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Unverified Commit bbcd5eea authored by Sylvain Gugger's avatar Sylvain Gugger Committed by GitHub
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Fix init import_structure sorting (#20477) 

* Fix init import_structure sorting

* Fix rebase
parent 3b91f96f
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Showing with 176 additions and 167 deletions
src/transformers/__init__.py
View file @ bbcd5eea
......@@ -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(
......
src/transformers/models/speech_to_text/__init__.py
View file @ bbcd5eea
......@@ -47,7 +47,12 @@ except OptionalDependencyNotAvailable:
else:
_import_structure["feature_extraction_speech_to_text"] = ["Speech2TextFeatureExtractor"]
if is_sentencepiece_available():
try:
if not (is_speech_available() and is_sentencepiece_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["processing_speech_to_text"] = ["Speech2TextProcessor"]
try:
......@@ -96,7 +101,12 @@ if TYPE_CHECKING:
else:
from .feature_extraction_speech_to_text import Speech2TextFeatureExtractor
if is_sentencepiece_available():
try:
if not (is_speech_available() and is_sentencepiece_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .processing_speech_to_text import Speech2TextProcessor
try:
......
utils/custom_init_isort.py
View file @ bbcd5eea
......@@ -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]
......
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