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modeling_auto.py
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modeling_auto.py
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# 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 _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"),
("bart", "BartModel"),
("beit", "BeitModel"),
("bert", "BertModel"),
("bert-generation", "BertGenerationEncoder"),
("big_bird", "BigBirdModel"),
("bigbird_pegasus", "BigBirdPegasusModel"),
("blenderbot", "BlenderbotModel"),
("blenderbot-small", "BlenderbotSmallModel"),
("bloom", "BloomModel"),
("camembert", "CamembertModel"),
("canine", "CanineModel"),
("clip", "CLIPModel"),
("codegen", "CodeGenModel"),
("convbert", "ConvBertModel"),
("convnext", "ConvNextModel"),
("ctrl", "CTRLModel"),
("cvt", "CvtModel"),
("data2vec-audio", "Data2VecAudioModel"),
("data2vec-text", "Data2VecTextModel"),
("data2vec-vision", "Data2VecVisionModel"),
("deberta", "DebertaModel"),
("deberta-v2", "DebertaV2Model"),
("decision_transformer", "DecisionTransformerModel"),
("decision_transformer_gpt2", "DecisionTransformerGPT2Model"),
("deit", "DeiTModel"),
("detr", "DetrModel"),
("distilbert", "DistilBertModel"),
("donut-swin", "DonutSwinModel"),
("dpr", "DPRQuestionEncoder"),
("dpt", "DPTModel"),
("electra", "ElectraModel"),
("flaubert", "FlaubertModel"),
("flava", "FlavaModel"),
("fnet", "FNetModel"),
("fsmt", "FSMTModel"),
("funnel", ("FunnelModel", "FunnelBaseModel")),
("glpn", "GLPNModel"),
("gpt2", "GPT2Model"),
("gpt_neo", "GPTNeoModel"),
("gpt_neox", "GPTNeoXModel"),
("gptj", "GPTJModel"),
("groupvit", "GroupViTModel"),
("hubert", "HubertModel"),
("ibert", "IBertModel"),
("imagegpt", "ImageGPTModel"),
("layoutlm", "LayoutLMModel"),
("layoutlmv2", "LayoutLMv2Model"),
("layoutlmv3", "LayoutLMv3Model"),
("led", "LEDModel"),
("levit", "LevitModel"),
("longformer", "LongformerModel"),
("longt5", "LongT5Model"),
("luke", "LukeModel"),
("lxmert", "LxmertModel"),
("m2m_100", "M2M100Model"),
("marian", "MarianModel"),
("maskformer", "MaskFormerModel"),
("mbart", "MBartModel"),
("mctct", "MCTCTModel"),
("megatron-bert", "MegatronBertModel"),
("mobilebert", "MobileBertModel"),
("mobilevit", "MobileViTModel"),
("mpnet", "MPNetModel"),
("mt5", "MT5Model"),
("mvp", "MvpModel"),
("nezha", "NezhaModel"),
("nllb", "M2M100Model"),
("nystromformer", "NystromformerModel"),
("openai-gpt", "OpenAIGPTModel"),
("opt", "OPTModel"),
("owlvit", "OwlViTModel"),
("pegasus", "PegasusModel"),
("pegasus_x", "PegasusXModel"),
("perceiver", "PerceiverModel"),
("plbart", "PLBartModel"),
("poolformer", "PoolFormerModel"),
("prophetnet", "ProphetNetModel"),
("qdqbert", "QDQBertModel"),
("reformer", "ReformerModel"),
("regnet", "RegNetModel"),
("rembert", "RemBertModel"),
("resnet", "ResNetModel"),
("retribert", "RetriBertModel"),
("roberta", "RobertaModel"),
("roformer", "RoFormerModel"),
("segformer", "SegformerModel"),
("sew", "SEWModel"),
("sew-d", "SEWDModel"),
("speech_to_text", "Speech2TextModel"),
("splinter", "SplinterModel"),
("squeezebert", "SqueezeBertModel"),
("swin", "SwinModel"),
("swinv2", "Swinv2Model"),
("t5", "T5Model"),
("tapas", "TapasModel"),
("trajectory_transformer", "TrajectoryTransformerModel"),
("transfo-xl", "TransfoXLModel"),
("unispeech", "UniSpeechModel"),
("unispeech-sat", "UniSpeechSatModel"),
("van", "VanModel"),
("videomae", "VideoMAEModel"),
