# coding=utf-8
# Copyright 2018 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.
""" Auto Tokenizer class."""
import importlib
import json
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import TOKENIZER_CONFIG_FILE
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import cached_file, extract_commit_hash, is_sentencepiece_available, is_tokenizers_available, logging
from ..encoder_decoder import EncoderDecoderConfig
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
config_class_to_model_type,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
logger = logging.get_logger(__name__)
if TYPE_CHECKING:
# This significantly improves completion suggestion performance when
# the transformers package is used with Microsoft's Pylance language server.
TOKENIZER_MAPPING_NAMES: OrderedDict[str, Tuple[Optional[str], Optional[str]]] = OrderedDict()
else:
TOKENIZER_MAPPING_NAMES = OrderedDict(
[
(
"albert",
(
"AlbertTokenizer" if is_sentencepiece_available() else None,
"AlbertTokenizerFast" if is_tokenizers_available() else None,
),
),
("bart", ("BartTokenizer", "BartTokenizerFast")),
(
"barthez",
(
"BarthezTokenizer" if is_sentencepiece_available() else None,
"BarthezTokenizerFast" if is_tokenizers_available() else None,
),
),
("bartpho", ("BartphoTokenizer", None)),
("bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
("bert-generation", ("BertGenerationTokenizer" if is_sentencepiece_available() else None, None)),
("bert-japanese", ("BertJapaneseTokenizer", None)),
("bertweet", ("BertweetTokenizer", None)),
(
"big_bird",
(
"BigBirdTokenizer" if is_sentencepiece_available() else None,
"BigBirdTokenizerFast" if is_tokenizers_available() else None,
),
),
("bigbird_pegasus", ("PegasusTokenizer", "PegasusTokenizerFast" if is_tokenizers_available() else None)),
("blenderbot", ("BlenderbotTokenizer", "BlenderbotTokenizerFast")),
("blenderbot-small", ("BlenderbotSmallTokenizer", None)),
("bloom", (None, "BloomTokenizerFast" if is_tokenizers_available() else None)),
("byt5", ("ByT5Tokenizer", None)),
(
"camembert",
(
"CamembertTokenizer" if is_sentencepiece_available() else None,
"CamembertTokenizerFast" if is_tokenizers_available() else None,
),
),
("canine", ("CanineTokenizer", None)),
("chinese_clip", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
(
"clip",
(
"CLIPTokenizer",
"CLIPTokenizerFast" if is_tokenizers_available() else None,
),
),
(
"clipseg",
(
"CLIPTokenizer",
"CLIPTokenizerFast" if is_tokenizers_available() else None,
),
),
("codegen", ("CodeGenTokenizer", "CodeGenTokenizerFast" if is_tokenizers_available() else None)),
("convbert", ("ConvBertTokenizer", "ConvBertTokenizerFast" if is_tokenizers_available() else None)),
(
"cpm",
(
"CpmTokenizer" if is_sentencepiece_available() else None,
"CpmTokenizerFast" if is_tokenizers_available() else None,
),
),
("ctrl", ("CTRLTokenizer", None)),
("data2vec-text", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)),
("deberta", ("DebertaTokenizer", "DebertaTokenizerFast" if is_tokenizers_available() else None)),
(
"deberta-v2",
(
"DebertaV2Tokenizer" if is_sentencepiece_available() else None,
"DebertaV2TokenizerFast" if is_tokenizers_available() else None,
),
),
("distilbert", ("DistilBertTokenizer", "DistilBertTokenizerFast" if is_tokenizers_available() else None)),
(
"dpr",
(
"DPRQuestionEncoderTokenizer",
"DPRQuestionEncoderTokenizerFast" if is_tokenizers_available() else None,
),
),
("electra", ("ElectraTokenizer", "ElectraTokenizerFast" if is_tokenizers_available() else None)),
("ernie", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
("esm", ("EsmTokenizer", None)),
("flaubert", ("FlaubertTokenizer", None)),
("fnet", ("FNetTokenizer", "FNetTokenizerFast" if is_tokenizers_available() else None)),
("fsmt", ("FSMTTokenizer", None)),
("funnel", ("FunnelTokenizer", "FunnelTokenizerFast" if is_tokenizers_available() else None)),
("gpt2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
("gpt_neo", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
("gpt_neox", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)),
("gpt_neox_japanese", ("GPTNeoXJapaneseTokenizer", None)),
("gptj", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
