# 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).

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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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      to it if that's the case.
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      [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.

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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).

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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.
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<!-- 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 Model class."""

import warnings
from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoBackboneClass, _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


logger = logging.get_logger(__name__)


MODEL_MAPPING_NAMES = OrderedDict(
    [
        # Base model mapping
        ("albert", "AlbertModel"),
        ("align", "AlignModel"),
        ("altclip", "AltCLIPModel"),
        ("audio-spectrogram-transformer", "ASTModel"),
        ("autoformer", "AutoformerModel"),
        ("bark", "BarkModel"),
        ("bart", "BartModel"),
        ("beit", "BeitModel"),
        ("bert", "BertModel"),
        ("bert-generation", "BertGenerationEncoder"),
        ("big_bird", "BigBirdModel"),
        ("bigbird_pegasus", "BigBirdPegasusModel"),
        ("biogpt", "BioGptModel"),
        ("bit", "BitModel"),
        ("blenderbot", "BlenderbotModel"),
        ("blenderbot-small", "BlenderbotSmallModel"),
        ("blip", "BlipModel"),
        ("blip-2", "Blip2Model"),
        ("bloom", "BloomModel"),
        ("bridgetower", "BridgeTowerModel"),
        ("camembert", "CamembertModel"),
        ("canine", "CanineModel"),
        ("chinese_clip", "ChineseCLIPModel"),
        ("clap", "ClapModel"),
        ("clip", "CLIPModel"),
        ("clipseg", "CLIPSegModel"),
        ("code_llama", "LlamaModel"),
        ("codegen", "CodeGenModel"),
        ("conditional_detr", "ConditionalDetrModel"),
        ("convbert", "ConvBertModel"),
        ("convnext", "ConvNextModel"),
        ("convnextv2", "ConvNextV2Model"),
        ("cpmant", "CpmAntModel"),
        ("ctrl", "CTRLModel"),
        ("cvt", "CvtModel"),
        ("data2vec-audio", "Data2VecAudioModel"),
        ("data2vec-text", "Data2VecTextModel"),
        ("data2vec-vision", "Data2VecVisionModel"),
        ("deberta", "DebertaModel"),
        ("deberta-v2", "DebertaV2Model"),
        ("decision_transformer", "DecisionTransformerModel"),
        ("deformable_detr", "DeformableDetrModel"),
        ("deit", "DeiTModel"),
        ("deta", "DetaModel"),
        ("detr", "DetrModel"),
        ("dinat", "DinatModel"),
        ("dinov2", "Dinov2Model"),
        ("distilbert", "DistilBertModel"),
        ("donut-swin", "DonutSwinModel"),
        ("dpr", "DPRQuestionEncoder"),
        ("dpt", "DPTModel"),
        ("efficientformer", "EfficientFormerModel"),
        ("efficientnet", "EfficientNetModel"),
        ("electra", "ElectraModel"),
        ("encodec", "EncodecModel"),
        ("ernie", "ErnieModel"),
        ("ernie_m", "ErnieMModel"),
        ("esm", "EsmModel"),
        ("falcon", "FalconModel"),
        ("flaubert", "FlaubertModel"),
        ("flava", "FlavaModel"),
        ("fnet", "FNetModel"),
        ("focalnet", "FocalNetModel"),
        ("fsmt", "FSMTModel"),
        ("funnel", ("FunnelModel", "FunnelBaseModel")),
        ("git", "GitModel"),
        ("glpn", "GLPNModel"),
        ("gpt-sw3", "GPT2Model"),
        ("gpt2", "GPT2Model"),
        ("gpt_bigcode", "GPTBigCodeModel"),
        ("gpt_neo", "GPTNeoModel"),
        ("gpt_neox", "GPTNeoXModel"),
        ("gpt_neox_japanese", "GPTNeoXJapaneseModel"),
        ("gptj", "GPTJModel"),
        ("gptsan-japanese", "GPTSanJapaneseForConditionalGeneration"),
        ("graphormer", "GraphormerModel"),
        ("groupvit", "GroupViTModel"),
        ("hubert", "HubertModel"),
        ("ibert", "IBertModel"),
        ("idefics", "IdeficsModel"),
        ("imagegpt", "ImageGPTModel"),
        ("informer", "InformerModel"),
        ("jukebox", "JukeboxModel"),
        ("layoutlm", "LayoutLMModel"),
        ("layoutlmv2", "LayoutLMv2Model"),
        ("layoutlmv3", "LayoutLMv3Model"),
        ("led", "LEDModel"),
        ("levit", "LevitModel"),
        ("lilt", "LiltModel"),
        ("llama", "LlamaModel"),
        ("longformer", "LongformerModel"),
        ("longt5", "LongT5Model"),
        ("luke", "LukeModel"),
        ("lxmert", "LxmertModel"),
        ("m2m_100", "M2M100Model"),
        ("marian", "MarianModel"),
        ("markuplm", "MarkupLMModel"),
        ("mask2former", "Mask2FormerModel"),
        ("maskformer", "MaskFormerModel"),
        ("maskformer-swin", "MaskFormerSwinModel"),
        ("mbart", "MBartModel"),
        ("mctct", "MCTCTModel"),
        ("mega", "MegaModel"),
        ("megatron-bert", "MegatronBertModel"),
        ("mgp-str", "MgpstrForSceneTextRecognition"),
        ("mobilebert", "MobileBertModel"),
        ("mobilenet_v1", "MobileNetV1Model"),
        ("mobilenet_v2", "MobileNetV2Model"),
        ("mobilevit", "MobileViTModel"),
        ("mobilevitv2", "MobileViTV2Model"),
        ("mpnet", "MPNetModel"),
        ("mpt", "MptModel"),
        ("mra", "MraModel"),
        ("mt5", "MT5Model"),
        ("mvp", "MvpModel"),
        ("nat", "NatModel"),
        ("nezha", "NezhaModel"),
        ("nllb-moe", "NllbMoeModel"),
        ("nystromformer", "NystromformerModel"),
        ("oneformer", "OneFormerModel"),
        ("open-llama", "OpenLlamaModel"),
        ("openai-gpt", "OpenAIGPTModel"),
        ("opt", "OPTModel"),
        ("owlvit", "OwlViTModel"),
        ("pegasus", "PegasusModel"),
        ("pegasus_x", "PegasusXModel"),
        ("perceiver", "PerceiverModel"),
        ("plbart", "PLBartModel"),
        ("poolformer", "PoolFormerModel"),
        ("prophetnet", "ProphetNetModel"),
        ("pvt", "PvtModel"),
        ("qdqbert", "QDQBertModel"),
        ("reformer", "ReformerModel"),
        ("regnet", "RegNetModel"),
        ("rembert", "RemBertModel"),
        ("resnet", "ResNetModel"),
        ("retribert", "RetriBertModel"),
        ("roberta", "RobertaModel"),
        ("roberta-prelayernorm", "RobertaPreLayerNormModel"),
        ("roc_bert", "RoCBertModel"),
        ("roformer", "RoFormerModel"),
        ("rwkv", "RwkvModel"),
        ("sam", "SamModel"),
        ("segformer", "SegformerModel"),
        ("sew", "SEWModel"),
        ("sew-d", "SEWDModel"),
        ("speech_to_text", "Speech2TextModel"),
        ("speecht5", "SpeechT5Model"),
        ("splinter", "SplinterModel"),
        ("squeezebert", "SqueezeBertModel"),
        ("swiftformer", "SwiftFormerModel"),
        ("swin", "SwinModel"),
        ("swin2sr", "Swin2SRModel"),
        ("swinv2", "Swinv2Model"),
        ("switch_transformers", "SwitchTransformersModel"),
        ("t5", "T5Model"),
        ("table-transformer", "TableTransformerModel"),
        ("tapas", "TapasModel"),
        ("time_series_transformer", "TimeSeriesTransformerModel"),
        ("timesformer", "TimesformerModel"),
        ("timm_backbone", "TimmBackbone"),
        ("trajectory_transformer", "TrajectoryTransformerModel"),
        ("transfo-xl", "TransfoXLModel"),
        ("tvlt", "TvltModel"),
        ("umt5", "UMT5Model"),
        ("unispeech", "UniSpeechModel"),
        ("unispeech-sat", "UniSpeechSatModel"),
        ("van", "VanModel"),
        ("videomae", "VideoMAEModel"),
        ("vilt", "ViltModel"),
        ("vision-text-dual-encoder", "VisionTextDualEncoderModel"),
        ("visual_bert", "VisualBertModel"),
        ("vit", "ViTModel"),
        ("vit_hybrid", "ViTHybridModel"),
        ("vit_mae", "ViTMAEModel"),
        ("vit_msn", "ViTMSNModel"),
        ("vitdet", "VitDetModel"),
        ("vivit", "VivitModel"),
        ("wav2vec2", "Wav2Vec2Model"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerModel"),
        ("wavlm", "WavLMModel"),
        ("whisper", "WhisperModel"),
        ("xclip", "XCLIPModel"),
        ("xglm", "XGLMModel"),
        ("xlm", "XLMModel"),
        ("xlm-prophetnet", "XLMProphetNetModel"),
        ("xlm-roberta", "XLMRobertaModel"),
        ("xlm-roberta-xl", "XLMRobertaXLModel"),
        ("xlnet", "XLNetModel"),
        ("xmod", "XmodModel"),
        ("yolos", "YolosModel"),
        ("yoso", "YosoModel"),
    ]
)

MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "AlbertForPreTraining"),
        ("bart", "BartForConditionalGeneration"),
        ("bert", "BertForPreTraining"),
        ("big_bird", "BigBirdForPreTraining"),
        ("bloom", "BloomForCausalLM"),
        ("camembert", "CamembertForMaskedLM"),
        ("ctrl", "CTRLLMHeadModel"),
        ("data2vec-text", "Data2VecTextForMaskedLM"),
        ("deberta", "DebertaForMaskedLM"),
        ("deberta-v2", "DebertaV2ForMaskedLM"),
        ("distilbert", "DistilBertForMaskedLM"),
        ("electra", "ElectraForPreTraining"),
        ("ernie", "ErnieForPreTraining"),
        ("flaubert", "FlaubertWithLMHeadModel"),
        ("flava", "FlavaForPreTraining"),
        ("fnet", "FNetForPreTraining"),
        ("fsmt", "FSMTForConditionalGeneration"),
        ("funnel", "FunnelForPreTraining"),
        ("gpt-sw3", "GPT2LMHeadModel"),
        ("gpt2", "GPT2LMHeadModel"),
        ("gpt_bigcode", "GPTBigCodeForCausalLM"),
        ("gptsan-japanese", "GPTSanJapaneseForConditionalGeneration"),
        ("ibert", "IBertForMaskedLM"),
        ("idefics", "IdeficsForVisionText2Text"),
        ("layoutlm", "LayoutLMForMaskedLM"),
        ("longformer", "LongformerForMaskedLM"),
        ("luke", "LukeForMaskedLM"),
        ("lxmert", "LxmertForPreTraining"),
        ("mega", "MegaForMaskedLM"),
        ("megatron-bert", "MegatronBertForPreTraining"),
        ("mobilebert", "MobileBertForPreTraining"),
        ("mpnet", "MPNetForMaskedLM"),
        ("mpt", "MptForCausalLM"),
        ("mra", "MraForMaskedLM"),
        ("mvp", "MvpForConditionalGeneration"),
        ("nezha", "NezhaForPreTraining"),
        ("nllb-moe", "NllbMoeForConditionalGeneration"),
        ("openai-gpt", "OpenAIGPTLMHeadModel"),
        ("retribert", "RetriBertModel"),
        ("roberta", "RobertaForMaskedLM"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForMaskedLM"),
        ("roc_bert", "RoCBertForPreTraining"),
        ("rwkv", "RwkvForCausalLM"),
        ("splinter", "SplinterForPreTraining"),
        ("squeezebert", "SqueezeBertForMaskedLM"),
        ("switch_transformers", "SwitchTransformersForConditionalGeneration"),
        ("t5", "T5ForConditionalGeneration"),
        ("tapas", "TapasForMaskedLM"),
        ("transfo-xl", "TransfoXLLMHeadModel"),
        ("tvlt", "TvltForPreTraining"),
        ("unispeech", "UniSpeechForPreTraining"),
        ("unispeech-sat", "UniSpeechSatForPreTraining"),
        ("videomae", "VideoMAEForPreTraining"),
        ("visual_bert", "VisualBertForPreTraining"),
        ("vit_mae", "ViTMAEForPreTraining"),
        ("wav2vec2", "Wav2Vec2ForPreTraining"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerForPreTraining"),
        ("xlm", "XLMWithLMHeadModel"),
        ("xlm-roberta", "XLMRobertaForMaskedLM"),
        ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"),
        ("xlnet", "XLNetLMHeadModel"),
        ("xmod", "XmodForMaskedLM"),
    ]
)

MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict(
    [
        # Model with LM heads mapping
        ("albert", "AlbertForMaskedLM"),
        ("bart", "BartForConditionalGeneration"),
        ("bert", "BertForMaskedLM"),
        ("big_bird", "BigBirdForMaskedLM"),
        ("bigbird_pegasus", "BigBirdPegasusForConditionalGeneration"),
        ("blenderbot-small", "BlenderbotSmallForConditionalGeneration"),
        ("bloom", "BloomForCausalLM"),
        ("camembert", "CamembertForMaskedLM"),
        ("codegen", "CodeGenForCausalLM"),
        ("convbert", "ConvBertForMaskedLM"),
        ("cpmant", "CpmAntForCausalLM"),
        ("ctrl", "CTRLLMHeadModel"),
        ("data2vec-text", "Data2VecTextForMaskedLM"),
        ("deberta", "DebertaForMaskedLM"),
        ("deberta-v2", "DebertaV2ForMaskedLM"),
        ("distilbert", "DistilBertForMaskedLM"),
        ("electra", "ElectraForMaskedLM"),
        ("encoder-decoder", "EncoderDecoderModel"),
        ("ernie", "ErnieForMaskedLM"),
        ("esm", "EsmForMaskedLM"),
        ("flaubert", "FlaubertWithLMHeadModel"),
        ("fnet", "FNetForMaskedLM"),
        ("fsmt", "FSMTForConditionalGeneration"),
        ("funnel", "FunnelForMaskedLM"),
        ("git", "GitForCausalLM"),
        ("gpt-sw3", "GPT2LMHeadModel"),
        ("gpt2", "GPT2LMHeadModel"),
        ("gpt_bigcode", "GPTBigCodeForCausalLM"),
        ("gpt_neo", "GPTNeoForCausalLM"),
        ("gpt_neox", "GPTNeoXForCausalLM"),
        ("gpt_neox_japanese", "GPTNeoXJapaneseForCausalLM"),
        ("gptj", "GPTJForCausalLM"),
        ("gptsan-japanese", "GPTSanJapaneseForConditionalGeneration"),
        ("ibert", "IBertForMaskedLM"),
        ("layoutlm", "LayoutLMForMaskedLM"),
        ("led", "LEDForConditionalGeneration"),
        ("longformer", "LongformerForMaskedLM"),
        ("longt5", "LongT5ForConditionalGeneration"),
        ("luke", "LukeForMaskedLM"),
        ("m2m_100", "M2M100ForConditionalGeneration"),
        ("marian", "MarianMTModel"),
        ("mega", "MegaForMaskedLM"),
        ("megatron-bert", "MegatronBertForCausalLM"),
        ("mobilebert", "MobileBertForMaskedLM"),
        ("mpnet", "MPNetForMaskedLM"),
        ("mpt", "MptForCausalLM"),
        ("mra", "MraForMaskedLM"),
        ("mvp", "MvpForConditionalGeneration"),
        ("nezha", "NezhaForMaskedLM"),
        ("nllb-moe", "NllbMoeForConditionalGeneration"),
        ("nystromformer", "NystromformerForMaskedLM"),
        ("openai-gpt", "OpenAIGPTLMHeadModel"),
        ("pegasus_x", "PegasusXForConditionalGeneration"),
        ("plbart", "PLBartForConditionalGeneration"),
        ("pop2piano", "Pop2PianoForConditionalGeneration"),
        ("qdqbert", "QDQBertForMaskedLM"),
        ("reformer", "ReformerModelWithLMHead"),
        ("rembert", "RemBertForMaskedLM"),
        ("roberta", "RobertaForMaskedLM"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForMaskedLM"),
        ("roc_bert", "RoCBertForMaskedLM"),
        ("roformer", "RoFormerForMaskedLM"),
        ("rwkv", "RwkvForCausalLM"),
        ("speech_to_text", "Speech2TextForConditionalGeneration"),
        ("squeezebert", "SqueezeBertForMaskedLM"),
        ("switch_transformers", "SwitchTransformersForConditionalGeneration"),
        ("t5", "T5ForConditionalGeneration"),
        ("tapas", "TapasForMaskedLM"),
        ("transfo-xl", "TransfoXLLMHeadModel"),
        ("wav2vec2", "Wav2Vec2ForMaskedLM"),
        ("whisper", "WhisperForConditionalGeneration"),
        ("xlm", "XLMWithLMHeadModel"),
        ("xlm-roberta", "XLMRobertaForMaskedLM"),
        ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"),
        ("xlnet", "XLNetLMHeadModel"),
        ("xmod", "XmodForMaskedLM"),
        ("yoso", "YosoForMaskedLM"),
    ]
)

MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "BartForCausalLM"),
        ("bert", "BertLMHeadModel"),
        ("bert-generation", "BertGenerationDecoder"),
        ("big_bird", "BigBirdForCausalLM"),
        ("bigbird_pegasus", "BigBirdPegasusForCausalLM"),
        ("biogpt", "BioGptForCausalLM"),
        ("blenderbot", "BlenderbotForCausalLM"),
        ("blenderbot-small", "BlenderbotSmallForCausalLM"),
        ("bloom", "BloomForCausalLM"),
        ("camembert", "CamembertForCausalLM"),
        ("code_llama", "LlamaForCausalLM"),
        ("codegen", "CodeGenForCausalLM"),
        ("cpmant", "CpmAntForCausalLM"),
        ("ctrl", "CTRLLMHeadModel"),
        ("data2vec-text", "Data2VecTextForCausalLM"),
        ("electra", "ElectraForCausalLM"),
        ("ernie", "ErnieForCausalLM"),
        ("falcon", "FalconForCausalLM"),
        ("git", "GitForCausalLM"),
        ("gpt-sw3", "GPT2LMHeadModel"),
        ("gpt2", "GPT2LMHeadModel"),
        ("gpt_bigcode", "GPTBigCodeForCausalLM"),
        ("gpt_neo", "GPTNeoForCausalLM"),
        ("gpt_neox", "GPTNeoXForCausalLM"),
        ("gpt_neox_japanese", "GPTNeoXJapaneseForCausalLM"),
        ("gptj", "GPTJForCausalLM"),
        ("llama", "LlamaForCausalLM"),
        ("marian", "MarianForCausalLM"),
        ("mbart", "MBartForCausalLM"),
        ("mega", "MegaForCausalLM"),
        ("megatron-bert", "MegatronBertForCausalLM"),
        ("mpt", "MptForCausalLM"),
        ("musicgen", "MusicgenForCausalLM"),
        ("mvp", "MvpForCausalLM"),
        ("open-llama", "OpenLlamaForCausalLM"),
        ("openai-gpt", "OpenAIGPTLMHeadModel"),
        ("opt", "OPTForCausalLM"),
        ("pegasus", "PegasusForCausalLM"),
        ("plbart", "PLBartForCausalLM"),
        ("prophetnet", "ProphetNetForCausalLM"),
        ("qdqbert", "QDQBertLMHeadModel"),
        ("reformer", "ReformerModelWithLMHead"),
        ("rembert", "RemBertForCausalLM"),
        ("roberta", "RobertaForCausalLM"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForCausalLM"),
        ("roc_bert", "RoCBertForCausalLM"),
        ("roformer", "RoFormerForCausalLM"),
        ("rwkv", "RwkvForCausalLM"),
        ("speech_to_text_2", "Speech2Text2ForCausalLM"),
        ("transfo-xl", "TransfoXLLMHeadModel"),
        ("trocr", "TrOCRForCausalLM"),
        ("xglm", "XGLMForCausalLM"),
        ("xlm", "XLMWithLMHeadModel"),
        ("xlm-prophetnet", "XLMProphetNetForCausalLM"),
        ("xlm-roberta", "XLMRobertaForCausalLM"),
        ("xlm-roberta-xl", "XLMRobertaXLForCausalLM"),
        ("xlnet", "XLNetLMHeadModel"),
        ("xmod", "XmodForCausalLM"),
    ]
)

MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES = OrderedDict(
    [
        ("deit", "DeiTForMaskedImageModeling"),
        ("focalnet", "FocalNetForMaskedImageModeling"),
        ("swin", "SwinForMaskedImageModeling"),
        ("swinv2", "Swinv2ForMaskedImageModeling"),
        ("vit", "ViTForMaskedImageModeling"),
    ]
)


MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES = OrderedDict(
    # Model for Causal Image Modeling mapping
    [
        ("imagegpt", "ImageGPTForCausalImageModeling"),
    ]
)

MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Image Classification mapping
        ("beit", "BeitForImageClassification"),
        ("bit", "BitForImageClassification"),
        ("convnext", "ConvNextForImageClassification"),
        ("convnextv2", "ConvNextV2ForImageClassification"),
        ("cvt", "CvtForImageClassification"),
        ("data2vec-vision", "Data2VecVisionForImageClassification"),
        ("deit", ("DeiTForImageClassification", "DeiTForImageClassificationWithTeacher")),
        ("dinat", "DinatForImageClassification"),
        ("dinov2", "Dinov2ForImageClassification"),
        (
            "efficientformer",
            (
                "EfficientFormerForImageClassification",
                "EfficientFormerForImageClassificationWithTeacher",
            ),
        ),
        ("efficientnet", "EfficientNetForImageClassification"),
        ("focalnet", "FocalNetForImageClassification"),
        ("imagegpt", "ImageGPTForImageClassification"),
        ("levit", ("LevitForImageClassification", "LevitForImageClassificationWithTeacher")),
        ("mobilenet_v1", "MobileNetV1ForImageClassification"),
        ("mobilenet_v2", "MobileNetV2ForImageClassification"),
        ("mobilevit", "MobileViTForImageClassification"),
        ("mobilevitv2", "MobileViTV2ForImageClassification"),
        ("nat", "NatForImageClassification"),
        (
            "perceiver",
            (
                "PerceiverForImageClassificationLearned",
                "PerceiverForImageClassificationFourier",
                "PerceiverForImageClassificationConvProcessing",
            ),
        ),
        ("poolformer", "PoolFormerForImageClassification"),
        ("pvt", "PvtForImageClassification"),
        ("regnet", "RegNetForImageClassification"),
        ("resnet", "ResNetForImageClassification"),
        ("segformer", "SegformerForImageClassification"),
        ("swiftformer", "SwiftFormerForImageClassification"),
        ("swin", "SwinForImageClassification"),
        ("swinv2", "Swinv2ForImageClassification"),
        ("van", "VanForImageClassification"),
        ("vit", "ViTForImageClassification"),
        ("vit_hybrid", "ViTHybridForImageClassification"),
        ("vit_msn", "ViTMSNForImageClassification"),
    ]
)

MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES = OrderedDict(
    [
        # Do not add new models here, this class will be deprecated in the future.
        # Model for Image Segmentation mapping
        ("detr", "DetrForSegmentation"),
    ]
)

MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Semantic Segmentation mapping
        ("beit", "BeitForSemanticSegmentation"),
        ("data2vec-vision", "Data2VecVisionForSemanticSegmentation"),
        ("dpt", "DPTForSemanticSegmentation"),
        ("mobilenet_v2", "MobileNetV2ForSemanticSegmentation"),
        ("mobilevit", "MobileViTForSemanticSegmentation"),
        ("mobilevitv2", "MobileViTV2ForSemanticSegmentation"),
        ("segformer", "SegformerForSemanticSegmentation"),
        ("upernet", "UperNetForSemanticSegmentation"),
    ]
)

MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Instance Segmentation mapping
        # MaskFormerForInstanceSegmentation can be removed from this mapping in v5
        ("maskformer", "MaskFormerForInstanceSegmentation"),
    ]
)

MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Universal Segmentation mapping
        ("detr", "DetrForSegmentation"),
        ("mask2former", "Mask2FormerForUniversalSegmentation"),
        ("maskformer", "MaskFormerForInstanceSegmentation"),
        ("oneformer", "OneFormerForUniversalSegmentation"),
    ]
)

MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        ("timesformer", "TimesformerForVideoClassification"),
        ("videomae", "VideoMAEForVideoClassification"),
        ("vivit", "VivitForVideoClassification"),
    ]
)

MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict(
    [
        ("blip", "BlipForConditionalGeneration"),
        ("blip-2", "Blip2ForConditionalGeneration"),
        ("git", "GitForCausalLM"),
        ("instructblip", "InstructBlipForConditionalGeneration"),
        ("pix2struct", "Pix2StructForConditionalGeneration"),
        ("vision-encoder-decoder", "VisionEncoderDecoderModel"),
    ]
)

MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "AlbertForMaskedLM"),
        ("bart", "BartForConditionalGeneration"),
        ("bert", "BertForMaskedLM"),
        ("big_bird", "BigBirdForMaskedLM"),
        ("camembert", "CamembertForMaskedLM"),
        ("convbert", "ConvBertForMaskedLM"),
        ("data2vec-text", "Data2VecTextForMaskedLM"),
        ("deberta", "DebertaForMaskedLM"),
        ("deberta-v2", "DebertaV2ForMaskedLM"),
        ("distilbert", "DistilBertForMaskedLM"),
        ("electra", "ElectraForMaskedLM"),
        ("ernie", "ErnieForMaskedLM"),
        ("esm", "EsmForMaskedLM"),
        ("flaubert", "FlaubertWithLMHeadModel"),
        ("fnet", "FNetForMaskedLM"),
        ("funnel", "FunnelForMaskedLM"),
        ("ibert", "IBertForMaskedLM"),
        ("layoutlm", "LayoutLMForMaskedLM"),
        ("longformer", "LongformerForMaskedLM"),
        ("luke", "LukeForMaskedLM"),
        ("mbart", "MBartForConditionalGeneration"),
        ("mega", "MegaForMaskedLM"),
        ("megatron-bert", "MegatronBertForMaskedLM"),
        ("mobilebert", "MobileBertForMaskedLM"),
        ("mpnet", "MPNetForMaskedLM"),
        ("mra", "MraForMaskedLM"),
        ("mvp", "MvpForConditionalGeneration"),
        ("nezha", "NezhaForMaskedLM"),
        ("nystromformer", "NystromformerForMaskedLM"),
        ("perceiver", "PerceiverForMaskedLM"),
        ("qdqbert", "QDQBertForMaskedLM"),
        ("reformer", "ReformerForMaskedLM"),
        ("rembert", "RemBertForMaskedLM"),
        ("roberta", "RobertaForMaskedLM"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForMaskedLM"),
        ("roc_bert", "RoCBertForMaskedLM"),
        ("roformer", "RoFormerForMaskedLM"),
        ("squeezebert", "SqueezeBertForMaskedLM"),
        ("tapas", "TapasForMaskedLM"),
        ("wav2vec2", "Wav2Vec2ForMaskedLM"),
        ("xlm", "XLMWithLMHeadModel"),
        ("xlm-roberta", "XLMRobertaForMaskedLM"),
        ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"),
        ("xmod", "XmodForMaskedLM"),
        ("yoso", "YosoForMaskedLM"),
    ]
)

MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Object Detection mapping
        ("conditional_detr", "ConditionalDetrForObjectDetection"),
        ("deformable_detr", "DeformableDetrForObjectDetection"),
        ("deta", "DetaForObjectDetection"),
        ("detr", "DetrForObjectDetection"),
        ("table-transformer", "TableTransformerForObjectDetection"),
        ("yolos", "YolosForObjectDetection"),
    ]
)

MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Zero Shot Object Detection mapping
        ("owlvit", "OwlViTForObjectDetection")
    ]
)

MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for depth estimation mapping
        ("dpt", "DPTForDepthEstimation"),
        ("glpn", "GLPNForDepthEstimation"),
    ]
)
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "BartForConditionalGeneration"),
        ("bigbird_pegasus", "BigBirdPegasusForConditionalGeneration"),
        ("blenderbot", "BlenderbotForConditionalGeneration"),
        ("blenderbot-small", "BlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "EncoderDecoderModel"),
        ("fsmt", "FSMTForConditionalGeneration"),
        ("gptsan-japanese", "GPTSanJapaneseForConditionalGeneration"),
        ("led", "LEDForConditionalGeneration"),
        ("longt5", "LongT5ForConditionalGeneration"),
        ("m2m_100", "M2M100ForConditionalGeneration"),
        ("marian", "MarianMTModel"),
        ("mbart", "MBartForConditionalGeneration"),
        ("mt5", "MT5ForConditionalGeneration"),
        ("mvp", "MvpForConditionalGeneration"),
        ("nllb-moe", "NllbMoeForConditionalGeneration"),
        ("pegasus", "PegasusForConditionalGeneration"),
        ("pegasus_x", "PegasusXForConditionalGeneration"),
        ("plbart", "PLBartForConditionalGeneration"),
        ("prophetnet", "ProphetNetForConditionalGeneration"),
        ("switch_transformers", "SwitchTransformersForConditionalGeneration"),
        ("t5", "T5ForConditionalGeneration"),
        ("umt5", "UMT5ForConditionalGeneration"),
        ("xlm-prophetnet", "XLMProphetNetForConditionalGeneration"),
    ]
)

MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict(
    [
        ("pop2piano", "Pop2PianoForConditionalGeneration"),
        ("speech-encoder-decoder", "SpeechEncoderDecoderModel"),
        ("speech_to_text", "Speech2TextForConditionalGeneration"),
        ("speecht5", "SpeechT5ForSpeechToText"),
        ("whisper", "WhisperForConditionalGeneration"),
    ]
)

MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "AlbertForSequenceClassification"),
        ("bart", "BartForSequenceClassification"),
        ("bert", "BertForSequenceClassification"),
        ("big_bird", "BigBirdForSequenceClassification"),
        ("bigbird_pegasus", "BigBirdPegasusForSequenceClassification"),
        ("biogpt", "BioGptForSequenceClassification"),
        ("bloom", "BloomForSequenceClassification"),
        ("camembert", "CamembertForSequenceClassification"),
        ("canine", "CanineForSequenceClassification"),
        ("code_llama", "LlamaForSequenceClassification"),
        ("convbert", "ConvBertForSequenceClassification"),
        ("ctrl", "CTRLForSequenceClassification"),
        ("data2vec-text", "Data2VecTextForSequenceClassification"),
        ("deberta", "DebertaForSequenceClassification"),
        ("deberta-v2", "DebertaV2ForSequenceClassification"),
        ("distilbert", "DistilBertForSequenceClassification"),
        ("electra", "ElectraForSequenceClassification"),
        ("ernie", "ErnieForSequenceClassification"),
        ("ernie_m", "ErnieMForSequenceClassification"),
        ("esm", "EsmForSequenceClassification"),
        ("falcon", "FalconForSequenceClassification"),
        ("flaubert", "FlaubertForSequenceClassification"),
        ("fnet", "FNetForSequenceClassification"),
        ("funnel", "FunnelForSequenceClassification"),
        ("gpt-sw3", "GPT2ForSequenceClassification"),
        ("gpt2", "GPT2ForSequenceClassification"),
        ("gpt_bigcode", "GPTBigCodeForSequenceClassification"),
        ("gpt_neo", "GPTNeoForSequenceClassification"),
        ("gpt_neox", "GPTNeoXForSequenceClassification"),
        ("gptj", "GPTJForSequenceClassification"),
        ("ibert", "IBertForSequenceClassification"),
        ("layoutlm", "LayoutLMForSequenceClassification"),
        ("layoutlmv2", "LayoutLMv2ForSequenceClassification"),
        ("layoutlmv3", "LayoutLMv3ForSequenceClassification"),
        ("led", "LEDForSequenceClassification"),
        ("lilt", "LiltForSequenceClassification"),
        ("llama", "LlamaForSequenceClassification"),
        ("longformer", "LongformerForSequenceClassification"),
        ("luke", "LukeForSequenceClassification"),
        ("markuplm", "MarkupLMForSequenceClassification"),
        ("mbart", "MBartForSequenceClassification"),
        ("mega", "MegaForSequenceClassification"),
        ("megatron-bert", "MegatronBertForSequenceClassification"),
        ("mobilebert", "MobileBertForSequenceClassification"),
        ("mpnet", "MPNetForSequenceClassification"),
        ("mpt", "MptForSequenceClassification"),
        ("mra", "MraForSequenceClassification"),
        ("mt5", "MT5ForSequenceClassification"),
        ("mvp", "MvpForSequenceClassification"),
        ("nezha", "NezhaForSequenceClassification"),
        ("nystromformer", "NystromformerForSequenceClassification"),
        ("open-llama", "OpenLlamaForSequenceClassification"),
        ("openai-gpt", "OpenAIGPTForSequenceClassification"),
        ("opt", "OPTForSequenceClassification"),
        ("perceiver", "PerceiverForSequenceClassification"),
        ("plbart", "PLBartForSequenceClassification"),
        ("qdqbert", "QDQBertForSequenceClassification"),
        ("reformer", "ReformerForSequenceClassification"),
        ("rembert", "RemBertForSequenceClassification"),
        ("roberta", "RobertaForSequenceClassification"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForSequenceClassification"),
        ("roc_bert", "RoCBertForSequenceClassification"),
        ("roformer", "RoFormerForSequenceClassification"),
        ("squeezebert", "SqueezeBertForSequenceClassification"),
        ("t5", "T5ForSequenceClassification"),
        ("tapas", "TapasForSequenceClassification"),
        ("transfo-xl", "TransfoXLForSequenceClassification"),
        ("umt5", "UMT5ForSequenceClassification"),
        ("xlm", "XLMForSequenceClassification"),
        ("xlm-roberta", "XLMRobertaForSequenceClassification"),
        ("xlm-roberta-xl", "XLMRobertaXLForSequenceClassification"),
        ("xlnet", "XLNetForSequenceClassification"),
        ("xmod", "XmodForSequenceClassification"),
        ("yoso", "YosoForSequenceClassification"),
    ]
)

MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "AlbertForQuestionAnswering"),
        ("bart", "BartForQuestionAnswering"),
        ("bert", "BertForQuestionAnswering"),
        ("big_bird", "BigBirdForQuestionAnswering"),
        ("bigbird_pegasus", "BigBirdPegasusForQuestionAnswering"),
        ("bloom", "BloomForQuestionAnswering"),
        ("camembert", "CamembertForQuestionAnswering"),
        ("canine", "CanineForQuestionAnswering"),
        ("convbert", "ConvBertForQuestionAnswering"),
        ("data2vec-text", "Data2VecTextForQuestionAnswering"),
        ("deberta", "DebertaForQuestionAnswering"),
        ("deberta-v2", "DebertaV2ForQuestionAnswering"),
        ("distilbert", "DistilBertForQuestionAnswering"),
        ("electra", "ElectraForQuestionAnswering"),
        ("ernie", "ErnieForQuestionAnswering"),
        ("ernie_m", "ErnieMForQuestionAnswering"),
        ("falcon", "FalconForQuestionAnswering"),
        ("flaubert", "FlaubertForQuestionAnsweringSimple"),
        ("fnet", "FNetForQuestionAnswering"),
        ("funnel", "FunnelForQuestionAnswering"),
        ("gpt2", "GPT2ForQuestionAnswering"),
        ("gpt_neo", "GPTNeoForQuestionAnswering"),
        ("gpt_neox", "GPTNeoXForQuestionAnswering"),
        ("gptj", "GPTJForQuestionAnswering"),
        ("ibert", "IBertForQuestionAnswering"),
        ("layoutlmv2", "LayoutLMv2ForQuestionAnswering"),
        ("layoutlmv3", "LayoutLMv3ForQuestionAnswering"),
        ("led", "LEDForQuestionAnswering"),
        ("lilt", "LiltForQuestionAnswering"),
        ("longformer", "LongformerForQuestionAnswering"),
        ("luke", "LukeForQuestionAnswering"),
        ("lxmert", "LxmertForQuestionAnswering"),
        ("markuplm", "MarkupLMForQuestionAnswering"),
        ("mbart", "MBartForQuestionAnswering"),
        ("mega", "MegaForQuestionAnswering"),
        ("megatron-bert", "MegatronBertForQuestionAnswering"),
        ("mobilebert", "MobileBertForQuestionAnswering"),
        ("mpnet", "MPNetForQuestionAnswering"),
        ("mpt", "MptForQuestionAnswering"),
        ("mra", "MraForQuestionAnswering"),
        ("mt5", "MT5ForQuestionAnswering"),
        ("mvp", "MvpForQuestionAnswering"),
        ("nezha", "NezhaForQuestionAnswering"),
        ("nystromformer", "NystromformerForQuestionAnswering"),
        ("opt", "OPTForQuestionAnswering"),
        ("qdqbert", "QDQBertForQuestionAnswering"),
        ("reformer", "ReformerForQuestionAnswering"),
        ("rembert", "RemBertForQuestionAnswering"),
        ("roberta", "RobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForQuestionAnswering"),
        ("roc_bert", "RoCBertForQuestionAnswering"),
        ("roformer", "RoFormerForQuestionAnswering"),
        ("splinter", "SplinterForQuestionAnswering"),
        ("squeezebert", "SqueezeBertForQuestionAnswering"),
        ("t5", "T5ForQuestionAnswering"),
        ("umt5", "UMT5ForQuestionAnswering"),
        ("xlm", "XLMForQuestionAnsweringSimple"),
        ("xlm-roberta", "XLMRobertaForQuestionAnswering"),
        ("xlm-roberta-xl", "XLMRobertaXLForQuestionAnswering"),
        ("xlnet", "XLNetForQuestionAnsweringSimple"),
        ("xmod", "XmodForQuestionAnswering"),
        ("yoso", "YosoForQuestionAnswering"),
    ]
)

MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
    [
        # Model for Table Question Answering mapping
        ("tapas", "TapasForQuestionAnswering"),
    ]
)

MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
    [
        ("blip-2", "Blip2ForConditionalGeneration"),
        ("vilt", "ViltForQuestionAnswering"),
    ]
)

MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
    [
        ("layoutlm", "LayoutLMForQuestionAnswering"),
        ("layoutlmv2", "LayoutLMv2ForQuestionAnswering"),
        ("layoutlmv3", "LayoutLMv3ForQuestionAnswering"),
    ]
)

MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "AlbertForTokenClassification"),
        ("bert", "BertForTokenClassification"),
        ("big_bird", "BigBirdForTokenClassification"),
        ("biogpt", "BioGptForTokenClassification"),
        ("bloom", "BloomForTokenClassification"),
        ("camembert", "CamembertForTokenClassification"),
        ("canine", "CanineForTokenClassification"),
        ("convbert", "ConvBertForTokenClassification"),
        ("data2vec-text", "Data2VecTextForTokenClassification"),
        ("deberta", "DebertaForTokenClassification"),
        ("deberta-v2", "DebertaV2ForTokenClassification"),
        ("distilbert", "DistilBertForTokenClassification"),
        ("electra", "ElectraForTokenClassification"),
        ("ernie", "ErnieForTokenClassification"),
        ("ernie_m", "ErnieMForTokenClassification"),
        ("esm", "EsmForTokenClassification"),
        ("falcon", "FalconForTokenClassification"),
        ("flaubert", "FlaubertForTokenClassification"),
        ("fnet", "FNetForTokenClassification"),
        ("funnel", "FunnelForTokenClassification"),
        ("gpt-sw3", "GPT2ForTokenClassification"),
        ("gpt2", "GPT2ForTokenClassification"),
        ("gpt_bigcode", "GPTBigCodeForTokenClassification"),
        ("gpt_neo", "GPTNeoForTokenClassification"),
        ("gpt_neox", "GPTNeoXForTokenClassification"),
        ("ibert", "IBertForTokenClassification"),
        ("layoutlm", "LayoutLMForTokenClassification"),
        ("layoutlmv2", "LayoutLMv2ForTokenClassification"),
        ("layoutlmv3", "LayoutLMv3ForTokenClassification"),
        ("lilt", "LiltForTokenClassification"),
        ("longformer", "LongformerForTokenClassification"),
        ("luke", "LukeForTokenClassification"),
        ("markuplm", "MarkupLMForTokenClassification"),
        ("mega", "MegaForTokenClassification"),
        ("megatron-bert", "MegatronBertForTokenClassification"),
        ("mobilebert", "MobileBertForTokenClassification"),
        ("mpnet", "MPNetForTokenClassification"),
        ("mpt", "MptForTokenClassification"),
        ("mra", "MraForTokenClassification"),
        ("nezha", "NezhaForTokenClassification"),
        ("nystromformer", "NystromformerForTokenClassification"),
        ("qdqbert", "QDQBertForTokenClassification"),
        ("rembert", "RemBertForTokenClassification"),
        ("roberta", "RobertaForTokenClassification"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForTokenClassification"),
        ("roc_bert", "RoCBertForTokenClassification"),
        ("roformer", "RoFormerForTokenClassification"),
        ("squeezebert", "SqueezeBertForTokenClassification"),
        ("xlm", "XLMForTokenClassification"),
        ("xlm-roberta", "XLMRobertaForTokenClassification"),
        ("xlm-roberta-xl", "XLMRobertaXLForTokenClassification"),
        ("xlnet", "XLNetForTokenClassification"),
        ("xmod", "XmodForTokenClassification"),
        ("yoso", "YosoForTokenClassification"),
    ]
)

MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "AlbertForMultipleChoice"),
        ("bert", "BertForMultipleChoice"),
        ("big_bird", "BigBirdForMultipleChoice"),
        ("camembert", "CamembertForMultipleChoice"),
        ("canine", "CanineForMultipleChoice"),
        ("convbert", "ConvBertForMultipleChoice"),
        ("data2vec-text", "Data2VecTextForMultipleChoice"),
        ("deberta-v2", "DebertaV2ForMultipleChoice"),
        ("distilbert", "DistilBertForMultipleChoice"),
        ("electra", "ElectraForMultipleChoice"),
        ("ernie", "ErnieForMultipleChoice"),
        ("ernie_m", "ErnieMForMultipleChoice"),
        ("flaubert", "FlaubertForMultipleChoice"),
        ("fnet", "FNetForMultipleChoice"),
        ("funnel", "FunnelForMultipleChoice"),
        ("ibert", "IBertForMultipleChoice"),
        ("longformer", "LongformerForMultipleChoice"),
        ("luke", "LukeForMultipleChoice"),
        ("mega", "MegaForMultipleChoice"),
        ("megatron-bert", "MegatronBertForMultipleChoice"),
        ("mobilebert", "MobileBertForMultipleChoice"),
        ("mpnet", "MPNetForMultipleChoice"),
        ("mra", "MraForMultipleChoice"),
        ("nezha", "NezhaForMultipleChoice"),
        ("nystromformer", "NystromformerForMultipleChoice"),
        ("qdqbert", "QDQBertForMultipleChoice"),
        ("rembert", "RemBertForMultipleChoice"),
        ("roberta", "RobertaForMultipleChoice"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForMultipleChoice"),
        ("roc_bert", "RoCBertForMultipleChoice"),
        ("roformer", "RoFormerForMultipleChoice"),
        ("squeezebert", "SqueezeBertForMultipleChoice"),
        ("xlm", "XLMForMultipleChoice"),
        ("xlm-roberta", "XLMRobertaForMultipleChoice"),
        ("xlm-roberta-xl", "XLMRobertaXLForMultipleChoice"),
        ("xlnet", "XLNetForMultipleChoice"),
        ("xmod", "XmodForMultipleChoice"),
        ("yoso", "YosoForMultipleChoice"),
    ]
)

MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict(
    [
        ("bert", "BertForNextSentencePrediction"),
        ("ernie", "ErnieForNextSentencePrediction"),
        ("fnet", "FNetForNextSentencePrediction"),
        ("megatron-bert", "MegatronBertForNextSentencePrediction"),
        ("mobilebert", "MobileBertForNextSentencePrediction"),
        ("nezha", "NezhaForNextSentencePrediction"),
        ("qdqbert", "QDQBertForNextSentencePrediction"),
    ]
)

MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Audio Classification mapping
        ("audio-spectrogram-transformer", "ASTForAudioClassification"),
        ("data2vec-audio", "Data2VecAudioForSequenceClassification"),
        ("hubert", "HubertForSequenceClassification"),
        ("sew", "SEWForSequenceClassification"),
        ("sew-d", "SEWDForSequenceClassification"),
        ("unispeech", "UniSpeechForSequenceClassification"),
        ("unispeech-sat", "UniSpeechSatForSequenceClassification"),
        ("wav2vec2", "Wav2Vec2ForSequenceClassification"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerForSequenceClassification"),
        ("wavlm", "WavLMForSequenceClassification"),
        ("whisper", "WhisperForAudioClassification"),
    ]
)

MODEL_FOR_CTC_MAPPING_NAMES = OrderedDict(
    [
        # Model for Connectionist temporal classification (CTC) mapping
        ("data2vec-audio", "Data2VecAudioForCTC"),
        ("hubert", "HubertForCTC"),
        ("mctct", "MCTCTForCTC"),
        ("sew", "SEWForCTC"),
        ("sew-d", "SEWDForCTC"),
        ("unispeech", "UniSpeechForCTC"),
        ("unispeech-sat", "UniSpeechSatForCTC"),
        ("wav2vec2", "Wav2Vec2ForCTC"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerForCTC"),
        ("wavlm", "WavLMForCTC"),
    ]
)

MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Audio Classification mapping
        ("data2vec-audio", "Data2VecAudioForAudioFrameClassification"),
        ("unispeech-sat", "UniSpeechSatForAudioFrameClassification"),
        ("wav2vec2", "Wav2Vec2ForAudioFrameClassification"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerForAudioFrameClassification"),
        ("wavlm", "WavLMForAudioFrameClassification"),
    ]
)

MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES = OrderedDict(
    [
        # Model for Audio Classification mapping
        ("data2vec-audio", "Data2VecAudioForXVector"),
        ("unispeech-sat", "UniSpeechSatForXVector"),
        ("wav2vec2", "Wav2Vec2ForXVector"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerForXVector"),
        ("wavlm", "WavLMForXVector"),
    ]
)

MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES = OrderedDict(
    [
        # Model for Text-To-Spectrogram mapping
        ("speecht5", "SpeechT5ForTextToSpeech"),
    ]
)

MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES = OrderedDict(
    [
        # Model for Text-To-Waveform mapping
        ("bark", "BarkModel"),
        ("musicgen", "MusicgenForConditionalGeneration"),
    ]
)

MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Zero Shot Image Classification mapping
        ("align", "AlignModel"),
        ("altclip", "AltCLIPModel"),
        ("blip", "BlipModel"),
        ("chinese_clip", "ChineseCLIPModel"),
        ("clip", "CLIPModel"),
        ("clipseg", "CLIPSegModel"),
    ]
)

MODEL_FOR_BACKBONE_MAPPING_NAMES = OrderedDict(
    [
        # Backbone mapping
        ("bit", "BitBackbone"),
        ("convnext", "ConvNextBackbone"),
        ("convnextv2", "ConvNextV2Backbone"),
        ("dinat", "DinatBackbone"),
        ("dinov2", "Dinov2Backbone"),
        ("focalnet", "FocalNetBackbone"),
        ("maskformer-swin", "MaskFormerSwinBackbone"),
        ("nat", "NatBackbone"),
        ("resnet", "ResNetBackbone"),
        ("swin", "SwinBackbone"),
        ("timm_backbone", "TimmBackbone"),
        ("vitdet", "VitDetBackbone"),
    ]
)

MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = OrderedDict(
    [
        ("sam", "SamModel"),
    ]
)

MODEL_FOR_TEXT_ENCODING_MAPPING_NAMES = OrderedDict(
    [
        ("albert", "AlbertModel"),
        ("bert", "BertModel"),
        ("big_bird", "BigBirdModel"),
        ("data2vec-text", "Data2VecTextModel"),
        ("deberta", "DebertaModel"),
        ("deberta-v2", "DebertaV2Model"),
        ("distilbert", "DistilBertModel"),
        ("electra", "ElectraModel"),
        ("flaubert", "FlaubertModel"),
        ("ibert", "IBertModel"),
        ("longformer", "LongformerModel"),
        ("mobilebert", "MobileBertModel"),
        ("mt5", "MT5EncoderModel"),
        ("nystromformer", "NystromformerModel"),
        ("reformer", "ReformerModel"),
        ("rembert", "RemBertModel"),
        ("roberta", "RobertaModel"),
        ("roberta-prelayernorm", "RobertaPreLayerNormModel"),
        ("roc_bert", "RoCBertModel"),
        ("roformer", "RoFormerModel"),
        ("squeezebert", "SqueezeBertModel"),
        ("t5", "T5EncoderModel"),
        ("umt5", "UMT5EncoderModel"),
        ("xlm", "XLMModel"),
        ("xlm-roberta", "XLMRobertaModel"),
        ("xlm-roberta-xl", "XLMRobertaXLModel"),
    ]
)

MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_MAPPING_NAMES)
MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_PRETRAINING_MAPPING_NAMES)
MODEL_WITH_LM_HEAD_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_WITH_LM_HEAD_MAPPING_NAMES)
MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES
)
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES
)
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES
)
MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES
)
MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES
)
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_VISION_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES
)
MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES
)
MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MASKED_LM_MAPPING_NAMES)
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES
)
MODEL_FOR_OBJECT_DETECTION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES)
MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES
)
MODEL_FOR_DEPTH_ESTIMATION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES)
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES
)
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_MULTIPLE_CHOICE_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES)
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_CTC_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CTC_MAPPING_NAMES)
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES)
MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_AUDIO_XVECTOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES)

MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES
)

MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES)

MODEL_FOR_BACKBONE_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_BACKBONE_MAPPING_NAMES)

MODEL_FOR_MASK_GENERATION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MASK_GENERATION_MAPPING_NAMES)

MODEL_FOR_TEXT_ENCODING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_TEXT_ENCODING_MAPPING_NAMES)


class AutoModelForMaskGeneration(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_MASK_GENERATION_MAPPING


class AutoModelForTextEncoding(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_TEXT_ENCODING_MAPPING


class AutoModel(_BaseAutoModelClass):
    _model_mapping = MODEL_MAPPING


AutoModel = auto_class_update(AutoModel)


class AutoModelForPreTraining(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_PRETRAINING_MAPPING


AutoModelForPreTraining = auto_class_update(AutoModelForPreTraining, head_doc="pretraining")


# Private on purpose, the public class will add the deprecation warnings.
class _AutoModelWithLMHead(_BaseAutoModelClass):
    _model_mapping = MODEL_WITH_LM_HEAD_MAPPING


_AutoModelWithLMHead = auto_class_update(_AutoModelWithLMHead, head_doc="language modeling")


class AutoModelForCausalLM(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING


AutoModelForCausalLM = auto_class_update(AutoModelForCausalLM, head_doc="causal language modeling")


class AutoModelForMaskedLM(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_MASKED_LM_MAPPING


AutoModelForMaskedLM = auto_class_update(AutoModelForMaskedLM, head_doc="masked language modeling")


class AutoModelForSeq2SeqLM(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


AutoModelForSeq2SeqLM = auto_class_update(
    AutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)


class AutoModelForSequenceClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


AutoModelForSequenceClassification = auto_class_update(
    AutoModelForSequenceClassification, head_doc="sequence classification"
)


class AutoModelForQuestionAnswering(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_QUESTION_ANSWERING_MAPPING


AutoModelForQuestionAnswering = auto_class_update(AutoModelForQuestionAnswering, head_doc="question answering")


class AutoModelForTableQuestionAnswering(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING


AutoModelForTableQuestionAnswering = auto_class_update(
    AutoModelForTableQuestionAnswering,
    head_doc="table question answering",
    checkpoint_for_example="google/tapas-base-finetuned-wtq",
)


class AutoModelForVisualQuestionAnswering(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING


AutoModelForVisualQuestionAnswering = auto_class_update(
    AutoModelForVisualQuestionAnswering,
    head_doc="visual question answering",
    checkpoint_for_example="dandelin/vilt-b32-finetuned-vqa",
)


class AutoModelForDocumentQuestionAnswering(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING


AutoModelForDocumentQuestionAnswering = auto_class_update(
    AutoModelForDocumentQuestionAnswering,
    head_doc="document question answering",
    checkpoint_for_example='impira/layoutlm-document-qa", revision="52e01b3',
)


class AutoModelForTokenClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING


AutoModelForTokenClassification = auto_class_update(AutoModelForTokenClassification, head_doc="token classification")


class AutoModelForMultipleChoice(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_MULTIPLE_CHOICE_MAPPING


AutoModelForMultipleChoice = auto_class_update(AutoModelForMultipleChoice, head_doc="multiple choice")


class AutoModelForNextSentencePrediction(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING


AutoModelForNextSentencePrediction = auto_class_update(
    AutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)


class AutoModelForImageClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


AutoModelForImageClassification = auto_class_update(AutoModelForImageClassification, head_doc="image classification")


class AutoModelForZeroShotImageClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING


AutoModelForZeroShotImageClassification = auto_class_update(
    AutoModelForZeroShotImageClassification, head_doc="zero-shot image classification"
)


class AutoModelForImageSegmentation(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_IMAGE_SEGMENTATION_MAPPING


AutoModelForImageSegmentation = auto_class_update(AutoModelForImageSegmentation, head_doc="image segmentation")


class AutoModelForSemanticSegmentation(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING


AutoModelForSemanticSegmentation = auto_class_update(
    AutoModelForSemanticSegmentation, head_doc="semantic segmentation"
)


class AutoModelForUniversalSegmentation(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING


AutoModelForUniversalSegmentation = auto_class_update(
    AutoModelForUniversalSegmentation, head_doc="universal image segmentation"
)


class AutoModelForInstanceSegmentation(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING


AutoModelForInstanceSegmentation = auto_class_update(
    AutoModelForInstanceSegmentation, head_doc="instance segmentation"
)


class AutoModelForObjectDetection(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING


AutoModelForObjectDetection = auto_class_update(AutoModelForObjectDetection, head_doc="object detection")


class AutoModelForZeroShotObjectDetection(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING


AutoModelForZeroShotObjectDetection = auto_class_update(
    AutoModelForZeroShotObjectDetection, head_doc="zero-shot object detection"
)


class AutoModelForDepthEstimation(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_DEPTH_ESTIMATION_MAPPING


AutoModelForDepthEstimation = auto_class_update(AutoModelForDepthEstimation, head_doc="depth estimation")


class AutoModelForVideoClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


AutoModelForVideoClassification = auto_class_update(AutoModelForVideoClassification, head_doc="video classification")


class AutoModelForVision2Seq(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_VISION_2_SEQ_MAPPING


AutoModelForVision2Seq = auto_class_update(AutoModelForVision2Seq, head_doc="vision-to-text modeling")


class AutoModelForAudioClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING


AutoModelForAudioClassification = auto_class_update(AutoModelForAudioClassification, head_doc="audio classification")


class AutoModelForCTC(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_CTC_MAPPING


AutoModelForCTC = auto_class_update(AutoModelForCTC, head_doc="connectionist temporal classification")


class AutoModelForSpeechSeq2Seq(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING


AutoModelForSpeechSeq2Seq = auto_class_update(
    AutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling"
)


class AutoModelForAudioFrameClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING


AutoModelForAudioFrameClassification = auto_class_update(
    AutoModelForAudioFrameClassification, head_doc="audio frame (token) classification"
)


class AutoModelForAudioXVector(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_AUDIO_XVECTOR_MAPPING


class AutoModelForTextToSpectrogram(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING


class AutoModelForTextToWaveform(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING


class AutoBackbone(_BaseAutoBackboneClass):
    _model_mapping = MODEL_FOR_BACKBONE_MAPPING


AutoModelForAudioXVector = auto_class_update(AutoModelForAudioXVector, head_doc="audio retrieval via x-vector")


class AutoModelForMaskedImageModeling(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING


AutoModelForMaskedImageModeling = auto_class_update(AutoModelForMaskedImageModeling, head_doc="masked image modeling")


class AutoModelWithLMHead(_AutoModelWithLMHead):
    @classmethod
    def from_config(cls, config):
        warnings.warn(
            "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
            "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and "
            "`AutoModelForSeq2SeqLM` for encoder-decoder models.",
            FutureWarning,
        )
        return super().from_config(config)

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        warnings.warn(
            "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
            "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and "
            "`AutoModelForSeq2SeqLM` for encoder-decoder models.",
            FutureWarning,
        )
        return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)

# 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 Model class."""

import warnings
from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoBackboneClass, _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


logger = logging.get_logger(__name__)