("vilt", "ViltModel"),
("vision-text-dual-encoder", "VisionTextDualEncoderModel"),
("visual_bert", "VisualBertModel"),
("vit", "ViTModel"),
("vit_mae", "ViTMAEModel"),
("wav2vec2", "Wav2Vec2Model"),
("wav2vec2-conformer", "Wav2Vec2ConformerModel"),
("wavlm", "WavLMModel"),
("xglm", "XGLMModel"),
("xlm", "XLMModel"),
("xlm-prophetnet", "XLMProphetNetModel"),
("xlm-roberta", "XLMRobertaModel"),
("xlm-roberta-xl", "XLMRobertaXLModel"),
("xlnet", "XLNetModel"),
("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"),
("flaubert", "FlaubertWithLMHeadModel"),
("flava", "FlavaForPreTraining"),
("fnet", "FNetForPreTraining"),
("fsmt", "FSMTForConditionalGeneration"),
("funnel", "FunnelForPreTraining"),
("gpt2", "GPT2LMHeadModel"),
("ibert", "IBertForMaskedLM"),
("layoutlm", "LayoutLMForMaskedLM"),
("longformer", "LongformerForMaskedLM"),
("luke", "LukeForMaskedLM"),
("lxmert", "LxmertForPreTraining"),
("megatron-bert", "MegatronBertForPreTraining"),
("mobilebert", "MobileBertForPreTraining"),
("mpnet", "MPNetForMaskedLM"),
("mvp", "MvpForConditionalGeneration"),
("nezha", "NezhaForPreTraining"),
("openai-gpt", "OpenAIGPTLMHeadModel"),
("retribert", "RetriBertModel"),
("roberta", "RobertaForMaskedLM"),
("splinter", "SplinterForPreTraining"),
("squeezebert", "SqueezeBertForMaskedLM"),
("t5", "T5ForConditionalGeneration"),
("tapas", "TapasForMaskedLM"),
("transfo-xl", "TransfoXLLMHeadModel"),
("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"),
]
)
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"),
("ctrl", "CTRLLMHeadModel"),
("data2vec-text", "Data2VecTextForMaskedLM"),
("deberta", "DebertaForMaskedLM"),
("deberta-v2", "DebertaV2ForMaskedLM"),
("distilbert", "DistilBertForMaskedLM"),
("electra", "ElectraForMaskedLM"),
("encoder-decoder", "EncoderDecoderModel"),
("flaubert", "FlaubertWithLMHeadModel"),
("fnet", "FNetForMaskedLM"),
("fsmt", "FSMTForConditionalGeneration"),
("funnel", "FunnelForMaskedLM"),
("gpt2", "GPT2LMHeadModel"),
("gpt_neo", "GPTNeoForCausalLM"),
("gpt_neox", "GPTNeoXForCausalLM"),
("gptj", "GPTJForCausalLM"),
("ibert", "IBertForMaskedLM"),
("layoutlm", "LayoutLMForMaskedLM"),
("led", "LEDForConditionalGeneration"),
("longformer", "LongformerForMaskedLM"),
("longt5", "LongT5ForConditionalGeneration"),
("luke", "LukeForMaskedLM"),
("m2m_100", "M2M100ForConditionalGeneration"),
("marian", "MarianMTModel"),
("megatron-bert", "MegatronBertForCausalLM"),
("mobilebert", "MobileBertForMaskedLM"),
("mpnet", "MPNetForMaskedLM"),
("mvp", "MvpForConditionalGeneration"),
("nezha", "NezhaForMaskedLM"),
("nllb", "M2M100ForConditionalGeneration"),
("nystromformer", "NystromformerForMaskedLM"),
("openai-gpt", "OpenAIGPTLMHeadModel"),
("pegasus_x", "PegasusXForConditionalGeneration"),
("plbart", "PLBartForConditionalGeneration"),
("qdqbert", "QDQBertForMaskedLM"),
("reformer", "ReformerModelWithLMHead"),
("rembert", "RemBertForMaskedLM"),
("roberta", "RobertaForMaskedLM"),
("roformer", "RoFormerForMaskedLM"),
("speech_to_text", "Speech2TextForConditionalGeneration"),
("squeezebert", "SqueezeBertForMaskedLM"),
("t5", "T5ForConditionalGeneration"),
("tapas", "TapasForMaskedLM"),
("transfo-xl", "TransfoXLLMHeadModel"),
("wav2vec2", "Wav2Vec2ForMaskedLM"),
("xlm", "XLMWithLMHeadModel"),
("xlm-roberta", "XLMRobertaForMaskedLM"),
("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"),
