# What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

add VITS model. # What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

# 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
import warnings
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, resolve_trust_remote_code
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import TOKENIZER_CONFIG_FILE
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,
)


if is_tokenizers_available():
    from ...tokenization_utils_fast import PreTrainedTokenizerFast
else:
    PreTrainedTokenizerFast = None


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,
                ),
            ),
            ("align", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
            ("bark", ("BertTokenizer", "BertTokenizerFast" 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)),
            ("biogpt", ("BioGptTokenizer", None)),
            ("blenderbot", ("BlenderbotTokenizer", "BlenderbotTokenizerFast")),
            ("blenderbot-small", ("BlenderbotSmallTokenizer", None)),
            ("blip", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
            ("blip-2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
            ("bloom", (None, "BloomTokenizerFast" if is_tokenizers_available() else None)),
            ("bridgetower", ("RobertaTokenizer", "RobertaTokenizerFast" 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)),
            (
                "clap",
                (
                    "RobertaTokenizer",
                    "RobertaTokenizerFast" if is_tokenizers_available() else None,
                ),
            ),
            (
                "clip",
                (
                    "CLIPTokenizer",
                    "CLIPTokenizerFast" if is_tokenizers_available() else None,
                ),
            ),
            (
                "clipseg",
                (
                    "CLIPTokenizer",
                    "CLIPTokenizerFast" if is_tokenizers_available() else None,
                ),
            ),
            (
                "code_llama",
                (
                    "CodeLlamaTokenizer" if is_sentencepiece_available() else None,
                    "CodeLlamaTokenizerFast" 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,
                ),
            ),
            ("cpmant", ("CpmAntTokenizer", None)),
            ("ctrl", ("CTRLTokenizer", None)),
            ("data2vec-audio", ("Wav2Vec2CTCTokenizer", 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)),
            ("ernie_m", ("ErnieMTokenizer" if is_sentencepiece_available() else None, 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)),
            ("git", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
            ("gpt-sw3", ("GPTSw3Tokenizer" if is_sentencepiece_available() else None, None)),
            ("gpt2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
            ("gpt_bigcode", ("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)),
            ("gptsan-japanese", ("GPTSanJapaneseTokenizer", 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)),
            ("idefics", (None, "LlamaTokenizerFast" if is_tokenizers_available() else None)),
            ("instructblip", ("GPT2Tokenizer", "GPT2TokenizerFast" 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)),
            (
                "llama",
                (
                    "LlamaTokenizer" if is_sentencepiece_available() else None,
                    "LlamaTokenizerFast" 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,
                ),
            ),
            ("mega", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)),
            ("megatron-bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
            ("mgp-str", ("MgpstrTokenizer", 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)),
            ("mpt", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)),
            ("mra", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)),
            (
                "mt5",
                (
                    "MT5Tokenizer" if is_sentencepiece_available() else None,
                    "MT5TokenizerFast" if is_tokenizers_available() else None,
                ),
            ),
            ("musicgen", ("T5Tokenizer", "T5TokenizerFast" 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,
                ),
            ),
            (
                "nllb-moe",
                (
                    "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,
                ),
            ),
            ("oneformer", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)),
            ("openai-gpt", ("OpenAIGPTTokenizer", "OpenAIGPTTokenizerFast" if is_tokenizers_available() else None)),
            ("opt", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else 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)),
            ("pix2struct", ("T5Tokenizer", "T5TokenizerFast" if is_tokenizers_available() else 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)),
            (
                "roberta-prelayernorm",
                ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None),
            ),
            ("roc_bert", ("RoCBertTokenizer", None)),
            ("roformer", ("RoFormerTokenizer", "RoFormerTokenizerFast" if is_tokenizers_available() else None)),
            ("rwkv", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)),
            ("speech_to_text", ("Speech2TextTokenizer" if is_sentencepiece_available() else None, None)),
            ("speech_to_text_2", ("Speech2Text2Tokenizer", None)),
            ("speecht5", ("SpeechT5Tokenizer" if is_sentencepiece_available() else None, 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)),
            (
                "umt5",
                (
                    "T5Tokenizer" if is_sentencepiece_available() else None,
                    "T5TokenizerFast" if is_tokenizers_available() else 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", "WhisperTokenizerFast" if is_tokenizers_available() else 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,
                ),
            ),
            (
                "xmod",
                (
                    "XLMRobertaTokenizer" if is_sentencepiece_available() else None,
                    "XLMRobertaTokenizerFast" 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,
    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.
        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 `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")
    ```"""
    use_auth_token = kwargs.pop("use_auth_token", 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