MODEL_MAPPING_NAMES = OrderedDict(
    [
        # Base model mapping
        ("albert", "AlbertModel"),
        ("align", "AlignModel"),
        ("altclip", "AltCLIPModel"),
        ("audio-spectrogram-transformer", "ASTModel"),
        ("autoformer", "AutoformerModel"),
        ("bark", "BarkModel"),
        ("bart", "BartModel"),
        ("beit", "BeitModel"),
        ("bert", "BertModel"),
        ("bert-generation", "BertGenerationEncoder"),
        ("big_bird", "BigBirdModel"),
        ("bigbird_pegasus", "BigBirdPegasusModel"),
        ("biogpt", "BioGptModel"),
        ("bit", "BitModel"),
        ("blenderbot", "BlenderbotModel"),
        ("blenderbot-small", "BlenderbotSmallModel"),
        ("blip", "BlipModel"),
        ("blip-2", "Blip2Model"),
        ("bloom", "BloomModel"),
        ("bridgetower", "BridgeTowerModel"),
        ("camembert", "CamembertModel"),
        ("canine", "CanineModel"),
        ("chinese_clip", "ChineseCLIPModel"),
        ("clap", "ClapModel"),
        ("clip", "CLIPModel"),
        ("clipseg", "CLIPSegModel"),
        ("code_llama", "LlamaModel"),
        ("codegen", "CodeGenModel"),
        ("conditional_detr", "ConditionalDetrModel"),
        ("convbert", "ConvBertModel"),
        ("convnext", "ConvNextModel"),
        ("convnextv2", "ConvNextV2Model"),
        ("cpmant", "CpmAntModel"),
        ("ctrl", "CTRLModel"),
        ("cvt", "CvtModel"),
        ("data2vec-audio", "Data2VecAudioModel"),
        ("data2vec-text", "Data2VecTextModel"),
        ("data2vec-vision", "Data2VecVisionModel"),
        ("deberta", "DebertaModel"),
        ("deberta-v2", "DebertaV2Model"),
        ("decision_transformer", "DecisionTransformerModel"),
        ("deformable_detr", "DeformableDetrModel"),
        ("deit", "DeiTModel"),
        ("deta", "DetaModel"),
        ("detr", "DetrModel"),
        ("dinat", "DinatModel"),
        ("dinov2", "Dinov2Model"),
        ("distilbert", "DistilBertModel"),
        ("donut-swin", "DonutSwinModel"),
        ("dpr", "DPRQuestionEncoder"),
        ("dpt", "DPTModel"),
        ("efficientformer", "EfficientFormerModel"),
        ("efficientnet", "EfficientNetModel"),
        ("electra", "ElectraModel"),
        ("encodec", "EncodecModel"),
        ("ernie", "ErnieModel"),
        ("ernie_m", "ErnieMModel"),
        ("esm", "EsmModel"),
        ("falcon", "FalconModel"),
        ("flaubert", "FlaubertModel"),
        ("flava", "FlavaModel"),
        ("fnet", "FNetModel"),
        ("focalnet", "FocalNetModel"),
        ("fsmt", "FSMTModel"),
        ("funnel", ("FunnelModel", "FunnelBaseModel")),
        ("git", "GitModel"),
        ("glpn", "GLPNModel"),
        ("gpt-sw3", "GPT2Model"),
        ("gpt2", "GPT2Model"),
        ("gpt_bigcode", "GPTBigCodeModel"),
        ("gpt_neo", "GPTNeoModel"),
        ("gpt_neox", "GPTNeoXModel"),
        ("gpt_neox_japanese", "GPTNeoXJapaneseModel"),
        ("gptj", "GPTJModel"),
        ("gptsan-japanese", "GPTSanJapaneseForConditionalGeneration"),
        ("graphormer", "GraphormerModel"),
        ("groupvit", "GroupViTModel"),
        ("hubert", "HubertModel"),
        ("ibert", "IBertModel"),
        ("idefics", "IdeficsModel"),
        ("imagegpt", "ImageGPTModel"),
        ("informer", "InformerModel"),
        ("jukebox", "JukeboxModel"),
        ("layoutlm", "LayoutLMModel"),
        ("layoutlmv2", "LayoutLMv2Model"),
        ("layoutlmv3", "LayoutLMv3Model"),
        ("led", "LEDModel"),
        ("levit", "LevitModel"),
        ("lilt", "LiltModel"),
        ("llama", "LlamaModel"),
        ("longformer", "LongformerModel"),
        ("longt5", "LongT5Model"),
        ("luke", "LukeModel"),
        ("lxmert", "LxmertModel"),
        ("m2m_100", "M2M100Model"),
        ("marian", "MarianModel"),
        ("markuplm", "MarkupLMModel"),
        ("mask2former", "Mask2FormerModel"),
        ("maskformer", "MaskFormerModel"),
        ("maskformer-swin", "MaskFormerSwinModel"),
        ("mbart", "MBartModel"),
        ("mctct", "MCTCTModel"),
        ("mega", "MegaModel"),
        ("megatron-bert", "MegatronBertModel"),
        ("mgp-str", "MgpstrForSceneTextRecognition"),
        ("mobilebert", "MobileBertModel"),
        ("mobilenet_v1", "MobileNetV1Model"),
        ("mobilenet_v2", "MobileNetV2Model"),
        ("mobilevit", "MobileViTModel"),
        ("mobilevitv2", "MobileViTV2Model"),
        ("mpnet", "MPNetModel"),
        ("mpt", "MptModel"),
        ("mra", "MraModel"),
        ("mt5", "MT5Model"),
        ("mvp", "MvpModel"),
        ("nat", "NatModel"),
        ("nezha", "NezhaModel"),
        ("nllb-moe", "NllbMoeModel"),
        ("nystromformer", "NystromformerModel"),
        ("oneformer", "OneFormerModel"),
        ("open-llama", "OpenLlamaModel"),
        ("openai-gpt", "OpenAIGPTModel"),
        ("opt", "OPTModel"),
        ("owlvit", "OwlViTModel"),
        ("pegasus", "PegasusModel"),
        ("pegasus_x", "PegasusXModel"),
        ("perceiver", "PerceiverModel"),
        ("plbart", "PLBartModel"),
        ("poolformer", "PoolFormerModel"),
        ("prophetnet", "ProphetNetModel"),
        ("pvt", "PvtModel"),
        ("qdqbert", "QDQBertModel"),
        ("reformer", "ReformerModel"),
        ("regnet", "RegNetModel"),
        ("rembert", "RemBertModel"),
        ("resnet", "ResNetModel"),
        ("retribert", "RetriBertModel"),
        ("roberta", "RobertaModel"),
        ("roberta-prelayernorm", "RobertaPreLayerNormModel"),
        ("roc_bert", "RoCBertModel"),
        ("roformer", "RoFormerModel"),
        ("rwkv", "RwkvModel"),
        ("sam", "SamModel"),
        ("segformer", "SegformerModel"),
        ("sew", "SEWModel"),
        ("sew-d", "SEWDModel"),
        ("speech_to_text", "Speech2TextModel"),
        ("speecht5", "SpeechT5Model"),
        ("splinter", "SplinterModel"),
        ("squeezebert", "SqueezeBertModel"),
        ("swiftformer", "SwiftFormerModel"),
        ("swin", "SwinModel"),
        ("swin2sr", "Swin2SRModel"),
        ("swinv2", "Swinv2Model"),
        ("switch_transformers", "SwitchTransformersModel"),
        ("t5", "T5Model"),
        ("table-transformer", "TableTransformerModel"),
        ("tapas", "TapasModel"),
        ("time_series_transformer", "TimeSeriesTransformerModel"),
        ("timesformer", "TimesformerModel"),
        ("timm_backbone", "TimmBackbone"),
        ("trajectory_transformer", "TrajectoryTransformerModel"),
        ("transfo-xl", "TransfoXLModel"),
        ("tvlt", "TvltModel"),
        ("umt5", "UMT5Model"),
        ("unispeech", "UniSpeechModel"),
        ("unispeech-sat", "UniSpeechSatModel"),
        ("van", "VanModel"),
        ("videomae", "VideoMAEModel"),
        ("vilt", "ViltModel"),
        ("vision-text-dual-encoder", "VisionTextDualEncoderModel"),
        ("visual_bert", "VisualBertModel"),
        ("vit", "ViTModel"),
        ("vit_hybrid", "ViTHybridModel"),
        ("vit_mae", "ViTMAEModel"),
        ("vit_msn", "ViTMSNModel"),
        ("vitdet", "VitDetModel"),
        ("vits", "VitsModel"),
        ("vivit", "VivitModel"),
        ("wav2vec2", "Wav2Vec2Model"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerModel"),
        ("wavlm", "WavLMModel"),
        ("whisper", "WhisperModel"),
        ("xclip", "XCLIPModel"),
        ("xglm", "XGLMModel"),
        ("xlm", "XLMModel"),
        ("xlm-prophetnet", "XLMProphetNetModel"),
        ("xlm-roberta", "XLMRobertaModel"),
        ("xlm-roberta-xl", "XLMRobertaXLModel"),
        ("xlnet", "XLNetModel"),
        ("xmod", "XmodModel"),
        ("yolos", "YolosModel"),
        ("yoso", "YosoModel"),
    ]
)

MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "AlbertForPreTraining"),
        ("bart", "BartForConditionalGeneration"),
        ("bert", "BertForPreTraining"),
        ("big_bird", "BigBirdForPreTraining"),
        ("bloom", "BloomForCausalLM"),
        ("camembert", "CamembertForMaskedLM"),
        ("ctrl", "CTRLLMHeadModel"),
        ("data2vec-text", "Data2VecTextForMaskedLM"),
        ("deberta", "DebertaForMaskedLM"),
        ("deberta-v2", "DebertaV2ForMaskedLM"),
        ("distilbert", "DistilBertForMaskedLM"),
        ("electra", "ElectraForPreTraining"),
        ("ernie", "ErnieForPreTraining"),
        ("flaubert", "FlaubertWithLMHeadModel"),
        ("flava", "FlavaForPreTraining"),
        ("fnet", "FNetForPreTraining"),
        ("fsmt", "FSMTForConditionalGeneration"),
        ("funnel", "FunnelForPreTraining"),
        ("gpt-sw3", "GPT2LMHeadModel"),
        ("gpt2", "GPT2LMHeadModel"),
        ("gpt_bigcode", "GPTBigCodeForCausalLM"),
        ("gptsan-japanese", "GPTSanJapaneseForConditionalGeneration"),
        ("ibert", "IBertForMaskedLM"),
        ("idefics", "IdeficsForVisionText2Text"),
        ("layoutlm", "LayoutLMForMaskedLM"),
        ("longformer", "LongformerForMaskedLM"),
        ("luke", "LukeForMaskedLM"),
        ("lxmert", "LxmertForPreTraining"),
        ("mega", "MegaForMaskedLM"),
        ("megatron-bert", "MegatronBertForPreTraining"),
        ("mobilebert", "MobileBertForPreTraining"),
        ("mpnet", "MPNetForMaskedLM"),
        ("mpt", "MptForCausalLM"),
        ("mra", "MraForMaskedLM"),
        ("mvp", "MvpForConditionalGeneration"),
        ("nezha", "NezhaForPreTraining"),
        ("nllb-moe", "NllbMoeForConditionalGeneration"),
        ("openai-gpt", "OpenAIGPTLMHeadModel"),
        ("retribert", "RetriBertModel"),
        ("roberta", "RobertaForMaskedLM"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForMaskedLM"),
        ("roc_bert", "RoCBertForPreTraining"),
        ("rwkv", "RwkvForCausalLM"),
        ("splinter", "SplinterForPreTraining"),
        ("squeezebert", "SqueezeBertForMaskedLM"),
        ("switch_transformers", "SwitchTransformersForConditionalGeneration"),
        ("t5", "T5ForConditionalGeneration"),
        ("tapas", "TapasForMaskedLM"),
        ("transfo-xl", "TransfoXLLMHeadModel"),
        ("tvlt", "TvltForPreTraining"),
        ("unispeech", "UniSpeechForPreTraining"),
        ("unispeech-sat", "UniSpeechSatForPreTraining"),
        ("videomae", "VideoMAEForPreTraining"),
        ("visual_bert", "VisualBertForPreTraining"),
        ("vit_mae", "ViTMAEForPreTraining"),
        ("wav2vec2", "Wav2Vec2ForPreTraining"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerForPreTraining"),
        ("xlm", "XLMWithLMHeadModel"),
        ("xlm-roberta", "XLMRobertaForMaskedLM"),
        ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"),
        ("xlnet", "XLNetLMHeadModel"),
        ("xmod", "XmodForMaskedLM"),
    ]
)

MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict(
    [
        # Model with LM heads mapping
        ("albert", "AlbertForMaskedLM"),
        ("bart", "BartForConditionalGeneration"),
        ("bert", "BertForMaskedLM"),
        ("big_bird", "BigBirdForMaskedLM"),
        ("bigbird_pegasus", "BigBirdPegasusForConditionalGeneration"),
        ("blenderbot-small", "BlenderbotSmallForConditionalGeneration"),
        ("bloom", "BloomForCausalLM"),
        ("camembert", "CamembertForMaskedLM"),
        ("codegen", "CodeGenForCausalLM"),
        ("convbert", "ConvBertForMaskedLM"),
        ("cpmant", "CpmAntForCausalLM"),
        ("ctrl", "CTRLLMHeadModel"),
        ("data2vec-text", "Data2VecTextForMaskedLM"),
        ("deberta", "DebertaForMaskedLM"),
        ("deberta-v2", "DebertaV2ForMaskedLM"),
        ("distilbert", "DistilBertForMaskedLM"),
        ("electra", "ElectraForMaskedLM"),
        ("encoder-decoder", "EncoderDecoderModel"),
        ("ernie", "ErnieForMaskedLM"),
        ("esm", "EsmForMaskedLM"),
        ("flaubert", "FlaubertWithLMHeadModel"),
        ("fnet", "FNetForMaskedLM"),
        ("fsmt", "FSMTForConditionalGeneration"),
        ("funnel", "FunnelForMaskedLM"),
        ("git", "GitForCausalLM"),
        ("gpt-sw3", "GPT2LMHeadModel"),
        ("gpt2", "GPT2LMHeadModel"),
        ("gpt_bigcode", "GPTBigCodeForCausalLM"),
        ("gpt_neo", "GPTNeoForCausalLM"),
        ("gpt_neox", "GPTNeoXForCausalLM"),
        ("gpt_neox_japanese", "GPTNeoXJapaneseForCausalLM"),
        ("gptj", "GPTJForCausalLM"),
        ("gptsan-japanese", "GPTSanJapaneseForConditionalGeneration"),
        ("ibert", "IBertForMaskedLM"),
        ("layoutlm", "LayoutLMForMaskedLM"),
        ("led", "LEDForConditionalGeneration"),
        ("longformer", "LongformerForMaskedLM"),
        ("longt5", "LongT5ForConditionalGeneration"),
        ("luke", "LukeForMaskedLM"),
        ("m2m_100", "M2M100ForConditionalGeneration"),
        ("marian", "MarianMTModel"),
        ("mega", "MegaForMaskedLM"),
        ("megatron-bert", "MegatronBertForCausalLM"),
        ("mobilebert", "MobileBertForMaskedLM"),
        ("mpnet", "MPNetForMaskedLM"),
        ("mpt", "MptForCausalLM"),
        ("mra", "MraForMaskedLM"),
        ("mvp", "MvpForConditionalGeneration"),
        ("nezha", "NezhaForMaskedLM"),
        ("nllb-moe", "NllbMoeForConditionalGeneration"),
        ("nystromformer", "NystromformerForMaskedLM"),
        ("openai-gpt", "OpenAIGPTLMHeadModel"),
        ("pegasus_x", "PegasusXForConditionalGeneration"),
        ("plbart", "PLBartForConditionalGeneration"),
        ("pop2piano", "Pop2PianoForConditionalGeneration"),
        ("qdqbert", "QDQBertForMaskedLM"),
        ("reformer", "ReformerModelWithLMHead"),
        ("rembert", "RemBertForMaskedLM"),
        ("roberta", "RobertaForMaskedLM"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForMaskedLM"),
        ("roc_bert", "RoCBertForMaskedLM"),
        ("roformer", "RoFormerForMaskedLM"),
        ("rwkv", "RwkvForCausalLM"),
        ("speech_to_text", "Speech2TextForConditionalGeneration"),
        ("squeezebert", "SqueezeBertForMaskedLM"),
        ("switch_transformers", "SwitchTransformersForConditionalGeneration"),
        ("t5", "T5ForConditionalGeneration"),
        ("tapas", "TapasForMaskedLM"),
        ("transfo-xl", "TransfoXLLMHeadModel"),
        ("wav2vec2", "Wav2Vec2ForMaskedLM"),
        ("whisper", "WhisperForConditionalGeneration"),
        ("xlm", "XLMWithLMHeadModel"),
        ("xlm-roberta", "XLMRobertaForMaskedLM"),
        ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"),
        ("xlnet", "XLNetLMHeadModel"),
        ("xmod", "XmodForMaskedLM"),
        ("yoso", "YosoForMaskedLM"),
    ]
)

MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "BartForCausalLM"),
        ("bert", "BertLMHeadModel"),
        ("bert-generation", "BertGenerationDecoder"),
        ("big_bird", "BigBirdForCausalLM"),
        ("bigbird_pegasus", "BigBirdPegasusForCausalLM"),
        ("biogpt", "BioGptForCausalLM"),
        ("blenderbot", "BlenderbotForCausalLM"),
        ("blenderbot-small", "BlenderbotSmallForCausalLM"),
        ("bloom", "BloomForCausalLM"),
        ("camembert", "CamembertForCausalLM"),
        ("code_llama", "LlamaForCausalLM"),
        ("codegen", "CodeGenForCausalLM"),
        ("cpmant", "CpmAntForCausalLM"),
        ("ctrl", "CTRLLMHeadModel"),
        ("data2vec-text", "Data2VecTextForCausalLM"),
        ("electra", "ElectraForCausalLM"),
        ("ernie", "ErnieForCausalLM"),
        ("falcon", "FalconForCausalLM"),
        ("git", "GitForCausalLM"),
        ("gpt-sw3", "GPT2LMHeadModel"),
        ("gpt2", "GPT2LMHeadModel"),
        ("gpt_bigcode", "GPTBigCodeForCausalLM"),
        ("gpt_neo", "GPTNeoForCausalLM"),
        ("gpt_neox", "GPTNeoXForCausalLM"),
        ("gpt_neox_japanese", "GPTNeoXJapaneseForCausalLM"),
        ("gptj", "GPTJForCausalLM"),
        ("llama", "LlamaForCausalLM"),
        ("marian", "MarianForCausalLM"),
        ("mbart", "MBartForCausalLM"),
        ("mega", "MegaForCausalLM"),
        ("megatron-bert", "MegatronBertForCausalLM"),
        ("mpt", "MptForCausalLM"),
        ("musicgen", "MusicgenForCausalLM"),
        ("mvp", "MvpForCausalLM"),
        ("open-llama", "OpenLlamaForCausalLM"),
        ("openai-gpt", "OpenAIGPTLMHeadModel"),
        ("opt", "OPTForCausalLM"),
        ("pegasus", "PegasusForCausalLM"),
        ("plbart", "PLBartForCausalLM"),
        ("prophetnet", "ProphetNetForCausalLM"),
        ("qdqbert", "QDQBertLMHeadModel"),
        ("reformer", "ReformerModelWithLMHead"),
        ("rembert", "RemBertForCausalLM"),
        ("roberta", "RobertaForCausalLM"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForCausalLM"),
        ("roc_bert", "RoCBertForCausalLM"),
        ("roformer", "RoFormerForCausalLM"),
        ("rwkv", "RwkvForCausalLM"),
        ("speech_to_text_2", "Speech2Text2ForCausalLM"),
        ("transfo-xl", "TransfoXLLMHeadModel"),
        ("trocr", "TrOCRForCausalLM"),
        ("xglm", "XGLMForCausalLM"),
        ("xlm", "XLMWithLMHeadModel"),
        ("xlm-prophetnet", "XLMProphetNetForCausalLM"),
        ("xlm-roberta", "XLMRobertaForCausalLM"),
        ("xlm-roberta-xl", "XLMRobertaXLForCausalLM"),
        ("xlnet", "XLNetLMHeadModel"),
        ("xmod", "XmodForCausalLM"),
    ]
)

MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES = OrderedDict(
    [
        ("deit", "DeiTForMaskedImageModeling"),
        ("focalnet", "FocalNetForMaskedImageModeling"),
        ("swin", "SwinForMaskedImageModeling"),
        ("swinv2", "Swinv2ForMaskedImageModeling"),
        ("vit", "ViTForMaskedImageModeling"),
    ]
)


MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES = OrderedDict(
    # Model for Causal Image Modeling mapping
    [
        ("imagegpt", "ImageGPTForCausalImageModeling"),
    ]
)

MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Image Classification mapping
        ("beit", "BeitForImageClassification"),
        ("bit", "BitForImageClassification"),
        ("convnext", "ConvNextForImageClassification"),
        ("convnextv2", "ConvNextV2ForImageClassification"),
        ("cvt", "CvtForImageClassification"),
        ("data2vec-vision", "Data2VecVisionForImageClassification"),
        ("deit", ("DeiTForImageClassification", "DeiTForImageClassificationWithTeacher")),
        ("dinat", "DinatForImageClassification"),
        ("dinov2", "Dinov2ForImageClassification"),
        (
            "efficientformer",
            (
                "EfficientFormerForImageClassification",
                "EfficientFormerForImageClassificationWithTeacher",
            ),
        ),
        ("efficientnet", "EfficientNetForImageClassification"),
        ("focalnet", "FocalNetForImageClassification"),
        ("imagegpt", "ImageGPTForImageClassification"),
        ("levit", ("LevitForImageClassification", "LevitForImageClassificationWithTeacher")),
        ("mobilenet_v1", "MobileNetV1ForImageClassification"),
        ("mobilenet_v2", "MobileNetV2ForImageClassification"),
        ("mobilevit", "MobileViTForImageClassification"),
        ("mobilevitv2", "MobileViTV2ForImageClassification"),
        ("nat", "NatForImageClassification"),
        (
            "perceiver",
            (
                "PerceiverForImageClassificationLearned",
                "PerceiverForImageClassificationFourier",
                "PerceiverForImageClassificationConvProcessing",
            ),
        ),
        ("poolformer", "PoolFormerForImageClassification"),
        ("pvt", "PvtForImageClassification"),
        ("regnet", "RegNetForImageClassification"),
        ("resnet", "ResNetForImageClassification"),
        ("segformer", "SegformerForImageClassification"),
        ("swiftformer", "SwiftFormerForImageClassification"),
        ("swin", "SwinForImageClassification"),
        ("swinv2", "Swinv2ForImageClassification"),
        ("van", "VanForImageClassification"),
        ("vit", "ViTForImageClassification"),
        ("vit_hybrid", "ViTHybridForImageClassification"),
        ("vit_msn", "ViTMSNForImageClassification"),
    ]
)

MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES = OrderedDict(
    [
        # Do not add new models here, this class will be deprecated in the future.
        # Model for Image Segmentation mapping
        ("detr", "DetrForSegmentation"),
    ]
)

MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Semantic Segmentation mapping
        ("beit", "BeitForSemanticSegmentation"),
        ("data2vec-vision", "Data2VecVisionForSemanticSegmentation"),
        ("dpt", "DPTForSemanticSegmentation"),
        ("mobilenet_v2", "MobileNetV2ForSemanticSegmentation"),
        ("mobilevit", "MobileViTForSemanticSegmentation"),
        ("mobilevitv2", "MobileViTV2ForSemanticSegmentation"),
        ("segformer", "SegformerForSemanticSegmentation"),
        ("upernet", "UperNetForSemanticSegmentation"),
    ]
)

MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Instance Segmentation mapping
        # MaskFormerForInstanceSegmentation can be removed from this mapping in v5
        ("maskformer", "MaskFormerForInstanceSegmentation"),
    ]
)

MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Universal Segmentation mapping
        ("detr", "DetrForSegmentation"),
        ("mask2former", "Mask2FormerForUniversalSegmentation"),
        ("maskformer", "MaskFormerForInstanceSegmentation"),
        ("oneformer", "OneFormerForUniversalSegmentation"),
    ]
)

MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        ("timesformer", "TimesformerForVideoClassification"),
        ("videomae", "VideoMAEForVideoClassification"),
        ("vivit", "VivitForVideoClassification"),
    ]
)

MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict(
    [
        ("blip", "BlipForConditionalGeneration"),
        ("blip-2", "Blip2ForConditionalGeneration"),
        ("git", "GitForCausalLM"),
        ("instructblip", "InstructBlipForConditionalGeneration"),
        ("pix2struct", "Pix2StructForConditionalGeneration"),
        ("vision-encoder-decoder", "VisionEncoderDecoderModel"),
    ]
)

MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "AlbertForMaskedLM"),
        ("bart", "BartForConditionalGeneration"),
        ("bert", "BertForMaskedLM"),
        ("big_bird", "BigBirdForMaskedLM"),
        ("camembert", "CamembertForMaskedLM"),
        ("convbert", "ConvBertForMaskedLM"),
        ("data2vec-text", "Data2VecTextForMaskedLM"),
        ("deberta", "DebertaForMaskedLM"),
        ("deberta-v2", "DebertaV2ForMaskedLM"),
        ("distilbert", "DistilBertForMaskedLM"),
        ("electra", "ElectraForMaskedLM"),
        ("ernie", "ErnieForMaskedLM"),
        ("esm", "EsmForMaskedLM"),
        ("flaubert", "FlaubertWithLMHeadModel"),
        ("fnet", "FNetForMaskedLM"),
        ("funnel", "FunnelForMaskedLM"),
        ("ibert", "IBertForMaskedLM"),
        ("layoutlm", "LayoutLMForMaskedLM"),
        ("longformer", "LongformerForMaskedLM"),
        ("luke", "LukeForMaskedLM"),
        ("mbart", "MBartForConditionalGeneration"),
        ("mega", "MegaForMaskedLM"),
        ("megatron-bert", "MegatronBertForMaskedLM"),
        ("mobilebert", "MobileBertForMaskedLM"),
        ("mpnet", "MPNetForMaskedLM"),
        ("mra", "MraForMaskedLM"),
        ("mvp", "MvpForConditionalGeneration"),
        ("nezha", "NezhaForMaskedLM"),
        ("nystromformer", "NystromformerForMaskedLM"),
        ("perceiver", "PerceiverForMaskedLM"),
        ("qdqbert", "QDQBertForMaskedLM"),
        ("reformer", "ReformerForMaskedLM"),
        ("rembert", "RemBertForMaskedLM"),
        ("roberta", "RobertaForMaskedLM"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForMaskedLM"),
        ("roc_bert", "RoCBertForMaskedLM"),
        ("roformer", "RoFormerForMaskedLM"),
        ("squeezebert", "SqueezeBertForMaskedLM"),
        ("tapas", "TapasForMaskedLM"),
        ("wav2vec2", "Wav2Vec2ForMaskedLM"),
        ("xlm", "XLMWithLMHeadModel"),
        ("xlm-roberta", "XLMRobertaForMaskedLM"),
        ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"),
        ("xmod", "XmodForMaskedLM"),
        ("yoso", "YosoForMaskedLM"),
    ]
)

MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Object Detection mapping
        ("conditional_detr", "ConditionalDetrForObjectDetection"),
        ("deformable_detr", "DeformableDetrForObjectDetection"),
        ("deta", "DetaForObjectDetection"),
        ("detr", "DetrForObjectDetection"),
        ("table-transformer", "TableTransformerForObjectDetection"),
        ("yolos", "YolosForObjectDetection"),
    ]
)

MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Zero Shot Object Detection mapping
        ("owlvit", "OwlViTForObjectDetection")
    ]
)

MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for depth estimation mapping
        ("dpt", "DPTForDepthEstimation"),
        ("glpn", "GLPNForDepthEstimation"),
    ]
)
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "BartForConditionalGeneration"),
        ("bigbird_pegasus", "BigBirdPegasusForConditionalGeneration"),
        ("blenderbot", "BlenderbotForConditionalGeneration"),
        ("blenderbot-small", "BlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "EncoderDecoderModel"),
        ("fsmt", "FSMTForConditionalGeneration"),
        ("gptsan-japanese", "GPTSanJapaneseForConditionalGeneration"),
        ("led", "LEDForConditionalGeneration"),
        ("longt5", "LongT5ForConditionalGeneration"),
        ("m2m_100", "M2M100ForConditionalGeneration"),
        ("marian", "MarianMTModel"),
        ("mbart", "MBartForConditionalGeneration"),
        ("mt5", "MT5ForConditionalGeneration"),
        ("mvp", "MvpForConditionalGeneration"),
        ("nllb-moe", "NllbMoeForConditionalGeneration"),
        ("pegasus", "PegasusForConditionalGeneration"),
        ("pegasus_x", "PegasusXForConditionalGeneration"),
        ("plbart", "PLBartForConditionalGeneration"),
        ("prophetnet", "ProphetNetForConditionalGeneration"),
        ("switch_transformers", "SwitchTransformersForConditionalGeneration"),
        ("t5", "T5ForConditionalGeneration"),
        ("umt5", "UMT5ForConditionalGeneration"),
        ("xlm-prophetnet", "XLMProphetNetForConditionalGeneration"),
    ]
)

MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict(
    [
        ("pop2piano", "Pop2PianoForConditionalGeneration"),
        ("speech-encoder-decoder", "SpeechEncoderDecoderModel"),
        ("speech_to_text", "Speech2TextForConditionalGeneration"),
        ("speecht5", "SpeechT5ForSpeechToText"),
        ("whisper", "WhisperForConditionalGeneration"),
    ]
)

MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "AlbertForSequenceClassification"),
        ("bart", "BartForSequenceClassification"),
        ("bert", "BertForSequenceClassification"),
        ("big_bird", "BigBirdForSequenceClassification"),
        ("bigbird_pegasus", "BigBirdPegasusForSequenceClassification"),
        ("biogpt", "BioGptForSequenceClassification"),
        ("bloom", "BloomForSequenceClassification"),
        ("camembert", "CamembertForSequenceClassification"),
        ("canine", "CanineForSequenceClassification"),
        ("code_llama", "LlamaForSequenceClassification"),
        ("convbert", "ConvBertForSequenceClassification"),
        ("ctrl", "CTRLForSequenceClassification"),
        ("data2vec-text", "Data2VecTextForSequenceClassification"),
        ("deberta", "DebertaForSequenceClassification"),
        ("deberta-v2", "DebertaV2ForSequenceClassification"),
        ("distilbert", "DistilBertForSequenceClassification"),
        ("electra", "ElectraForSequenceClassification"),
        ("ernie", "ErnieForSequenceClassification"),
        ("ernie_m", "ErnieMForSequenceClassification"),
        ("esm", "EsmForSequenceClassification"),
        ("falcon", "FalconForSequenceClassification"),
        ("flaubert", "FlaubertForSequenceClassification"),
        ("fnet", "FNetForSequenceClassification"),
        ("funnel", "FunnelForSequenceClassification"),
        ("gpt-sw3", "GPT2ForSequenceClassification"),
        ("gpt2", "GPT2ForSequenceClassification"),
        ("gpt_bigcode", "GPTBigCodeForSequenceClassification"),
        ("gpt_neo", "GPTNeoForSequenceClassification"),
        ("gpt_neox", "GPTNeoXForSequenceClassification"),
        ("gptj", "GPTJForSequenceClassification"),
        ("ibert", "IBertForSequenceClassification"),
        ("layoutlm", "LayoutLMForSequenceClassification"),
        ("layoutlmv2", "LayoutLMv2ForSequenceClassification"),
        ("layoutlmv3", "LayoutLMv3ForSequenceClassification"),
        ("led", "LEDForSequenceClassification"),
        ("lilt", "LiltForSequenceClassification"),
        ("llama", "LlamaForSequenceClassification"),
        ("longformer", "LongformerForSequenceClassification"),
        ("luke", "LukeForSequenceClassification"),
        ("markuplm", "MarkupLMForSequenceClassification"),
        ("mbart", "MBartForSequenceClassification"),
        ("mega", "MegaForSequenceClassification"),
        ("megatron-bert", "MegatronBertForSequenceClassification"),
        ("mobilebert", "MobileBertForSequenceClassification"),
        ("mpnet", "MPNetForSequenceClassification"),
        ("mpt", "MptForSequenceClassification"),
        ("mra", "MraForSequenceClassification"),
        ("mt5", "MT5ForSequenceClassification"),
        ("mvp", "MvpForSequenceClassification"),
        ("nezha", "NezhaForSequenceClassification"),
        ("nystromformer", "NystromformerForSequenceClassification"),
        ("open-llama", "OpenLlamaForSequenceClassification"),
        ("openai-gpt", "OpenAIGPTForSequenceClassification"),
        ("opt", "OPTForSequenceClassification"),
        ("perceiver", "PerceiverForSequenceClassification"),
        ("plbart", "PLBartForSequenceClassification"),
        ("qdqbert", "QDQBertForSequenceClassification"),
        ("reformer", "ReformerForSequenceClassification"),
        ("rembert", "RemBertForSequenceClassification"),
        ("roberta", "RobertaForSequenceClassification"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForSequenceClassification"),
        ("roc_bert", "RoCBertForSequenceClassification"),
        ("roformer", "RoFormerForSequenceClassification"),
        ("squeezebert", "SqueezeBertForSequenceClassification"),
        ("t5", "T5ForSequenceClassification"),
        ("tapas", "TapasForSequenceClassification"),
        ("transfo-xl", "TransfoXLForSequenceClassification"),
        ("umt5", "UMT5ForSequenceClassification"),
        ("xlm", "XLMForSequenceClassification"),
        ("xlm-roberta", "XLMRobertaForSequenceClassification"),
        ("xlm-roberta-xl", "XLMRobertaXLForSequenceClassification"),
        ("xlnet", "XLNetForSequenceClassification"),
        ("xmod", "XmodForSequenceClassification"),
        ("yoso", "YosoForSequenceClassification"),
    ]
)

MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "AlbertForQuestionAnswering"),
        ("bart", "BartForQuestionAnswering"),
        ("bert", "BertForQuestionAnswering"),
        ("big_bird", "BigBirdForQuestionAnswering"),
        ("bigbird_pegasus", "BigBirdPegasusForQuestionAnswering"),
        ("bloom", "BloomForQuestionAnswering"),
        ("camembert", "CamembertForQuestionAnswering"),
        ("canine", "CanineForQuestionAnswering"),
        ("convbert", "ConvBertForQuestionAnswering"),
        ("data2vec-text", "Data2VecTextForQuestionAnswering"),
        ("deberta", "DebertaForQuestionAnswering"),
        ("deberta-v2", "DebertaV2ForQuestionAnswering"),
        ("distilbert", "DistilBertForQuestionAnswering"),
        ("electra", "ElectraForQuestionAnswering"),
        ("ernie", "ErnieForQuestionAnswering"),
        ("ernie_m", "ErnieMForQuestionAnswering"),
        ("falcon", "FalconForQuestionAnswering"),
        ("flaubert", "FlaubertForQuestionAnsweringSimple"),
        ("fnet", "FNetForQuestionAnswering"),
        ("funnel", "FunnelForQuestionAnswering"),
        ("gpt2", "GPT2ForQuestionAnswering"),
        ("gpt_neo", "GPTNeoForQuestionAnswering"),
        ("gpt_neox", "GPTNeoXForQuestionAnswering"),
        ("gptj", "GPTJForQuestionAnswering"),
        ("ibert", "IBertForQuestionAnswering"),
        ("layoutlmv2", "LayoutLMv2ForQuestionAnswering"),
        ("layoutlmv3", "LayoutLMv3ForQuestionAnswering"),
        ("led", "LEDForQuestionAnswering"),
        ("lilt", "LiltForQuestionAnswering"),
        ("longformer", "LongformerForQuestionAnswering"),
        ("luke", "LukeForQuestionAnswering"),
        ("lxmert", "LxmertForQuestionAnswering"),
        ("markuplm", "MarkupLMForQuestionAnswering"),
        ("mbart", "MBartForQuestionAnswering"),
        ("mega", "MegaForQuestionAnswering"),
        ("megatron-bert", "MegatronBertForQuestionAnswering"),
        ("mobilebert", "MobileBertForQuestionAnswering"),
        ("mpnet", "MPNetForQuestionAnswering"),
        ("mpt", "MptForQuestionAnswering"),
        ("mra", "MraForQuestionAnswering"),
        ("mt5", "MT5ForQuestionAnswering"),
        ("mvp", "MvpForQuestionAnswering"),
        ("nezha", "NezhaForQuestionAnswering"),
        ("nystromformer", "NystromformerForQuestionAnswering"),
        ("opt", "OPTForQuestionAnswering"),
        ("qdqbert", "QDQBertForQuestionAnswering"),
        ("reformer", "ReformerForQuestionAnswering"),
        ("rembert", "RemBertForQuestionAnswering"),
        ("roberta", "RobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForQuestionAnswering"),
        ("roc_bert", "RoCBertForQuestionAnswering"),
        ("roformer", "RoFormerForQuestionAnswering"),
        ("splinter", "SplinterForQuestionAnswering"),
        ("squeezebert", "SqueezeBertForQuestionAnswering"),
        ("t5", "T5ForQuestionAnswering"),
        ("umt5", "UMT5ForQuestionAnswering"),
        ("xlm", "XLMForQuestionAnsweringSimple"),
        ("xlm-roberta", "XLMRobertaForQuestionAnswering"),
        ("xlm-roberta-xl", "XLMRobertaXLForQuestionAnswering"),
        ("xlnet", "XLNetForQuestionAnsweringSimple"),
        ("xmod", "XmodForQuestionAnswering"),
        ("yoso", "YosoForQuestionAnswering"),
    ]
)

MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
    [
        # Model for Table Question Answering mapping
        ("tapas", "TapasForQuestionAnswering"),
    ]
)

MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
    [
        ("blip-2", "Blip2ForConditionalGeneration"),
        ("vilt", "ViltForQuestionAnswering"),
    ]
)

MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
    [
        ("layoutlm", "LayoutLMForQuestionAnswering"),
        ("layoutlmv2", "LayoutLMv2ForQuestionAnswering"),
        ("layoutlmv3", "LayoutLMv3ForQuestionAnswering"),
    ]
)

MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "AlbertForTokenClassification"),
        ("bert", "BertForTokenClassification"),
        ("big_bird", "BigBirdForTokenClassification"),
        ("biogpt", "BioGptForTokenClassification"),
        ("bloom", "BloomForTokenClassification"),
        ("camembert", "CamembertForTokenClassification"),
        ("canine", "CanineForTokenClassification"),
        ("convbert", "ConvBertForTokenClassification"),
        ("data2vec-text", "Data2VecTextForTokenClassification"),
        ("deberta", "DebertaForTokenClassification"),
        ("deberta-v2", "DebertaV2ForTokenClassification"),
        ("distilbert", "DistilBertForTokenClassification"),
        ("electra", "ElectraForTokenClassification"),
        ("ernie", "ErnieForTokenClassification"),
        ("ernie_m", "ErnieMForTokenClassification"),
        ("esm", "EsmForTokenClassification"),
        ("falcon", "FalconForTokenClassification"),
        ("flaubert", "FlaubertForTokenClassification"),
        ("fnet", "FNetForTokenClassification"),
        ("funnel", "FunnelForTokenClassification"),
        ("gpt-sw3", "GPT2ForTokenClassification"),
        ("gpt2", "GPT2ForTokenClassification"),
        ("gpt_bigcode", "GPTBigCodeForTokenClassification"),
        ("gpt_neo", "GPTNeoForTokenClassification"),
        ("gpt_neox", "GPTNeoXForTokenClassification"),
        ("ibert", "IBertForTokenClassification"),
        ("layoutlm", "LayoutLMForTokenClassification"),
        ("layoutlmv2", "LayoutLMv2ForTokenClassification"),
        ("layoutlmv3", "LayoutLMv3ForTokenClassification"),
        ("lilt", "LiltForTokenClassification"),
        ("longformer", "LongformerForTokenClassification"),
        ("luke", "LukeForTokenClassification"),
        ("markuplm", "MarkupLMForTokenClassification"),
        ("mega", "MegaForTokenClassification"),
        ("megatron-bert", "MegatronBertForTokenClassification"),
        ("mobilebert", "MobileBertForTokenClassification"),
        ("mpnet", "MPNetForTokenClassification"),
        ("mpt", "MptForTokenClassification"),
        ("mra", "MraForTokenClassification"),
        ("nezha", "NezhaForTokenClassification"),
        ("nystromformer", "NystromformerForTokenClassification"),
        ("qdqbert", "QDQBertForTokenClassification"),
        ("rembert", "RemBertForTokenClassification"),
        ("roberta", "RobertaForTokenClassification"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForTokenClassification"),
        ("roc_bert", "RoCBertForTokenClassification"),
        ("roformer", "RoFormerForTokenClassification"),
        ("squeezebert", "SqueezeBertForTokenClassification"),
        ("xlm", "XLMForTokenClassification"),
        ("xlm-roberta", "XLMRobertaForTokenClassification"),
        ("xlm-roberta-xl", "XLMRobertaXLForTokenClassification"),
        ("xlnet", "XLNetForTokenClassification"),
        ("xmod", "XmodForTokenClassification"),
        ("yoso", "YosoForTokenClassification"),
    ]
)

MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "AlbertForMultipleChoice"),
        ("bert", "BertForMultipleChoice"),
        ("big_bird", "BigBirdForMultipleChoice"),
        ("camembert", "CamembertForMultipleChoice"),
        ("canine", "CanineForMultipleChoice"),
        ("convbert", "ConvBertForMultipleChoice"),
        ("data2vec-text", "Data2VecTextForMultipleChoice"),
        ("deberta-v2", "DebertaV2ForMultipleChoice"),
        ("distilbert", "DistilBertForMultipleChoice"),
        ("electra", "ElectraForMultipleChoice"),
        ("ernie", "ErnieForMultipleChoice"),
        ("ernie_m", "ErnieMForMultipleChoice"),
        ("flaubert", "FlaubertForMultipleChoice"),
        ("fnet", "FNetForMultipleChoice"),
        ("funnel", "FunnelForMultipleChoice"),
        ("ibert", "IBertForMultipleChoice"),
        ("longformer", "LongformerForMultipleChoice"),
        ("luke", "LukeForMultipleChoice"),
        ("mega", "MegaForMultipleChoice"),
        ("megatron-bert", "MegatronBertForMultipleChoice"),
        ("mobilebert", "MobileBertForMultipleChoice"),
        ("mpnet", "MPNetForMultipleChoice"),
        ("mra", "MraForMultipleChoice"),
        ("nezha", "NezhaForMultipleChoice"),
        ("nystromformer", "NystromformerForMultipleChoice"),
        ("qdqbert", "QDQBertForMultipleChoice"),
        ("rembert", "RemBertForMultipleChoice"),
        ("roberta", "RobertaForMultipleChoice"),
        ("roberta-prelayernorm", "RobertaPreLayerNormForMultipleChoice"),
        ("roc_bert", "RoCBertForMultipleChoice"),
        ("roformer", "RoFormerForMultipleChoice"),
        ("squeezebert", "SqueezeBertForMultipleChoice"),
        ("xlm", "XLMForMultipleChoice"),
        ("xlm-roberta", "XLMRobertaForMultipleChoice"),
        ("xlm-roberta-xl", "XLMRobertaXLForMultipleChoice"),
        ("xlnet", "XLNetForMultipleChoice"),
        ("xmod", "XmodForMultipleChoice"),
        ("yoso", "YosoForMultipleChoice"),
    ]
)

MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict(
    [
        ("bert", "BertForNextSentencePrediction"),
        ("ernie", "ErnieForNextSentencePrediction"),
        ("fnet", "FNetForNextSentencePrediction"),
        ("megatron-bert", "MegatronBertForNextSentencePrediction"),
        ("mobilebert", "MobileBertForNextSentencePrediction"),
        ("nezha", "NezhaForNextSentencePrediction"),
        ("qdqbert", "QDQBertForNextSentencePrediction"),
    ]
)

MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Audio Classification mapping
        ("audio-spectrogram-transformer", "ASTForAudioClassification"),
        ("data2vec-audio", "Data2VecAudioForSequenceClassification"),
        ("hubert", "HubertForSequenceClassification"),
        ("sew", "SEWForSequenceClassification"),
        ("sew-d", "SEWDForSequenceClassification"),
        ("unispeech", "UniSpeechForSequenceClassification"),
        ("unispeech-sat", "UniSpeechSatForSequenceClassification"),
        ("wav2vec2", "Wav2Vec2ForSequenceClassification"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerForSequenceClassification"),
        ("wavlm", "WavLMForSequenceClassification"),
        ("whisper", "WhisperForAudioClassification"),
    ]
)

MODEL_FOR_CTC_MAPPING_NAMES = OrderedDict(
    [
        # Model for Connectionist temporal classification (CTC) mapping
        ("data2vec-audio", "Data2VecAudioForCTC"),
        ("hubert", "HubertForCTC"),
        ("mctct", "MCTCTForCTC"),
        ("sew", "SEWForCTC"),
        ("sew-d", "SEWDForCTC"),
        ("unispeech", "UniSpeechForCTC"),
        ("unispeech-sat", "UniSpeechSatForCTC"),
        ("wav2vec2", "Wav2Vec2ForCTC"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerForCTC"),
        ("wavlm", "WavLMForCTC"),
    ]
)

MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Audio Classification mapping
        ("data2vec-audio", "Data2VecAudioForAudioFrameClassification"),
        ("unispeech-sat", "UniSpeechSatForAudioFrameClassification"),
        ("wav2vec2", "Wav2Vec2ForAudioFrameClassification"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerForAudioFrameClassification"),
        ("wavlm", "WavLMForAudioFrameClassification"),
    ]
)

MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES = OrderedDict(
    [
        # Model for Audio Classification mapping
        ("data2vec-audio", "Data2VecAudioForXVector"),
        ("unispeech-sat", "UniSpeechSatForXVector"),
        ("wav2vec2", "Wav2Vec2ForXVector"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerForXVector"),
        ("wavlm", "WavLMForXVector"),
    ]
)

MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES = OrderedDict(
    [
        # Model for Text-To-Spectrogram mapping
        ("speecht5", "SpeechT5ForTextToSpeech"),
    ]
)

MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES = OrderedDict(
    [
        # Model for Text-To-Waveform mapping
        ("bark", "BarkModel"),
        ("musicgen", "MusicgenForConditionalGeneration"),
    ]
)

MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
    [
        # Model for Zero Shot Image Classification mapping
        ("align", "AlignModel"),
        ("altclip", "AltCLIPModel"),
        ("blip", "BlipModel"),
        ("chinese_clip", "ChineseCLIPModel"),
        ("clip", "CLIPModel"),
        ("clipseg", "CLIPSegModel"),
    ]
)

MODEL_FOR_BACKBONE_MAPPING_NAMES = OrderedDict(
    [
        # Backbone mapping
        ("bit", "BitBackbone"),
        ("convnext", "ConvNextBackbone"),
        ("convnextv2", "ConvNextV2Backbone"),
        ("dinat", "DinatBackbone"),
        ("dinov2", "Dinov2Backbone"),
        ("focalnet", "FocalNetBackbone"),
        ("maskformer-swin", "MaskFormerSwinBackbone"),
        ("nat", "NatBackbone"),
        ("resnet", "ResNetBackbone"),
        ("swin", "SwinBackbone"),
        ("timm_backbone", "TimmBackbone"),
        ("vitdet", "VitDetBackbone"),
    ]
)

MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = OrderedDict(
    [
        ("sam", "SamModel"),
    ]
)

MODEL_FOR_TEXT_ENCODING_MAPPING_NAMES = OrderedDict(
    [
        ("albert", "AlbertModel"),
        ("bert", "BertModel"),
        ("big_bird", "BigBirdModel"),
        ("data2vec-text", "Data2VecTextModel"),
        ("deberta", "DebertaModel"),
        ("deberta-v2", "DebertaV2Model"),
        ("distilbert", "DistilBertModel"),
        ("electra", "ElectraModel"),
        ("flaubert", "FlaubertModel"),
        ("ibert", "IBertModel"),
        ("longformer", "LongformerModel"),
        ("mobilebert", "MobileBertModel"),
        ("mt5", "MT5EncoderModel"),
        ("nystromformer", "NystromformerModel"),
        ("reformer", "ReformerModel"),
        ("rembert", "RemBertModel"),
        ("roberta", "RobertaModel"),
        ("roberta-prelayernorm", "RobertaPreLayerNormModel"),
        ("roc_bert", "RoCBertModel"),
        ("roformer", "RoFormerModel"),
        ("squeezebert", "SqueezeBertModel"),
        ("t5", "T5EncoderModel"),
        ("umt5", "UMT5EncoderModel"),
        ("xlm", "XLMModel"),
        ("xlm-roberta", "XLMRobertaModel"),
        ("xlm-roberta-xl", "XLMRobertaXLModel"),
    ]
)

MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_MAPPING_NAMES)
MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_PRETRAINING_MAPPING_NAMES)
MODEL_WITH_LM_HEAD_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_WITH_LM_HEAD_MAPPING_NAMES)
MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES
)
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES
)
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES
)
MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES
)
MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES
)
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_VISION_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES
)
MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES
)
MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MASKED_LM_MAPPING_NAMES)
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES
)
MODEL_FOR_OBJECT_DETECTION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES)
MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES
)
MODEL_FOR_DEPTH_ESTIMATION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES)
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES
)
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_MULTIPLE_CHOICE_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES)
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_CTC_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CTC_MAPPING_NAMES)
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES)
MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES
)
MODEL_FOR_AUDIO_XVECTOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES)

MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING = _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES
)

MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES)

MODEL_FOR_BACKBONE_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_BACKBONE_MAPPING_NAMES)

MODEL_FOR_MASK_GENERATION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MASK_GENERATION_MAPPING_NAMES)

MODEL_FOR_TEXT_ENCODING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_TEXT_ENCODING_MAPPING_NAMES)


class AutoModelForMaskGeneration(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_MASK_GENERATION_MAPPING


class AutoModelForTextEncoding(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_TEXT_ENCODING_MAPPING


class AutoModel(_BaseAutoModelClass):
    _model_mapping = MODEL_MAPPING


AutoModel = auto_class_update(AutoModel)


class AutoModelForPreTraining(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_PRETRAINING_MAPPING


AutoModelForPreTraining = auto_class_update(AutoModelForPreTraining, head_doc="pretraining")


# Private on purpose, the public class will add the deprecation warnings.
class _AutoModelWithLMHead(_BaseAutoModelClass):
    _model_mapping = MODEL_WITH_LM_HEAD_MAPPING


_AutoModelWithLMHead = auto_class_update(_AutoModelWithLMHead, head_doc="language modeling")


class AutoModelForCausalLM(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING


AutoModelForCausalLM = auto_class_update(AutoModelForCausalLM, head_doc="causal language modeling")


class AutoModelForMaskedLM(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_MASKED_LM_MAPPING


AutoModelForMaskedLM = auto_class_update(AutoModelForMaskedLM, head_doc="masked language modeling")


class AutoModelForSeq2SeqLM(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


AutoModelForSeq2SeqLM = auto_class_update(
    AutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)


class AutoModelForSequenceClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


AutoModelForSequenceClassification = auto_class_update(
    AutoModelForSequenceClassification, head_doc="sequence classification"
)


class AutoModelForQuestionAnswering(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_QUESTION_ANSWERING_MAPPING


AutoModelForQuestionAnswering = auto_class_update(AutoModelForQuestionAnswering, head_doc="question answering")


class AutoModelForTableQuestionAnswering(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING


AutoModelForTableQuestionAnswering = auto_class_update(
    AutoModelForTableQuestionAnswering,
    head_doc="table question answering",
    checkpoint_for_example="google/tapas-base-finetuned-wtq",
)


class AutoModelForVisualQuestionAnswering(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING


AutoModelForVisualQuestionAnswering = auto_class_update(
    AutoModelForVisualQuestionAnswering,
    head_doc="visual question answering",
    checkpoint_for_example="dandelin/vilt-b32-finetuned-vqa",
)


class AutoModelForDocumentQuestionAnswering(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING


AutoModelForDocumentQuestionAnswering = auto_class_update(
    AutoModelForDocumentQuestionAnswering,
    head_doc="document question answering",
    checkpoint_for_example='impira/layoutlm-document-qa", revision="52e01b3',
)


class AutoModelForTokenClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING


AutoModelForTokenClassification = auto_class_update(AutoModelForTokenClassification, head_doc="token classification")


class AutoModelForMultipleChoice(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_MULTIPLE_CHOICE_MAPPING


AutoModelForMultipleChoice = auto_class_update(AutoModelForMultipleChoice, head_doc="multiple choice")


class AutoModelForNextSentencePrediction(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING


AutoModelForNextSentencePrediction = auto_class_update(
    AutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)


class AutoModelForImageClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


AutoModelForImageClassification = auto_class_update(AutoModelForImageClassification, head_doc="image classification")


class AutoModelForZeroShotImageClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING


AutoModelForZeroShotImageClassification = auto_class_update(
    AutoModelForZeroShotImageClassification, head_doc="zero-shot image classification"
)


class AutoModelForImageSegmentation(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_IMAGE_SEGMENTATION_MAPPING


AutoModelForImageSegmentation = auto_class_update(AutoModelForImageSegmentation, head_doc="image segmentation")


class AutoModelForSemanticSegmentation(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING


AutoModelForSemanticSegmentation = auto_class_update(
    AutoModelForSemanticSegmentation, head_doc="semantic segmentation"
)


class AutoModelForUniversalSegmentation(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING


AutoModelForUniversalSegmentation = auto_class_update(
    AutoModelForUniversalSegmentation, head_doc="universal image segmentation"
)


class AutoModelForInstanceSegmentation(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING


AutoModelForInstanceSegmentation = auto_class_update(
    AutoModelForInstanceSegmentation, head_doc="instance segmentation"
)


class AutoModelForObjectDetection(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING


AutoModelForObjectDetection = auto_class_update(AutoModelForObjectDetection, head_doc="object detection")


class AutoModelForZeroShotObjectDetection(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING


AutoModelForZeroShotObjectDetection = auto_class_update(
    AutoModelForZeroShotObjectDetection, head_doc="zero-shot object detection"
)


class AutoModelForDepthEstimation(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_DEPTH_ESTIMATION_MAPPING


AutoModelForDepthEstimation = auto_class_update(AutoModelForDepthEstimation, head_doc="depth estimation")


class AutoModelForVideoClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


AutoModelForVideoClassification = auto_class_update(AutoModelForVideoClassification, head_doc="video classification")


class AutoModelForVision2Seq(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_VISION_2_SEQ_MAPPING


AutoModelForVision2Seq = auto_class_update(AutoModelForVision2Seq, head_doc="vision-to-text modeling")


class AutoModelForAudioClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING


AutoModelForAudioClassification = auto_class_update(AutoModelForAudioClassification, head_doc="audio classification")


class AutoModelForCTC(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_CTC_MAPPING


AutoModelForCTC = auto_class_update(AutoModelForCTC, head_doc="connectionist temporal classification")


class AutoModelForSpeechSeq2Seq(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING


AutoModelForSpeechSeq2Seq = auto_class_update(
    AutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling"
)


class AutoModelForAudioFrameClassification(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING


AutoModelForAudioFrameClassification = auto_class_update(
    AutoModelForAudioFrameClassification, head_doc="audio frame (token) classification"
)


class AutoModelForAudioXVector(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_AUDIO_XVECTOR_MAPPING


class AutoModelForTextToSpectrogram(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING


class AutoModelForTextToWaveform(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING


class AutoBackbone(_BaseAutoBackboneClass):
    _model_mapping = MODEL_FOR_BACKBONE_MAPPING


AutoModelForAudioXVector = auto_class_update(AutoModelForAudioXVector, head_doc="audio retrieval via x-vector")


class AutoModelForMaskedImageModeling(_BaseAutoModelClass):
    _model_mapping = MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING


AutoModelForMaskedImageModeling = auto_class_update(AutoModelForMaskedImageModeling, head_doc="masked image modeling")


class AutoModelWithLMHead(_AutoModelWithLMHead):
    @classmethod
    def from_config(cls, config):
        warnings.warn(
            "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
            "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and "
            "`AutoModelForSeq2SeqLM` for encoder-decoder models.",
            FutureWarning,
        )
        return super().from_config(config)

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        warnings.warn(
            "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
            "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and "
            "`AutoModelForSeq2SeqLM` for encoder-decoder models.",
            FutureWarning,
        )
        return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
