# 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 Config class."""

import importlib
import os
import re
import warnings
from collections import OrderedDict
from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...utils import CONFIG_NAME, logging


logger = logging.get_logger(__name__)


CONFIG_MAPPING_NAMES = OrderedDict(
    [
        # Add configs here
        ("albert", "AlbertConfig"),
        ("align", "AlignConfig"),
        ("altclip", "AltCLIPConfig"),
        ("audio-spectrogram-transformer", "ASTConfig"),
        ("autoformer", "AutoformerConfig"),
        ("bark", "BarkConfig"),
        ("bart", "BartConfig"),
        ("beit", "BeitConfig"),
        ("bert", "BertConfig"),
        ("bert-generation", "BertGenerationConfig"),
        ("big_bird", "BigBirdConfig"),
        ("bigbird_pegasus", "BigBirdPegasusConfig"),
        ("biogpt", "BioGptConfig"),
        ("bit", "BitConfig"),
        ("blenderbot", "BlenderbotConfig"),
        ("blenderbot-small", "BlenderbotSmallConfig"),
        ("blip", "BlipConfig"),
        ("blip-2", "Blip2Config"),
        ("bloom", "BloomConfig"),
        ("bridgetower", "BridgeTowerConfig"),
        ("bros", "BrosConfig"),
        ("camembert", "CamembertConfig"),
        ("canine", "CanineConfig"),
        ("chinese_clip", "ChineseCLIPConfig"),
        ("chinese_clip_vision_model", "ChineseCLIPVisionConfig"),
        ("clap", "ClapConfig"),
        ("clip", "CLIPConfig"),
        ("clip_vision_model", "CLIPVisionConfig"),
        ("clipseg", "CLIPSegConfig"),
        ("clvp", "ClvpConfig"),
        ("code_llama", "LlamaConfig"),
        ("codegen", "CodeGenConfig"),
        ("cohere", "CohereConfig"),
        ("conditional_detr", "ConditionalDetrConfig"),
        ("convbert", "ConvBertConfig"),
        ("convnext", "ConvNextConfig"),
        ("convnextv2", "ConvNextV2Config"),
        ("cpmant", "CpmAntConfig"),
        ("ctrl", "CTRLConfig"),
        ("cvt", "CvtConfig"),
        ("data2vec-audio", "Data2VecAudioConfig"),
        ("data2vec-text", "Data2VecTextConfig"),
        ("data2vec-vision", "Data2VecVisionConfig"),
        ("dbrx", "DbrxConfig"),
        ("deberta", "DebertaConfig"),
        ("deberta-v2", "DebertaV2Config"),
        ("decision_transformer", "DecisionTransformerConfig"),
        ("deformable_detr", "DeformableDetrConfig"),
        ("deit", "DeiTConfig"),
        ("depth_anything", "DepthAnythingConfig"),
        ("deta", "DetaConfig"),
        ("detr", "DetrConfig"),
        ("dinat", "DinatConfig"),
        ("dinov2", "Dinov2Config"),
        ("distilbert", "DistilBertConfig"),
        ("donut-swin", "DonutSwinConfig"),
        ("dpr", "DPRConfig"),
        ("dpt", "DPTConfig"),
        ("efficientformer", "EfficientFormerConfig"),
        ("efficientnet", "EfficientNetConfig"),
        ("electra", "ElectraConfig"),
        ("encodec", "EncodecConfig"),
        ("encoder-decoder", "EncoderDecoderConfig"),
        ("ernie", "ErnieConfig"),
        ("ernie_m", "ErnieMConfig"),
        ("esm", "EsmConfig"),
        ("falcon", "FalconConfig"),
        ("fastspeech2_conformer", "FastSpeech2ConformerConfig"),
        ("flaubert", "FlaubertConfig"),
        ("flava", "FlavaConfig"),
        ("fnet", "FNetConfig"),
        ("focalnet", "FocalNetConfig"),
        ("fsmt", "FSMTConfig"),
        ("funnel", "FunnelConfig"),
        ("fuyu", "FuyuConfig"),
        ("gemma", "GemmaConfig"),
        ("git", "GitConfig"),
        ("glpn", "GLPNConfig"),
        ("gpt-sw3", "GPT2Config"),
        ("gpt2", "GPT2Config"),
        ("gpt_bigcode", "GPTBigCodeConfig"),
        ("gpt_neo", "GPTNeoConfig"),
        ("gpt_neox", "GPTNeoXConfig"),
        ("gpt_neox_japanese", "GPTNeoXJapaneseConfig"),
        ("gptj", "GPTJConfig"),
        ("gptsan-japanese", "GPTSanJapaneseConfig"),
        ("graphormer", "GraphormerConfig"),
        ("grounding-dino", "GroundingDinoConfig"),
        ("groupvit", "GroupViTConfig"),
        ("hubert", "HubertConfig"),
        ("ibert", "IBertConfig"),
        ("idefics", "IdeficsConfig"),
        ("idefics2", "Idefics2Config"),
        ("imagegpt", "ImageGPTConfig"),
        ("informer", "InformerConfig"),
        ("instructblip", "InstructBlipConfig"),
        ("jamba", "JambaConfig"),
        ("jetmoe", "JetMoeConfig"),
        ("jukebox", "JukeboxConfig"),
        ("kosmos-2", "Kosmos2Config"),
        ("layoutlm", "LayoutLMConfig"),
        ("layoutlmv2", "LayoutLMv2Config"),
        ("layoutlmv3", "LayoutLMv3Config"),
        ("led", "LEDConfig"),
        ("levit", "LevitConfig"),
        ("lilt", "LiltConfig"),
        ("llama", "LlamaConfig"),
        ("llava", "LlavaConfig"),
        ("llava_next", "LlavaNextConfig"),
        ("longformer", "LongformerConfig"),
        ("longt5", "LongT5Config"),
        ("luke", "LukeConfig"),
        ("lxmert", "LxmertConfig"),
        ("m2m_100", "M2M100Config"),
        ("mamba", "MambaConfig"),
        ("marian", "MarianConfig"),
        ("markuplm", "MarkupLMConfig"),
        ("mask2former", "Mask2FormerConfig"),
        ("maskformer", "MaskFormerConfig"),
        ("maskformer-swin", "MaskFormerSwinConfig"),
        ("mbart", "MBartConfig"),
        ("mctct", "MCTCTConfig"),
        ("mega", "MegaConfig"),
        ("megatron-bert", "MegatronBertConfig"),