    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,
        token=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


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."
        )

    @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 determine 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`):
                Use a [fast Rust-based tokenizer](https://huggingface.co/docs/tokenizers/index) if it is supported for
                a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer
                is returned instead.
            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)
        ```"""
        use_auth_token = kwargs.pop("use_auth_token", 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 kwargs.get("token", None) is not None:
                raise ValueError(
                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
                )
            kwargs["token"] = use_auth_token

        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", None)

        # 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:
                if tokenizer_fast_class_name is not None:
                    tokenizer_class = tokenizer_class_from_name(tokenizer_fast_class_name)
                else:
                    logger.warning(
                        "`use_fast` is set to `True` but the tokenizer class does not have a fast version. "
                        " Falling back to the slow version."
                    )
            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"]

        has_remote_code = tokenizer_auto_map is not None
        has_local_code = config_tokenizer_class is not None or type(config) in TOKENIZER_MAPPING
        trust_remote_code = resolve_trust_remote_code(
            trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
        )

        if has_remote_code and trust_remote_code:
            if use_fast and tokenizer_auto_map[1] is not None:
                class_ref = tokenizer_auto_map[1]
            else:
                class_ref = tokenizer_auto_map[0]
            tokenizer_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs)
            _ = kwargs.pop("code_revision", None)
            if os.path.isdir(pretrained_model_name_or_path):
                tokenizer_class.register_for_auto_class()
            return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
        elif config_tokenizer_class is not None:
            tokenizer_class = None
            if 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())}."
        )

    def register(config_class, slow_tokenizer_class=None, fast_tokenizer_class=None, exist_ok=False):
        """
        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), exist_ok=exist_ok)

# 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
import warnings
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, resolve_trust_remote_code
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import TOKENIZER_CONFIG_FILE
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,
)


if is_tokenizers_available():
    from ...tokenization_utils_fast import PreTrainedTokenizerFast
else:
    PreTrainedTokenizerFast = None