("groupvit", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)),
("herbert", ("HerbertTokenizer", "HerbertTokenizerFast" if is_tokenizers_available() else None)),
("hubert", ("Wav2Vec2CTCTokenizer", None)),
("ibert", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)),
("jukebox", ("JukeboxTokenizer", None)),
("layoutlm", ("LayoutLMTokenizer", "LayoutLMTokenizerFast" if is_tokenizers_available() else None)),
("layoutlmv2", ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast" if is_tokenizers_available() else None)),
("layoutlmv3", ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast" if is_tokenizers_available() else None)),
("layoutxlm", ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast" if is_tokenizers_available() else None)),
("led", ("LEDTokenizer", "LEDTokenizerFast" if is_tokenizers_available() else None)),
("lilt", ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast" if is_tokenizers_available() else None)),
("longformer", ("LongformerTokenizer", "LongformerTokenizerFast" if is_tokenizers_available() else None)),
(
"longt5",
(
"T5Tokenizer" if is_sentencepiece_available() else None,
"T5TokenizerFast" if is_tokenizers_available() else None,
),
),
("luke", ("LukeTokenizer", None)),
("lxmert", ("LxmertTokenizer", "LxmertTokenizerFast" if is_tokenizers_available() else None)),
("m2m_100", ("M2M100Tokenizer" if is_sentencepiece_available() else None, None)),
("marian", ("MarianTokenizer" if is_sentencepiece_available() else None, None)),
(
"mbart",
(
"MBartTokenizer" if is_sentencepiece_available() else None,
"MBartTokenizerFast" if is_tokenizers_available() else None,
),
),
(
"mbart50",
(
"MBart50Tokenizer" if is_sentencepiece_available() else None,
"MBart50TokenizerFast" if is_tokenizers_available() else None,
),
),
("megatron-bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
("mluke", ("MLukeTokenizer" if is_sentencepiece_available() else None, None)),
("mobilebert", ("MobileBertTokenizer", "MobileBertTokenizerFast" if is_tokenizers_available() else None)),
("mpnet", ("MPNetTokenizer", "MPNetTokenizerFast" if is_tokenizers_available() else None)),
(
"mt5",
(
"MT5Tokenizer" if is_sentencepiece_available() else None,
"MT5TokenizerFast" if is_tokenizers_available() else None,
),
),
("mvp", ("MvpTokenizer", "MvpTokenizerFast" if is_tokenizers_available() else None)),
("nezha", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
(
"nllb",
(
"NllbTokenizer" if is_sentencepiece_available() else None,
"NllbTokenizerFast" if is_tokenizers_available() else None,
),
),
(
"nystromformer",
(
"AlbertTokenizer" if is_sentencepiece_available() else None,
"AlbertTokenizerFast" if is_tokenizers_available() else None,
),
),
("openai-gpt", ("OpenAIGPTTokenizer", "OpenAIGPTTokenizerFast" if is_tokenizers_available() else None)),
("opt", ("GPT2Tokenizer", None)),
("owlvit", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)),
(
"pegasus",
(
"PegasusTokenizer" if is_sentencepiece_available() else None,
"PegasusTokenizerFast" if is_tokenizers_available() else None,
),
),
(
"pegasus_x",
(
"PegasusTokenizer" if is_sentencepiece_available() else None,
"PegasusTokenizerFast" if is_tokenizers_available() else None,
),
),
(
"perceiver",
(
"PerceiverTokenizer",
None,
),
),
("phobert", ("PhobertTokenizer", None)),
("plbart", ("PLBartTokenizer" if is_sentencepiece_available() else None, None)),
("prophetnet", ("ProphetNetTokenizer", None)),
("qdqbert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
("rag", ("RagTokenizer", None)),
("realm", ("RealmTokenizer", "RealmTokenizerFast" if is_tokenizers_available() else None)),
(
"reformer",
(
"ReformerTokenizer" if is_sentencepiece_available() else None,
"ReformerTokenizerFast" if is_tokenizers_available() else None,
),
),
(
"rembert",
(
"RemBertTokenizer" if is_sentencepiece_available() else None,
"RemBertTokenizerFast" if is_tokenizers_available() else None,
),
),
("retribert", ("RetriBertTokenizer", "RetriBertTokenizerFast" if is_tokenizers_available() else None)),
("roberta", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)),
("roc_bert", ("RoCBertTokenizer", None)),
("roformer", ("RoFormerTokenizer", "RoFormerTokenizerFast" if is_tokenizers_available() else None)),