        ("mgp-str", "MgpstrConfig"),
        ("mistral", "MistralConfig"),
        ("mixtral", "MixtralConfig"),
        ("mobilebert", "MobileBertConfig"),
        ("mobilenet_v1", "MobileNetV1Config"),
        ("mobilenet_v2", "MobileNetV2Config"),
        ("mobilevit", "MobileViTConfig"),
        ("mobilevitv2", "MobileViTV2Config"),
        ("mpnet", "MPNetConfig"),
        ("mpt", "MptConfig"),
        ("mra", "MraConfig"),
        ("mt5", "MT5Config"),
        ("musicgen", "MusicgenConfig"),
        ("musicgen_melody", "MusicgenMelodyConfig"),
        ("mvp", "MvpConfig"),
        ("nat", "NatConfig"),
        ("nezha", "NezhaConfig"),
        ("nllb-moe", "NllbMoeConfig"),
        ("nougat", "VisionEncoderDecoderConfig"),
        ("nystromformer", "NystromformerConfig"),
        ("olmo", "OlmoConfig"),
        ("oneformer", "OneFormerConfig"),
        ("open-llama", "OpenLlamaConfig"),
        ("openai-gpt", "OpenAIGPTConfig"),
        ("opt", "OPTConfig"),
        ("owlv2", "Owlv2Config"),
        ("owlvit", "OwlViTConfig"),
        ("paligemma", "PaliGemmaConfig"),
        ("patchtsmixer", "PatchTSMixerConfig"),
        ("patchtst", "PatchTSTConfig"),
        ("pegasus", "PegasusConfig"),
        ("pegasus_x", "PegasusXConfig"),
        ("perceiver", "PerceiverConfig"),
        ("persimmon", "PersimmonConfig"),
        ("phi", "PhiConfig"),
        ("phi3", "Phi3Config"),
        ("pix2struct", "Pix2StructConfig"),
        ("plbart", "PLBartConfig"),
        ("poolformer", "PoolFormerConfig"),
        ("pop2piano", "Pop2PianoConfig"),
        ("prophetnet", "ProphetNetConfig"),
        ("pvt", "PvtConfig"),
        ("pvt_v2", "PvtV2Config"),
        ("qdqbert", "QDQBertConfig"),
        ("qwen2", "Qwen2Config"),
        ("qwen2_moe", "Qwen2MoeConfig"),
        ("rag", "RagConfig"),
        ("realm", "RealmConfig"),
        ("recurrent_gemma", "RecurrentGemmaConfig"),
        ("reformer", "ReformerConfig"),
        ("regnet", "RegNetConfig"),
        ("rembert", "RemBertConfig"),
        ("resnet", "ResNetConfig"),
        ("retribert", "RetriBertConfig"),
        ("roberta", "RobertaConfig"),
        ("roberta-prelayernorm", "RobertaPreLayerNormConfig"),
        ("roc_bert", "RoCBertConfig"),
        ("roformer", "RoFormerConfig"),
        ("rwkv", "RwkvConfig"),
        ("sam", "SamConfig"),
        ("seamless_m4t", "SeamlessM4TConfig"),
        ("seamless_m4t_v2", "SeamlessM4Tv2Config"),
        ("segformer", "SegformerConfig"),
        ("seggpt", "SegGptConfig"),
        ("sew", "SEWConfig"),
        ("sew-d", "SEWDConfig"),
        ("siglip", "SiglipConfig"),
        ("siglip_vision_model", "SiglipVisionConfig"),
        ("speech-encoder-decoder", "SpeechEncoderDecoderConfig"),
        ("speech_to_text", "Speech2TextConfig"),
        ("speech_to_text_2", "Speech2Text2Config"),
        ("speecht5", "SpeechT5Config"),
        ("splinter", "SplinterConfig"),
        ("squeezebert", "SqueezeBertConfig"),
        ("stablelm", "StableLmConfig"),
        ("starcoder2", "Starcoder2Config"),
        ("superpoint", "SuperPointConfig"),
        ("swiftformer", "SwiftFormerConfig"),
        ("swin", "SwinConfig"),
        ("swin2sr", "Swin2SRConfig"),
        ("swinv2", "Swinv2Config"),
        ("switch_transformers", "SwitchTransformersConfig"),
        ("t5", "T5Config"),
        ("table-transformer", "TableTransformerConfig"),
        ("tapas", "TapasConfig"),
        ("time_series_transformer", "TimeSeriesTransformerConfig"),
        ("timesformer", "TimesformerConfig"),
        ("timm_backbone", "TimmBackboneConfig"),
        ("trajectory_transformer", "TrajectoryTransformerConfig"),
        ("transfo-xl", "TransfoXLConfig"),
        ("trocr", "TrOCRConfig"),
        ("tvlt", "TvltConfig"),
        ("tvp", "TvpConfig"),
        ("udop", "UdopConfig"),
        ("umt5", "UMT5Config"),
        ("unispeech", "UniSpeechConfig"),
        ("unispeech-sat", "UniSpeechSatConfig"),
        ("univnet", "UnivNetConfig"),
        ("upernet", "UperNetConfig"),
        ("van", "VanConfig"),
        ("video_llava", "VideoLlavaConfig"),
        ("videomae", "VideoMAEConfig"),
        ("vilt", "ViltConfig"),
        ("vipllava", "VipLlavaConfig"),
        ("vision-encoder-decoder", "VisionEncoderDecoderConfig"),
        ("vision-text-dual-encoder", "VisionTextDualEncoderConfig"),
        ("visual_bert", "VisualBertConfig"),
        ("vit", "ViTConfig"),
        ("vit_hybrid", "ViTHybridConfig"),
        ("vit_mae", "ViTMAEConfig"),
        ("vit_msn", "ViTMSNConfig"),
        ("vitdet", "VitDetConfig"),
        ("vitmatte", "VitMatteConfig"),
        ("vits", "VitsConfig"),
        ("vivit", "VivitConfig"),
        ("wav2vec2", "Wav2Vec2Config"),
        ("wav2vec2-bert", "Wav2Vec2BertConfig"),
        ("wav2vec2-conformer", "Wav2Vec2ConformerConfig"),
        ("wavlm", "WavLMConfig"),
        ("whisper", "WhisperConfig"),
        ("xclip", "XCLIPConfig"),
        ("xglm", "XGLMConfig"),
        ("xlm", "XLMConfig"),
        ("xlm-prophetnet", "XLMProphetNetConfig"),
        ("xlm-roberta", "XLMRobertaConfig"),
        ("xlm-roberta-xl", "XLMRobertaXLConfig"),
        ("xlnet", "XLNetConfig"),
        ("xmod", "XmodConfig"),
        ("yolos", "YolosConfig"),
        ("yoso", "YosoConfig"),
    ]
)


