From bbcd5eea3b7981b5a5f73e0a9b7dd50ebcf6a30b Mon Sep 17 00:00:00 2001 From: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Date: Tue, 29 Nov 2022 09:46:10 -0500 Subject: [PATCH] Fix init import_structure sorting (#20477) * Fix init import_structure sorting * Fix rebase --- src/transformers/__init__.py | 319 +++++++++--------- .../models/speech_to_text/__init__.py | 18 +- utils/custom_init_isort.py | 6 +- 3 files changed, 176 insertions(+), 167 deletions(-) diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py index 107ee9186..f38918cf2 100644 --- a/src/transformers/__init__.py +++ b/src/transformers/__init__.py @@ -569,10 +569,10 @@ else: _import_structure["models.m2m_100"].append("M2M100Tokenizer") _import_structure["models.marian"].append("MarianTokenizer") _import_structure["models.mbart"].append("MBartTokenizer") - _import_structure["models.nllb"].append("NllbTokenizer") _import_structure["models.mbart50"].append("MBart50Tokenizer") _import_structure["models.mluke"].append("MLukeTokenizer") _import_structure["models.mt5"].append("MT5Tokenizer") + _import_structure["models.nllb"].append("NllbTokenizer") _import_structure["models.pegasus"].append("PegasusTokenizer") _import_structure["models.plbart"].append("PLBartTokenizer") _import_structure["models.reformer"].append("ReformerTokenizer") @@ -722,14 +722,14 @@ else: _import_structure["image_utils"] = ["ImageFeatureExtractionMixin"] _import_structure["models.beit"].extend(["BeitFeatureExtractor", "BeitImageProcessor"]) _import_structure["models.clip"].extend(["CLIPFeatureExtractor", "CLIPImageProcessor"]) + _import_structure["models.conditional_detr"].append("ConditionalDetrFeatureExtractor") _import_structure["models.convnext"].extend(["ConvNextFeatureExtractor", "ConvNextImageProcessor"]) _import_structure["models.deformable_detr"].append("DeformableDetrFeatureExtractor") _import_structure["models.deit"].extend(["DeiTFeatureExtractor", "DeiTImageProcessor"]) _import_structure["models.detr"].append("DetrFeatureExtractor") - _import_structure["models.conditional_detr"].append("ConditionalDetrFeatureExtractor") _import_structure["models.donut"].extend(["DonutFeatureExtractor", "DonutImageProcessor"]) _import_structure["models.dpt"].extend(["DPTFeatureExtractor", "DPTImageProcessor"]) - _import_structure["models.flava"].extend(["FlavaFeatureExtractor", "FlavaProcessor", "FlavaImageProcessor"]) + _import_structure["models.flava"].extend(["FlavaFeatureExtractor", "FlavaImageProcessor", "FlavaProcessor"]) _import_structure["models.glpn"].extend(["GLPNFeatureExtractor", "GLPNImageProcessor"]) _import_structure["models.imagegpt"].extend(["ImageGPTFeatureExtractor", "ImageGPTImageProcessor"]) _import_structure["models.layoutlmv2"].extend(["LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor"]) @@ -819,70 +819,44 @@ else: "TextDatasetForNextSentencePrediction", ] _import_structure["deepspeed"] = [] - _import_structure["generation_utils"] = [] _import_structure["generation"].extend( [ - "Constraint", - "ConstraintListState", - "DisjunctiveConstraint", - "PhrasalConstraint", "BeamScorer", "BeamSearchScorer", "ConstrainedBeamSearchScorer", + "Constraint", + "ConstraintListState", + "DisjunctiveConstraint", "ForcedBOSTokenLogitsProcessor", "ForcedEOSTokenLogitsProcessor", + "GenerationMixin", "HammingDiversityLogitsProcessor", "InfNanRemoveLogitsProcessor", "LogitsProcessor", "LogitsProcessorList", "LogitsWarper", + "MaxLengthCriteria", + "MaxTimeCriteria", "MinLengthLogitsProcessor", "NoBadWordsLogitsProcessor", "NoRepeatNGramLogitsProcessor", + "PhrasalConstraint", "PrefixConstrainedLogitsProcessor", "RepetitionPenaltyLogitsProcessor", + "StoppingCriteria", + "StoppingCriteriaList", "TemperatureLogitsWarper", "TopKLogitsWarper", "TopPLogitsWarper", "TypicalLogitsWarper", - "MaxLengthCriteria", - "MaxTimeCriteria", - "StoppingCriteria", - "StoppingCriteriaList", - "GenerationMixin", "top_k_top_p_filtering", ] ) + _import_structure["generation_utils"] = [] _import_structure["modeling_outputs"] = [] _import_structure["modeling_utils"] = ["PreTrainedModel"] # PyTorch