("speech_to_text", ("Speech2TextTokenizer" if is_sentencepiece_available() else None, None)),
("speech_to_text_2", ("Speech2Text2Tokenizer", None)),
("splinter", ("SplinterTokenizer", "SplinterTokenizerFast")),
(
"squeezebert",
("SqueezeBertTokenizer", "SqueezeBertTokenizerFast" if is_tokenizers_available() else None),
),
(
"switch_transformers",
(
"T5Tokenizer" if is_sentencepiece_available() else None,
"T5TokenizerFast" if is_tokenizers_available() else None,
),
),
(
"t5",
(
"T5Tokenizer" if is_sentencepiece_available() else None,
"T5TokenizerFast" if is_tokenizers_available() else None,
),
),
("tapas", ("TapasTokenizer", None)),
("tapex", ("TapexTokenizer", None)),
("transfo-xl", ("TransfoXLTokenizer", None)),
("vilt", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
("visual_bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
("wav2vec2", ("Wav2Vec2CTCTokenizer", None)),
("wav2vec2-conformer", ("Wav2Vec2CTCTokenizer", None)),
("wav2vec2_phoneme", ("Wav2Vec2PhonemeCTCTokenizer", None)),
("whisper", ("WhisperTokenizer" if is_sentencepiece_available() else None, None)),
("xclip", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)),
(
"xglm",
(
"XGLMTokenizer" if is_sentencepiece_available() else None,
"XGLMTokenizerFast" if is_tokenizers_available() else None,
),
),
("xlm", ("XLMTokenizer", None)),
("xlm-prophetnet", ("XLMProphetNetTokenizer" if is_sentencepiece_available() else None, None)),
(
"xlm-roberta",
(
"XLMRobertaTokenizer" if is_sentencepiece_available() else None,
"XLMRobertaTokenizerFast" if is_tokenizers_available() else None,
),
),
(
"xlm-roberta-xl",
(
"XLMRobertaTokenizer" if is_sentencepiece_available() else None,
"XLMRobertaTokenizerFast" if is_tokenizers_available() else None,
),
),
(
"xlnet",
(
"XLNetTokenizer" if is_sentencepiece_available() else None,
"XLNetTokenizerFast" if is_tokenizers_available() else None,
),
),
(
"yoso",
(
"AlbertTokenizer" if is_sentencepiece_available() else None,
"AlbertTokenizerFast" if is_tokenizers_available() else None,
),
),
]
)
TOKENIZER_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TOKENIZER_MAPPING_NAMES)
CONFIG_TO_TYPE = {v: k for k, v in CONFIG_MAPPING_NAMES.items()}
def tokenizer_class_from_name(class_name: str):
if class_name == "PreTrainedTokenizerFast":
return PreTrainedTokenizerFast
for module_name, tokenizers in TOKENIZER_MAPPING_NAMES.items():
if class_name in tokenizers:
module_name = model_type_to_module_name(module_name)
module = importlib.import_module(f".{module_name}", "transformers.models")
try:
return getattr(module, class_name)
except AttributeError:
continue
for config, tokenizers in TOKENIZER_MAPPING._extra_content.items():
for tokenizer in tokenizers:
if getattr(tokenizer, "__name__", None) == class_name:
return tokenizer
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
main_module = importlib.import_module("transformers")
if hasattr(main_module, class_name):
return getattr(main_module, class_name)
return None
def get_tokenizer_config(
pretrained_model_name_or_path: Union[str, os.PathLike],
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: bool = False,
proxies: Optional[Dict[str, str]] = None,
use_auth_token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
subfolder: str = "",
**kwargs,
):
"""
Loads the tokenizer configuration from a pretrained model tokenizer configuration.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration 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 a configuration file saved using the
[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received file. Attempts 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.
use_auth_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`).
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.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
subfolder (`str`, *optional*, defaults to `""`):
In case the tokenizer config is located inside a subfolder of the model repo on huggingface.co, you can
specify the folder name here.
<Tip>
Passing `use_auth_token=True` is required when you want to use a private model.
</Tip>
Returns:
`Dict`: The configuration of the tokenizer.