MODEL_NAMES_MAPPING = OrderedDict(
    [
        # Add full (and cased) model names here
        ("albert", "ALBERT"),
        ("align", "ALIGN"),
        ("altclip", "AltCLIP"),
        ("audio-spectrogram-transformer", "Audio Spectrogram Transformer"),
        ("autoformer", "Autoformer"),
        ("bark", "Bark"),
        ("bart", "BART"),
        ("barthez", "BARThez"),
        ("bartpho", "BARTpho"),
        ("beit", "BEiT"),
        ("bert", "BERT"),
        ("bert-generation", "Bert Generation"),
        ("bert-japanese", "BertJapanese"),
        ("bertweet", "BERTweet"),
        ("big_bird", "BigBird"),
        ("bigbird_pegasus", "BigBird-Pegasus"),
        ("biogpt", "BioGpt"),
        ("bit", "BiT"),
        ("blenderbot", "Blenderbot"),
        ("blenderbot-small", "BlenderbotSmall"),
        ("blip", "BLIP"),
        ("blip-2", "BLIP-2"),
        ("bloom", "BLOOM"),
        ("bort", "BORT"),
        ("bridgetower", "BridgeTower"),
        ("bros", "BROS"),
        ("byt5", "ByT5"),
        ("camembert", "CamemBERT"),
        ("canine", "CANINE"),
        ("chinese_clip", "Chinese-CLIP"),
        ("chinese_clip_vision_model", "ChineseCLIPVisionModel"),
        ("clap", "CLAP"),
        ("clip", "CLIP"),
        ("clip_vision_model", "CLIPVisionModel"),
        ("clipseg", "CLIPSeg"),
        ("clvp", "CLVP"),
        ("code_llama", "CodeLlama"),
        ("codegen", "CodeGen"),
        ("cohere", "Cohere"),
        ("conditional_detr", "Conditional DETR"),
        ("convbert", "ConvBERT"),
        ("convnext", "ConvNeXT"),
        ("convnextv2", "ConvNeXTV2"),
        ("cpm", "CPM"),
        ("cpmant", "CPM-Ant"),
        ("ctrl", "CTRL"),
        ("cvt", "CvT"),
        ("data2vec-audio", "Data2VecAudio"),
        ("data2vec-text", "Data2VecText"),
        ("data2vec-vision", "Data2VecVision"),
        ("dbrx", "DBRX"),
        ("deberta", "DeBERTa"),
        ("deberta-v2", "DeBERTa-v2"),
        ("decision_transformer", "Decision Transformer"),
        ("deformable_detr", "Deformable DETR"),
        ("deit", "DeiT"),
        ("deplot", "DePlot"),
        ("depth_anything", "Depth Anything"),
        ("deta", "DETA"),
        ("detr", "DETR"),
        ("dialogpt", "DialoGPT"),
        ("dinat", "DiNAT"),
        ("dinov2", "DINOv2"),
        ("distilbert", "DistilBERT"),
        ("dit", "DiT"),
        ("donut-swin", "DonutSwin"),
        ("dpr", "DPR"),
        ("dpt", "DPT"),
        ("efficientformer", "EfficientFormer"),
        ("efficientnet", "EfficientNet"),
        ("electra", "ELECTRA"),
        ("encodec", "EnCodec"),
        ("encoder-decoder", "Encoder decoder"),
        ("ernie", "ERNIE"),
        ("ernie_m", "ErnieM"),
        ("esm", "ESM"),
        ("falcon", "Falcon"),
        ("fastspeech2_conformer", "FastSpeech2Conformer"),
        ("flan-t5", "FLAN-T5"),
        ("flan-ul2", "FLAN-UL2"),
        ("flaubert", "FlauBERT"),
        ("flava", "FLAVA"),
        ("fnet", "FNet"),
        ("focalnet", "FocalNet"),
        ("fsmt", "FairSeq Machine-Translation"),
        ("funnel", "Funnel Transformer"),
        ("fuyu", "Fuyu"),
        ("gemma", "Gemma"),
        ("git", "GIT"),
        ("glpn", "GLPN"),
        ("gpt-sw3", "GPT-Sw3"),
        ("gpt2", "OpenAI GPT-2"),
        ("gpt_bigcode", "GPTBigCode"),
        ("gpt_neo", "GPT Neo"),
        ("gpt_neox", "GPT NeoX"),
        ("gpt_neox_japanese", "GPT NeoX Japanese"),
        ("gptj", "GPT-J"),
        ("gptsan-japanese", "GPTSAN-japanese"),
        ("graphormer", "Graphormer"),
        ("grounding-dino", "Grounding DINO"),
        ("groupvit", "GroupViT"),
        ("herbert", "HerBERT"),
        ("hubert", "Hubert"),
        ("ibert", "I-BERT"),
        ("idefics", "IDEFICS"),
        ("idefics2", "Idefics2"),
        ("imagegpt", "ImageGPT"),
        ("informer", "Informer"),
        ("instructblip", "InstructBLIP"),
        ("jamba", "Jamba"),
        ("jetmoe", "JetMoe"),
        ("jukebox", "Jukebox"),
        ("kosmos-2", "KOSMOS-2"),
        ("layoutlm", "LayoutLM"),
        ("layoutlmv2", "LayoutLMv2"),
        ("layoutlmv3", "LayoutLMv3"),
        ("layoutxlm", "LayoutXLM"),
        ("led", "LED"),
        ("levit", "LeViT"),
        ("lilt", "LiLT"),
        ("llama", "LLaMA"),
        ("llama2", "Llama2"),
        ("llama3", "Llama3"),
        ("llava", "LLaVa"),
        ("llava_next", "LLaVA-NeXT"),
        ("longformer", "Longformer"),
        ("longt5", "LongT5"),
        ("luke", "LUKE"),
        ("lxmert", "LXMERT"),
        ("m2m_100", "M2M100"),
        ("madlad-400", "MADLAD-400"),
        ("mamba", "Mamba"),
        ("marian", "Marian"),
        ("markuplm", "MarkupLM"),
        ("mask2former", "Mask2Former"),
        ("maskformer", "MaskFormer"),
        ("maskformer-swin", "MaskFormerSwin"),
        ("matcha", "MatCha"),
        ("mbart", "mBART"),
        ("mbart50", "mBART-50"),
        ("mctct", "M-CTC-T"),
        ("mega", "MEGA"),
        ("megatron-bert", "Megatron-BERT"),