models structure - - _import_structure["models.roc_bert"].extend( - [ - "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", - "RoCBertForMaskedLM", - "RoCBertForCausalLM", - "RoCBertForMultipleChoice", - "RoCBertForQuestionAnswering", - "RoCBertForSequenceClassification", - "RoCBertForTokenClassification", - "RoCBertLayer", - "RoCBertModel", - "RoCBertForPreTraining", - "RoCBertPreTrainedModel", - "load_tf_weights_in_roc_bert", - ] - ) - - _import_structure["models.time_series_transformer"].extend( - [ - "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", - "TimeSeriesTransformerForPrediction", - "TimeSeriesTransformerModel", - "TimeSeriesTransformerPreTrainedModel", - ] - ) _import_structure["models.albert"].extend( [ "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -897,12 +871,13 @@ else: "load_tf_weights_in_albert", ] ) + _import_structure["models.audio_spectrogram_transformer"].extend( [ "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", + "ASTForAudioClassification", "ASTModel", "ASTPreTrainedModel", - "ASTForAudioClassification", ] ) _import_structure["models.auto"].extend( @@ -913,8 +888,8 @@ else: "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING", "MODEL_FOR_CAUSAL_LM_MAPPING", "MODEL_FOR_CTC_MAPPING", - "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING", + "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING", "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING", @@ -934,18 +909,18 @@ else: "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING", "MODEL_FOR_VISION_2_SEQ_MAPPING", "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING", + "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING", "MODEL_MAPPING", "MODEL_WITH_LM_HEAD_MAPPING", - "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING", - "AutoModel", "AutoBackbone", + "AutoModel", "AutoModelForAudioClassification", "AutoModelForAudioFrameClassification", "AutoModelForAudioXVector", "AutoModelForCausalLM", "AutoModelForCTC", - "AutoModelForDocumentQuestionAnswering", "AutoModelForDepthEstimation", + "AutoModelForDocumentQuestionAnswering", "AutoModelForImageClassification", "AutoModelForImageSegmentation", "AutoModelForInstanceSegmentation", @@ -965,8 +940,8 @@ else: "AutoModelForVideoClassification", "AutoModelForVision2Seq", "AutoModelForVisualQuestionAnswering", - "AutoModelWithLMHead", "AutoModelForZeroShotObjectDetection", + "AutoModelWithLMHead", ] ) _import_structure["models.bart"].extend( @@ -981,17 +956,6 @@ else: "PretrainedBartModel", ] ) - _import_structure["models.mvp"].extend( - [ - "MVP_PRETRAINED_MODEL_ARCHIVE_LIST", - "MvpForCausalLM", - "MvpForConditionalGeneration", - "MvpForQuestionAnswering", - "MvpForSequenceClassification", - "MvpModel", - "MvpPreTrainedModel", - ] - ) _import_structure["models.beit"].extend( [ "BEIT_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -1054,17 +1018,6 @@ else: "BigBirdPegasusPreTrainedModel", ] ) - _import_structure["models.bloom"].extend( - [ - "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST", - "BloomForCausalLM", - "BloomModel", - "BloomPreTrainedModel", - "BloomForSequenceClassification", - "BloomForTokenClassification", - "BloomForQuestionAnswering", - ] - ) _import_structure["models.blenderbot"].extend( [ "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -1083,6 +1036,17 @@ else: "BlenderbotSmallPreTrainedModel", ] ) + _import_structure["models.bloom"].extend( + [ + "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST", + "BloomForCausalLM", + "BloomForQuestionAnswering", + "BloomForSequenceClassification", + "BloomForTokenClassification", + "BloomModel", + "BloomPreTrainedModel", + ] + ) _import_structure["models.camembert"].extend( [ "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -1123,20 +1087,19 @@ else: _import_structure["models.clipseg"].extend( [ "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST", + "CLIPSegForImageSegmentation", "CLIPSegModel", "CLIPSegPreTrainedModel", "CLIPSegTextModel", "CLIPSegVisionModel", - "CLIPSegForImageSegmentation", ] ) - _import_structure["models.x_clip"].extend( + _import_structure["models.codegen"].extend( [ - "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST", - "XCLIPModel", - "XCLIPPreTrainedModel", - "XCLIPTextModel", - "XCLIPVisionModel", + "CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST", + "CodeGenForCausalLM", + "CodeGenModel", + "CodeGenPreTrainedModel", ] ) _import_structure["models.convbert"].extend( @@ -1245,6 +1208,14 @@ else: "DeiTPreTrainedModel", ] ) + _import_structure["models.dinat"].extend( + [ + "DINAT_PRETRAINED_MODEL_ARCHIVE_LIST", + "DinatForImageClassification", + "DinatModel", + "DinatPreTrainedModel", + ] + ) _import_structure["models.distilbert"].extend( [ "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -1257,14 +1228,6 @@ else: "DistilBertPreTrainedModel", ] ) - _import_structure["models.dinat"].extend( - [ - "DINAT_PRETRAINED_MODEL_ARCHIVE_LIST", - "DinatForImageClassification", - "DinatModel", - "DinatPreTrainedModel", - ] - ) _import_structure["models.donut"].extend( [ "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -1347,8 +1310,8 @@ else: "FlaubertForSequenceClassification", "FlaubertForTokenClassification", "FlaubertModel", - "FlaubertWithLMHeadModel", "FlaubertPreTrainedModel", + "FlaubertWithLMHeadModel", ] ) _import_structure["models.flava"].extend( @@ -1461,14 +1424,6 @@ else: "GroupViTVisionModel", ] ) - _import_structure["models.codegen"].extend( - [ - "CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST", - "CodeGenForCausalLM", - "CodeGenModel", - "CodeGenPreTrainedModel", - ] - ) _import_structure["models.hubert"].extend( [ "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -1505,17 +1460,17 @@ else: "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST", "JukeboxModel", "JukeboxPreTrainedModel", - "JukeboxVQVAE", "JukeboxPrior", + "JukeboxVQVAE", ] ) _import_structure["models.layoutlm"].extend( [ "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST", "LayoutLMForMaskedLM", + "LayoutLMForQuestionAnswering", "LayoutLMForSequenceClassification", "LayoutLMForTokenClassification", - "LayoutLMForQuestionAnswering", "LayoutLMModel", "LayoutLMPreTrainedModel", ] @@ -1559,6 +1514,16 @@ else: "LevitPreTrainedModel", ] ) + _import_structure["models.lilt"].extend( + [ + "LILT_PRETRAINED_MODEL_ARCHIVE_LIST", + "LiltForQuestionAnswering", + "LiltForSequenceClassification", + "LiltForTokenClassification", + "LiltModel", + "LiltPreTrainedModel", + ] + ) _import_structure["models.longformer"].extend( [ "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -1587,11 +1552,11 @@ else: "LukeForEntityClassification", "LukeForEntityPairClassification", "LukeForEntitySpanClassification", + "LukeForMaskedLM", "LukeForMultipleChoice", "LukeForQuestionAnswering", "LukeForSequenceClassification", "LukeForTokenClassification", - "LukeForMaskedLM", "LukeModel", "LukePreTrainedModel", ] @@ -1616,15 +1581,6 @@ else: ] ) _import_structure["models.marian"].extend(["MarianForCausalLM", "MarianModel", "MarianMTModel"]) - _import_structure["models.maskformer"].extend( - [ - "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", - "MaskFormerForInstanceSegmentation", - "MaskFormerModel", - "MaskFormerPreTrainedModel", - "MaskFormerSwinBackbone", - ] - ) _import_structure["models.markuplm"].extend( [ "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -1635,6 +1591,15 @@ else: "MarkupLMPreTrainedModel", ] ) + _import_structure["models.maskformer"].extend( + [ + "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", + "MaskFormerForInstanceSegmentation", + "MaskFormerModel", + "MaskFormerPreTrainedModel", + "MaskFormerSwinBackbone", + ] + ) _import_structure["models.mbart"].extend( [ "MBartForCausalLM", @@ -1727,6 +1692,17 @@ else: ] ) _import_structure["models.mt5"].extend(["MT5EncoderModel", "MT5ForConditionalGeneration", "MT5Model"]) + _import_structure["models.mvp"].extend( + [ + "MVP_PRETRAINED_MODEL_ARCHIVE_LIST", + "MvpForCausalLM", + "MvpForConditionalGeneration", + "MvpForQuestionAnswering", + "MvpForSequenceClassification", + "MvpModel", + "MvpPreTrainedModel", + ] + ) _import_structure["models.nat"].extend( [ "NAT_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -1739,9 +1715,9 @@ else: [ "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST", "NezhaForMaskedLM", - "NezhaForPreTraining", - "NezhaForNextSentencePrediction", "NezhaForMultipleChoice", + "NezhaForNextSentencePrediction", + "NezhaForPreTraining", "NezhaForQuestionAnswering", "NezhaForSequenceClassification", "NezhaForTokenClassification", @@ -1777,20 +1753,20 @@ else: [ "OPT_PRETRAINED_MODEL_ARCHIVE_LIST", "OPTForCausalLM", + "OPTForQuestionAnswering", + "OPTForSequenceClassification", "OPTModel", "OPTPreTrainedModel", - "OPTForSequenceClassification", - "OPTForQuestionAnswering", ] ) _import_structure["models.owlvit"].extend( [ "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST", + "OwlViTForObjectDetection", "OwlViTModel", "OwlViTPreTrainedModel", "OwlViTTextModel", "OwlViTVisionModel", - "OwlViTForObjectDetection", ] ) _import_structure["models.pegasus"].extend( @@ -1919,10 +1895,10 @@ else: _import_structure["models.resnet"].extend( [ "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST", + "ResNetBackbone", "ResNetForImageClassification", "ResNetModel", "ResNetPreTrainedModel", - "ResNetBackbone", ] ) _import_structure["models.retribert"].extend( @@ -1941,14 +1917,20 @@ else: "RobertaPreTrainedModel", ] ) - _import_structure["models.lilt"].extend( + _import_structure["models.roc_bert"].extend( [ - "LILT_PRETRAINED_MODEL_ARCHIVE_LIST", - "LiltForQuestionAnswering", - "LiltForSequenceClassification", - "LiltForTokenClassification", - "LiltModel", - "LiltPreTrainedModel", + "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "RoCBertForCausalLM", + "RoCBertForMaskedLM", + "RoCBertForMultipleChoice", + "RoCBertForPreTraining", + "RoCBertForQuestionAnswering", + "RoCBertForSequenceClassification", + "RoCBertForTokenClassification", + "RoCBertLayer", + "RoCBertModel", + "RoCBertPreTrainedModel", + "load_tf_weights_in_roc_bert", ] ) _import_structure["models.roformer"].extend( @@ -2004,14 +1986,6 @@ else: "Speech2TextPreTrainedModel", ] ) - _import_structure["models.whisper"].extend( - [ - "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", - "WhisperForConditionalGeneration", - "WhisperModel", - "WhisperPreTrainedModel", - ] - ) _import_structure["models.speech_to_text_2"].extend(["Speech2Text2ForCausalLM", "Speech2Text2PreTrainedModel"]) _import_structure["models.splinter"].extend( [ @@ -2054,15 +2028,15 @@ else: "Swinv2PreTrainedModel", ] ) - _import_structure["models.tapas"].extend( + _import_structure["models.switch_transformers"].extend( [ - "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", - "TapasForMaskedLM", - "TapasForQuestionAnswering", - "TapasForSequenceClassification", - "TapasModel", - "TapasPreTrainedModel", - "load_tf_weights_in_tapas", + "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST", + "SwitchTransformersEncoderModel", + "SwitchTransformersForConditionalGeneration", + "SwitchTransformersModel", + "SwitchTransformersPreTrainedModel", + "SwitchTransformersSparseMLP", + "SwitchTransformersTop1Router", ] ) _import_structure["models.t5"].extend( @@ -2075,15 +2049,23 @@ else: "load_tf_weights_in_t5", ] ) - _import_structure["models.switch_transformers"].extend( + _import_structure["models.tapas"].extend( [ - "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST", - "SwitchTransformersEncoderModel", - "SwitchTransformersForConditionalGeneration", - "SwitchTransformersModel", - "SwitchTransformersPreTrainedModel", - "SwitchTransformersTop1Router", - "SwitchTransformersSparseMLP", + "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", + "TapasForMaskedLM", + "TapasForQuestionAnswering", + "TapasForSequenceClassification", + "TapasModel", + "TapasPreTrainedModel", + "load_tf_weights_in_tapas", + ] + ) + _import_structure["models.time_series_transformer"].extend( + [ + "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", + "TimeSeriesTransformerForPrediction", + "TimeSeriesTransformerModel", + "TimeSeriesTransformerPreTrainedModel", ] ) _import_structure["models.trajectory_transformer"].extend( @@ -2137,14 +2119,23 @@ else: "VanPreTrainedModel", ] ) + _import_structure["models.videomae"].extend( + [ + "VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST", + "VideoMAEForPreTraining", + "VideoMAEForVideoClassification", + "VideoMAEModel", + "VideoMAEPreTrainedModel", + ] + ) _import_structure["models.vilt"].extend( [ "VILT_PRETRAINED_MODEL_ARCHIVE_LIST", "ViltForImageAndTextRetrieval", "ViltForImagesAndTextClassification", - "ViltForTokenClassification", "ViltForMaskedLM", "ViltForQuestionAnswering", + "ViltForTokenClassification", "ViltLayer", "ViltModel", "ViltPreTrainedModel", @@ -2186,20 +2177,11 @@ else: _import_structure["models.vit_msn"].extend( [ "VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST", - "ViTMSNModel", "ViTMSNForImageClassification", + "ViTMSNModel", "ViTMSNPreTrainedModel", ] ) - _import_structure["models.videomae"].extend( - [ - "VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST", - "VideoMAEForPreTraining", - "VideoMAEModel", - "VideoMAEPreTrainedModel", - "VideoMAEForVideoClassification", - ] - ) _import_structure["models.wav2vec2"].extend( [ "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -2236,6 +2218,23 @@ else: "WavLMPreTrainedModel", ] ) + _import_structure["models.whisper"].extend( + [ + "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", + "WhisperForConditionalGeneration", + "WhisperModel", + "WhisperPreTrainedModel", + ] + ) + _import_structure["models.x_clip"].extend( + [ + "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST", + "XCLIPModel", + "XCLIPPreTrainedModel", + "XCLIPTextModel", + "XCLIPVisionModel", + ] + ) _import_structure["models.xglm"].extend( [ "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -2358,11 +2357,11 @@ else: _import_structure["activations_tf"] = [] _import_structure["benchmark.benchmark_args_tf"] = ["TensorFlowBenchmarkArguments"] _import_structure["benchmark.benchmark_tf"] = ["TensorFlowBenchmark"] - _import_structure["generation_tf_utils"] = [] _import_structure["generation"].extend( [ "TFForcedBOSTokenLogitsProcessor", "TFForcedEOSTokenLogitsProcessor", + "TFGenerationMixin", "TFLogitsProcessor", "TFLogitsProcessorList", "TFLogitsWarper", @@ -2373,10 +2372,10 @@ else: "TFTemperatureLogitsWarper", "TFTopKLogitsWarper", "TFTopPLogitsWarper", - "TFGenerationMixin", "tf_top_k_top_p_filtering", ] ) + _import_structure["generation_tf_utils"] = [] _import_structure["keras_callbacks"] = ["KerasMetricCallback", "PushToHubCallback"] _import_structure["modeling_tf_outputs"] = [] _import_structure["modeling_tf_utils"] = [ @@ -2403,13 +2402,13 @@ else: _import_structure["models.auto"].extend( [ "TF_MODEL_FOR_CAUSAL_LM_MAPPING", + "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_MASKED_IMAGE_MODELING_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_DOCUMENT_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", @@ -2422,12 +2421,12 @@ else: "TF_MODEL_WITH_LM_HEAD_MAPPING", "TFAutoModel", "TFAutoModelForCausalLM", + "TFAutoModelForDocumentQuestionAnswering", "TFAutoModelForImageClassification", "TFAutoModelForMaskedLM", "TFAutoModelForMultipleChoice", "TFAutoModelForNextSentencePrediction", "TFAutoModelForPreTraining", - "TFAutoModelForDocumentQuestionAnswering", "TFAutoModelForQuestionAnswering", "TFAutoModelForSemanticSegmentation", "TFAutoModelForSeq2SeqLM", @@ -2679,8 +2678,8 @@ else: [ "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLayoutLMForMaskedLM", - "TFLayoutLMForSequenceClassification", "TFLayoutLMForQuestionAnswering", + "TFLayoutLMForSequenceClassification", "TFLayoutLMForTokenClassification", "TFLayoutLMMainLayer", "TFLayoutLMModel", @@ -2743,10 +2742,10 @@ else: _import_structure["models.mobilevit"].extend( [ "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", - "TFMobileViTPreTrainedModel", - "TFMobileViTModel", "TFMobileViTForImageClassification", "TFMobileViTForSemanticSegmentation", + "TFMobileViTModel", + "TFMobileViTPreTrainedModel", ] ) _import_structure["models.mpnet"].extend( @@ -2999,11 +2998,11 @@ except OptionalDependencyNotAvailable: name for name in dir(dummy_flax_objects) if not name.startswith("_") ] else: - _import_structure["generation_flax_utils"] = [] _import_structure["generation"].extend( [ "FlaxForcedBOSTokenLogitsProcessor", "FlaxForcedEOSTokenLogitsProcessor", + "FlaxGenerationMixin", "FlaxLogitsProcessor", "FlaxLogitsProcessorList", "FlaxLogitsWarper", @@ -3011,9 +3010,9 @@ else: "FlaxTemperatureLogitsWarper", "FlaxTopKLogitsWarper", "FlaxTopPLogitsWarper", - "FlaxGenerationMixin", ] ) + _import_structure["generation_flax_utils"] = [] _import_structure["modeling_flax_outputs"] = [] _import_structure["modeling_flax_utils"] = ["FlaxPreTrainedModel"] _import_structure["models.albert"].extend( diff --git a/src/transformers/models/speech_to_text/__init__.py b/src/transformers/models/speech_to_text/__init__.py index 20eba2bf6..ea6822cf9 100644 --- a/src/transformers/models/speech_to_text/__init__.py +++ b/src/transformers/models/speech_to_text/__init__.py @@ -47,8 +47,13 @@ except OptionalDependencyNotAvailable: else: _import_structure["feature_extraction_speech_to_text"] = ["Speech2TextFeatureExtractor"] - if is_sentencepiece_available(): - _import_structure["processing_speech_to_text"] = ["Speech2TextProcessor"] +try: + if not (is_speech_available() and is_sentencepiece_available()): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["processing_speech_to_text"] = ["Speech2TextProcessor"] try: if not is_tf_available(): @@ -96,8 +101,13 @@ if TYPE_CHECKING: else: from .feature_extraction_speech_to_text import Speech2TextFeatureExtractor - if is_sentencepiece_available(): - from .processing_speech_to_text import Speech2TextProcessor + try: + if not (is_speech_available() and is_sentencepiece_available()): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .processing_speech_to_text import Speech2TextProcessor try: if not is_tf_available(): diff --git a/utils/custom_init_isort.py b/utils/custom_init_isort.py index 375cdb662..c17ce1395 100644 --- a/utils/custom_init_isort.py +++ b/utils/custom_init_isort.py @@ -200,9 +200,9 @@ def sort_imports(file, check_only=True): indent = get_indent(block_lines[1]) # Slit the internal block into blocks of indent level 1. internal_blocks = split_code_in_indented_blocks(internal_block_code, indent_level=indent) - # We have two categories of import key: list or _import_structu[key].append/extend - pattern = _re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key - # Grab the keys, but there is a trap: some lines are empty or jsut comments. + # We have two categories of import key: list or _import_structure[key].append/extend + pattern = _re_direct_key if "_import_structure = {" in block_lines[0] else _re_indirect_key + # Grab the keys, but there is a trap: some lines are empty or just comments. keys = [(pattern.search(b).groups()[0] if pattern.search(b) is not None else None) for b in internal_blocks] # We only sort the lines with a key. keys_to_sort = [(i, key) for i, key in enumerate(keys) if key is not None]