("xlnet", "XLNetLMHeadModel"),
("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"),
("blenderbot", "BlenderbotForCausalLM"),
("blenderbot-small", "BlenderbotSmallForCausalLM"),
("bloom", "BloomForCausalLM"),
("camembert", "CamembertForCausalLM"),
("codegen", "CodeGenForCausalLM"),
("ctrl", "CTRLLMHeadModel"),
("data2vec-text", "Data2VecTextForCausalLM"),
("electra", "ElectraForCausalLM"),
("gpt2", "GPT2LMHeadModel"),
("gpt_neo", "GPTNeoForCausalLM"),
("gpt_neox", "GPTNeoXForCausalLM"),
("gptj", "GPTJForCausalLM"),
("marian", "MarianForCausalLM"),
("mbart", "MBartForCausalLM"),
("megatron-bert", "MegatronBertForCausalLM"),
("mvp", "MvpForCausalLM"),
("openai-gpt", "OpenAIGPTLMHeadModel"),
("opt", "OPTForCausalLM"),
("pegasus", "PegasusForCausalLM"),
("plbart", "PLBartForCausalLM"),
("prophetnet", "ProphetNetForCausalLM"),
("qdqbert", "QDQBertLMHeadModel"),
("reformer", "ReformerModelWithLMHead"),
("rembert", "RemBertForCausalLM"),
("roberta", "RobertaForCausalLM"),
("roformer", "RoFormerForCausalLM"),
("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"),
]
)
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES = OrderedDict(
[
("deit", "DeiTForMaskedImageModeling"),
("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"),
("convnext", "ConvNextForImageClassification"),
("cvt", "CvtForImageClassification"),
("data2vec-vision", "Data2VecVisionForImageClassification"),
("deit", ("DeiTForImageClassification", "DeiTForImageClassificationWithTeacher")),
("imagegpt", "ImageGPTForImageClassification"),
("levit", ("LevitForImageClassification", "LevitForImageClassificationWithTeacher")),
("mobilevit", "MobileViTForImageClassification"),
(
"perceiver",
(
"PerceiverForImageClassificationLearned",
"PerceiverForImageClassificationFourier",
"PerceiverForImageClassificationConvProcessing",
),
),
("poolformer", "PoolFormerForImageClassification"),
("regnet", "RegNetForImageClassification"),
("resnet", "ResNetForImageClassification"),
("segformer", "SegformerForImageClassification"),
("swin", "SwinForImageClassification"),
("swinv2", "Swinv2ForImageClassification"),
("van", "VanForImageClassification"),
("vit", "ViTForImageClassification"),
]
)
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"),
("mobilevit", "MobileViTForSemanticSegmentation"),
("segformer", "SegformerForSemanticSegmentation"),
]
)
MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES = OrderedDict(
[
# Model for Instance Segmentation mapping
("maskformer", "MaskFormerForInstanceSegmentation"),
]
)
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
("videomae", "VideoMAEForVideoClassification"),
]
)
MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict(
[
("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"),
("flaubert", "FlaubertWithLMHeadModel"),
("fnet", "FNetForMaskedLM"),
("funnel", "FunnelForMaskedLM"),
("ibert", "IBertForMaskedLM"),
("layoutlm", "LayoutLMForMaskedLM"),
("longformer", "LongformerForMaskedLM"),
("luke", "LukeForMaskedLM"),
("mbart", "MBartForConditionalGeneration"),
("megatron-bert", "MegatronBertForMaskedLM"),
("mobilebert", "MobileBertForMaskedLM"),
("mpnet", "MPNetForMaskedLM"),
("mvp", "MvpForConditionalGeneration"),
("nezha", "NezhaForMaskedLM"),
("nystromformer", "NystromformerForMaskedLM"),
("perceiver", "PerceiverForMaskedLM"),
("qdqbert", "QDQBertForMaskedLM"),
("reformer", "ReformerForMaskedLM"),