        ("megatron_gpt2", "Megatron-GPT2"),
        ("mgp-str", "MGP-STR"),
        ("mistral", "Mistral"),
        ("mixtral", "Mixtral"),
        ("mluke", "mLUKE"),
        ("mms", "MMS"),
        ("mobilebert", "MobileBERT"),
        ("mobilenet_v1", "MobileNetV1"),
        ("mobilenet_v2", "MobileNetV2"),
        ("mobilevit", "MobileViT"),
        ("mobilevitv2", "MobileViTV2"),
        ("mpnet", "MPNet"),
        ("mpt", "MPT"),
        ("mra", "MRA"),
        ("mt5", "MT5"),
        ("musicgen", "MusicGen"),
        ("musicgen_melody", "MusicGen Melody"),
        ("mvp", "MVP"),
        ("nat", "NAT"),
        ("nezha", "Nezha"),
        ("nllb", "NLLB"),
        ("nllb-moe", "NLLB-MOE"),
        ("nougat", "Nougat"),
        ("nystromformer", "Nyströmformer"),
        ("olmo", "OLMo"),
        ("oneformer", "OneFormer"),
        ("open-llama", "OpenLlama"),
        ("openai-gpt", "OpenAI GPT"),
        ("opt", "OPT"),
        ("owlv2", "OWLv2"),
        ("owlvit", "OWL-ViT"),
        ("paligemma", "PaliGemma"),
        ("patchtsmixer", "PatchTSMixer"),
        ("patchtst", "PatchTST"),
        ("pegasus", "Pegasus"),
        ("pegasus_x", "PEGASUS-X"),
        ("perceiver", "Perceiver"),
        ("persimmon", "Persimmon"),
        ("phi", "Phi"),
        ("phi3", "Phi3"),
        ("phobert", "PhoBERT"),
        ("pix2struct", "Pix2Struct"),
        ("plbart", "PLBart"),
        ("poolformer", "PoolFormer"),
        ("pop2piano", "Pop2Piano"),
        ("prophetnet", "ProphetNet"),
        ("pvt", "PVT"),
        ("pvt_v2", "PVTv2"),
        ("qdqbert", "QDQBert"),
        ("qwen2", "Qwen2"),
        ("qwen2_moe", "Qwen2MoE"),
        ("rag", "RAG"),
        ("realm", "REALM"),
        ("recurrent_gemma", "RecurrentGemma"),
        ("reformer", "Reformer"),
        ("regnet", "RegNet"),
        ("rembert", "RemBERT"),
        ("resnet", "ResNet"),
        ("retribert", "RetriBERT"),
        ("roberta", "RoBERTa"),
        ("roberta-prelayernorm", "RoBERTa-PreLayerNorm"),
        ("roc_bert", "RoCBert"),
        ("roformer", "RoFormer"),
        ("rwkv", "RWKV"),
        ("sam", "SAM"),
        ("seamless_m4t", "SeamlessM4T"),
        ("seamless_m4t_v2", "SeamlessM4Tv2"),
        ("segformer", "SegFormer"),
        ("seggpt", "SegGPT"),
        ("sew", "SEW"),
        ("sew-d", "SEW-D"),
        ("siglip", "SigLIP"),
        ("siglip_vision_model", "SiglipVisionModel"),
        ("speech-encoder-decoder", "Speech Encoder decoder"),
        ("speech_to_text", "Speech2Text"),
        ("speech_to_text_2", "Speech2Text2"),
        ("speecht5", "SpeechT5"),
        ("splinter", "Splinter"),
        ("squeezebert", "SqueezeBERT"),
        ("stablelm", "StableLm"),
        ("starcoder2", "Starcoder2"),
        ("superpoint", "SuperPoint"),
        ("swiftformer", "SwiftFormer"),
        ("swin", "Swin Transformer"),
        ("swin2sr", "Swin2SR"),
        ("swinv2", "Swin Transformer V2"),
        ("switch_transformers", "SwitchTransformers"),
        ("t5", "T5"),
        ("t5v1.1", "T5v1.1"),
        ("table-transformer", "Table Transformer"),
        ("tapas", "TAPAS"),
        ("tapex", "TAPEX"),
        ("time_series_transformer", "Time Series Transformer"),
        ("timesformer", "TimeSformer"),
        ("timm_backbone", "TimmBackbone"),
        ("trajectory_transformer", "Trajectory Transformer"),
        ("transfo-xl", "Transformer-XL"),
        ("trocr", "TrOCR"),
        ("tvlt", "TVLT"),
        ("tvp", "TVP"),
        ("udop", "UDOP"),
        ("ul2", "UL2"),
        ("umt5", "UMT5"),
        ("unispeech", "UniSpeech"),
        ("unispeech-sat", "UniSpeechSat"),
        ("univnet", "UnivNet"),
        ("upernet", "UPerNet"),
        ("van", "VAN"),
        ("video_llava", "VideoLlava"),
        ("videomae", "VideoMAE"),
        ("vilt", "ViLT"),
        ("vipllava", "VipLlava"),
        ("vision-encoder-decoder", "Vision Encoder decoder"),
        ("vision-text-dual-encoder", "VisionTextDualEncoder"),
        ("visual_bert", "VisualBERT"),
        ("vit", "ViT"),
        ("vit_hybrid", "ViT Hybrid"),
        ("vit_mae", "ViTMAE"),
        ("vit_msn", "ViTMSN"),
        ("vitdet", "VitDet"),
        ("vitmatte", "ViTMatte"),
        ("vits", "VITS"),
        ("vivit", "ViViT"),
        ("wav2vec2", "Wav2Vec2"),
        ("wav2vec2-bert", "Wav2Vec2-BERT"),
        ("wav2vec2-conformer", "Wav2Vec2-Conformer"),
        ("wav2vec2_phoneme", "Wav2Vec2Phoneme"),
        ("wavlm", "WavLM"),
        ("whisper", "Whisper"),
        ("xclip", "X-CLIP"),
        ("xglm", "XGLM"),
        ("xlm", "XLM"),
        ("xlm-prophetnet", "XLM-ProphetNet"),
        ("xlm-roberta", "XLM-RoBERTa"),
        ("xlm-roberta-xl", "XLM-RoBERTa-XL"),
        ("xlm-v", "XLM-V"),
        ("xlnet", "XLNet"),
        ("xls_r", "XLS-R"),
        ("xlsr_wav2vec2", "XLSR-Wav2Vec2"),
        ("xmod", "X-MOD"),
        ("yolos", "YOLOS"),
        ("yoso", "YOSO"),
    ]
)