Examples:
```python
# Download configuration from huggingface.co and cache.
tokenizer_config = get_tokenizer_config("bert-base-uncased")
# This model does not have a tokenizer config so the result will be an empty dict.
tokenizer_config = get_tokenizer_config("xlm-roberta-base")
# Save a pretrained tokenizer locally and you can reload its config
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
tokenizer.save_pretrained("tokenizer-test")
tokenizer_config = get_tokenizer_config("tokenizer-test")
```"""
commit_hash = kwargs.get("_commit_hash", None)
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=use_auth_token,
revision=revision,
local_files_only=local_files_only,
subfolder=subfolder,
_raise_exceptions_for_missing_entries=False,
_raise_exceptions_for_connection_errors=False,
_commit_hash=commit_hash,
)
if resolved_config_file is None:
logger.info("Could not locate the tokenizer configuration file, will try to use the model config instead.")
return {}
commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
with open(resolved_config_file, encoding="utf-8") as reader:
result = json.load(reader)
result["_commit_hash"] = commit_hash
return result
[docs]class AutoTokenizer:
r"""
This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when
created with the [`AutoTokenizer.from_pretrained`] class method.
This class cannot be instantiated directly using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"AutoTokenizer is designed to be instantiated "
"using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method."
)
[docs] @classmethod
@replace_list_option_in_docstrings(TOKENIZER_MAPPING_NAMES)
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
r"""
Instantiate one of the tokenizer classes of the library from a pretrained model vocabulary.
The tokenizer class to instantiate is selected based on the `model_type` property of the config object (either
passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by
falling back to using pattern matching on `pretrained_model_name_or_path`:
List options
Params:
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 [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
- 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`. (Not
applicable to all derived classes)
inputs (additional positional arguments, *optional*):
Will be passed along to the Tokenizer `__init__()` method.
config ([`PretrainedConfig`], *optional*)
The configuration object used to dertermine the tokenizer class to instantiate.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration 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 model weights and configuration files and override the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will 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.
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.
use_fast (`bool`, *optional*, defaults to `True`):
Whether or not to try to load the fast version of the tokenizer.
tokenizer_type (`str`, *optional*):
Tokenizer type to be loaded.
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to `True` for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.
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.
Examples:
```python
>>> from transformers import AutoTokenizer
>>> # Download vocabulary from huggingface.co and cache.
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> # Download vocabulary from huggingface.co (user-uploaded) and cache.
>>> tokenizer = AutoTokenizer.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 = AutoTokenizer.from_pretrained("./test/bert_saved_model/")
>>> # Download vocabulary from huggingface.co and define model-specific arguments
>>> tokenizer = AutoTokenizer.from_pretrained("roberta-base", add_prefix_space=True)
```"""
config = kwargs.pop("config", None)
kwargs["_from_auto"] = True
use_fast = kwargs.pop("use_fast", True)
tokenizer_type = kwargs.pop("tokenizer_type", None)
trust_remote_code = kwargs.pop("trust_remote_code", False)
# First, let's see whether the tokenizer_type is passed so that we can leverage it
if tokenizer_type is not None:
tokenizer_class = None
tokenizer_class_tuple = TOKENIZER_MAPPING_NAMES.get(tokenizer_type, None)
if tokenizer_class_tuple is None:
raise ValueError(
f"Passed `tokenizer_type` {tokenizer_type} does not exist. `tokenizer_type` should be one of "
f"{', '.join(c for c in TOKENIZER_MAPPING_NAMES.keys())}."
)
tokenizer_class_name, tokenizer_fast_class_name = tokenizer_class_tuple
if use_fast and tokenizer_fast_class_name is not None:
tokenizer_class = tokenizer_class_from_name(tokenizer_fast_class_name)
if tokenizer_class is None:
tokenizer_class = tokenizer_class_from_name(tokenizer_class_name)
if tokenizer_class is None:
raise ValueError(f"Tokenizer class {tokenizer_class_name} is not currently imported.")
return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
# Next, let's try to use the tokenizer_config file to get the tokenizer class.
tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
if "_commit_hash" in tokenizer_config:
kwargs["_commit_hash"] = tokenizer_config["_commit_hash"]
config_tokenizer_class = tokenizer_config.get("tokenizer_class")
tokenizer_auto_map = None
if "auto_map" in tokenizer_config:
if isinstance(tokenizer_config["auto_map"], (tuple, list)):
# Legacy format for dynamic tokenizers
tokenizer_auto_map = tokenizer_config["auto_map"]
else:
tokenizer_auto_map = tokenizer_config["auto_map"].get("AutoTokenizer", None)
# If that did not work, let's try to use the config.
if config_tokenizer_class is None:
if not isinstance(config, PretrainedConfig):
config = AutoConfig.from_pretrained(
pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
)
config_tokenizer_class = config.tokenizer_class
if hasattr(config, "auto_map") and "AutoTokenizer" in config.auto_map:
tokenizer_auto_map = config.auto_map["AutoTokenizer"]
# If we have the tokenizer class from the tokenizer config or the model config we're good!
if config_tokenizer_class is not None:
tokenizer_class = None
if tokenizer_auto_map is not None:
if not trust_remote_code:
raise ValueError(
f"Loading {pretrained_model_name_or_path} requires you to execute the tokenizer file in that"
" repo on your local machine. Make sure you have read the code there to avoid malicious use,"
" then set the option `trust_remote_code=True` to remove this error."