("rembert", "RemBertForMaskedLM"),
("roberta", "RobertaForMaskedLM"),
("roformer", "RoFormerForMaskedLM"),
("squeezebert", "SqueezeBertForMaskedLM"),
("tapas", "TapasForMaskedLM"),
("wav2vec2", "Wav2Vec2ForMaskedLM"),
("xlm", "XLMWithLMHeadModel"),
("xlm-roberta", "XLMRobertaForMaskedLM"),
("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"),
("yoso", "YosoForMaskedLM"),
]
)
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict(
[
# Model for Object Detection mapping
("detr", "DetrForObjectDetection"),
("yolos", "YolosForObjectDetection"),
]
)
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"),
("led", "LEDForConditionalGeneration"),
("longt5", "LongT5ForConditionalGeneration"),
("m2m_100", "M2M100ForConditionalGeneration"),
("marian", "MarianMTModel"),
("mbart", "MBartForConditionalGeneration"),
("mt5", "MT5ForConditionalGeneration"),
("mvp", "MvpForConditionalGeneration"),
("nllb", "M2M100ForConditionalGeneration"),
("pegasus", "PegasusForConditionalGeneration"),
("pegasus_x", "PegasusXForConditionalGeneration"),
("plbart", "PLBartForConditionalGeneration"),
("prophetnet", "ProphetNetForConditionalGeneration"),
("t5", "T5ForConditionalGeneration"),
("xlm-prophetnet", "XLMProphetNetForConditionalGeneration"),
]
)
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict(
[
("speech-encoder-decoder", "SpeechEncoderDecoderModel"),
("speech_to_text", "Speech2TextForConditionalGeneration"),
]
)
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "AlbertForSequenceClassification"),
("bart", "BartForSequenceClassification"),
("bert", "BertForSequenceClassification"),
("big_bird", "BigBirdForSequenceClassification"),
("bigbird_pegasus", "BigBirdPegasusForSequenceClassification"),
("bloom", "BloomForSequenceClassification"),
("camembert", "CamembertForSequenceClassification"),
("canine", "CanineForSequenceClassification"),
("convbert", "ConvBertForSequenceClassification"),
("ctrl", "CTRLForSequenceClassification"),
("data2vec-text", "Data2VecTextForSequenceClassification"),
("deberta", "DebertaForSequenceClassification"),
("deberta-v2", "DebertaV2ForSequenceClassification"),
("distilbert", "DistilBertForSequenceClassification"),
("electra", "ElectraForSequenceClassification"),
("flaubert", "FlaubertForSequenceClassification"),
("fnet", "FNetForSequenceClassification"),
("funnel", "FunnelForSequenceClassification"),
("gpt2", "GPT2ForSequenceClassification"),
("gpt_neo", "GPTNeoForSequenceClassification"),
("gptj", "GPTJForSequenceClassification"),
("ibert", "IBertForSequenceClassification"),
("layoutlm", "LayoutLMForSequenceClassification"),
("layoutlmv2", "LayoutLMv2ForSequenceClassification"),
("layoutlmv3", "LayoutLMv3ForSequenceClassification"),
("led", "LEDForSequenceClassification"),
("longformer", "LongformerForSequenceClassification"),
("luke", "LukeForSequenceClassification"),
("mbart", "MBartForSequenceClassification"),
("megatron-bert", "MegatronBertForSequenceClassification"),
("mobilebert", "MobileBertForSequenceClassification"),
("mpnet", "MPNetForSequenceClassification"),
("mvp", "MvpForSequenceClassification"),
("nezha", "NezhaForSequenceClassification"),
("nystromformer", "NystromformerForSequenceClassification"),
("openai-gpt", "OpenAIGPTForSequenceClassification"),
("opt", "OPTForSequenceClassification"),
("perceiver", "PerceiverForSequenceClassification"),
("plbart", "PLBartForSequenceClassification"),
