diff --git "a/public/gpt-2/transformers/__init__.py.orig" "b/public/gpt-2/transformers/__init__.py.orig" new file mode 100644--- /dev/null +++ "b/public/gpt-2/transformers/__init__.py.orig" @@ -0,0 +1,3095 @@ +# flake8: noqa +# There's no way to ignore "F401 '...' imported but unused" warnings in this +# module, but to preserve other warnings. So, don't check this module at all. + +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# 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. + +# When adding a new object to this init, remember to add it twice: once inside the `_import_structure` dictionary and +# once inside the `if TYPE_CHECKING` branch. The `TYPE_CHECKING` should have import statements as usual, but they are +# only there for type checking. The `_import_structure` is a dictionary submodule to list of object names, and is used +# to defer the actual importing for when the objects are requested. This way `import transformers` provides the names +# in the namespace without actually importing anything (and especially none of the backends). + +__version__ = "4.9.1" + +# Work around to update TensorFlow's absl.logging threshold which alters the +# default Python logging output behavior when present. +# see: https://github.com/abseil/abseil-py/issues/99 +# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493 +try: + import absl.logging +except ImportError: + pass +else: + absl.logging.set_verbosity("info") + absl.logging.set_stderrthreshold("info") + absl.logging._warn_preinit_stderr = False + +from typing import TYPE_CHECKING + +# Check the dependencies satisfy the minimal versions required. +from . import dependency_versions_check +from .file_utils import ( + _LazyModule, + is_flax_available, + is_sentencepiece_available, + is_speech_available, + is_tf_available, + is_timm_available, + is_tokenizers_available, + is_torch_available, + is_vision_available, +) +from .utils import logging + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +# Base objects, independent of any specific backend +_import_structure = { + "configuration_utils": ["PretrainedConfig"], + "data": [ + "DataProcessor", + "InputExample", + "InputFeatures", + "SingleSentenceClassificationProcessor", + "SquadExample", + "SquadFeatures", + "SquadV1Processor", + "SquadV2Processor", + "glue_compute_metrics", + "glue_convert_examples_to_features", + "glue_output_modes", + "glue_processors", + "glue_tasks_num_labels", + "squad_convert_examples_to_features", + "xnli_compute_metrics", + "xnli_output_modes", + "xnli_processors", + "xnli_tasks_num_labels", + ], + "feature_extraction_sequence_utils": ["BatchFeature", "SequenceFeatureExtractor"], + "file_utils": [ + "CONFIG_NAME", + "MODEL_CARD_NAME", + "PYTORCH_PRETRAINED_BERT_CACHE", + "PYTORCH_TRANSFORMERS_CACHE", + "SPIECE_UNDERLINE", + "TF2_WEIGHTS_NAME", + "TF_WEIGHTS_NAME", + "TRANSFORMERS_CACHE", + "WEIGHTS_NAME", + "TensorType", + "add_end_docstrings", + "add_start_docstrings", + "cached_path", + "is_apex_available", + "is_datasets_available", + "is_faiss_available", + "is_flax_available", + "is_psutil_available", + "is_py3nvml_available", + "is_scipy_available", + "is_sentencepiece_available", + "is_sklearn_available", + "is_speech_available", + "is_tf_available", + "is_timm_available", + "is_tokenizers_available", + "is_torch_available", + "is_torch_tpu_available", + "is_vision_available", + ], + "hf_argparser": ["HfArgumentParser"], + "integrations": [ + "is_comet_available", + "is_optuna_available", + "is_ray_available", + "is_ray_tune_available", + "is_tensorboard_available", + "is_wandb_available", + ], + "modelcard": ["ModelCard"], + "modeling_tf_pytorch_utils": [ + "convert_tf_weight_name_to_pt_weight_name", + "load_pytorch_checkpoint_in_tf2_model", + "load_pytorch_model_in_tf2_model", + "load_pytorch_weights_in_tf2_model", + "load_tf2_checkpoint_in_pytorch_model", + "load_tf2_model_in_pytorch_model", + "load_tf2_weights_in_pytorch_model", + ], + # Models + "models": [], + "models.albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig"], + "models.auto": [ + "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", + "CONFIG_MAPPING", + "FEATURE_EXTRACTOR_MAPPING", + "MODEL_NAMES_MAPPING", + "TOKENIZER_MAPPING", + "AutoConfig", + "AutoFeatureExtractor", + "AutoTokenizer", + ], + "models.bart": ["BartConfig", "BartTokenizer"], + "models.barthez": [], + "models.bert": [ + "BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", + "BasicTokenizer", + "BertConfig", + "BertTokenizer", + "WordpieceTokenizer", + ], + "models.bert_generation": ["BertGenerationConfig"], + "models.bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"], + "models.bertweet": ["BertweetTokenizer"], + "models.big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdTokenizer"], + "models.bigbird_pegasus": [ + "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", + "BigBirdPegasusConfig", + ], + "models.blenderbot": ["BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlenderbotConfig", "BlenderbotTokenizer"], + "models.blenderbot_small": [ + "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP", + "BlenderbotSmallConfig", + "BlenderbotSmallTokenizer", + ], + "models.byt5": ["ByT5Tokenizer"], + "models.camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig"], + "models.canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig", "CanineTokenizer"], + "models.clip": [ + "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", + "CLIPConfig", + "CLIPTextConfig", + "CLIPTokenizer", + "CLIPVisionConfig", + ], + "models.convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertTokenizer"], + "models.cpm": ["CpmTokenizer"], + "models.ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig", "CTRLTokenizer"], + "models.deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaTokenizer"], + "models.deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config"], + "models.deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig"], + "models.detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig"], + "models.distilbert": ["DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DistilBertConfig", "DistilBertTokenizer"], + "models.dpr": [ + "DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", + "DPRConfig", + "DPRContextEncoderTokenizer", + "DPRQuestionEncoderTokenizer", + "DPRReaderOutput", + "DPRReaderTokenizer", + ], + "models.electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraTokenizer"], + "models.encoder_decoder": ["EncoderDecoderConfig"], + "models.flaubert": ["FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlaubertConfig", "FlaubertTokenizer"], + "models.fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig", "FSMTTokenizer"], + "models.funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig", "FunnelTokenizer"], + "models.gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2Tokenizer"], + "models.gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig"], + "models.herbert": ["HerbertTokenizer"], + "models.hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"], + "models.ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig"], + "models.layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMTokenizer"], + "models.led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig", "LEDTokenizer"], + "models.longformer": ["LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongformerConfig", "LongformerTokenizer"], + "models.luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig", "LukeTokenizer"], + "models.lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig", "LxmertTokenizer"], + "models.m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config"], + "models.marian": ["MarianConfig"], + "models.mbart": ["MBartConfig"], + "models.megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"], + "models.mmbt": ["MMBTConfig"], + "models.mobilebert": ["MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileBertConfig", "MobileBertTokenizer"], + "models.mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig", "MPNetTokenizer"], + "models.mt5": ["MT5Config"], + "models.openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig", "OpenAIGPTTokenizer"], + "models.pegasus": ["PegasusConfig"], + "models.phobert": ["PhobertTokenizer"], + "models.prophetnet": ["PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ProphetNetConfig", "ProphetNetTokenizer"], + "models.rag": ["RagConfig", "RagRetriever", "RagTokenizer"], + "models.reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"], + "models.retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig", "RetriBertTokenizer"], + "models.roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaTokenizer"], + "models.roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerTokenizer"], + "models.speech_to_text": [ + "SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", + "Speech2TextConfig", + ], + "models.squeezebert": ["SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "SqueezeBertConfig", "SqueezeBertTokenizer"], + "models.t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config"], + "models.tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig", "TapasTokenizer"], + "models.transfo_xl": [ + "TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", + "TransfoXLConfig", + "TransfoXLCorpus", + "TransfoXLTokenizer", + ], + "models.visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"], + "models.vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"], + "models.wav2vec2": [ + "WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", + "Wav2Vec2Config", + "Wav2Vec2CTCTokenizer", + "Wav2Vec2FeatureExtractor", + "Wav2Vec2Processor", + "Wav2Vec2Tokenizer", + ], + "models.xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMTokenizer"], + "models.xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"], + "models.xlm_roberta": ["XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaConfig"], + "models.xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"], + "pipelines": [ + "AutomaticSpeechRecognitionPipeline", + "Conversation", + "ConversationalPipeline", + "CsvPipelineDataFormat", + "FeatureExtractionPipeline", + "FillMaskPipeline", + "ImageClassificationPipeline", + "JsonPipelineDataFormat", + "NerPipeline", + "PipedPipelineDataFormat", + "Pipeline", + "PipelineDataFormat", + "QuestionAnsweringPipeline", + "SummarizationPipeline", + "TableQuestionAnsweringPipeline", + "Text2TextGenerationPipeline", + "TextClassificationPipeline", + "TextGenerationPipeline", + "TokenClassificationPipeline", + "TranslationPipeline", + "ZeroShotClassificationPipeline", + "pipeline", + ], + "tokenization_utils": ["PreTrainedTokenizer"], + "tokenization_utils_base": [ + "AddedToken", + "BatchEncoding", + "CharSpan", + "PreTrainedTokenizerBase", + "SpecialTokensMixin", + "TokenSpan", + ], + "trainer_callback": [ + "DefaultFlowCallback", + "EarlyStoppingCallback", + "PrinterCallback", + "ProgressCallback", + "TrainerCallback", + "TrainerControl", + "TrainerState", + ], + "trainer_utils": ["EvalPrediction", "IntervalStrategy", "SchedulerType", "set_seed"], + "training_args": ["TrainingArguments"], + "training_args_seq2seq": ["Seq2SeqTrainingArguments"], + "training_args_tf": ["TFTrainingArguments"], + "utils": ["logging"], +} + +# sentencepiece-backed objects +if is_sentencepiece_available(): + _import_structure["models.albert"].append("AlbertTokenizer") + _import_structure["models.barthez"].append("BarthezTokenizer") + _import_structure["models.bert_generation"].append("BertGenerationTokenizer") + _import_structure["models.camembert"].append("CamembertTokenizer") + _import_structure["models.deberta_v2"].append("DebertaV2Tokenizer") + _import_structure["models.m2m_100"].append("M2M100Tokenizer") + _import_structure["models.marian"].append("MarianTokenizer") + _import_structure["models.mbart"].append("MBartTokenizer") + _import_structure["models.mbart"].append("MBart50Tokenizer") + _import_structure["models.mt5"].append("MT5Tokenizer") + _import_structure["models.pegasus"].append("PegasusTokenizer") + _import_structure["models.reformer"].append("ReformerTokenizer") + _import_structure["models.speech_to_text"].append("Speech2TextTokenizer") + _import_structure["models.t5"].append("T5Tokenizer") + _import_structure["models.xlm_prophetnet"].append("XLMProphetNetTokenizer") + _import_structure["models.xlm_roberta"].append("XLMRobertaTokenizer") + _import_structure["models.xlnet"].append("XLNetTokenizer") +else: + from .utils import dummy_sentencepiece_objects + + _import_structure["utils.dummy_sentencepiece_objects"] = [ + name for name in dir(dummy_sentencepiece_objects) if not name.startswith("_") + ] + +# tokenizers-backed objects +if is_tokenizers_available(): + # Fast tokenizers + _import_structure["models.roformer"].append("RoFormerTokenizerFast") + _import_structure["models.clip"].append("CLIPTokenizerFast") + _import_structure["models.convbert"].append("ConvBertTokenizerFast") + _import_structure["models.albert"].append("AlbertTokenizerFast") + _import_structure["models.bart"].append("BartTokenizerFast") + _import_structure["models.barthez"].append("BarthezTokenizerFast") + _import_structure["models.bert"].append("BertTokenizerFast") + _import_structure["models.big_bird"].append("BigBirdTokenizerFast") + _import_structure["models.camembert"].append("CamembertTokenizerFast") + _import_structure["models.deberta"].append("DebertaTokenizerFast") + _import_structure["models.distilbert"].append("DistilBertTokenizerFast") + _import_structure["models.dpr"].extend( + ["DPRContextEncoderTokenizerFast", "DPRQuestionEncoderTokenizerFast", "DPRReaderTokenizerFast"] + ) + _import_structure["models.electra"].append("ElectraTokenizerFast") + _import_structure["models.funnel"].append("FunnelTokenizerFast") + _import_structure["models.gpt2"].append("GPT2TokenizerFast") + _import_structure["models.herbert"].append("HerbertTokenizerFast") + _import_structure["models.layoutlm"].append("LayoutLMTokenizerFast") + _import_structure["models.led"].append("LEDTokenizerFast") + _import_structure["models.longformer"].append("LongformerTokenizerFast") + _import_structure["models.lxmert"].append("LxmertTokenizerFast") + _import_structure["models.mbart"].append("MBartTokenizerFast") + _import_structure["models.mbart"].append("MBart50TokenizerFast") + _import_structure["models.mobilebert"].append("MobileBertTokenizerFast") + _import_structure["models.mpnet"].append("MPNetTokenizerFast") + _import_structure["models.mt5"].append("MT5TokenizerFast") + _import_structure["models.openai"].append("OpenAIGPTTokenizerFast") + _import_structure["models.pegasus"].append("PegasusTokenizerFast") + _import_structure["models.reformer"].append("ReformerTokenizerFast") + _import_structure["models.retribert"].append("RetriBertTokenizerFast") + _import_structure["models.roberta"].append("RobertaTokenizerFast") + _import_structure["models.squeezebert"].append("SqueezeBertTokenizerFast") + _import_structure["models.t5"].append("T5TokenizerFast") + _import_structure["models.xlm_roberta"].append("XLMRobertaTokenizerFast") + _import_structure["models.xlnet"].append("XLNetTokenizerFast") + _import_structure["tokenization_utils_fast"] = ["PreTrainedTokenizerFast"] + +else: + from .utils import dummy_tokenizers_objects + + _import_structure["utils.dummy_tokenizers_objects"] = [ + name for name in dir(dummy_tokenizers_objects) if not name.startswith("_") + ] + +if is_sentencepiece_available() and is_tokenizers_available(): + _import_structure["convert_slow_tokenizer"] = ["SLOW_TO_FAST_CONVERTERS", "convert_slow_tokenizer"] +else: + from .utils import dummy_sentencepiece_and_tokenizers_objects + + _import_structure["utils.dummy_sentencepiece_and_tokenizers_objects"] = [ + name for name in dir(dummy_sentencepiece_and_tokenizers_objects) if not name.startswith("_") + ] + +# Speech-specific objects +if is_speech_available(): + _import_structure["models.speech_to_text"].append("Speech2TextFeatureExtractor") + +else: + from .utils import dummy_speech_objects + + _import_structure["utils.dummy_speech_objects"] = [ + name for name in dir(dummy_speech_objects) if not name.startswith("_") + ] + +if is_sentencepiece_available() and is_speech_available(): + _import_structure["models.speech_to_text"].append("Speech2TextProcessor") +else: + from .utils import dummy_sentencepiece_and_speech_objects + + _import_structure["utils.dummy_sentencepiece_and_speech_objects"] = [ + name for name in dir(dummy_sentencepiece_and_speech_objects) if not name.startswith("_") + ] + +# Vision-specific objects +if is_vision_available(): + _import_structure["image_utils"] = ["ImageFeatureExtractionMixin"] + _import_structure["models.clip"].append("CLIPFeatureExtractor") + _import_structure["models.clip"].append("CLIPProcessor") + _import_structure["models.deit"].append("DeiTFeatureExtractor") + _import_structure["models.detr"].append("DetrFeatureExtractor") + _import_structure["models.vit"].append("ViTFeatureExtractor") +else: + from .utils import dummy_vision_objects + + _import_structure["utils.dummy_vision_objects"] = [ + name for name in dir(dummy_vision_objects) if not name.startswith("_") + ] + +# Timm-backed objects +if is_timm_available() and is_vision_available(): + _import_structure["models.detr"].extend( + [ + "DETR_PRETRAINED_MODEL_ARCHIVE_LIST", + "DetrForObjectDetection", + "DetrForSegmentation", + "DetrModel", + "DetrPreTrainedModel", + ] + ) +else: + from .utils import dummy_timm_objects + + _import_structure["utils.dummy_timm_objects"] = [ + name for name in dir(dummy_timm_objects) if not name.startswith("_") + ] + +# PyTorch-backed objects +if is_torch_available(): + _import_structure["benchmark.benchmark"] = ["PyTorchBenchmark"] + _import_structure["benchmark.benchmark_args"] = ["PyTorchBenchmarkArguments"] + _import_structure["data.data_collator"] = [ + "DataCollator", + "DataCollatorForLanguageModeling", + "DataCollatorForPermutationLanguageModeling", + "DataCollatorForSeq2Seq", + "DataCollatorForSOP", + "DataCollatorForTokenClassification", + "DataCollatorForWholeWordMask", + "DataCollatorWithPadding", + "default_data_collator", + ] + _import_structure["data.datasets"] = [ + "GlueDataset", + "GlueDataTrainingArguments", + "LineByLineTextDataset", + "LineByLineWithRefDataset", + "LineByLineWithSOPTextDataset", + "SquadDataset", + "SquadDataTrainingArguments", + "TextDataset", + "TextDatasetForNextSentencePrediction", + ] + _import_structure["generation_beam_search"] = ["BeamScorer", "BeamSearchScorer"] + _import_structure["generation_logits_process"] = [ + "ForcedBOSTokenLogitsProcessor", + "ForcedEOSTokenLogitsProcessor", + "HammingDiversityLogitsProcessor", + "InfNanRemoveLogitsProcessor", + "LogitsProcessor", + "LogitsProcessorList", + "LogitsWarper", + "MinLengthLogitsProcessor", + "NoBadWordsLogitsProcessor", + "NoRepeatNGramLogitsProcessor", + "PrefixConstrainedLogitsProcessor", + "RepetitionPenaltyLogitsProcessor", + "TemperatureLogitsWarper", + "TopKLogitsWarper", + "TopPLogitsWarper", + ] + _import_structure["generation_stopping_criteria"] = [ + "MaxLengthCriteria", + "MaxTimeCriteria", + "StoppingCriteria", + "StoppingCriteriaList", + ] + _import_structure["generation_utils"] = ["top_k_top_p_filtering"] + _import_structure["modeling_utils"] = ["Conv1D", "PreTrainedModel", "apply_chunking_to_forward", "prune_layer"] + + # PyTorch models structure + _import_structure["models.albert"].extend( + [ + "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "AlbertForMaskedLM", + "AlbertForMultipleChoice", + "AlbertForPreTraining", + "AlbertForQuestionAnswering", + "AlbertForSequenceClassification", + "AlbertForTokenClassification", + "AlbertModel", + "AlbertPreTrainedModel", + "load_tf_weights_in_albert", + ] + ) + _import_structure["models.auto"].extend( + [ + "MODEL_FOR_CAUSAL_LM_MAPPING", + "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", + "MODEL_FOR_MASKED_LM_MAPPING", + "MODEL_FOR_MULTIPLE_CHOICE_MAPPING", + "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", + "MODEL_FOR_OBJECT_DETECTION_MAPPING", + "MODEL_FOR_PRETRAINING_MAPPING", + "MODEL_FOR_QUESTION_ANSWERING_MAPPING", + "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", + "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", + "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", + "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", + "MODEL_MAPPING", + "MODEL_WITH_LM_HEAD_MAPPING", + "AutoModel", + "AutoModelForCausalLM", + "AutoModelForImageClassification", + "AutoModelForMaskedLM", + "AutoModelForMultipleChoice", + "AutoModelForNextSentencePrediction", + "AutoModelForPreTraining", + "AutoModelForQuestionAnswering", + "AutoModelForSeq2SeqLM", + "AutoModelForSequenceClassification", + "AutoModelForTableQuestionAnswering", + "AutoModelForTokenClassification", + "AutoModelWithLMHead", + ] + ) + + _import_structure["models.bart"].extend( + [ + "BART_PRETRAINED_MODEL_ARCHIVE_LIST", + "BartForCausalLM", + "BartForConditionalGeneration", + "BartForQuestionAnswering", + "BartForSequenceClassification", + "BartModel", + "BartPretrainedModel", + "PretrainedBartModel", + ] + ) + _import_structure["models.bert"].extend( + [ + "BERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "BertForMaskedLM", + "BertForMultipleChoice", + "BertForNextSentencePrediction", + "BertForPreTraining", + "BertForQuestionAnswering", + "BertForSequenceClassification", + "BertForTokenClassification", + "BertLayer", + "BertLMHeadModel", + "BertModel", + "BertPreTrainedModel", + "load_tf_weights_in_bert", + ] + ) + _import_structure["models.bert_generation"].extend( + [ + "BertGenerationDecoder", + "BertGenerationEncoder", + "BertGenerationPreTrainedModel", + "load_tf_weights_in_bert_generation", + ] + ) + _import_structure["models.big_bird"].extend( + [ + "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST", + "BigBirdForCausalLM", + "BigBirdForMaskedLM", + "BigBirdForMultipleChoice", + "BigBirdForPreTraining", + "BigBirdForQuestionAnswering", + "BigBirdForSequenceClassification", + "BigBirdForTokenClassification", + "BigBirdLayer", + "BigBirdModel", + "BigBirdPreTrainedModel", + "load_tf_weights_in_big_bird", + ] + ) + _import_structure["models.bigbird_pegasus"].extend( + [ + "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST", + "BigBirdPegasusForCausalLM", + "BigBirdPegasusForConditionalGeneration", + "BigBirdPegasusForQuestionAnswering", + "BigBirdPegasusForSequenceClassification", + "BigBirdPegasusModel", + "BigBirdPegasusPreTrainedModel", + ] + ) + _import_structure["models.blenderbot"].extend( + [ + "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST", + "BlenderbotForCausalLM", + "BlenderbotForConditionalGeneration", + "BlenderbotModel", + "BlenderbotPreTrainedModel", + ] + ) + _import_structure["models.blenderbot_small"].extend( + [ + "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST", + "BlenderbotSmallForCausalLM", + "BlenderbotSmallForConditionalGeneration", + "BlenderbotSmallModel", + "BlenderbotSmallPreTrainedModel", + ] + ) + _import_structure["models.camembert"].extend( + [ + "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "CamembertForCausalLM", + "CamembertForMaskedLM", + "CamembertForMultipleChoice", + "CamembertForQuestionAnswering", + "CamembertForSequenceClassification", + "CamembertForTokenClassification", + "CamembertModel", + ] + ) + _import_structure["models.canine"].extend( + [ + "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST", + "CanineForMultipleChoice", + "CanineForQuestionAnswering", + "CanineForSequenceClassification", + "CanineForTokenClassification", + "CanineLayer", + "CanineModel", + "CaninePreTrainedModel", + "load_tf_weights_in_canine", + ] + ) + _import_structure["models.clip"].extend( + [ + "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST", + "CLIPModel", + "CLIPPreTrainedModel", + "CLIPTextModel", + "CLIPVisionModel", + ] + ) + _import_structure["models.convbert"].extend( + [ + "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "ConvBertForMaskedLM", + "ConvBertForMultipleChoice", + "ConvBertForQuestionAnswering", + "ConvBertForSequenceClassification", + "ConvBertForTokenClassification", + "ConvBertLayer", + "ConvBertModel", + "ConvBertPreTrainedModel", + "load_tf_weights_in_convbert", + ] + ) + _import_structure["models.ctrl"].extend( + [ + "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST", + "CTRLForSequenceClassification", + "CTRLLMHeadModel", + "CTRLModel", + "CTRLPreTrainedModel", + ] + ) + _import_structure["models.deberta"].extend( + [ + "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", + "DebertaForMaskedLM", + "DebertaForQuestionAnswering", + "DebertaForSequenceClassification", + "DebertaForTokenClassification", + "DebertaModel", + "DebertaPreTrainedModel", + ] + ) + _import_structure["models.deberta_v2"].extend( + [ + "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST", + "DebertaV2ForMaskedLM", + "DebertaV2ForQuestionAnswering", + "DebertaV2ForSequenceClassification", + "DebertaV2ForTokenClassification", + "DebertaV2Model", + "DebertaV2PreTrainedModel", + ] + ) + _import_structure["models.deit"].extend( + [ + "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST", + "DeiTForImageClassification", + "DeiTForImageClassificationWithTeacher", + "DeiTModel", + "DeiTPreTrainedModel", + ] + ) + _import_structure["models.distilbert"].extend( + [ + "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "DistilBertForMaskedLM", + "DistilBertForMultipleChoice", + "DistilBertForQuestionAnswering", + "DistilBertForSequenceClassification", + "DistilBertForTokenClassification", + "DistilBertModel", + "DistilBertPreTrainedModel", + ] + ) + _import_structure["models.dpr"].extend( + [ + "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", + "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", + "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST", + "DPRContextEncoder", + "DPRPretrainedContextEncoder", + "DPRPretrainedQuestionEncoder", + "DPRPretrainedReader", + "DPRQuestionEncoder", + "DPRReader", + ] + ) + _import_structure["models.electra"].extend( + [ + "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", + "ElectraForMaskedLM", + "ElectraForMultipleChoice", + "ElectraForPreTraining", + "ElectraForQuestionAnswering", + "ElectraForSequenceClassification", + "ElectraForTokenClassification", + "ElectraModel", + "ElectraPreTrainedModel", + "load_tf_weights_in_electra", + ] + ) + _import_structure["models.encoder_decoder"].append("EncoderDecoderModel") + _import_structure["models.flaubert"].extend( + [ + "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "FlaubertForMultipleChoice", + "FlaubertForQuestionAnswering", + "FlaubertForQuestionAnsweringSimple", + "FlaubertForSequenceClassification", + "FlaubertForTokenClassification", + "FlaubertModel", + "FlaubertWithLMHeadModel", + ] + ) + _import_structure["models.fsmt"].extend(["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]) + _import_structure["models.funnel"].extend( + [ + "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST", + "FunnelBaseModel", + "FunnelForMaskedLM", + "FunnelForMultipleChoice", + "FunnelForPreTraining", + "FunnelForQuestionAnswering", + "FunnelForSequenceClassification", + "FunnelForTokenClassification", + "FunnelModel", + "FunnelPreTrainedModel", + "load_tf_weights_in_funnel", + ] + ) + _import_structure["models.gpt2"].extend( + [ + "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST", + "GPT2DoubleHeadsModel", + "GPT2ForSequenceClassification", + "GPT2LMHeadModel", + "GPT2Model", + "GPT2PreTrainedModel", + "load_tf_weights_in_gpt2", + ] + ) + _import_structure["models.gpt_neo"].extend( + [ + "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST", + "GPTNeoForCausalLM", + "GPTNeoForSequenceClassification", + "GPTNeoModel", + "GPTNeoPreTrainedModel", + "load_tf_weights_in_gpt_neo", + ] + ) + _import_structure["models.hubert"].extend( + [ + "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "HubertForCTC", + "HubertModel", + "HubertPreTrainedModel", + ] + ) + _import_structure["models.ibert"].extend( + [ + "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "IBertForMaskedLM", + "IBertForMultipleChoice", + "IBertForQuestionAnswering", + "IBertForSequenceClassification", + "IBertForTokenClassification", + "IBertModel", + "IBertPreTrainedModel", + ] + ) + _import_structure["models.layoutlm"].extend( + [ + "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST", + "LayoutLMForMaskedLM", + "LayoutLMForSequenceClassification", + "LayoutLMForTokenClassification", + "LayoutLMModel", + "LayoutLMPreTrainedModel", + ] + ) + _import_structure["models.led"].extend( + [ + "LED_PRETRAINED_MODEL_ARCHIVE_LIST", + "LEDForConditionalGeneration", + "LEDForQuestionAnswering", + "LEDForSequenceClassification", + "LEDModel", + "LEDPreTrainedModel", + ] + ) + _import_structure["models.longformer"].extend( + [ + "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", + "LongformerForMaskedLM", + "LongformerForMultipleChoice", + "LongformerForQuestionAnswering", + "LongformerForSequenceClassification", + "LongformerForTokenClassification", + "LongformerModel", + "LongformerPreTrainedModel", + "LongformerSelfAttention", + ] + ) + _import_structure["models.luke"].extend( + [ + "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST", + "LukeForEntityClassification", + "LukeForEntityPairClassification", + "LukeForEntitySpanClassification", + "LukeModel", + "LukePreTrainedModel", + ] + ) + _import_structure["models.lxmert"].extend( + [ + "LxmertEncoder", + "LxmertForPreTraining", + "LxmertForQuestionAnswering", + "LxmertModel", + "LxmertPreTrainedModel", + "LxmertVisualFeatureEncoder", + "LxmertXLayer", + ] + ) + _import_structure["models.m2m_100"].extend( + [ + "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST", + "M2M100ForConditionalGeneration", + "M2M100Model", + "M2M100PreTrainedModel", + ] + ) + _import_structure["models.marian"].extend(["MarianForCausalLM", "MarianModel", "MarianMTModel"]) + _import_structure["models.mbart"].extend( + [ + "MBartForCausalLM", + "MBartForConditionalGeneration", + "MBartForQuestionAnswering", + "MBartForSequenceClassification", + "MBartModel", + "MBartPreTrainedModel", + ] + ) + _import_structure["models.megatron_bert"].extend( + [ + "MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "MegatronBertForCausalLM", + "MegatronBertForMaskedLM", + "MegatronBertForMultipleChoice", + "MegatronBertForNextSentencePrediction", + "MegatronBertForPreTraining", + "MegatronBertForQuestionAnswering", + "MegatronBertForSequenceClassification", + "MegatronBertForTokenClassification", + "MegatronBertModel", + "MegatronBertPreTrainedModel", + ] + ) + _import_structure["models.mmbt"].extend(["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]) + _import_structure["models.mobilebert"].extend( + [ + "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "MobileBertForMaskedLM", + "MobileBertForMultipleChoice", + "MobileBertForNextSentencePrediction", + "MobileBertForPreTraining", + "MobileBertForQuestionAnswering", + "MobileBertForSequenceClassification", + "MobileBertForTokenClassification", + "MobileBertLayer", + "MobileBertModel", + "MobileBertPreTrainedModel", + "load_tf_weights_in_mobilebert", + ] + ) + _import_structure["models.mpnet"].extend( + [ + "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST", + "MPNetForMaskedLM", + "MPNetForMultipleChoice", + "MPNetForQuestionAnswering", + "MPNetForSequenceClassification", + "MPNetForTokenClassification", + "MPNetLayer", + "MPNetModel", + "MPNetPreTrainedModel", + ] + ) + _import_structure["models.mt5"].extend(["MT5EncoderModel", "MT5ForConditionalGeneration", "MT5Model"]) + _import_structure["models.openai"].extend( + [ + "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST", + "OpenAIGPTDoubleHeadsModel", + "OpenAIGPTForSequenceClassification", + "OpenAIGPTLMHeadModel", + "OpenAIGPTModel", + "OpenAIGPTPreTrainedModel", + "load_tf_weights_in_openai_gpt", + ] + ) + _import_structure["models.pegasus"].extend( + ["PegasusForCausalLM", "PegasusForConditionalGeneration", "PegasusModel", "PegasusPreTrainedModel"] + ) + _import_structure["models.prophetnet"].extend( + [ + "PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST", + "ProphetNetDecoder", + "ProphetNetEncoder", + "ProphetNetForCausalLM", + "ProphetNetForConditionalGeneration", + "ProphetNetModel", + "ProphetNetPreTrainedModel", + ] + ) + _import_structure["models.rag"].extend( + ["RagModel", "RagPreTrainedModel", "RagSequenceForGeneration", "RagTokenForGeneration"] + ) + _import_structure["models.reformer"].extend( + [ + "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", + "ReformerAttention", + "ReformerForMaskedLM", + "ReformerForQuestionAnswering", + "ReformerForSequenceClassification", + "ReformerLayer", + "ReformerModel", + "ReformerModelWithLMHead", + "ReformerPreTrainedModel", + ] + ) + _import_structure["models.retribert"].extend( + ["RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RetriBertModel", "RetriBertPreTrainedModel"] + ) + _import_structure["models.roberta"].extend( + [ + "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", + "RobertaForCausalLM", + "RobertaForMaskedLM", + "RobertaForMultipleChoice", + "RobertaForQuestionAnswering", + "RobertaForSequenceClassification", + "RobertaForTokenClassification", + "RobertaModel", + "RobertaPreTrainedModel", + ] + ) + _import_structure["models.roformer"].extend( + [ + "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", + "RoFormerForCausalLM", + "RoFormerForMaskedLM", + "RoFormerForMultipleChoice", + "RoFormerForQuestionAnswering", + "RoFormerForSequenceClassification", + "RoFormerForTokenClassification", + "RoFormerLayer", + "RoFormerModel", + "RoFormerPreTrainedModel", + "load_tf_weights_in_roformer", + ] + ) + _import_structure["models.speech_to_text"].extend( + [ + "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST", + "Speech2TextForConditionalGeneration", + "Speech2TextModel", + "Speech2TextPreTrainedModel", + ] + ) + _import_structure["models.squeezebert"].extend( + [ + "SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "SqueezeBertForMaskedLM", + "SqueezeBertForMultipleChoice", + "SqueezeBertForQuestionAnswering", + "SqueezeBertForSequenceClassification", + "SqueezeBertForTokenClassification", + "SqueezeBertModel", + "SqueezeBertModule", + "SqueezeBertPreTrainedModel", + ] + ) + _import_structure["models.t5"].extend( + [ + "T5_PRETRAINED_MODEL_ARCHIVE_LIST", + "T5EncoderModel", + "T5ForConditionalGeneration", + "T5Model", + "T5PreTrainedModel", + "load_tf_weights_in_t5", + ] + ) + _import_structure["models.tapas"].extend( + [ + "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", + "TapasForMaskedLM", + "TapasForQuestionAnswering", + "TapasForSequenceClassification", + "TapasModel", + "TapasPreTrainedModel", + ] + ) + _import_structure["models.transfo_xl"].extend( + [ + "TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", + "AdaptiveEmbedding", + "TransfoXLForSequenceClassification", + "TransfoXLLMHeadModel", + "TransfoXLModel", + "TransfoXLPreTrainedModel", + "load_tf_weights_in_transfo_xl", + ] + ) + _import_structure["models.visual_bert"].extend( + [ + "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "VisualBertForMultipleChoice", + "VisualBertForPreTraining", + "VisualBertForQuestionAnswering", + "VisualBertForRegionToPhraseAlignment", + "VisualBertForVisualReasoning", + "VisualBertLayer", + "VisualBertModel", + "VisualBertPreTrainedModel", + ] + ) + _import_structure["models.vit"].extend( + [ + "VIT_PRETRAINED_MODEL_ARCHIVE_LIST", + "ViTForImageClassification", + "ViTModel", + "ViTPreTrainedModel", + ] + ) + _import_structure["models.wav2vec2"].extend( + [ + "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST", + "Wav2Vec2ForCTC", + "Wav2Vec2ForMaskedLM", + "Wav2Vec2ForPreTraining", + "Wav2Vec2Model", + "Wav2Vec2PreTrainedModel", + ] + ) + _import_structure["models.xlm"].extend( + [ + "XLM_PRETRAINED_MODEL_ARCHIVE_LIST", + "XLMForMultipleChoice", + "XLMForQuestionAnswering", + "XLMForQuestionAnsweringSimple", + "XLMForSequenceClassification", + "XLMForTokenClassification", + "XLMModel", + "XLMPreTrainedModel", + "XLMWithLMHeadModel", + ] + ) + _import_structure["models.xlm_prophetnet"].extend( + [ + "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST", + "XLMProphetNetDecoder", + "XLMProphetNetEncoder", + "XLMProphetNetForCausalLM", + "XLMProphetNetForConditionalGeneration", + "XLMProphetNetModel", + ] + ) + _import_structure["models.xlm_roberta"].extend( + [ + "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", + "XLMRobertaForCausalLM", + "XLMRobertaForMaskedLM", + "XLMRobertaForMultipleChoice", + "XLMRobertaForQuestionAnswering", + "XLMRobertaForSequenceClassification", + "XLMRobertaForTokenClassification", + "XLMRobertaModel", + ] + ) + _import_structure["models.xlnet"].extend( + [ + "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST", + "XLNetForMultipleChoice", + "XLNetForQuestionAnswering", + "XLNetForQuestionAnsweringSimple", + "XLNetForSequenceClassification", + "XLNetForTokenClassification", + "XLNetLMHeadModel", + "XLNetModel", + "XLNetPreTrainedModel", + "load_tf_weights_in_xlnet", + ] + ) + _import_structure["optimization"] = [ + "Adafactor", + "AdamW", + "get_constant_schedule", + "get_constant_schedule_with_warmup", + "get_cosine_schedule_with_warmup", + "get_cosine_with_hard_restarts_schedule_with_warmup", + "get_linear_schedule_with_warmup", + "get_polynomial_decay_schedule_with_warmup", + "get_scheduler", + ] + _import_structure["trainer"] = ["Trainer"] + _import_structure["trainer_pt_utils"] = ["torch_distributed_zero_first"] + _import_structure["trainer_seq2seq"] = ["Seq2SeqTrainer"] +else: + from .utils import dummy_pt_objects + + _import_structure["utils.dummy_pt_objects"] = [name for name in dir(dummy_pt_objects) if not name.startswith("_")] + +# TensorFlow-backed objects +if is_tf_available(): + _import_structure["benchmark.benchmark_args_tf"] = ["TensorFlowBenchmarkArguments"] + _import_structure["benchmark.benchmark_tf"] = ["TensorFlowBenchmark"] + _import_structure["generation_tf_utils"] = ["tf_top_k_top_p_filtering"] + _import_structure["modeling_tf_utils"] = [ + "TFPreTrainedModel", + "TFSequenceSummary", + "TFSharedEmbeddings", + "shape_list", + ] + # TensorFlow models structure + _import_structure["models.albert"].extend( + [ + "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFAlbertForMaskedLM", + "TFAlbertForMultipleChoice", + "TFAlbertForPreTraining", + "TFAlbertForQuestionAnswering", + "TFAlbertForSequenceClassification", + "TFAlbertForTokenClassification", + "TFAlbertMainLayer", + "TFAlbertModel", + "TFAlbertPreTrainedModel", + ] + ) + _import_structure["models.auto"].extend( + [ + "TF_MODEL_FOR_CAUSAL_LM_MAPPING", + "TF_MODEL_FOR_MASKED_LM_MAPPING", + "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", + "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", + "TF_MODEL_FOR_PRETRAINING_MAPPING", + "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING", + "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", + "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", + "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", + "TF_MODEL_MAPPING", + "TF_MODEL_WITH_LM_HEAD_MAPPING", + "TFAutoModel", + "TFAutoModelForCausalLM", + "TFAutoModelForMaskedLM", + "TFAutoModelForMultipleChoice", + "TFAutoModelForPreTraining", + "TFAutoModelForQuestionAnswering", + "TFAutoModelForSeq2SeqLM", + "TFAutoModelForSequenceClassification", + "TFAutoModelForTokenClassification", + "TFAutoModelWithLMHead", + ] + ) + _import_structure["models.bart"].extend(["TFBartForConditionalGeneration", "TFBartModel", "TFBartPretrainedModel"]) + _import_structure["models.bert"].extend( + [ + "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFBertEmbeddings", + "TFBertForMaskedLM", + "TFBertForMultipleChoice", + "TFBertForNextSentencePrediction", + "TFBertForPreTraining", + "TFBertForQuestionAnswering", + "TFBertForSequenceClassification", + "TFBertForTokenClassification", + "TFBertLMHeadModel", + "TFBertMainLayer", + "TFBertModel", + "TFBertPreTrainedModel", + ] + ) + _import_structure["models.blenderbot"].extend( + ["TFBlenderbotForConditionalGeneration", "TFBlenderbotModel", "TFBlenderbotPreTrainedModel"] + ) + _import_structure["models.blenderbot_small"].extend( + ["TFBlenderbotSmallForConditionalGeneration", "TFBlenderbotSmallModel", "TFBlenderbotSmallPreTrainedModel"] + ) + _import_structure["models.camembert"].extend( + [ + "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFCamembertForMaskedLM", + "TFCamembertForMultipleChoice", + "TFCamembertForQuestionAnswering", + "TFCamembertForSequenceClassification", + "TFCamembertForTokenClassification", + "TFCamembertModel", + ] + ) + _import_structure["models.convbert"].extend( + [ + "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFConvBertForMaskedLM", + "TFConvBertForMultipleChoice", + "TFConvBertForQuestionAnswering", + "TFConvBertForSequenceClassification", + "TFConvBertForTokenClassification", + "TFConvBertLayer", + "TFConvBertModel", + "TFConvBertPreTrainedModel", + ] + ) + _import_structure["models.ctrl"].extend( + [ + "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFCTRLForSequenceClassification", + "TFCTRLLMHeadModel", + "TFCTRLModel", + "TFCTRLPreTrainedModel", + ] + ) + _import_structure["models.distilbert"].extend( + [ + "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFDistilBertForMaskedLM", + "TFDistilBertForMultipleChoice", + "TFDistilBertForQuestionAnswering", + "TFDistilBertForSequenceClassification", + "TFDistilBertForTokenClassification", + "TFDistilBertMainLayer", + "TFDistilBertModel", + "TFDistilBertPreTrainedModel", + ] + ) + _import_structure["models.dpr"].extend( + [ + "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", + "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", + "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFDPRContextEncoder", + "TFDPRPretrainedContextEncoder", + "TFDPRPretrainedQuestionEncoder", + "TFDPRPretrainedReader", + "TFDPRQuestionEncoder", + "TFDPRReader", + ] + ) + _import_structure["models.electra"].extend( + [ + "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFElectraForMaskedLM", + "TFElectraForMultipleChoice", + "TFElectraForPreTraining", + "TFElectraForQuestionAnswering", + "TFElectraForSequenceClassification", + "TFElectraForTokenClassification", + "TFElectraModel", + "TFElectraPreTrainedModel", + ] + ) + _import_structure["models.flaubert"].extend( + [ + "TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFFlaubertForMultipleChoice", + "TFFlaubertForQuestionAnsweringSimple", + "TFFlaubertForSequenceClassification", + "TFFlaubertForTokenClassification", + "TFFlaubertModel", + "TFFlaubertPreTrainedModel", + "TFFlaubertWithLMHeadModel", + ] + ) + _import_structure["models.funnel"].extend( + [ + "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFFunnelBaseModel", + "TFFunnelForMaskedLM", + "TFFunnelForMultipleChoice", + "TFFunnelForPreTraining", + "TFFunnelForQuestionAnswering", + "TFFunnelForSequenceClassification", + "TFFunnelForTokenClassification", + "TFFunnelModel", + "TFFunnelPreTrainedModel", + ] + ) + _import_structure["models.gpt2"].extend( + [ + "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFGPT2DoubleHeadsModel", + "TFGPT2ForSequenceClassification", + "TFGPT2LMHeadModel", + "TFGPT2MainLayer", + "TFGPT2Model", + "TFGPT2PreTrainedModel", + ] + ) + _import_structure["models.hubert"].extend( + [ + "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFHubertForCTC", + "TFHubertModel", + "TFHubertPreTrainedModel", + ] + ) + _import_structure["models.layoutlm"].extend( + [ + "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFLayoutLMForMaskedLM", + "TFLayoutLMForSequenceClassification", + "TFLayoutLMForTokenClassification", + "TFLayoutLMMainLayer", + "TFLayoutLMModel", + "TFLayoutLMPreTrainedModel", + ] + ) + _import_structure["models.led"].extend(["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]) + _import_structure["models.longformer"].extend( + [ + "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFLongformerForMaskedLM", + "TFLongformerForMultipleChoice", + "TFLongformerForQuestionAnswering", + "TFLongformerForSequenceClassification", + "TFLongformerForTokenClassification", + "TFLongformerModel", + "TFLongformerPreTrainedModel", + "TFLongformerSelfAttention", + ] + ) + _import_structure["models.lxmert"].extend( + [ + "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFLxmertForPreTraining", + "TFLxmertMainLayer", + "TFLxmertModel", + "TFLxmertPreTrainedModel", + "TFLxmertVisualFeatureEncoder", + ] + ) + _import_structure["models.marian"].extend(["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]) + _import_structure["models.mbart"].extend( + ["TFMBartForConditionalGeneration", "TFMBartModel", "TFMBartPreTrainedModel"] + ) + _import_structure["models.mobilebert"].extend( + [ + "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFMobileBertForMaskedLM", + "TFMobileBertForMultipleChoice", + "TFMobileBertForNextSentencePrediction", + "TFMobileBertForPreTraining", + "TFMobileBertForQuestionAnswering", + "TFMobileBertForSequenceClassification", + "TFMobileBertForTokenClassification", + "TFMobileBertMainLayer", + "TFMobileBertModel", + "TFMobileBertPreTrainedModel", + ] + ) + _import_structure["models.mpnet"].extend( + [ + "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFMPNetForMaskedLM", + "TFMPNetForMultipleChoice", + "TFMPNetForQuestionAnswering", + "TFMPNetForSequenceClassification", + "TFMPNetForTokenClassification", + "TFMPNetMainLayer", + "TFMPNetModel", + "TFMPNetPreTrainedModel", + ] + ) + _import_structure["models.mt5"].extend(["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]) + _import_structure["models.openai"].extend( + [ + "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFOpenAIGPTDoubleHeadsModel", + "TFOpenAIGPTForSequenceClassification", + "TFOpenAIGPTLMHeadModel", + "TFOpenAIGPTMainLayer", + "TFOpenAIGPTModel", + "TFOpenAIGPTPreTrainedModel", + ] + ) + _import_structure["models.pegasus"].extend( + ["TFPegasusForConditionalGeneration", "TFPegasusModel", "TFPegasusPreTrainedModel"] + ) + _import_structure["models.rag"].extend( + [ + "TFRagModel", + "TFRagPreTrainedModel", + "TFRagSequenceForGeneration", + "TFRagTokenForGeneration", + ] + ) + _import_structure["models.roberta"].extend( + [ + "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFRobertaForMaskedLM", + "TFRobertaForMultipleChoice", + "TFRobertaForQuestionAnswering", + "TFRobertaForSequenceClassification", + "TFRobertaForTokenClassification", + "TFRobertaMainLayer", + "TFRobertaModel", + "TFRobertaPreTrainedModel", + ] + ) + _import_structure["models.roformer"].extend( + [ + "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFRoFormerForCausalLM", + "TFRoFormerForMaskedLM", + "TFRoFormerForMultipleChoice", + "TFRoFormerForQuestionAnswering", + "TFRoFormerForSequenceClassification", + "TFRoFormerForTokenClassification", + "TFRoFormerLayer", + "TFRoFormerModel", + "TFRoFormerPreTrainedModel", + ] + ) + _import_structure["models.t5"].extend( + [ + "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFT5EncoderModel", + "TFT5ForConditionalGeneration", + "TFT5Model", + "TFT5PreTrainedModel", + ] + ) + _import_structure["models.transfo_xl"].extend( + [ + "TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFAdaptiveEmbedding", + "TFTransfoXLForSequenceClassification", + "TFTransfoXLLMHeadModel", + "TFTransfoXLMainLayer", + "TFTransfoXLModel", + "TFTransfoXLPreTrainedModel", + ] + ) + _import_structure["models.wav2vec2"].extend( + [ + "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFWav2Vec2ForCTC", + "TFWav2Vec2Model", + "TFWav2Vec2PreTrainedModel", + ] + ) + _import_structure["models.xlm"].extend( + [ + "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFXLMForMultipleChoice", + "TFXLMForQuestionAnsweringSimple", + "TFXLMForSequenceClassification", + "TFXLMForTokenClassification", + "TFXLMMainLayer", + "TFXLMModel", + "TFXLMPreTrainedModel", + "TFXLMWithLMHeadModel", + ] + ) + _import_structure["models.xlm_roberta"].extend( + [ + "TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFXLMRobertaForMaskedLM", + "TFXLMRobertaForMultipleChoice", + "TFXLMRobertaForQuestionAnswering", + "TFXLMRobertaForSequenceClassification", + "TFXLMRobertaForTokenClassification", + "TFXLMRobertaModel", + ] + ) + _import_structure["models.xlnet"].extend( + [ + "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFXLNetForMultipleChoice", + "TFXLNetForQuestionAnsweringSimple", + "TFXLNetForSequenceClassification", + "TFXLNetForTokenClassification", + "TFXLNetLMHeadModel", + "TFXLNetMainLayer", + "TFXLNetModel", + "TFXLNetPreTrainedModel", + ] + ) + _import_structure["optimization_tf"] = ["AdamWeightDecay", "GradientAccumulator", "WarmUp", "create_optimizer"] + _import_structure["trainer_tf"] = ["TFTrainer"] + +else: + from .utils import dummy_tf_objects + + _import_structure["utils.dummy_tf_objects"] = [name for name in dir(dummy_tf_objects) if not name.startswith("_")] + +# FLAX-backed objects +if is_flax_available(): + _import_structure["generation_flax_logits_process"] = [ + "FlaxForcedBOSTokenLogitsProcessor", + "FlaxForcedEOSTokenLogitsProcessor", + "FlaxLogitsProcessor", + "FlaxLogitsProcessorList", + "FlaxLogitsWarper", + "FlaxMinLengthLogitsProcessor", + "FlaxTemperatureLogitsWarper", + "FlaxTopKLogitsWarper", + "FlaxTopPLogitsWarper", + ] + _import_structure["modeling_flax_utils"] = ["FlaxPreTrainedModel"] + _import_structure["models.auto"].extend( + [ + "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING", + "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", + "FLAX_MODEL_FOR_MASKED_LM_MAPPING", + "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", + "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", + "FLAX_MODEL_FOR_PRETRAINING_MAPPING", + "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING", + "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", + "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", + "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", + "FLAX_MODEL_MAPPING", + "FlaxAutoModel", + "FlaxAutoModelForCausalLM", + "FlaxAutoModelForImageClassification", + "FlaxAutoModelForMaskedLM", + "FlaxAutoModelForMultipleChoice", + "FlaxAutoModelForNextSentencePrediction", + "FlaxAutoModelForPreTraining", + "FlaxAutoModelForQuestionAnswering", + "FlaxAutoModelForSeq2SeqLM", + "FlaxAutoModelForSequenceClassification", + "FlaxAutoModelForTokenClassification", + ] + ) + _import_structure["models.bart"].extend( + [ + "FlaxBartForConditionalGeneration", + "FlaxBartForQuestionAnswering", + "FlaxBartForSequenceClassification", + "FlaxBartModel", + "FlaxBartPreTrainedModel", + ] + ) + _import_structure["models.bert"].extend( + [ + "FlaxBertForMaskedLM", + "FlaxBertForMultipleChoice", + "FlaxBertForNextSentencePrediction", + "FlaxBertForPreTraining", + "FlaxBertForQuestionAnswering", + "FlaxBertForSequenceClassification", + "FlaxBertForTokenClassification", + "FlaxBertModel", + "FlaxBertPreTrainedModel", + ] + ) + _import_structure["models.big_bird"].extend( + [ + "FlaxBigBirdForMaskedLM", + "FlaxBigBirdForMultipleChoice", + "FlaxBigBirdForPreTraining", + "FlaxBigBirdForQuestionAnswering", + "FlaxBigBirdForSequenceClassification", + "FlaxBigBirdForTokenClassification", + "FlaxBigBirdModel", + "FlaxBigBirdPreTrainedModel", + ] + ) + _import_structure["models.clip"].extend( + [ + "FlaxCLIPModel", + "FlaxCLIPPreTrainedModel", + "FlaxCLIPTextModel", + "FlaxCLIPTextPreTrainedModel", + "FlaxCLIPVisionModel", + "FlaxCLIPVisionPreTrainedModel", + ] + ) + _import_structure["models.electra"].extend( + [ + "FlaxElectraForMaskedLM", + "FlaxElectraForMultipleChoice", + "FlaxElectraForPreTraining", + "FlaxElectraForQuestionAnswering", + "FlaxElectraForSequenceClassification", + "FlaxElectraForTokenClassification", + "FlaxElectraModel", + "FlaxElectraPreTrainedModel", + ] + ) + _import_structure["models.gpt2"].extend(["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]) + _import_structure["models.gpt_neo"].extend( + ["FlaxGPTNeoForCausalLM", "FlaxGPTNeoModel", "FlaxGPTNeoPreTrainedModel"] + ) + _import_structure["models.marian"].extend( + [ + "FlaxMarianModel", + "FlaxMarianMTModel", + "FlaxMarianPreTrainedModel", + ] + ) + _import_structure["models.mbart"].extend( + [ + "FlaxMBartForConditionalGeneration", + "FlaxMBartForQuestionAnswering", + "FlaxMBartForSequenceClassification", + "FlaxMBartModel", + "FlaxMBartPreTrainedModel", + ] + ) + _import_structure["models.roberta"].extend( + [ + "FlaxRobertaForMaskedLM", + "FlaxRobertaForMultipleChoice", + "FlaxRobertaForQuestionAnswering", + "FlaxRobertaForSequenceClassification", + "FlaxRobertaForTokenClassification", + "FlaxRobertaModel", + "FlaxRobertaPreTrainedModel", + ] + ) + _import_structure["models.t5"].extend(["FlaxT5ForConditionalGeneration", "FlaxT5Model", "FlaxT5PreTrainedModel"]) + _import_structure["models.vit"].extend(["FlaxViTForImageClassification", "FlaxViTModel", "FlaxViTPreTrainedModel"]) + _import_structure["models.wav2vec2"].extend( + ["FlaxWav2Vec2ForCTC", "FlaxWav2Vec2ForPreTraining", "FlaxWav2Vec2Model", "FlaxWav2Vec2PreTrainedModel"] + ) +else: + from .utils import dummy_flax_objects + + _import_structure["utils.dummy_flax_objects"] = [ + name for name in dir(dummy_flax_objects) if not name.startswith("_") + ] + +# Direct imports for type-checking +if TYPE_CHECKING: + # Configuration + from .configuration_utils import PretrainedConfig + + # Data + from .data import ( + DataProcessor, + InputExample, + InputFeatures, + SingleSentenceClassificationProcessor, + SquadExample, + SquadFeatures, + SquadV1Processor, + SquadV2Processor, + glue_compute_metrics, + glue_convert_examples_to_features, + glue_output_modes, + glue_processors, + glue_tasks_num_labels, + squad_convert_examples_to_features, + xnli_compute_metrics, + xnli_output_modes, + xnli_processors, + xnli_tasks_num_labels, + ) + + # Feature Extractor + from .feature_extraction_utils import BatchFeature, SequenceFeatureExtractor + + # Files and general utilities + from .file_utils import ( + CONFIG_NAME, + MODEL_CARD_NAME, + PYTORCH_PRETRAINED_BERT_CACHE, + PYTORCH_TRANSFORMERS_CACHE, + SPIECE_UNDERLINE, + TF2_WEIGHTS_NAME, + TF_WEIGHTS_NAME, + TRANSFORMERS_CACHE, + WEIGHTS_NAME, + TensorType, + add_end_docstrings, + add_start_docstrings, + cached_path, + is_apex_available, + is_datasets_available, + is_faiss_available, + is_flax_available, + is_psutil_available, + is_py3nvml_available, + is_scipy_available, + is_sentencepiece_available, + is_sklearn_available, + is_speech_available, + is_tf_available, + is_timm_available, + is_tokenizers_available, + is_torch_available, + is_torch_tpu_available, + is_vision_available, + ) + from .hf_argparser import HfArgumentParser + + # Integrations + from .integrations import ( + is_comet_available, + is_optuna_available, + is_ray_available, + is_ray_tune_available, + is_tensorboard_available, + is_wandb_available, + ) + + # Model Cards + from .modelcard import ModelCard + + # TF 2.0 <=> PyTorch conversion utilities + from .modeling_tf_pytorch_utils import ( + convert_tf_weight_name_to_pt_weight_name, + load_pytorch_checkpoint_in_tf2_model, + load_pytorch_model_in_tf2_model, + load_pytorch_weights_in_tf2_model, + load_tf2_checkpoint_in_pytorch_model, + load_tf2_model_in_pytorch_model, + load_tf2_weights_in_pytorch_model, + ) + from .models.albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig + from .models.auto import ( + ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, + CONFIG_MAPPING, + FEATURE_EXTRACTOR_MAPPING, + MODEL_NAMES_MAPPING, + TOKENIZER_MAPPING, + AutoConfig, + AutoFeatureExtractor, + AutoTokenizer, + ) + from .models.bart import BartConfig, BartTokenizer + from .models.bert import ( + BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, + BasicTokenizer, + BertConfig, + BertTokenizer, + WordpieceTokenizer, + ) + from .models.bert_generation import BertGenerationConfig + from .models.bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer + from .models.bertweet import BertweetTokenizer + from .models.big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdTokenizer + from .models.bigbird_pegasus import BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdPegasusConfig + from .models.blenderbot import BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig, BlenderbotTokenizer + from .models.blenderbot_small import ( + BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP, + BlenderbotSmallConfig, + BlenderbotSmallTokenizer, + ) + from .models.byt5 import ByT5Tokenizer + from .models.camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig + from .models.canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig, CanineTokenizer + from .models.clip import ( + CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, + CLIPConfig, + CLIPTextConfig, + CLIPTokenizer, + CLIPVisionConfig, + ) + from .models.convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertTokenizer + from .models.cpm import CpmTokenizer + from .models.ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig, CTRLTokenizer + from .models.deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaTokenizer + from .models.deberta_v2 import DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaV2Config + from .models.deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig + from .models.detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig + from .models.distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig, DistilBertTokenizer + from .models.dpr import ( + DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, + DPRConfig, + DPRContextEncoderTokenizer, + DPRQuestionEncoderTokenizer, + DPRReaderOutput, + DPRReaderTokenizer, + ) + from .models.electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraTokenizer + from .models.encoder_decoder import EncoderDecoderConfig + from .models.flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertTokenizer + from .models.fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig, FSMTTokenizer + from .models.funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig, FunnelTokenizer + from .models.gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2Tokenizer + from .models.gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig + from .models.herbert import HerbertTokenizer + from .models.hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig + from .models.ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig + from .models.layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMTokenizer + from .models.led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig, LEDTokenizer + from .models.longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig, LongformerTokenizer + from .models.luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig, LukeTokenizer + from .models.lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig, LxmertTokenizer + from .models.m2m_100 import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, M2M100Config + from .models.marian import MarianConfig + from .models.mbart import MBartConfig + from .models.megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig + from .models.mmbt import MMBTConfig + from .models.mobilebert import MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileBertConfig, MobileBertTokenizer + from .models.mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig, MPNetTokenizer + from .models.mt5 import MT5Config + from .models.openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig, OpenAIGPTTokenizer + from .models.pegasus import PegasusConfig + from .models.phobert import PhobertTokenizer + from .models.prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig, ProphetNetTokenizer + from .models.rag import RagConfig, RagRetriever, RagTokenizer + from .models.reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig + from .models.retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig, RetriBertTokenizer + from .models.roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaTokenizer + from .models.roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerTokenizer + from .models.speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2TextConfig + from .models.squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertTokenizer + from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config + from .models.tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig, TapasTokenizer + from .models.transfo_xl import ( + TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, + TransfoXLConfig, + TransfoXLCorpus, + TransfoXLTokenizer, + ) + from .models.visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig + from .models.vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig + from .models.wav2vec2 import ( + WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, + Wav2Vec2Config, + Wav2Vec2CTCTokenizer, + Wav2Vec2FeatureExtractor, + Wav2Vec2Processor, + Wav2Vec2Tokenizer, + ) + from .models.xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMTokenizer + from .models.xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig + from .models.xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig + from .models.xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig + + # Pipelines + from .pipelines import ( + AutomaticSpeechRecognitionPipeline, + Conversation, + ConversationalPipeline, + CsvPipelineDataFormat, + FeatureExtractionPipeline, + FillMaskPipeline, + ImageClassificationPipeline, + JsonPipelineDataFormat, + NerPipeline, + PipedPipelineDataFormat, + Pipeline, + PipelineDataFormat, + QuestionAnsweringPipeline, + SummarizationPipeline, + TableQuestionAnsweringPipeline, + Text2TextGenerationPipeline, + TextClassificationPipeline, + TextGenerationPipeline, + TokenClassificationPipeline, + TranslationPipeline, + ZeroShotClassificationPipeline, + pipeline, + ) + + # Tokenization + from .tokenization_utils import PreTrainedTokenizer + from .tokenization_utils_base import ( + AddedToken, + BatchEncoding, + CharSpan, + PreTrainedTokenizerBase, + SpecialTokensMixin, + TokenSpan, + ) + + # Trainer + from .trainer_callback import ( + DefaultFlowCallback, + EarlyStoppingCallback, + PrinterCallback, + ProgressCallback, + TrainerCallback, + TrainerControl, + TrainerState, + ) + from .trainer_utils import EvalPrediction, IntervalStrategy, SchedulerType, set_seed + from .training_args import TrainingArguments + from .training_args_seq2seq import Seq2SeqTrainingArguments + from .training_args_tf import TFTrainingArguments + from .utils import logging + + if is_sentencepiece_available(): + from .models.albert import AlbertTokenizer + from .models.barthez import BarthezTokenizer + from .models.bert_generation import BertGenerationTokenizer + from .models.camembert import CamembertTokenizer + from .models.deberta_v2 import DebertaV2Tokenizer + from .models.m2m_100 import M2M100Tokenizer + from .models.marian import MarianTokenizer + from .models.mbart import MBart50Tokenizer, MBartTokenizer + from .models.mt5 import MT5Tokenizer + from .models.pegasus import PegasusTokenizer + from .models.reformer import ReformerTokenizer + from .models.speech_to_text import Speech2TextTokenizer + from .models.t5 import T5Tokenizer + from .models.xlm_prophetnet import XLMProphetNetTokenizer + from .models.xlm_roberta import XLMRobertaTokenizer + from .models.xlnet import XLNetTokenizer + else: + from .utils.dummy_sentencepiece_objects import * + + if is_tokenizers_available(): + from .models.albert import AlbertTokenizerFast + from .models.bart import BartTokenizerFast + from .models.barthez import BarthezTokenizerFast + from .models.bert import BertTokenizerFast + from .models.big_bird import BigBirdTokenizerFast + from .models.camembert import CamembertTokenizerFast + from .models.clip import CLIPTokenizerFast + from .models.convbert import ConvBertTokenizerFast + from .models.deberta import DebertaTokenizerFast + from .models.distilbert import DistilBertTokenizerFast + from .models.dpr import DPRContextEncoderTokenizerFast, DPRQuestionEncoderTokenizerFast, DPRReaderTokenizerFast + from .models.electra import ElectraTokenizerFast + from .models.funnel import FunnelTokenizerFast + from .models.gpt2 import GPT2TokenizerFast + from .models.herbert import HerbertTokenizerFast + from .models.layoutlm import LayoutLMTokenizerFast + from .models.led import LEDTokenizerFast + from .models.longformer import LongformerTokenizerFast + from .models.lxmert import LxmertTokenizerFast + from .models.mbart import MBart50TokenizerFast, MBartTokenizerFast + from .models.mobilebert import MobileBertTokenizerFast + from .models.mpnet import MPNetTokenizerFast + from .models.mt5 import MT5TokenizerFast + from .models.openai import OpenAIGPTTokenizerFast + from .models.pegasus import PegasusTokenizerFast + from .models.reformer import ReformerTokenizerFast + from .models.retribert import RetriBertTokenizerFast + from .models.roberta import RobertaTokenizerFast + from .models.roformer import RoFormerTokenizerFast + from .models.squeezebert import SqueezeBertTokenizerFast + from .models.t5 import T5TokenizerFast + from .models.xlm_roberta import XLMRobertaTokenizerFast + from .models.xlnet import XLNetTokenizerFast + from .tokenization_utils_fast import PreTrainedTokenizerFast + + else: + from .utils.dummy_tokenizers_objects import * + + if is_sentencepiece_available() and is_tokenizers_available(): + from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, convert_slow_tokenizer + else: + from .utils.dummies_sentencepiece_and_tokenizers_objects import * + + if is_speech_available(): + from .models.speech_to_text import Speech2TextFeatureExtractor + + else: + from .utils.dummy_speech_objects import * + + if is_speech_available() and is_sentencepiece_available(): + from .models.speech_to_text import Speech2TextProcessor + else: + from .utils.dummy_sentencepiece_and_speech_objects import * + + if is_vision_available(): + from .image_utils import ImageFeatureExtractionMixin + from .models.clip import CLIPFeatureExtractor, CLIPProcessor + from .models.deit import DeiTFeatureExtractor + from .models.detr import DetrFeatureExtractor + from .models.vit import ViTFeatureExtractor + else: + from .utils.dummy_vision_objects import * + + # Modeling + if is_timm_available() and is_vision_available(): + from .models.detr import ( + DETR_PRETRAINED_MODEL_ARCHIVE_LIST, + DetrForObjectDetection, + DetrForSegmentation, + DetrModel, + DetrPreTrainedModel, + ) + else: + from .utils.dummy_timm_objects import * + + if is_torch_available(): + # Benchmarks + from .benchmark.benchmark import PyTorchBenchmark + from .benchmark.benchmark_args import PyTorchBenchmarkArguments + from .data.data_collator import ( + DataCollator, + DataCollatorForLanguageModeling, + DataCollatorForPermutationLanguageModeling, + DataCollatorForSeq2Seq, + DataCollatorForSOP, + DataCollatorForTokenClassification, + DataCollatorForWholeWordMask, + DataCollatorWithPadding, + default_data_collator, + ) + from .data.datasets import ( + GlueDataset, + GlueDataTrainingArguments, + LineByLineTextDataset, + LineByLineWithRefDataset, + LineByLineWithSOPTextDataset, + SquadDataset, + SquadDataTrainingArguments, + TextDataset, + TextDatasetForNextSentencePrediction, + ) + from .generation_beam_search import BeamScorer, BeamSearchScorer + from .generation_logits_process import ( + ForcedBOSTokenLogitsProcessor, + ForcedEOSTokenLogitsProcessor, + HammingDiversityLogitsProcessor, + InfNanRemoveLogitsProcessor, + LogitsProcessor, + LogitsProcessorList, + LogitsWarper, + MinLengthLogitsProcessor, + NoBadWordsLogitsProcessor, + NoRepeatNGramLogitsProcessor, + PrefixConstrainedLogitsProcessor, + RepetitionPenaltyLogitsProcessor, + TemperatureLogitsWarper, + TopKLogitsWarper, + TopPLogitsWarper, + ) + from .generation_stopping_criteria import ( + MaxLengthCriteria, + MaxTimeCriteria, + StoppingCriteria, + StoppingCriteriaList, + ) + from .generation_utils import top_k_top_p_filtering + from .modeling_utils import Conv1D, PreTrainedModel, apply_chunking_to_forward, prune_layer + from .models.albert import ( + ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + AlbertForMaskedLM, + AlbertForMultipleChoice, + AlbertForPreTraining, + AlbertForQuestionAnswering, + AlbertForSequenceClassification, + AlbertForTokenClassification, + AlbertModel, + AlbertPreTrainedModel, + load_tf_weights_in_albert, + ) + from .models.auto import ( + MODEL_FOR_CAUSAL_LM_MAPPING, + MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, + MODEL_FOR_MASKED_LM_MAPPING, + MODEL_FOR_MULTIPLE_CHOICE_MAPPING, + MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, + MODEL_FOR_OBJECT_DETECTION_MAPPING, + MODEL_FOR_PRETRAINING_MAPPING, + MODEL_FOR_QUESTION_ANSWERING_MAPPING, + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, + MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, + MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, + MODEL_MAPPING, + MODEL_WITH_LM_HEAD_MAPPING, + AutoModel, + AutoModelForCausalLM, + AutoModelForImageClassification, + AutoModelForMaskedLM, + AutoModelForMultipleChoice, + AutoModelForNextSentencePrediction, + AutoModelForPreTraining, + AutoModelForQuestionAnswering, + AutoModelForSeq2SeqLM, + AutoModelForSequenceClassification, + AutoModelForTableQuestionAnswering, + AutoModelForTokenClassification, + AutoModelWithLMHead, + ) + from .models.bart import ( + BART_PRETRAINED_MODEL_ARCHIVE_LIST, + BartForCausalLM, + BartForConditionalGeneration, + BartForQuestionAnswering, + BartForSequenceClassification, + BartModel, + BartPretrainedModel, + PretrainedBartModel, + ) + from .models.bert import ( + BERT_PRETRAINED_MODEL_ARCHIVE_LIST, + BertForMaskedLM, + BertForMultipleChoice, + BertForNextSentencePrediction, + BertForPreTraining, + BertForQuestionAnswering, + BertForSequenceClassification, + BertForTokenClassification, + BertLayer, + BertLMHeadModel, + BertModel, + BertPreTrainedModel, + load_tf_weights_in_bert, + ) + from .models.bert_generation import ( + BertGenerationDecoder, + BertGenerationEncoder, + BertGenerationPreTrainedModel, + load_tf_weights_in_bert_generation, + ) + from .models.big_bird import ( + BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST, + BigBirdForCausalLM, + BigBirdForMaskedLM, + BigBirdForMultipleChoice, + BigBirdForPreTraining, + BigBirdForQuestionAnswering, + BigBirdForSequenceClassification, + BigBirdForTokenClassification, + BigBirdLayer, + BigBirdModel, + BigBirdPreTrainedModel, + load_tf_weights_in_big_bird, + ) + from .models.bigbird_pegasus import ( + BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST, + BigBirdPegasusForCausalLM, + BigBirdPegasusForConditionalGeneration, + BigBirdPegasusForQuestionAnswering, + BigBirdPegasusForSequenceClassification, + BigBirdPegasusModel, + BigBirdPegasusPreTrainedModel, + ) + from .models.blenderbot import ( + BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST, + BlenderbotForCausalLM, + BlenderbotForConditionalGeneration, + BlenderbotModel, + BlenderbotPreTrainedModel, + ) + from .models.blenderbot_small import ( + BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST, + BlenderbotSmallForCausalLM, + BlenderbotSmallForConditionalGeneration, + BlenderbotSmallModel, + BlenderbotSmallPreTrainedModel, + ) + from .models.camembert import ( + CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + CamembertForCausalLM, + CamembertForMaskedLM, + CamembertForMultipleChoice, + CamembertForQuestionAnswering, + CamembertForSequenceClassification, + CamembertForTokenClassification, + CamembertModel, + ) + from .models.canine import ( + CANINE_PRETRAINED_MODEL_ARCHIVE_LIST, + CanineForMultipleChoice, + CanineForQuestionAnswering, + CanineForSequenceClassification, + CanineForTokenClassification, + CanineLayer, + CanineModel, + CaninePreTrainedModel, + load_tf_weights_in_canine, + ) + from .models.clip import ( + CLIP_PRETRAINED_MODEL_ARCHIVE_LIST, + CLIPModel, + CLIPPreTrainedModel, + CLIPTextModel, + CLIPVisionModel, + ) + from .models.convbert import ( + CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + ConvBertForMaskedLM, + ConvBertForMultipleChoice, + ConvBertForQuestionAnswering, + ConvBertForSequenceClassification, + ConvBertForTokenClassification, + ConvBertLayer, + ConvBertModel, + ConvBertPreTrainedModel, + load_tf_weights_in_convbert, + ) + from .models.ctrl import ( + CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, + CTRLForSequenceClassification, + CTRLLMHeadModel, + CTRLModel, + CTRLPreTrainedModel, + ) + from .models.deberta import ( + DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, + DebertaForMaskedLM, + DebertaForQuestionAnswering, + DebertaForSequenceClassification, + DebertaForTokenClassification, + DebertaModel, + DebertaPreTrainedModel, + ) + from .models.deberta_v2 import ( + DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST, + DebertaV2ForMaskedLM, + DebertaV2ForQuestionAnswering, + DebertaV2ForSequenceClassification, + DebertaV2ForTokenClassification, + DebertaV2Model, + DebertaV2PreTrainedModel, + ) + from .models.deit import ( + DEIT_PRETRAINED_MODEL_ARCHIVE_LIST, + DeiTForImageClassification, + DeiTForImageClassificationWithTeacher, + DeiTModel, + DeiTPreTrainedModel, + ) + from .models.distilbert import ( + DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + DistilBertForMaskedLM, + DistilBertForMultipleChoice, + DistilBertForQuestionAnswering, + DistilBertForSequenceClassification, + DistilBertForTokenClassification, + DistilBertModel, + DistilBertPreTrainedModel, + ) + from .models.dpr import ( + DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, + DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, + DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, + DPRContextEncoder, + DPRPretrainedContextEncoder, + DPRPretrainedQuestionEncoder, + DPRPretrainedReader, + DPRQuestionEncoder, + DPRReader, + ) + from .models.electra import ( + ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, + ElectraForMaskedLM, + ElectraForMultipleChoice, + ElectraForPreTraining, + ElectraForQuestionAnswering, + ElectraForSequenceClassification, + ElectraForTokenClassification, + ElectraModel, + ElectraPreTrainedModel, + load_tf_weights_in_electra, + ) + from .models.encoder_decoder import EncoderDecoderModel + from .models.flaubert import ( + FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + FlaubertForMultipleChoice, + FlaubertForQuestionAnswering, + FlaubertForQuestionAnsweringSimple, + FlaubertForSequenceClassification, + FlaubertForTokenClassification, + FlaubertModel, + FlaubertWithLMHeadModel, + ) + from .models.fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel + from .models.funnel import ( + FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST, + FunnelBaseModel, + FunnelForMaskedLM, + FunnelForMultipleChoice, + FunnelForPreTraining, + FunnelForQuestionAnswering, + FunnelForSequenceClassification, + FunnelForTokenClassification, + FunnelModel, + FunnelPreTrainedModel, + load_tf_weights_in_funnel, + ) + from .models.gpt2 import ( + GPT2_PRETRAINED_MODEL_ARCHIVE_LIST, + GPT2DoubleHeadsModel, + GPT2ForSequenceClassification, + GPT2LMHeadModel, + GPT2Model, + GPT2PreTrainedModel, + load_tf_weights_in_gpt2, + ) + from .models.gpt_neo import ( + GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST, + GPTNeoForCausalLM, + GPTNeoForSequenceClassification, + GPTNeoModel, + GPTNeoPreTrainedModel, + load_tf_weights_in_gpt_neo, + ) + from .models.hubert import ( + HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + HubertForCTC, + HubertModel, + HubertPreTrainedModel, + ) + from .models.ibert import ( + IBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + IBertForMaskedLM, + IBertForMultipleChoice, + IBertForQuestionAnswering, + IBertForSequenceClassification, + IBertForTokenClassification, + IBertModel, + IBertPreTrainedModel, + ) + from .models.layoutlm import ( + LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, + LayoutLMForMaskedLM, + LayoutLMForSequenceClassification, + LayoutLMForTokenClassification, + LayoutLMModel, + LayoutLMPreTrainedModel, + ) + from .models.led import ( + LED_PRETRAINED_MODEL_ARCHIVE_LIST, + LEDForConditionalGeneration, + LEDForQuestionAnswering, + LEDForSequenceClassification, + LEDModel, + LEDPreTrainedModel, + ) + from .models.longformer import ( + LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, + LongformerForMaskedLM, + LongformerForMultipleChoice, + LongformerForQuestionAnswering, + LongformerForSequenceClassification, + LongformerForTokenClassification, + LongformerModel, + LongformerPreTrainedModel, + LongformerSelfAttention, + ) + from .models.luke import ( + LUKE_PRETRAINED_MODEL_ARCHIVE_LIST, + LukeForEntityClassification, + LukeForEntityPairClassification, + LukeForEntitySpanClassification, + LukeModel, + LukePreTrainedModel, + ) + from .models.lxmert import ( + LxmertEncoder, + LxmertForPreTraining, + LxmertForQuestionAnswering, + LxmertModel, + LxmertPreTrainedModel, + LxmertVisualFeatureEncoder, + LxmertXLayer, + ) + from .models.m2m_100 import ( + M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST, + M2M100ForConditionalGeneration, + M2M100Model, + M2M100PreTrainedModel, + ) + from .models.marian import MarianForCausalLM, MarianModel, MarianMTModel + from .models.mbart import ( + MBartForCausalLM, + MBartForConditionalGeneration, + MBartForQuestionAnswering, + MBartForSequenceClassification, + MBartModel, + MBartPreTrainedModel, + ) + from .models.megatron_bert import ( + MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, + MegatronBertForCausalLM, + MegatronBertForMaskedLM, + MegatronBertForMultipleChoice, + MegatronBertForNextSentencePrediction, + MegatronBertForPreTraining, + MegatronBertForQuestionAnswering, + MegatronBertForSequenceClassification, + MegatronBertForTokenClassification, + MegatronBertModel, + MegatronBertPreTrainedModel, + ) + from .models.mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings + from .models.mobilebert import ( + MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + MobileBertForMaskedLM, + MobileBertForMultipleChoice, + MobileBertForNextSentencePrediction, + MobileBertForPreTraining, + MobileBertForQuestionAnswering, + MobileBertForSequenceClassification, + MobileBertForTokenClassification, + MobileBertLayer, + MobileBertModel, + MobileBertPreTrainedModel, + load_tf_weights_in_mobilebert, + ) + from .models.mpnet import ( + MPNET_PRETRAINED_MODEL_ARCHIVE_LIST, + MPNetForMaskedLM, + MPNetForMultipleChoice, + MPNetForQuestionAnswering, + MPNetForSequenceClassification, + MPNetForTokenClassification, + MPNetLayer, + MPNetModel, + MPNetPreTrainedModel, + ) + from .models.mt5 import MT5EncoderModel, MT5ForConditionalGeneration, MT5Model + from .models.openai import ( + OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST, + OpenAIGPTDoubleHeadsModel, + OpenAIGPTForSequenceClassification, + OpenAIGPTLMHeadModel, + OpenAIGPTModel, + OpenAIGPTPreTrainedModel, + load_tf_weights_in_openai_gpt, + ) + from .models.pegasus import ( + PegasusForCausalLM, + PegasusForConditionalGeneration, + PegasusModel, + PegasusPreTrainedModel, + ) + from .models.prophetnet import ( + PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST, + ProphetNetDecoder, + ProphetNetEncoder, + ProphetNetForCausalLM, + ProphetNetForConditionalGeneration, + ProphetNetModel, + ProphetNetPreTrainedModel, + ) + from .models.rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration + from .models.reformer import ( + REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, + ReformerAttention, + ReformerForMaskedLM, + ReformerForQuestionAnswering, + ReformerForSequenceClassification, + ReformerLayer, + ReformerModel, + ReformerModelWithLMHead, + ReformerPreTrainedModel, + ) + from .models.retribert import RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST, RetriBertModel, RetriBertPreTrainedModel + from .models.roberta import ( + ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, + RobertaForCausalLM, + RobertaForMaskedLM, + RobertaForMultipleChoice, + RobertaForQuestionAnswering, + RobertaForSequenceClassification, + RobertaForTokenClassification, + RobertaModel, + RobertaPreTrainedModel, + ) + from .models.roformer import ( + ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, + RoFormerForCausalLM, + RoFormerForMaskedLM, + RoFormerForMultipleChoice, + RoFormerForQuestionAnswering, + RoFormerForSequenceClassification, + RoFormerForTokenClassification, + RoFormerLayer, + RoFormerModel, + RoFormerPreTrainedModel, + load_tf_weights_in_roformer, + ) + from .models.speech_to_text import ( + SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, + Speech2TextForConditionalGeneration, + Speech2TextModel, + Speech2TextPreTrainedModel, + ) + from .models.squeezebert import ( + SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + SqueezeBertForMaskedLM, + SqueezeBertForMultipleChoice, + SqueezeBertForQuestionAnswering, + SqueezeBertForSequenceClassification, + SqueezeBertForTokenClassification, + SqueezeBertModel, + SqueezeBertModule, + SqueezeBertPreTrainedModel, + ) + from .models.t5 import ( + T5_PRETRAINED_MODEL_ARCHIVE_LIST, + T5EncoderModel, + T5ForConditionalGeneration, + T5Model, + T5PreTrainedModel, + load_tf_weights_in_t5, + ) + from .models.tapas import ( + TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, + TapasForMaskedLM, + TapasForQuestionAnswering, + TapasForSequenceClassification, + TapasModel, + TapasPreTrainedModel, + ) + from .models.transfo_xl import ( + TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, + AdaptiveEmbedding, + TransfoXLForSequenceClassification, + TransfoXLLMHeadModel, + TransfoXLModel, + TransfoXLPreTrainedModel, + load_tf_weights_in_transfo_xl, + ) + from .models.visual_bert import ( # load_tf_weights_in_visual_bert, + VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, + VisualBertForMultipleChoice, + VisualBertForPreTraining, + VisualBertForQuestionAnswering, + VisualBertForRegionToPhraseAlignment, + VisualBertForVisualReasoning, + VisualBertLayer, + VisualBertModel, + VisualBertPreTrainedModel, + ) + from .models.vit import ( + VIT_PRETRAINED_MODEL_ARCHIVE_LIST, + ViTForImageClassification, + ViTModel, + ViTPreTrainedModel, + ) + from .models.wav2vec2 import ( + WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, + Wav2Vec2ForCTC, + Wav2Vec2ForMaskedLM, + Wav2Vec2ForPreTraining, + Wav2Vec2Model, + Wav2Vec2PreTrainedModel, + ) + from .models.xlm import ( + XLM_PRETRAINED_MODEL_ARCHIVE_LIST, + XLMForMultipleChoice, + XLMForQuestionAnswering, + XLMForQuestionAnsweringSimple, + XLMForSequenceClassification, + XLMForTokenClassification, + XLMModel, + XLMPreTrainedModel, + XLMWithLMHeadModel, + ) + from .models.xlm_prophetnet import ( + XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST, + XLMProphetNetDecoder, + XLMProphetNetEncoder, + XLMProphetNetForCausalLM, + XLMProphetNetForConditionalGeneration, + XLMProphetNetModel, + ) + from .models.xlm_roberta import ( + XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, + XLMRobertaForCausalLM, + XLMRobertaForMaskedLM, + XLMRobertaForMultipleChoice, + XLMRobertaForQuestionAnswering, + XLMRobertaForSequenceClassification, + XLMRobertaForTokenClassification, + XLMRobertaModel, + ) + from .models.xlnet import ( + XLNET_PRETRAINED_MODEL_ARCHIVE_LIST, + XLNetForMultipleChoice, + XLNetForQuestionAnswering, + XLNetForQuestionAnsweringSimple, + XLNetForSequenceClassification, + XLNetForTokenClassification, + XLNetLMHeadModel, + XLNetModel, + XLNetPreTrainedModel, + load_tf_weights_in_xlnet, + ) + + # Optimization + from .optimization import ( + Adafactor, + AdamW, + get_constant_schedule, + get_constant_schedule_with_warmup, + get_cosine_schedule_with_warmup, + get_cosine_with_hard_restarts_schedule_with_warmup, + get_linear_schedule_with_warmup, + get_polynomial_decay_schedule_with_warmup, + get_scheduler, + ) + + # Trainer + from .trainer import Trainer + from .trainer_pt_utils import torch_distributed_zero_first + from .trainer_seq2seq import Seq2SeqTrainer + else: + from .utils.dummy_pt_objects import * + + # TensorFlow + if is_tf_available(): + + from .benchmark.benchmark_args_tf import TensorFlowBenchmarkArguments + + # Benchmarks + from .benchmark.benchmark_tf import TensorFlowBenchmark + from .generation_tf_utils import tf_top_k_top_p_filtering + from .modeling_tf_layoutlm import ( + TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, + TFLayoutLMForMaskedLM, + TFLayoutLMForSequenceClassification, + TFLayoutLMForTokenClassification, + TFLayoutLMMainLayer, + TFLayoutLMModel, + TFLayoutLMPreTrainedModel, + ) + from .modeling_tf_utils import TFPreTrainedModel, TFSequenceSummary, TFSharedEmbeddings, shape_list + from .models.albert import ( + TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + TFAlbertForMaskedLM, + TFAlbertForMultipleChoice, + TFAlbertForPreTraining, + TFAlbertForQuestionAnswering, + TFAlbertForSequenceClassification, + TFAlbertForTokenClassification, + TFAlbertMainLayer, + TFAlbertModel, + TFAlbertPreTrainedModel, + ) + from .models.auto import ( + TF_MODEL_FOR_CAUSAL_LM_MAPPING, + TF_MODEL_FOR_MASKED_LM_MAPPING, + TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, + TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, + TF_MODEL_FOR_PRETRAINING_MAPPING, + TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, + TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, + TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, + TF_MODEL_MAPPING, + TF_MODEL_WITH_LM_HEAD_MAPPING, + TFAutoModel, + TFAutoModelForCausalLM, + TFAutoModelForMaskedLM, + TFAutoModelForMultipleChoice, + TFAutoModelForPreTraining, + TFAutoModelForQuestionAnswering, + TFAutoModelForSeq2SeqLM, + TFAutoModelForSequenceClassification, + TFAutoModelForTokenClassification, + TFAutoModelWithLMHead, + ) + from .models.bart import TFBartForConditionalGeneration, TFBartModel, TFBartPretrainedModel + from .models.bert import ( + TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, + TFBertEmbeddings, + TFBertForMaskedLM, + TFBertForMultipleChoice, + TFBertForNextSentencePrediction, + TFBertForPreTraining, + TFBertForQuestionAnswering, + TFBertForSequenceClassification, + TFBertForTokenClassification, + TFBertLMHeadModel, + TFBertMainLayer, + TFBertModel, + TFBertPreTrainedModel, + ) + from .models.blenderbot import ( + TFBlenderbotForConditionalGeneration, + TFBlenderbotModel, + TFBlenderbotPreTrainedModel, + ) + from .models.blenderbot_small import ( + TFBlenderbotSmallForConditionalGeneration, + TFBlenderbotSmallModel, + TFBlenderbotSmallPreTrainedModel, + ) + from .models.camembert import ( + TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + TFCamembertForMaskedLM, + TFCamembertForMultipleChoice, + TFCamembertForQuestionAnswering, + TFCamembertForSequenceClassification, + TFCamembertForTokenClassification, + TFCamembertModel, + ) + from .models.convbert import ( + TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + TFConvBertForMaskedLM, + TFConvBertForMultipleChoice, + TFConvBertForQuestionAnswering, + TFConvBertForSequenceClassification, + TFConvBertForTokenClassification, + TFConvBertLayer, + TFConvBertModel, + TFConvBertPreTrainedModel, + ) + from .models.ctrl import ( + TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, + TFCTRLForSequenceClassification, + TFCTRLLMHeadModel, + TFCTRLModel, + TFCTRLPreTrainedModel, + ) + from .models.distilbert import ( + TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + TFDistilBertForMaskedLM, + TFDistilBertForMultipleChoice, + TFDistilBertForQuestionAnswering, + TFDistilBertForSequenceClassification, + TFDistilBertForTokenClassification, + TFDistilBertMainLayer, + TFDistilBertModel, + TFDistilBertPreTrainedModel, + ) + from .models.dpr import ( + TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, + TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, + TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, + TFDPRContextEncoder, + TFDPRPretrainedContextEncoder, + TFDPRPretrainedQuestionEncoder, + TFDPRPretrainedReader, + TFDPRQuestionEncoder, + TFDPRReader, + ) + from .models.electra import ( + TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, + TFElectraForMaskedLM, + TFElectraForMultipleChoice, + TFElectraForPreTraining, + TFElectraForQuestionAnswering, + TFElectraForSequenceClassification, + TFElectraForTokenClassification, + TFElectraModel, + TFElectraPreTrainedModel, + ) + from .models.flaubert import ( + TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + TFFlaubertForMultipleChoice, + TFFlaubertForQuestionAnsweringSimple, + TFFlaubertForSequenceClassification, + TFFlaubertForTokenClassification, + TFFlaubertModel, + TFFlaubertPreTrainedModel, + TFFlaubertWithLMHeadModel, + ) + from .models.funnel import ( + TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST, + TFFunnelBaseModel, + TFFunnelForMaskedLM, + TFFunnelForMultipleChoice, + TFFunnelForPreTraining, + TFFunnelForQuestionAnswering, + TFFunnelForSequenceClassification, + TFFunnelForTokenClassification, + TFFunnelModel, + TFFunnelPreTrainedModel, + ) + from .models.gpt2 import ( + TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST, + TFGPT2DoubleHeadsModel, + TFGPT2ForSequenceClassification, + TFGPT2LMHeadModel, + TFGPT2MainLayer, + TFGPT2Model, + TFGPT2PreTrainedModel, + ) + from .models.hubert import ( + TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + TFHubertForCTC, + TFHubertModel, + TFHubertPreTrainedModel, + ) + from .models.led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel + from .models.longformer import ( + TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, + TFLongformerForMaskedLM, + TFLongformerForMultipleChoice, + TFLongformerForQuestionAnswering, + TFLongformerForSequenceClassification, + TFLongformerForTokenClassification, + TFLongformerModel, + TFLongformerPreTrainedModel, + TFLongformerSelfAttention, + ) + from .models.lxmert import ( + TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST, + TFLxmertForPreTraining, + TFLxmertMainLayer, + TFLxmertModel, + TFLxmertPreTrainedModel, + TFLxmertVisualFeatureEncoder, + ) + from .models.marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel + from .models.mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel + from .models.mobilebert import ( + TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + TFMobileBertForMaskedLM, + TFMobileBertForMultipleChoice, + TFMobileBertForNextSentencePrediction, + TFMobileBertForPreTraining, + TFMobileBertForQuestionAnswering, + TFMobileBertForSequenceClassification, + TFMobileBertForTokenClassification, + TFMobileBertMainLayer, + TFMobileBertModel, + TFMobileBertPreTrainedModel, + ) + from .models.mpnet import ( + TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST, + TFMPNetForMaskedLM, + TFMPNetForMultipleChoice, + TFMPNetForQuestionAnswering, + TFMPNetForSequenceClassification, + TFMPNetForTokenClassification, + TFMPNetMainLayer, + TFMPNetModel, + TFMPNetPreTrainedModel, + ) + from .models.mt5 import TFMT5EncoderModel, TFMT5ForConditionalGeneration, TFMT5Model + from .models.openai import ( + TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST, + TFOpenAIGPTDoubleHeadsModel, + TFOpenAIGPTForSequenceClassification, + TFOpenAIGPTLMHeadModel, + TFOpenAIGPTMainLayer, + TFOpenAIGPTModel, + TFOpenAIGPTPreTrainedModel, + ) + from .models.pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel + from .models.rag import TFRagModel, TFRagPreTrainedModel, TFRagSequenceForGeneration, TFRagTokenForGeneration + from .models.roberta import ( + TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, + TFRobertaForMaskedLM, + TFRobertaForMultipleChoice, + TFRobertaForQuestionAnswering, + TFRobertaForSequenceClassification, + TFRobertaForTokenClassification, + TFRobertaMainLayer, + TFRobertaModel, + TFRobertaPreTrainedModel, + ) + from .models.roformer import ( + TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, + TFRoFormerForCausalLM, + TFRoFormerForMaskedLM, + TFRoFormerForMultipleChoice, + TFRoFormerForQuestionAnswering, + TFRoFormerForSequenceClassification, + TFRoFormerForTokenClassification, + TFRoFormerLayer, + TFRoFormerModel, + TFRoFormerPreTrainedModel, + ) + from .models.t5 import ( + TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST, + TFT5EncoderModel, + TFT5ForConditionalGeneration, + TFT5Model, + TFT5PreTrainedModel, + ) + from .models.transfo_xl import ( + TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, + TFAdaptiveEmbedding, + TFTransfoXLForSequenceClassification, + TFTransfoXLLMHeadModel, + TFTransfoXLMainLayer, + TFTransfoXLModel, + TFTransfoXLPreTrainedModel, + ) + from .models.wav2vec2 import ( + TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, + TFWav2Vec2ForCTC, + TFWav2Vec2Model, + TFWav2Vec2PreTrainedModel, + ) + from .models.xlm import ( + TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST, + TFXLMForMultipleChoice, + TFXLMForQuestionAnsweringSimple, + TFXLMForSequenceClassification, + TFXLMForTokenClassification, + TFXLMMainLayer, + TFXLMModel, + TFXLMPreTrainedModel, + TFXLMWithLMHeadModel, + ) + from .models.xlm_roberta import ( + TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, + TFXLMRobertaForMaskedLM, + TFXLMRobertaForMultipleChoice, + TFXLMRobertaForQuestionAnswering, + TFXLMRobertaForSequenceClassification, + TFXLMRobertaForTokenClassification, + TFXLMRobertaModel, + ) + from .models.xlnet import ( + TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST, + TFXLNetForMultipleChoice, + TFXLNetForQuestionAnsweringSimple, + TFXLNetForSequenceClassification, + TFXLNetForTokenClassification, + TFXLNetLMHeadModel, + TFXLNetMainLayer, + TFXLNetModel, + TFXLNetPreTrainedModel, + ) + + # Optimization + from .optimization_tf import AdamWeightDecay, GradientAccumulator, WarmUp, create_optimizer + + # Trainer + from .trainer_tf import TFTrainer + + else: + # Import the same objects as dummies to get them in the namespace. + # They will raise an import error if the user tries to instantiate / use them. + from .utils.dummy_tf_objects import * + + if is_flax_available(): + from .generation_flax_logits_process import ( + FlaxForcedBOSTokenLogitsProcessor, + FlaxForcedEOSTokenLogitsProcessor, + FlaxLogitsProcessor, + FlaxLogitsProcessorList, + FlaxLogitsWarper, + FlaxMinLengthLogitsProcessor, + FlaxTemperatureLogitsWarper, + FlaxTopKLogitsWarper, + FlaxTopPLogitsWarper, + ) + from .modeling_flax_utils import FlaxPreTrainedModel + from .models.auto import ( + FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, + FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, + FLAX_MODEL_FOR_MASKED_LM_MAPPING, + FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, + FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, + FLAX_MODEL_FOR_PRETRAINING_MAPPING, + FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING, + FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, + FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, + FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, + FLAX_MODEL_MAPPING, + FlaxAutoModel, + FlaxAutoModelForCausalLM, + FlaxAutoModelForImageClassification, + FlaxAutoModelForMaskedLM, + FlaxAutoModelForMultipleChoice, + FlaxAutoModelForNextSentencePrediction, + FlaxAutoModelForPreTraining, + FlaxAutoModelForQuestionAnswering, + FlaxAutoModelForSeq2SeqLM, + FlaxAutoModelForSequenceClassification, + FlaxAutoModelForTokenClassification, + ) + from .models.bart import ( + FlaxBartForConditionalGeneration, + FlaxBartForQuestionAnswering, + FlaxBartForSequenceClassification, + FlaxBartModel, + FlaxBartPreTrainedModel, + ) + from .models.bert import ( + FlaxBertForMaskedLM, + FlaxBertForMultipleChoice, + FlaxBertForNextSentencePrediction, + FlaxBertForPreTraining, + FlaxBertForQuestionAnswering, + FlaxBertForSequenceClassification, + FlaxBertForTokenClassification, + FlaxBertModel, + FlaxBertPreTrainedModel, + ) + from .models.big_bird import ( + FlaxBigBirdForMaskedLM, + FlaxBigBirdForMultipleChoice, + FlaxBigBirdForPreTraining, + FlaxBigBirdForQuestionAnswering, + FlaxBigBirdForSequenceClassification, + FlaxBigBirdForTokenClassification, + FlaxBigBirdModel, + FlaxBigBirdPreTrainedModel, + ) + from .models.clip import ( + FlaxCLIPModel, + FlaxCLIPPreTrainedModel, + FlaxCLIPTextModel, + FlaxCLIPTextPreTrainedModel, + FlaxCLIPVisionModel, + FlaxCLIPVisionPreTrainedModel, + ) + from .models.electra import ( + FlaxElectraForMaskedLM, + FlaxElectraForMultipleChoice, + FlaxElectraForPreTraining, + FlaxElectraForQuestionAnswering, + FlaxElectraForSequenceClassification, + FlaxElectraForTokenClassification, + FlaxElectraModel, + FlaxElectraPreTrainedModel, + ) + from .models.gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model, FlaxGPT2PreTrainedModel + from .models.gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel + from .models.marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel + from .models.mbart import ( + FlaxMBartForConditionalGeneration, + FlaxMBartForQuestionAnswering, + FlaxMBartForSequenceClassification, + FlaxMBartModel, + FlaxMBartPreTrainedModel, + ) + from .models.roberta import ( + FlaxRobertaForMaskedLM, + FlaxRobertaForMultipleChoice, + FlaxRobertaForQuestionAnswering, + FlaxRobertaForSequenceClassification, + FlaxRobertaForTokenClassification, + FlaxRobertaModel, + FlaxRobertaPreTrainedModel, + ) + from .models.t5 import FlaxT5ForConditionalGeneration, FlaxT5Model, FlaxT5PreTrainedModel + from .models.vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel + from .models.wav2vec2 import ( + FlaxWav2Vec2ForCTC, + FlaxWav2Vec2ForPreTraining, + FlaxWav2Vec2Model, + FlaxWav2Vec2PreTrainedModel, + ) + else: + # Import the same objects as dummies to get them in the namespace. + # They will raise an import error if the user tries to instantiate / use them. + from .utils.dummy_flax_objects import * + +else: + import sys + + sys.modules[__name__] = _LazyModule( + __name__, globals()["__file__"], _import_structure, extra_objects={"__version__": __version__} + ) + + +if not is_tf_available() and not is_torch_available() and not is_flax_available(): + logger.warning( + "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. " + "Models won't be available and only tokenizers, configuration " + "and file/data utilities can be used." + )