# This is tied to the processing `-` -> `_` in `model_type_to_module_name`. For example, instead of putting
# `transfo-xl` (as in `CONFIG_MAPPING_NAMES`), we should use `transfo_xl`.
DEPRECATED_MODELS = [
    "bort",
    "deta",
    "efficientformer",
    "ernie_m",
    "gptsan_japanese",
    "graphormer",
    "jukebox",
    "mctct",
    "mega",
    "mmbt",
    "nat",
    "nezha",
    "open_llama",
    "qdqbert",
    "realm",
    "retribert",
    "speech_to_text_2",
    "tapex",
    "trajectory_transformer",
    "transfo_xl",
    "tvlt",
    "van",
    "vit_hybrid",
    "xlm_prophetnet",
]

SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict(
    [
        ("openai-gpt", "openai"),
        ("data2vec-audio", "data2vec"),
        ("data2vec-text", "data2vec"),
        ("data2vec-vision", "data2vec"),
        ("donut-swin", "donut"),
        ("kosmos-2", "kosmos2"),
        ("maskformer-swin", "maskformer"),
        ("xclip", "x_clip"),
        ("clip_vision_model", "clip"),
        ("siglip_vision_model", "siglip"),
        ("chinese_clip_vision_model", "chinese_clip"),
    ]
)