("qdqbert", "QDQBertForSequenceClassification"),
("reformer", "ReformerForSequenceClassification"),
("rembert", "RemBertForSequenceClassification"),
("roberta", "RobertaForSequenceClassification"),
("roformer", "RoFormerForSequenceClassification"),
("squeezebert", "SqueezeBertForSequenceClassification"),
("tapas", "TapasForSequenceClassification"),
("transfo-xl", "TransfoXLForSequenceClassification"),
("xlm", "XLMForSequenceClassification"),
("xlm-roberta", "XLMRobertaForSequenceClassification"),
("xlm-roberta-xl", "XLMRobertaXLForSequenceClassification"),
("xlnet", "XLNetForSequenceClassification"),
("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"),
("camembert", "CamembertForQuestionAnswering"),
("canine", "CanineForQuestionAnswering"),
("convbert", "ConvBertForQuestionAnswering"),
("data2vec-text", "Data2VecTextForQuestionAnswering"),
("deberta", "DebertaForQuestionAnswering"),
("deberta-v2", "DebertaV2ForQuestionAnswering"),
("distilbert", "DistilBertForQuestionAnswering"),
("electra", "ElectraForQuestionAnswering"),
("flaubert", "FlaubertForQuestionAnsweringSimple"),
("fnet", "FNetForQuestionAnswering"),
("funnel", "FunnelForQuestionAnswering"),
("gptj", "GPTJForQuestionAnswering"),
("ibert", "IBertForQuestionAnswering"),
("layoutlmv2", "LayoutLMv2ForQuestionAnswering"),
("layoutlmv3", "LayoutLMv3ForQuestionAnswering"),
("led", "LEDForQuestionAnswering"),
("longformer", "LongformerForQuestionAnswering"),
("luke", "LukeForQuestionAnswering"),
("lxmert", "LxmertForQuestionAnswering"),
("mbart", "MBartForQuestionAnswering"),
("megatron-bert", "MegatronBertForQuestionAnswering"),
("mobilebert", "MobileBertForQuestionAnswering"),
("mpnet", "MPNetForQuestionAnswering"),
("mvp", "MvpForQuestionAnswering"),
("nezha", "NezhaForQuestionAnswering"),
("nystromformer", "NystromformerForQuestionAnswering"),
("qdqbert", "QDQBertForQuestionAnswering"),
("reformer", "ReformerForQuestionAnswering"),
("rembert", "RemBertForQuestionAnswering"),
("roberta", "RobertaForQuestionAnswering"),
("roformer", "RoFormerForQuestionAnswering"),
("splinter", "SplinterForQuestionAnswering"),
("squeezebert", "SqueezeBertForQuestionAnswering"),
("xlm", "XLMForQuestionAnsweringSimple"),
("xlm-roberta", "XLMRobertaForQuestionAnswering"),
("xlm-roberta-xl", "XLMRobertaXLForQuestionAnswering"),
("xlnet", "XLNetForQuestionAnsweringSimple"),
("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(
[
("vilt", "ViltForQuestionAnswering"),
]
)
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
# Model for Token Classification mapping
("albert", "AlbertForTokenClassification"),
("bert", "BertForTokenClassification"),
("big_bird", "BigBirdForTokenClassification"),
("bloom", "BloomForTokenClassification"),
("camembert", "CamembertForTokenClassification"),
("canine", "CanineForTokenClassification"),
("convbert", "ConvBertForTokenClassification"),
("data2vec-text", "Data2VecTextForTokenClassification"),
("deberta", "DebertaForTokenClassification"),
("deberta-v2", "DebertaV2ForTokenClassification"),
("distilbert", "DistilBertForTokenClassification"),
("electra", "ElectraForTokenClassification"),
("flaubert", "FlaubertForTokenClassification"),
("fnet", "FNetForTokenClassification"),
("funnel", "FunnelForTokenClassification"),
("gpt2", "GPT2ForTokenClassification"),
("ibert", "IBertForTokenClassification"),
("layoutlm", "LayoutLMForTokenClassification"),