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,
                ),
            ),
            ("align", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
            ("bark", ("BertTokenizer", "BertTokenizerFast" 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)),
            ("biogpt", ("BioGptTokenizer", None)),
            ("blenderbot", ("BlenderbotTokenizer", "BlenderbotTokenizerFast")),
            ("blenderbot-small", ("BlenderbotSmallTokenizer", None)),
            ("blip", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
            ("blip-2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
            ("bloom", (None, "BloomTokenizerFast" if is_tokenizers_available() else None)),
            ("bridgetower", ("RobertaTokenizer", "RobertaTokenizerFast" 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)),
            (
                "clap",
                (
                    "RobertaTokenizer",
                    "RobertaTokenizerFast" if is_tokenizers_available() else None,
                ),
            ),
            (
                "clip",
                (
                    "CLIPTokenizer",
                    "CLIPTokenizerFast" if is_tokenizers_available() else None,
                ),
            ),
            (
                "clipseg",
                (
                    "CLIPTokenizer",
                    "CLIPTokenizerFast" if is_tokenizers_available() else None,
                ),
            ),
            (
                "code_llama",
                (
                    "CodeLlamaTokenizer" if is_sentencepiece_available() else None,
                    "CodeLlamaTokenizerFast" 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,
                ),
            ),
            ("cpmant", ("CpmAntTokenizer", None)),
            ("ctrl", ("CTRLTokenizer", None)),
            ("data2vec-audio", ("Wav2Vec2CTCTokenizer", 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)),
            ("ernie_m", ("ErnieMTokenizer" if is_sentencepiece_available() else None, 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)),
            ("git", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
            ("gpt-sw3", ("GPTSw3Tokenizer" if is_sentencepiece_available() else None, None)),
            ("gpt2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
            ("gpt_bigcode", ("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)),
            ("gptsan-japanese", ("GPTSanJapaneseTokenizer", 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)),
            ("idefics", (None, "LlamaTokenizerFast" if is_tokenizers_available() else None)),
            ("instructblip", ("GPT2Tokenizer", "GPT2TokenizerFast" 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)),
            (
                "llama",
                (
                    "LlamaTokenizer" if is_sentencepiece_available() else None,
                    "LlamaTokenizerFast" 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,
                ),
            ),
            ("mega", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)),
            ("megatron-bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
            ("mgp-str", ("MgpstrTokenizer", 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)),
            ("mpt", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)),
            ("mra", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)),
            (
                "mt5",
                (
                    "MT5Tokenizer" if is_sentencepiece_available() else None,
                    "MT5TokenizerFast" if is_tokenizers_available() else None,
                ),
            ),
            ("musicgen", ("T5Tokenizer", "T5TokenizerFast" 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,
                ),
            ),
            (
                "nllb-moe",
                (
                    "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,
                ),
            ),
            ("oneformer", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)),
            ("openai-gpt", ("OpenAIGPTTokenizer", "OpenAIGPTTokenizerFast" if is_tokenizers_available() else None)),
            ("opt", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else 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)),
            ("pix2struct", ("T5Tokenizer", "T5TokenizerFast" if is_tokenizers_available() else 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)),
            (
                "roberta-prelayernorm",
                ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None),
            ),
            ("roc_bert", ("RoCBertTokenizer", None)),
            ("roformer", ("RoFormerTokenizer", "RoFormerTokenizerFast" if is_tokenizers_available() else None)),
            ("rwkv", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)),
            ("speech_to_text", ("Speech2TextTokenizer" if is_sentencepiece_available() else None, None)),
            ("speech_to_text_2", ("Speech2Text2Tokenizer", None)),
            ("speecht5", ("SpeechT5Tokenizer" if is_sentencepiece_available() else None, 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)),
            (
                "umt5",
                (
                    "T5Tokenizer" if is_sentencepiece_available() else None,
                    "T5TokenizerFast" if is_tokenizers_available() else None,
                ),
            ),
            ("vilt", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
            ("visual_bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
            ("vits", ("VitsTokenizer", None)),
            ("wav2vec2", ("Wav2Vec2CTCTokenizer", None)),
            ("wav2vec2-conformer", ("Wav2Vec2CTCTokenizer", None)),
            ("wav2vec2_phoneme", ("Wav2Vec2PhonemeCTCTokenizer", None)),
            ("whisper", ("WhisperTokenizer", "WhisperTokenizerFast" if is_tokenizers_available() else 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,
                ),
            ),
            (
                "xmod",
                (
                    "XLMRobertaTokenizer" if is_sentencepiece_available() else None,
                    "XLMRobertaTokenizerFast" 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,
    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.
        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 `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")
    ```"""
    use_auth_token = kwargs.pop("use_auth_token", 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

    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,
        token=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


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."
        )

    @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 determine 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`):
                Use a [fast Rust-based tokenizer](https://huggingface.co/docs/tokenizers/index) if it is supported for
                a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer
                is returned instead.
            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)
        ```"""
        use_auth_token = kwargs.pop("use_auth_token", 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 kwargs.get("token", None) is not None:
                raise ValueError(
                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
                )
            kwargs["token"] = use_auth_token

        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", None)

        # 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:
                if tokenizer_fast_class_name is not None:
                    tokenizer_class = tokenizer_class_from_name(tokenizer_fast_class_name)
                else:
                    logger.warning(
                        "`use_fast` is set to `True` but the tokenizer class does not have a fast version. "
                        " Falling back to the slow version."
                    )
            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"]

        has_remote_code = tokenizer_auto_map is not None
        has_local_code = config_tokenizer_class is not None or type(config) in TOKENIZER_MAPPING
        trust_remote_code = resolve_trust_remote_code(
            trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
        )

        if has_remote_code and trust_remote_code:
            if use_fast and tokenizer_auto_map[1] is not None:
                class_ref = tokenizer_auto_map[1]
            else:
                class_ref = tokenizer_auto_map[0]
            tokenizer_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs)
            _ = kwargs.pop("code_revision", None)
            if os.path.isdir(pretrained_model_name_or_path):
                tokenizer_class.register_for_auto_class()
            return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
        elif config_tokenizer_class is not None:
            tokenizer_class = None
            if 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())}."
        )

    def register(config_class, slow_tokenizer_class=None, fast_tokenizer_class=None, exist_ok=False):
        """
        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), exist_ok=exist_ok)
