_names
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if return_attention_mask is None:
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return_attention_mask = "attention_mask" in self.model_input_names
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if return_overflowing_tokens and encoding.overflowing is not None:
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encodings = [encoding] + encoding.overflowing
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else:
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encodings = [encoding]
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encoding_dict = defaultdict(list)
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for e in encodings:
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encoding_dict["input_ids"].append(e.ids)
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if return_token_type_ids:
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encoding_dict["token_type_ids"].append(e.type_ids)
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if return_attention_mask:
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encoding_dict["attention_mask"].append(e.attention_mask)
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if return_special_tokens_mask:
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encoding_dict["special_tokens_mask"].append(e.special_tokens_mask)
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if return_offsets_mapping:
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encoding_dict["offset_mapping"].append(e.offsets)
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if return_length:
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encoding_dict["length"].append(len(e.ids))
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return encoding_dict, encodings
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def convert_tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
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"""
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Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
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vocabulary.
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Args:
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tokens (`str` or `List[str]`): One or several token(s) to convert to token id(s).
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Returns:
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`int` or `List[int]`: The token id or list of token ids.
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"""
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if tokens is None:
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return None
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if isinstance(tokens, str):
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return self._convert_token_to_id_with_added_voc(tokens)
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return [self._convert_token_to_id_with_added_voc(token) for token in tokens]
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def _convert_token_to_id_with_added_voc(self, token: str) -> int:
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index = self._tokenizer.token_to_id(token)
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if index is None:
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return self.unk_token_id
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return index
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def _convert_id_to_token(self, index: int) -> Optional[str]:
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return self._tokenizer.id_to_token(int(index))
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def _add_tokens(self, new_tokens: List[Union[str, AddedToken]], special_tokens=False) -> int:
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if special_tokens:
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return self._tokenizer.add_special_tokens(new_tokens)
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return self._tokenizer.add_tokens(new_tokens)
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def num_special_tokens_to_add(self, pair: bool = False) -> int:
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"""
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Returns the number of added tokens when encoding a sequence with special tokens.
|
||
|
|
|
||
|
|
<Tip>
|
||
|
|
|
||
|
|
This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not put
|
||
|
|
this inside your training loop.
|
||
|
|
|
||
|
|
</Tip>
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pair (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether the number of added tokens should be computed in the case of a sequence pair or a single
|
||
|
|
sequence.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`int`: Number of special tokens added to sequences.
|
||
|
|
"""
|
||
|
|
return self._tokenizer.num_special_tokens_to_add(pair)
|
||
|
|
|
||
|
|
def convert_ids_to_tokens(
|
||
|
|
self, ids: Union[int, List[int]], skip_special_tokens: bool = False
|
||
|
|
) -> Union[str, List[str]]:
|
||
|
|
"""
|
||
|
|
Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
|
||
|
|
added tokens.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
ids (`int` or `List[int]`):
|
||
|
|
The token id (or token ids) to convert to tokens.
|
||
|
|
skip_special_tokens (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether or not to remove special tokens in the decoding.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`str` or `List[str]`: The decoded token(s).
|
||
|
|
"""
|
||
|
|
if isinstance(ids, int):
|
||
|
|
return self._tokenizer.id_to_token(ids)
|
||
|
|
tokens = []
|
||
|
|
for index in ids:
|
||
|
|
index = int(index)
|
||
|
|
if skip_special_tokens and index in self.all_special_ids:
|
||
|
|
continue
|
||
|
|
tokens.append(self._tokenizer.id_to_token(index))
|
||
|
|
return tokens
|
||
|
|
|
||
|
|
def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:
|
||
|
|
return self.encode_plus(text=text, text_pair=pair, add_special_tokens=add_special_tokens, **kwargs).tokens()
|
||
|
|
|
||
|
|
def set_truncation_and_padding(
|
||
|
|
self,
|
||
|
|
padding_strategy: PaddingStrategy,
|
||
|
|
truncation_strategy: TruncationStrategy,
|
||
|
|
max_length: int,
|
||
|
|
stride: int,
|
||
|
|
pad_to_multiple_of: Optional[int],
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Define the truncation and the padding strategies for fast tokenizers (provided by HuggingFace tokenizers
|
||
|
|
library) and restore the tokenizer settings afterwards.