def model_type_to_module_name(key):
    """Converts a config key to the corresponding module."""
    # Special treatment
    if key in SPECIAL_MODEL_TYPE_TO_MODULE_NAME:
        key = SPECIAL_MODEL_TYPE_TO_MODULE_NAME[key]

        if key in DEPRECATED_MODELS:
            key = f"deprecated.{key}"
        return key

    key = key.replace("-", "_")
    if key in DEPRECATED_MODELS:
        key = f"deprecated.{key}"

    return key


def config_class_to_model_type(config):
    """Converts a config class name to the corresponding model type"""
    for key, cls in CONFIG_MAPPING_NAMES.items():
        if cls == config:
            return key
    # if key not found check in extra content
    for key, cls in CONFIG_MAPPING._extra_content.items():
        if cls.__name__ == config:
            return key
    return None


class _LazyConfigMapping(OrderedDict):
    """
    A dictionary that lazily load its values when they are requested.
    """

    def __init__(self, mapping):
        self._mapping = mapping
        self._extra_content = {}
        self._modules = {}

    def __getitem__(self, key):
        if key in self._extra_content:
            return self._extra_content[key]
        if key not in self._mapping:
            raise KeyError(key)
        value = self._mapping[key]
        module_name = model_type_to_module_name(key)
        if module_name not in self._modules:
            self._modules[module_name] = importlib.import_module(f".{module_name}", "transformers.models")
        if hasattr(self._modules[module_name], value):
            return getattr(self._modules[module_name], value)

        # Some of the mappings have entries model_type -> config of another model type. In that case we try to grab the
        # object at the top level.
        transformers_module = importlib.import_module("transformers")
        return getattr(transformers_module, value)

    def keys(self):
        return list(self._mapping.keys()) + list(self._extra_content.keys())

    def values(self):
        return [self[k] for k in self._mapping.keys()] + list(self._extra_content.values())

    def items(self):
        return [(k, self[k]) for k in self._mapping.keys()] + list(self._extra_content.items())

    def __iter__(self):
        return iter(list(self._mapping.keys()) + list(self._extra_content.keys()))

    def __contains__(self, item):
        return item in self._mapping or item in self._extra_content

    def register(self, key, value, exist_ok=False):
        """
        Register a new configuration in this mapping.
        """
        if key in self._mapping.keys() and not exist_ok:
            raise ValueError(f"'{key}' is already used by a Transformers config, pick another name.")
        self._extra_content[key] = value


CONFIG_MAPPING = _LazyConfigMapping(CONFIG_MAPPING_NAMES)


class _LazyLoadAllMappings(OrderedDict):
    """
    A mapping that will load all pairs of key values at the first access (either by indexing, requestions keys, values,
    etc.)