("layoutlmv2", "LayoutLMv2ForTokenClassification"),
("layoutlmv3", "LayoutLMv3ForTokenClassification"),
("longformer", "LongformerForTokenClassification"),
("luke", "LukeForTokenClassification"),
("megatron-bert", "MegatronBertForTokenClassification"),
("mobilebert", "MobileBertForTokenClassification"),
("mpnet", "MPNetForTokenClassification"),
("nezha", "NezhaForTokenClassification"),
("nystromformer", "NystromformerForTokenClassification"),
("qdqbert", "QDQBertForTokenClassification"),
("rembert", "RemBertForTokenClassification"),
("roberta", "RobertaForTokenClassification"),
("roformer", "RoFormerForTokenClassification"),
("squeezebert", "SqueezeBertForTokenClassification"),
("xlm", "XLMForTokenClassification"),
("xlm-roberta", "XLMRobertaForTokenClassification"),
("xlm-roberta-xl", "XLMRobertaXLForTokenClassification"),
("xlnet", "XLNetForTokenClassification"),
("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"),
("flaubert", "FlaubertForMultipleChoice"),
("fnet", "FNetForMultipleChoice"),
("funnel", "FunnelForMultipleChoice"),
("ibert", "IBertForMultipleChoice"),
("longformer", "LongformerForMultipleChoice"),
("luke", "LukeForMultipleChoice"),
("megatron-bert", "MegatronBertForMultipleChoice"),
("mobilebert", "MobileBertForMultipleChoice"),
("mpnet", "MPNetForMultipleChoice"),
("nezha", "NezhaForMultipleChoice"),
("nystromformer", "NystromformerForMultipleChoice"),
("qdqbert", "QDQBertForMultipleChoice"),
("rembert", "RemBertForMultipleChoice"),
("roberta", "RobertaForMultipleChoice"),
("roformer", "RoFormerForMultipleChoice"),
("squeezebert", "SqueezeBertForMultipleChoice"),
("xlm", "XLMForMultipleChoice"),
("xlm-roberta", "XLMRobertaForMultipleChoice"),
("xlm-roberta-xl", "XLMRobertaXLForMultipleChoice"),
("xlnet", "XLNetForMultipleChoice"),
("yoso", "YosoForMultipleChoice"),
]
)
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict(
[
("bert", "BertForNextSentencePrediction"),
("fnet", "FNetForNextSentencePrediction"),
("megatron-bert", "MegatronBertForNextSentencePrediction"),
("mobilebert", "MobileBertForNextSentencePrediction"),
("nezha", "NezhaForNextSentencePrediction"),
("qdqbert", "QDQBertForNextSentencePrediction"),
]
)
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
[
# Model for Audio Classification mapping
("data2vec-audio", "Data2VecAudioForSequenceClassification"),
("hubert", "HubertForSequenceClassification"),
("sew", "SEWForSequenceClassification"),
("sew-d", "SEWDForSequenceClassification"),
("unispeech", "UniSpeechForSequenceClassification"),
("unispeech-sat", "UniSpeechSatForSequenceClassification"),
("wav2vec2", "Wav2Vec2ForSequenceClassification"),
("wav2vec2-conformer", "Wav2Vec2ConformerForSequenceClassification"),
("wavlm", "WavLMForSequenceClassification"),
]
)
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_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_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_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_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_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)
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 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 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 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 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"
)