|
||
|
|
|
||
|
|
The provided tokenizer has no padding / truncation strategy before the managed section. If your tokenizer set a
|
||
|
|
padding / truncation strategy before, then it will be reset to no padding / truncation when exiting the managed
|
||
|
|
section.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
padding_strategy ([`~utils.PaddingStrategy`]):
|
||
|
|
The kind of padding that will be applied to the input
|
||
|
|
truncation_strategy ([`~tokenization_utils_base.TruncationStrategy`]):
|
||
|
|
The kind of truncation that will be applied to the input
|
||
|
|
max_length (`int`):
|
||
|
|
The maximum size of a sequence.
|
||
|
|
stride (`int`):
|
||
|
|
The stride to use when handling overflow.
|
||
|
|
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).
|
||
|
|
"""
|
||
|
|
_truncation = self._tokenizer.truncation
|
||
|
|
_padding = self._tokenizer.padding
|
||
|
|
# Set truncation and padding on the backend tokenizer
|
||
|
|
if truncation_strategy == TruncationStrategy.DO_NOT_TRUNCATE:
|
||
|
|
if _truncation is not None:
|
||
|
|
self._tokenizer.no_truncation()
|
||
|
|
else:
|
||
|
|
target = {
|
||
|
|
"max_length": max_length,
|
||
|
|
"stride": stride,
|
||
|
|
"strategy": truncation_strategy.value,
|
||
|
|
"direction": self.truncation_side,
|
||
|
|
}
|
||
|
|
|
||
|
|
# _truncation might contain more keys that the target `transformers`
|
||
|
|
# supports. Use only the target keys to trigger `enable_truncation`.
|
||
|
|
# This should enable this code to works on various `tokenizers`
|
||
|
|
# targets.
|
||
|
|
if _truncation is None:
|
||
|
|
current = None
|
||
|
|
else:
|
||
|
|
current = {k: _truncation.get(k, None) for k in target}
|
||
|
|
|
||
|
|
if current != target:
|
||
|
|
self._tokenizer.enable_truncation(**target)
|
||
|
|
|
||
|
|
if padding_strategy == PaddingStrategy.DO_NOT_PAD:
|
||
|
|
if _padding is not None:
|
||
|
|
self._tokenizer.no_padding()
|
||
|
|
else:
|
||
|
|
length = max_length if padding_strategy == PaddingStrategy.MAX_LENGTH else None
|
||
|
|
target = {
|
||
|
|
"length": length,
|
||
|
|
"direction": self.padding_side,
|
||
|
|
"pad_id": self.pad_token_id,
|
||
|
|
"pad_token": self.pad_token,
|
||
|
|
"pad_type_id": self.pad_token_type_id,
|
||
|
|
"pad_to_multiple_of": pad_to_multiple_of,
|
||
|
|
}
|
||
|
|
if _padding != target:
|
||
|
|
self._tokenizer.enable_padding(**target)
|
||
|
|
|
||
|
|
def _batch_encode_plus(
|
||
|
|
self,
|
||
|
|
batch_text_or_text_pairs: Union[
|
||
|
|
List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair]
|
||
|
|
],
|
||
|
|
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[str] = 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,
|
||
|
|
) -> BatchEncoding:
|
||
|
|
if not isinstance(batch_text_or_text_pairs, (tuple, list)):
|
||
|
|
raise TypeError(
|
||
|
|
f"batch_text_or_text_pairs has to be a list or a tuple (got {type(batch_text_or_text_pairs)})"
|
||
|
|
)
|
||
|
|
|
||
|
|
# Set the truncation and padding strategy and restore the initial configuration
|
||
|
|
self.set_truncation_and_padding(
|
||
|
|
padding_strategy=padding_strategy,
|
||
|
|
truncation_strategy=truncation_strategy,
|
||
|
|
max_length=max_length,
|
||
|
|
stride=stride,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
)
|
||
|
|
|
||
|
|
encodings = self._tokenizer.encode_batch(
|
||
|
|
batch_text_or_text_pairs,
|
||
|
|
add_special_tokens=add_special_tokens,
|
||
|
|
is_pretokenized=is_split_into_words,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Convert encoding to dict
|
||
|
|
# `Tokens` has type: Tuple[
|
||
|
|
# List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
|
||
|
|
# List[EncodingFast]
|
||
|
|
# ]
|
||
|
|
# with nested dimensions corresponding to batch, overflows, sequence length
|
||
|
|
tokens_and_encodings = [
|
||
|
|
self._convert_encoding(
|
||
|
|