    Args:
        mapping: The mapping to load.
    """

    def __init__(self, mapping):
        self._mapping = mapping
        self._initialized = False
        self._data = {}

    def _initialize(self):
        if self._initialized:
            return

        for model_type, map_name in self._mapping.items():
            module_name = model_type_to_module_name(model_type)
            module = importlib.import_module(f".{module_name}", "transformers.models")
            mapping = getattr(module, map_name)
            self._data.update(mapping)

        self._initialized = True

    def __getitem__(self, key):
        self._initialize()
        return self._data[key]

    def keys(self):
        self._initialize()
        return self._data.keys()

    def values(self):
        self._initialize()
        return self._data.values()

    def items(self):
        self._initialize()
        return self._data.keys()

    def __iter__(self):
        self._initialize()
        return iter(self._data)

    def __contains__(self, item):
        self._initialize()
        return item in self._data


def _get_class_name(model_class: Union[str, List[str]]):
    if isinstance(model_class, (list, tuple)):
        return " or ".join([f"[`{c}`]" for c in model_class if c is not None])
    return f"[`{model_class}`]"


def _list_model_options(indent, config_to_class=None, use_model_types=True):
    if config_to_class is None and not use_model_types:
        raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
    if use_model_types:
        if config_to_class is None:
            model_type_to_name = {model_type: f"[`{config}`]" for model_type, config in CONFIG_MAPPING_NAMES.items()}
        else:
            model_type_to_name = {
                model_type: _get_class_name(model_class)
                for model_type, model_class in config_to_class.items()
                if model_type in MODEL_NAMES_MAPPING
            }
        lines = [
            f"{indent}- **{model_type}** -- {model_type_to_name[model_type]} ({MODEL_NAMES_MAPPING[model_type]} model)"
            for model_type in sorted(model_type_to_name.keys())
        ]
    else:
        config_to_name = {
            CONFIG_MAPPING_NAMES[config]: _get_class_name(clas)
            for config, clas in config_to_class.items()
            if config in CONFIG_MAPPING_NAMES
        }
        config_to_model_name = {
            config: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING_NAMES.items()
        }
        lines = [
            f"{indent}- [`{config_name}`] configuration class:"
            f" {config_to_name[config_name]} ({config_to_model_name[config_name]} model)"
            for config_name in sorted(config_to_name.keys())
        ]
    return "\n".join(lines)


def replace_list_option_in_docstrings(config_to_class=None, use_model_types=True):
    def docstring_decorator(fn):
        docstrings = fn.__doc__
        if docstrings is None:
            # Example: -OO
            return fn
        lines = docstrings.split("\n")
        i = 0
        while i < len(lines) and re.search(r"^(\s*)List options\s*$", lines[i]) is None:
            i += 1
        if i < len(lines):
            indent = re.search(r"^(\s*)List options\s*$", lines[i]).groups()[0]
            if use_model_types:
                indent = f"{indent}    "
            lines[i] = _list_model_options(indent, config_to_class=config_to_class, use_model_types=use_model_types)
            docstrings = "\n".join(lines)
        else:
            raise ValueError(
                f"The function {fn} should have an empty 'List options' in its docstring as placeholder, current"
                f" docstring is:\n{docstrings}"
            )
        fn.__doc__ = docstrings
        return fn

    return docstring_decorator


class AutoConfig:
    r"""
    This is a generic configuration class that will be instantiated as one of the configuration classes of the library
    when created with the [`~AutoConfig.from_pretrained`] class method.

    This class cannot be instantiated directly using `__init__()` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoConfig is designed to be instantiated "
            "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
        )

    @classmethod
    def for_model(cls, model_type: str, *args, **kwargs):
        if model_type in CONFIG_MAPPING:
            config_class = CONFIG_MAPPING[model_type]
            return config_class(*args, **kwargs)
        raise ValueError(
            f"Unrecognized model identifier: {model_type}. Should contain one of {', '.join(CONFIG_MAPPING.keys())}"
        )

    @classmethod
    @replace_list_option_in_docstrings()
    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        r"""
        Instantiate one of the configuration classes of the library from a pretrained model configuration.