)
if kwargs.get("revision", None) is None:
logger.warning(
"Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure"
" no malicious code has been contributed in a newer revision."
)
if use_fast and tokenizer_auto_map[1] is not None:
class_ref = tokenizer_auto_map[1]
else:
class_ref = tokenizer_auto_map[0]
module_file, class_name = class_ref.split(".")
tokenizer_class = get_class_from_dynamic_module(
pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
)
elif use_fast and not config_tokenizer_class.endswith("Fast"):
tokenizer_class_candidate = f"{config_tokenizer_class}Fast"
tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate)
if tokenizer_class is None:
tokenizer_class_candidate = config_tokenizer_class
tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate)
if tokenizer_class is None:
raise ValueError(
f"Tokenizer class {tokenizer_class_candidate} does not exist or is not currently imported."
)
return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
# Otherwise we have to be creative.
# if model is an encoder decoder, the encoder tokenizer class is used by default
if isinstance(config, EncoderDecoderConfig):
if type(config.decoder) is not type(config.encoder): # noqa: E721
logger.warning(
f"The encoder model config class: {config.encoder.__class__} is different from the decoder model "
f"config class: {config.decoder.__class__}. It is not recommended to use the "
"`AutoTokenizer.from_pretrained()` method in this case. Please use the encoder and decoder "
"specific tokenizer classes."
)
config = config.encoder
model_type = config_class_to_model_type(type(config).__name__)
if model_type is not None:
tokenizer_class_py, tokenizer_class_fast = TOKENIZER_MAPPING[type(config)]
if tokenizer_class_fast and (use_fast or tokenizer_class_py is None):
return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
else:
if tokenizer_class_py is not None:
return tokenizer_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
else:
raise ValueError(
"This tokenizer cannot be instantiated. Please make sure you have `sentencepiece` installed "
"in order to use this tokenizer."
)
raise ValueError(
f"Unrecognized configuration class {config.__class__} to build an AutoTokenizer.\n"
f"Model type should be one of {', '.join(c.__name__ for c in TOKENIZER_MAPPING.keys())}."
)
[docs] def register(config_class, slow_tokenizer_class=None, fast_tokenizer_class=None):
"""
Register a new tokenizer in this mapping.
Args:
config_class ([`PretrainedConfig`]):
The configuration corresponding to the model to register.
slow_tokenizer_class ([`PretrainedTokenizer`], *optional*):
The slow tokenizer to register.
slow_tokenizer_class ([`PretrainedTokenizerFast`], *optional*):
The fast tokenizer to register.
"""
if slow_tokenizer_class is None and fast_tokenizer_class is None:
raise ValueError("You need to pass either a `slow_tokenizer_class` or a `fast_tokenizer_class")
if slow_tokenizer_class is not None and issubclass(slow_tokenizer_class, PreTrainedTokenizerFast):
raise ValueError("You passed a fast tokenizer in the `slow_tokenizer_class`.")
if fast_tokenizer_class is not None and issubclass(fast_tokenizer_class, PreTrainedTokenizer):
raise ValueError("You passed a slow tokenizer in the `fast_tokenizer_class`.")
if (
slow_tokenizer_class is not None
and fast_tokenizer_class is not None
and issubclass(fast_tokenizer_class, PreTrainedTokenizerFast)
and fast_tokenizer_class.slow_tokenizer_class != slow_tokenizer_class
):
raise ValueError(
"The fast tokenizer class you are passing has a `slow_tokenizer_class` attribute that is not "
"consistent with the slow tokenizer class you passed (fast tokenizer has "
f"{fast_tokenizer_class.slow_tokenizer_class} and you passed {slow_tokenizer_class}. Fix one of those "
"so they match!"
)
# Avoid resetting a set slow/fast tokenizer if we are passing just the other ones.
if config_class in TOKENIZER_MAPPING._extra_content:
existing_slow, existing_fast = TOKENIZER_MAPPING[config_class]
if slow_tokenizer_class is None:
slow_tokenizer_class = existing_slow
if fast_tokenizer_class is None:
fast_tokenizer_class = existing_fast
TOKENIZER_MAPPING.register(config_class, (slow_tokenizer_class, fast_tokenizer_class))