encoding=encoding,
|
||
|
|
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,
|
||
|
|
)
|
||
|
|
for encoding in encodings
|
||
|
|
]
|
||
|
|
|
||
|
|
# Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
|
||
|
|
# From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
|
||
|
|
# (we say ~ because the number of overflow varies with the example in the batch)
|
||
|
|
#
|
||
|
|
# To match each overflowing sample with the original sample in the batch
|
||
|
|
# we add an overflow_to_sample_mapping array (see below)
|
||
|
|
sanitized_tokens = {}
|
||
|
|
for key in tokens_and_encodings[0][0].keys():
|
||
|
|
stack = [e for item, _ in tokens_and_encodings for e in item[key]]
|
||
|
|
sanitized_tokens[key] = stack
|
||
|
|
sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
|
||
|
|
|
||
|
|
# If returning overflowing tokens, we need to return a mapping
|
||
|
|
# from the batch idx to the original sample
|
||
|
|
if return_overflowing_tokens:
|
||
|
|
overflow_to_sample_mapping = []
|
||
|
|
for i, (toks, _) in enumerate(tokens_and_encodings):
|
||
|
|
overflow_to_sample_mapping += [i] * len(toks["input_ids"])
|
||
|
|
sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
|
||
|
|
|
||
|
|
for input_ids in sanitized_tokens["input_ids"]:
|
||
|
|
self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
|
||
|
|
return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
|
||
|
|
|
||
|
|
def _encode_plus(
|
||
|
|
self,
|
||
|
|
text: Union[TextInput, PreTokenizedInput],
|
||
|
|
text_pair: Optional[Union[TextInput, PreTokenizedInput]] = 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[bool] = 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:
|
||
|
|
batched_input = [(text, text_pair)] if text_pair else [text]
|
||
|
|
batched_output = self._batch_encode_plus(
|
||
|
|
batched_input,
|
||
|
|
is_split_into_words=is_split_into_words,
|
||
|
|
add_special_tokens=add_special_tokens,
|
||
|
|
padding_strategy=padding_strategy,
|
||
|
|
truncation_strategy=truncation_strategy,
|
||
|
|
max_length=max_length,
|
||
|
|
stride=stride,
|
||
|
|
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,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Return tensor is None, then we can remove the leading batch axis
|
||
|
|
# Overflowing tokens are returned as a batch of output so we keep them in this case
|
||
|
|
if return_tensors is None and not return_overflowing_tokens:
|
||
|
|
batched_output = BatchEncoding(
|
||
|
|
{
|
||
|
|
key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
|
||
|
|
for key, value in batched_output.items()
|
||
|
|
},
|
||
|
|
batched_output.encodings,
|
||
|
|
)
|
||
|
|
|
||
|
|
self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
|
||
|
|
|
||
|
|
return batched_output
|
||
|
|
|
||
|
|
def convert_tokens_to_string(self, tokens: List[str]) -> str:
|
||
|
|
return self.backend_tokenizer.decoder.decode(tokens)
|
||
|
|
|
||
|
|
def _decode(
|
||
|
|
self,
|
||
|
|
token_ids: Union[int, List[int]],
|
||
|
|
skip_special_tokens: bool = False,
|
||
|
|
clean_up_tokenization_spaces: bool = None,
|
||
|
|
**kwargs,
|
||
|
|
) -> str:
|
||
|
|
self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
|
||
|
|
|
||
|
|
if isinstance(token_ids, int):
|
||
|
|
token_ids = [token_ids]
|
||
|
|
text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
|
||
|
|
|
||
|
|
clean_up_tokenization_spaces = (
|
||
|
|
clean_up_tokenization_spaces
|
||
|
|
if clean_up_tokenization_spaces is not None
|
||
|
|
else self.clean_up_tokenization_spaces
|
||
|
|
)
|
||
|
|
if clean_up_tokenization_spaces:
|
||
|
|
clean_text = self.clean_up_tokenization(text)
|
||
|
|
return clean_text
|
||
|
|
else:
|
||
|
|
return text
|
||
|
|
|
||
|
|
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 as well as in a unique JSON
|
||
|
|
file containing {config + vocab + added-tokens}.