        The configuration class to instantiate is selected based on the `model_type` property of the config object that
        is loaded, or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

        List options

        Args:
            pretrained_model_name_or_path (`str` or `os.PathLike`):
                Can be either:

                    - A string, the *model id* of a pretrained model configuration hosted inside a model repo on
                      huggingface.co.
                    - A path to a *directory* containing a configuration file saved using the
                      [`~PretrainedConfig.save_pretrained`] method, or the [`~PreTrainedModel.save_pretrained`] method,
                      e.g., `./my_model_directory/`.
                    - A path or url to a saved configuration JSON *file*, e.g.,
                      `./my_model_directory/configuration.json`.
            cache_dir (`str` or `os.PathLike`, *optional*):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download the model weights and configuration files and override the
                cached versions if they exist.
            resume_download:
                Deprecated and ignored. All downloads are now resumed by default when possible.
                Will be removed in v5 of Transformers.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                identifier allowed by git.
            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                If `False`, then this function returns just the final configuration object.

                If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
                dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
                part of `kwargs` which has not been used to update `config` and is otherwise ignored.
            trust_remote_code (`bool`, *optional*, defaults to `False`):
                Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
                should only be set to `True` for repositories you trust and in which you have read the code, as it will
                execute code present on the Hub on your local machine.
            kwargs(additional keyword arguments, *optional*):
                The values in kwargs of any keys which are configuration attributes will be used to override the loaded
                values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
                by the `return_unused_kwargs` keyword parameter.

        Examples:

        ```python
        >>> from transformers import AutoConfig

        >>> # Download configuration from huggingface.co and cache.
        >>> config = AutoConfig.from_pretrained("google-bert/bert-base-uncased")

        >>> # Download configuration from huggingface.co (user-uploaded) and cache.
        >>> config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")

        >>> # If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
        >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/")

        >>> # Load a specific configuration file.
        >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")

        >>> # Change some config attributes when loading a pretrained config.
        >>> config = AutoConfig.from_pretrained("google-bert/bert-base-uncased", output_attentions=True, foo=False)
        >>> config.output_attentions
        True

        >>> config, unused_kwargs = AutoConfig.from_pretrained(
        ...     "google-bert/bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
        ... )
        >>> config.output_attentions
        True

        >>> unused_kwargs
        {'foo': False}
        ```"""
        use_auth_token = kwargs.pop("use_auth_token", None)
        if use_auth_token is not None:
            warnings.warn(
                "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
                FutureWarning,
            )
            if kwargs.get("token", None) is not None:
                raise ValueError(
                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
                )
            kwargs["token"] = use_auth_token

        kwargs["_from_auto"] = True
        kwargs["name_or_path"] = pretrained_model_name_or_path
        trust_remote_code = kwargs.pop("trust_remote_code", None)
        code_revision = kwargs.pop("code_revision", None)

        config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
        has_remote_code = "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]
        has_local_code = "model_type" in config_dict and config_dict["model_type"] in CONFIG_MAPPING
        trust_remote_code = resolve_trust_remote_code(
            trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
        )

        if has_remote_code and trust_remote_code:
            class_ref = config_dict["auto_map"]["AutoConfig"]
            config_class = get_class_from_dynamic_module(
                class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs
            )
            if os.path.isdir(pretrained_model_name_or_path):
                config_class.register_for_auto_class()
            return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
        elif "model_type" in config_dict:
            try:
                config_class = CONFIG_MAPPING[config_dict["model_type"]]
            except KeyError:
                raise ValueError(
                    f"The checkpoint you are trying to load has model type `{config_dict['model_type']}` "
                    "but Transformers does not recognize this architecture. This could be because of an "
                    "issue with the checkpoint, or because your version of Transformers is out of date."
                )
            return config_class.from_dict(config_dict, **unused_kwargs)
        else:
            # Fallback: use pattern matching on the string.
            # We go from longer names to shorter names to catch roberta before bert (for instance)
            for pattern in sorted(CONFIG_MAPPING.keys(), key=len, reverse=True):
                if pattern in str(pretrained_model_name_or_path):
                    return CONFIG_MAPPING[pattern].from_dict(config_dict, **unused_kwargs)

        raise ValueError(
            f"Unrecognized model in {pretrained_model_name_or_path}. "
            f"Should have a `model_type` key in its {CONFIG_NAME}, or contain one of the following strings "
            f"in its name: {', '.join(CONFIG_MAPPING.keys())}"
        )

    @staticmethod
    def register(model_type, config, exist_ok=False):
        """
        Register a new configuration for this class.

        Args:
            model_type (`str`): The model type like "bert" or "gpt".
            config ([`PretrainedConfig`]): The config to register.
        """
        if issubclass(config, PretrainedConfig) and config.model_type != model_type:
            raise ValueError(
                "The config you are passing has a `model_type` attribute that is not consistent with the model type "
                f"you passed (config has {config.model_type} and you passed {model_type}. Fix one of those so they "
                "match!"
            )
        CONFIG_MAPPING.register(model_type, config, exist_ok=exist_ok)