|
||
|
|
"""
|
||
|
|
save_directory = str(save_directory)
|
||
|
|
|
||
|
|
if self.slow_tokenizer_class is None and legacy_format is True:
|
||
|
|
raise ValueError(
|
||
|
|
"Your tokenizer does not have a legacy version defined and therefore cannot register this version. You"
|
||
|
|
" might consider leaving the legacy_format at `None` or setting it to `False`."
|
||
|
|
)
|
||
|
|
|
||
|
|
save_slow = (
|
||
|
|
(legacy_format is None or legacy_format is True)
|
||
|
|
and self.slow_tokenizer_class is not None
|
||
|
|
and self.can_save_slow_tokenizer
|
||
|
|
)
|
||
|
|
save_fast = legacy_format is None or legacy_format is False
|
||
|
|
|
||
|
|
if save_slow:
|
||
|
|
added_tokens_file = os.path.join(
|
||
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + ADDED_TOKENS_FILE
|
||
|
|
)
|
||
|
|
# make sure to be foward compatible
|
||
|
|
added_vocab = {tok: index for tok, index in self.added_tokens_encoder.items() if index >= self.vocab_size}
|
||
|
|
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)
|
||
|
|
|
||
|
|
vocab_files = self.save_vocabulary(save_directory, filename_prefix=filename_prefix)
|
||
|
|
file_names = file_names + vocab_files + (added_tokens_file,)
|
||
|
|
|
||
|
|
if save_fast:
|
||
|
|
tokenizer_file = os.path.join(
|
||
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + TOKENIZER_FILE
|
||
|
|
)
|
||
|
|
self.backend_tokenizer.save(tokenizer_file)
|
||
|
|
file_names = file_names + (tokenizer_file,)
|
||
|
|
|
||
|
|
return file_names
|
||
|
|
|
||
|
|
def train_new_from_iterator(
|
||
|
|
self,
|
||
|
|
text_iterator,
|
||
|
|
vocab_size,
|
||
|
|
length=None,
|
||
|
|
new_special_tokens=None,
|
||
|
|
special_tokens_map=None,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Trains a tokenizer on a new corpus with the same defaults (in terms of special tokens or tokenization pipeline)
|
||
|
|
as the current one.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
text_iterator (generator of `List[str]`):
|
||
|
|
The training corpus. Should be a generator of batches of texts, for instance a list of lists of texts
|
||
|
|
if you have everything in memory.
|
||
|
|
vocab_size (`int`):
|
||
|
|
The size of the vocabulary you want for your tokenizer.
|
||
|
|
length (`int`, *optional*):
|
||
|
|
The total number of sequences in the iterator. This is used to provide meaningful progress tracking
|
||
|
|
new_special_tokens (list of `str` or `AddedToken`, *optional*):
|
||
|
|
A list of new special tokens to add to the tokenizer you are training.
|
||
|
|
special_tokens_map (`Dict[str, str]`, *optional*):
|
||
|
|
If you want to rename some of the special tokens this tokenizer uses, pass along a mapping old special
|
||
|
|
token name to new special token name in this argument.
|
||
|
|
kwargs (`Dict[str, Any]`, *optional*):
|
||
|
|
Additional keyword arguments passed along to the trainer from the 🤗 Tokenizers library.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
[`PreTrainedTokenizerFast`]: A new tokenizer of the same type as the original one, trained on
|
||
|
|
`text_iterator`.
|
||
|
|
|
||
|
|
"""
|
||
|
|
tokenizer_json = json.loads(self._tokenizer.to_str())
|
||
|
|
# Remove added tokens for now (uses IDs of tokens)
|
||
|
|
added_tokens = tokenizer_json.pop("added_tokens")
|
||
|
|
# Remove post processor for now (uses IDs of tokens)
|
||
|
|
post_processor = tokenizer_json.pop("post_processor")
|
||
|
|
|
||
|
|
unk_token = None
|
||
|
|
# Remove vocab
|
||
|
|
if tokenizer_json["model"]["type"] == "BPE":
|
||
|
|
tokenizer_json["model"]["vocab"] = {}
|
||
|
|
tokenizer_json["model"]["merges"] = []
|
||
|
|
elif tokenizer_json["model"]["type"] == "Unigram":
|
||
|
|
if tokenizer_json["model"]["unk_id"] is not None:
|
||
|
|
unk_id = tokenizer_json["model"]["unk_id"]
|
||
|
|
unk_token = tokenizer_json["model"]["vocab"][unk_id][0]
|
||
|
|
if special_tokens_map is not None and unk_token in special_tokens_map:
|
||
|
|
unk_token = special_tokens_map[unk_token]
|
||
|
|
tokenizer_json["model"]["unk_id"] = 0
|
||
|
|
tokenizer_json["model"]["vocab"] = [[unk_token, 0.0]]
|
||
|
|
elif tokenizer_json["model"]["type"] in ["WordLevel", "WordPiece"]:
|
||
|
|
tokenizer_json["model"]["vocab"] = {}
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
f"This method does not support this type of tokenizer (found {tokenizer_json['model']['type']}) "
|
||
|
|
"only BPE, Unigram, WordLevel and WordPiece."
|
||
|
|
)
|
||
|
|
|
||
|
|
if (
|
||
|
|
special_tokens_map is not None
|
||
|
|
and "unk_token" in tokenizer_json["model"]
|
||
|
|
and tokenizer_json["model"]["unk_token"] in special_tokens_map
|
||
|
|
):
|
||
|
|
tokenizer_json["model"]["unk_token"] = special_tokens_map[tokenizer_json["model"]["unk_token"]]
|
||
|
|
|
||
|
|
tokenizer = TokenizerFast.from_str(json.dumps(tokenizer_json))
|
||
|
|
|
||
|
|
# Get the special tokens from the current tokenizer if none are specified.
|
||
|
|
special_tokens = []
|
||
|
|
for added_token in added_tokens:
|
||
|
|
special = added_token.pop("special", None)
|
||
|
|
_ = added_token.pop("id", None)
|
||
|
|
if tokenizer_json["model"]["type"] != "Unigram" and not special:
|
||
|
|
continue
|
||
|
|
if special_tokens_map is not None and added_token["content"] in special_tokens_map:
|
||
|
|
added_token["content"] = special_tokens_map[added_token["content"]]
|
||
|
|
special_tokens.append(AddedToken(**added_token))
|
||
|
|
|
||
|
|
if new_special_tokens is not None:
|
||
|
|
special_tokens.extend(new_special_tokens)
|
||
|
|
|
||
|
|
# Trainer needs to know the end of word / continuing subword thingies in BPE
|
||
|
|
if (
|
||
|
|
tokenizer_json["model"]["type"] == "BPE"
|
||
|
|
and "continuing_subword_prefix" not in kwargs
|
||
|
|
and tokenizer_json["model"]["continuing_subword_prefix"] is not None
|
||
|
|
):
|
||
|
|
kwargs["continuing_subword_prefix"] = tokenizer_json["model"]["continuing_subword_prefix"]
|
||
|
|
if (
|
||
|
|
tokenizer_json["model"]["type"] == "BPE"
|
||
|
|
and "end_of_word_suffix" not in kwargs
|
||
|
|
and tokenizer_json["model"]["end_of_word_suffix"] is not None
|
||
|
|
):
|
||
|
|
kwargs["end_of_word_suffix"] = tokenizer_json["model"]["end_of_word_suffix"]
|
||
|
|
if tokenizer_json["model"]["type"] == "Unigram" and unk_token is not None:
|
||
|
|
kwargs["unk_token"] = unk_token
|
||
|
|
if tokenizer_json["pre_tokenizer"] is not None and tokenizer_json["pre_tokenizer"]["type"] == "ByteLevel":
|
||
|
|
kwargs["initial_alphabet"] = pre_tokenizers_fast.ByteLevel.alphabet()
|
||
|
|
|
||
|
|
trainer_class = MODEL_TO_TRAINER_MAPPING[tokenizer_json["model"]["type"]]
|
||
|
|
trainer = trainer_class(vocab_size=vocab_size, special_tokens=special_tokens, **kwargs)
|
||
|
|
tokenizer.train_from_iterator(text_iterator, length=length, trainer=trainer)
|
||
|
|
|
||
|
|
if post_processor is not None:
|
||
|
|
trained_tokenizer_json = json.loads(tokenizer.to_str())
|
||
|
|
# Almost done, we just have to adjust the token IDs in the post processor
|
||
|
|
if "special_tokens" in post_processor:
|
||
|
|
for key in post_processor["special_tokens"]:
|
||
|
|
tokens = post_processor["special_tokens"][key]["tokens"]
|
||
|
|
if special_tokens_map is not None:
|
||
|
|
tokens = [special_tokens_map.get(token, token) for token in tokens]
|
||
|
|
post_processor["special_tokens"][key]["tokens"] = tokens
|
||
|
|
post_processor["special_tokens"][key]["ids"] = [tokenizer.token_to_id(token) for token in tokens]
|
||
|
|
|
||
|
|
for special_token in ["cls", "sep"]:
|
||
|
|
if special_token in post_processor:
|
||
|
|
token, _ = post_processor[special_token]
|
||
|
|
if special_tokens_map is not None and token in special_tokens_map:
|
||
|
|
token = special_tokens_map[token]
|
||
|
|
token_id = tokenizer.token_to_id(token)
|
||
|
|
post_processor[special_token] = [token, token_id]
|
||
|
|
|
||
|
|
trained_tokenizer_json["post_processor"] = post_processor
|
||
|
|
tokenizer = TokenizerFast.from_str(json.dumps(trained_tokenizer_json))
|
||
|
|
|
||
|
|
kwargs = self.init_kwargs.copy()
|
||
|
|
# Map pad/cls/mask token at the Transformers level
|
||
|
|
special_tokens_list = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy()
|
||
|
|
special_tokens_list.remove("additional_special_tokens")
|
||
|
|
for token in special_tokens_list:
|
||
|
|
# Get the private one to avoid unnecessary warnings.
|
||
|
|
if getattr(self, f"_{token}") is not None:
|
||
|
|
special_token = getattr(self, token)
|
||
|
|
if special_tokens_map is not None and special_token in special_tokens_map:
|
||
|
|
special_token = special_tokens_map[special_token]
|
||
|
|
|
||
|
|
special_token_full = getattr(self, f"_{token}")
|
||
|
|
if isinstance(special_token_full, AddedToken):
|
||
|
|
# Create an added token with the same parameters except the content
|
||
|
|
kwargs[token] = AddedToken(
|
||
|
|
special_token,
|
||
|
|
single_word=special_token_full.single_word,
|
||
|
|
lstrip=special_token_full.lstrip,
|
||
|
|
rstrip=special_token_full.rstrip,
|
||
|
|
normalized=special_token_full.normalized,
|
||
|
|
special=True,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
kwargs[token] = special_token
|
||
|
|
|
||
|
|
additional_special_tokens = self.additional_special_tokens
|
||
|
|
if new_special_tokens is not None:
|
||
|
|
additional_special_tokens.extend(new_special_tokens)
|
||
|
|
if len(additional_special_tokens) > 0:
|
||
|
|
kwargs["additional_special_tokens"] = additional_special_tokens
|
||
|
|
|
||
|
|
return self.__class__(tokenizer_object=tokenizer, **kwargs)
|