# 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__ = "4.52.1" from pathlib import Path from typing import TYPE_CHECKING # Check the dependencies satisfy the minimal versions required. from . import dependency_versions_check from .utils import ( OptionalDependencyNotAvailable, _LazyModule, is_bitsandbytes_available, is_essentia_available, is_flax_available, is_g2p_en_available, is_keras_nlp_available, is_librosa_available, is_pretty_midi_available, is_scipy_available, is_sentencepiece_available, is_speech_available, is_tensorflow_text_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torchaudio_available, is_torchvision_available, is_vision_available, logging, ) from .utils.import_utils import define_import_structure logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Base objects, independent of any specific backend _import_structure = { "audio_utils": [], "commands": [], "configuration_utils": ["PretrainedConfig"], "convert_graph_to_onnx": [], "convert_slow_tokenizers_checkpoints_to_fast": [], "convert_tf_hub_seq_to_seq_bert_to_pytorch": [], "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", ], "data.data_collator": [ "DataCollator", "DataCollatorForLanguageModeling", "DataCollatorForMultipleChoice", "DataCollatorForPermutationLanguageModeling", "DataCollatorForSeq2Seq", "DataCollatorForSOP", "DataCollatorForTokenClassification", "DataCollatorForWholeWordMask", "DataCollatorWithFlattening", "DataCollatorWithPadding", "DefaultDataCollator", "default_data_collator", ], "data.metrics": [], "data.processors": [], "debug_utils": [], "dependency_versions_check": [], "dependency_versions_table": [], "dynamic_module_utils": [], "feature_extraction_sequence_utils": ["SequenceFeatureExtractor"], "feature_extraction_utils": ["BatchFeature", "FeatureExtractionMixin"], "file_utils": [], "generation": [ "AsyncTextIteratorStreamer", "CompileConfig", "GenerationConfig", "TextIteratorStreamer", "TextStreamer", "WatermarkingConfig", ], "hf_argparser": ["HfArgumentParser"], "hyperparameter_search": [], "image_transforms": [], "integrations": [ "is_clearml_available", "is_comet_available", "is_dvclive_available", "is_neptune_available", "is_optuna_available", "is_ray_available", "is_ray_tune_available", "is_sigopt_available", "is_swanlab_available", "is_tensorboard_available", "is_wandb_available", ], "loss": [], "modelcard": ["ModelCard"], # Losses "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 "onnx": [], "pipelines": [ "AudioClassificationPipeline", "AutomaticSpeechRecognitionPipeline", "CsvPipelineDataFormat", "DepthEstimationPipeline", "DocumentQuestionAnsweringPipeline", "FeatureExtractionPipeline", "FillMaskPipeline", "ImageClassificationPipeline", "ImageFeatureExtractionPipeline", "ImageSegmentationPipeline", "ImageTextToTextPipeline", "ImageToImagePipeline", "ImageToTextPipeline", "JsonPipelineDataFormat", "MaskGenerationPipeline", "NerPipeline", "ObjectDetectionPipeline", "PipedPipelineDataFormat", "Pipeline", "PipelineDataFormat", "QuestionAnsweringPipeline", "SummarizationPipeline", "TableQuestionAnsweringPipeline", "Text2TextGenerationPipeline", "TextClassificationPipeline", "TextGenerationPipeline", "TextToAudioPipeline", "TokenClassificationPipeline", "TranslationPipeline", "VideoClassificationPipeline", "VisualQuestionAnsweringPipeline", "ZeroShotAudioClassificationPipeline", "ZeroShotClassificationPipeline", "ZeroShotImageClassificationPipeline", "ZeroShotObjectDetectionPipeline", "pipeline", ], "processing_utils": ["ProcessorMixin"], "quantizers": [], "testing_utils": [], "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", "enable_full_determinism", "set_seed", ], "training_args": ["TrainingArguments"], "training_args_seq2seq": ["Seq2SeqTrainingArguments"], "training_args_tf": ["TFTrainingArguments"], "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", "is_apex_available", "is_av_available", "is_bitsandbytes_available", "is_datasets_available", "is_faiss_available", "is_flax_available", "is_keras_nlp_available", "is_phonemizer_available", "is_psutil_available", "is_py3nvml_available", "is_pyctcdecode_available", "is_sacremoses_available", "is_safetensors_available", "is_scipy_available", "is_sentencepiece_available", "is_sklearn_available", "is_speech_available", "is_tensorflow_text_available", "is_tf_available", "is_timm_available", "is_tokenizers_available", "is_torch_available", "is_torch_hpu_available", "is_torch_mlu_available", "is_torch_musa_available", "is_torch_neuroncore_available", "is_torch_npu_available", "is_torchvision_available", "is_torch_xla_available", "is_torch_xpu_available", "is_vision_available", "logging", ], "utils.quantization_config": [ "AqlmConfig", "AutoRoundConfig", "AwqConfig", "BitNetQuantConfig", "BitsAndBytesConfig", "CompressedTensorsConfig", "EetqConfig", "FbgemmFp8Config", "FineGrainedFP8Config", "GPTQConfig", "HiggsConfig", "HqqConfig", "QuantoConfig", "QuarkConfig", "SpQRConfig", "TorchAoConfig", "VptqConfig", ], "video_utils": [], } # tokenizers-backed objects try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_tokenizers_objects _import_structure["utils.dummy_tokenizers_objects"] = [ name for name in dir(dummy_tokenizers_objects) if not name.startswith("_") ] else: # Fast tokenizers structure _import_structure["tokenization_utils_fast"] = ["PreTrainedTokenizerFast"] try: if not (is_sentencepiece_available() and is_tokenizers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: 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("_") ] else: _import_structure["convert_slow_tokenizer"] = [ "SLOW_TO_FAST_CONVERTERS", "convert_slow_tokenizer", ] # Vision-specific objects try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_vision_objects _import_structure["utils.dummy_vision_objects"] = [ name for name in dir(dummy_vision_objects) if not name.startswith("_") ] else: _import_structure["image_processing_base"] = ["ImageProcessingMixin"] _import_structure["image_processing_utils"] = ["BaseImageProcessor"] _import_structure["image_utils"] = ["ImageFeatureExtractionMixin"] try: if not is_torchvision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_torchvision_objects _import_structure["utils.dummy_torchvision_objects"] = [ name for name in dir(dummy_torchvision_objects) if not name.startswith("_") ] else: _import_structure["image_processing_utils_fast"] = ["BaseImageProcessorFast"] _import_structure["video_processing_utils"] = ["BaseVideoProcessor"] # PyTorch-backed objects try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_pt_objects _import_structure["utils.dummy_pt_objects"] = [name for name in dir(dummy_pt_objects) if not name.startswith("_")] else: _import_structure["model_debugging_utils"] = [ "model_addition_debugger_context", ] _import_structure["activations"] = [] _import_structure["cache_utils"] = [ "Cache", "CacheConfig", "DynamicCache", "EncoderDecoderCache", "HQQQuantizedCache", "HybridCache", "MambaCache", "OffloadedCache", "OffloadedStaticCache", "QuantizedCache", "QuantizedCacheConfig", "QuantoQuantizedCache", "SinkCache", "SlidingWindowCache", "StaticCache", ] _import_structure["data.datasets"] = [ "GlueDataset", "GlueDataTrainingArguments", "LineByLineTextDataset", "LineByLineWithRefDataset", "LineByLineWithSOPTextDataset", "SquadDataset", "SquadDataTrainingArguments", "TextDataset", "TextDatasetForNextSentencePrediction", ] _import_structure["generation"].extend( [ "AlternatingCodebooksLogitsProcessor", "BayesianDetectorConfig", "BayesianDetectorModel", "BeamScorer", "BeamSearchScorer", "ClassifierFreeGuidanceLogitsProcessor", "ConstrainedBeamSearchScorer", "Constraint", "ConstraintListState", "DisjunctiveConstraint", "EncoderNoRepeatNGramLogitsProcessor", "EncoderRepetitionPenaltyLogitsProcessor", "EosTokenCriteria", "EpsilonLogitsWarper", "EtaLogitsWarper", "ExponentialDecayLengthPenalty", "ForcedBOSTokenLogitsProcessor", "ForcedEOSTokenLogitsProcessor", "GenerationMixin", "HammingDiversityLogitsProcessor", "InfNanRemoveLogitsProcessor", "LogitNormalization", "LogitsProcessor", "LogitsProcessorList", "MaxLengthCriteria", "MaxTimeCriteria", "MinLengthLogitsProcessor", "MinNewTokensLengthLogitsProcessor", "MinPLogitsWarper", "NoBadWordsLogitsProcessor", "NoRepeatNGramLogitsProcessor", "PhrasalConstraint", "PrefixConstrainedLogitsProcessor", "RepetitionPenaltyLogitsProcessor", "SequenceBiasLogitsProcessor", "StoppingCriteria", "StoppingCriteriaList", "StopStringCriteria", "SuppressTokensAtBeginLogitsProcessor", "SuppressTokensLogitsProcessor", "SynthIDTextWatermarkDetector", "SynthIDTextWatermarkingConfig", "SynthIDTextWatermarkLogitsProcessor", "TemperatureLogitsWarper", "TopKLogitsWarper", "TopPLogitsWarper", "TypicalLogitsWarper", "UnbatchedClassifierFreeGuidanceLogitsProcessor", "WatermarkDetector", "WatermarkLogitsProcessor", "WhisperTimeStampLogitsProcessor", ] ) # PyTorch domain libraries integration _import_structure["integrations.executorch"] = [ "TorchExportableModuleWithStaticCache", "convert_and_export_with_cache", ] _import_structure["modeling_flash_attention_utils"] = [] _import_structure["modeling_layers"] = ["GradientCheckpointingLayer"] _import_structure["modeling_outputs"] = [] _import_structure["modeling_rope_utils"] = ["ROPE_INIT_FUNCTIONS", "dynamic_rope_update"] _import_structure["modeling_utils"] = ["PreTrainedModel", "AttentionInterface"] _import_structure["optimization"] = [ "Adafactor", "get_constant_schedule", "get_constant_schedule_with_warmup", "get_cosine_schedule_with_warmup", "get_cosine_with_hard_restarts_schedule_with_warmup", "get_inverse_sqrt_schedule", "get_linear_schedule_with_warmup", "get_polynomial_decay_schedule_with_warmup", "get_scheduler", "get_wsd_schedule", ] _import_structure["pytorch_utils"] = [ "Conv1D", "apply_chunking_to_forward", "prune_layer", ] _import_structure["sagemaker"] = [] _import_structure["time_series_utils"] = [] _import_structure["trainer"] = ["Trainer"] _import_structure["trainer_pt_utils"] = ["torch_distributed_zero_first"] _import_structure["trainer_seq2seq"] = ["Seq2SeqTrainer"] # TensorFlow-backed objects try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_tf_objects _import_structure["utils.dummy_tf_objects"] = [name for name in dir(dummy_tf_objects) if not name.startswith("_")] else: _import_structure["activations_tf"] = [] _import_structure["generation"].extend( [ "TFForcedBOSTokenLogitsProcessor", "TFForcedEOSTokenLogitsProcessor", "TFForceTokensLogitsProcessor", "TFGenerationMixin", "TFLogitsProcessor", "TFLogitsProcessorList", "TFLogitsWarper", "TFMinLengthLogitsProcessor", "TFNoBadWordsLogitsProcessor", "TFNoRepeatNGramLogitsProcessor", "TFRepetitionPenaltyLogitsProcessor", "TFSuppressTokensAtBeginLogitsProcessor", "TFSuppressTokensLogitsProcessor", "TFTemperatureLogitsWarper", "TFTopKLogitsWarper", "TFTopPLogitsWarper", ] ) _import_structure["keras_callbacks"] = ["KerasMetricCallback", "PushToHubCallback"] _import_structure["modeling_tf_outputs"] = [] _import_structure["modeling_tf_utils"] = [ "TFPreTrainedModel", "TFSequenceSummary", "TFSharedEmbeddings", "shape_list", ] _import_structure["optimization_tf"] = [ "AdamWeightDecay", "GradientAccumulator", "WarmUp", "create_optimizer", ] _import_structure["tf_utils"] = [] # FLAX-backed objects try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_flax_objects _import_structure["utils.dummy_flax_objects"] = [ name for name in dir(dummy_flax_objects) if not name.startswith("_") ] else: _import_structure["generation"].extend( [ "FlaxForcedBOSTokenLogitsProcessor", "FlaxForcedEOSTokenLogitsProcessor", "FlaxForceTokensLogitsProcessor", "FlaxGenerationMixin", "FlaxLogitsProcessor", "FlaxLogitsProcessorList", "FlaxLogitsWarper", "FlaxMinLengthLogitsProcessor", "FlaxTemperatureLogitsWarper", "FlaxSuppressTokensAtBeginLogitsProcessor", "FlaxSuppressTokensLogitsProcessor", "FlaxTopKLogitsWarper", "FlaxTopPLogitsWarper", "FlaxWhisperTimeStampLogitsProcessor", ] ) _import_structure["modeling_flax_outputs"] = [] _import_structure["modeling_flax_utils"] = ["FlaxPreTrainedModel"] # Direct imports for type-checking if TYPE_CHECKING: # All modeling imports 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, ) from .data.data_collator import ( DataCollator, DataCollatorForLanguageModeling, DataCollatorForMultipleChoice, DataCollatorForPermutationLanguageModeling, DataCollatorForSeq2Seq, DataCollatorForSOP, DataCollatorForTokenClassification, DataCollatorForWholeWordMask, DataCollatorWithFlattening, DataCollatorWithPadding, DefaultDataCollator, default_data_collator, ) from .feature_extraction_sequence_utils import SequenceFeatureExtractor # Feature Extractor from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin # Generation from .generation import ( AsyncTextIteratorStreamer, CompileConfig, GenerationConfig, TextIteratorStreamer, TextStreamer, WatermarkingConfig, ) from .hf_argparser import HfArgumentParser # Integrations from .integrations import ( is_clearml_available, is_comet_available, is_dvclive_available, is_neptune_available, is_optuna_available, is_ray_available, is_ray_tune_available, is_sigopt_available, is_swanlab_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 import * # Pipelines from .pipelines import ( AudioClassificationPipeline, AutomaticSpeechRecognitionPipeline, CsvPipelineDataFormat, DepthEstimationPipeline, DocumentQuestionAnsweringPipeline, FeatureExtractionPipeline, FillMaskPipeline, ImageClassificationPipeline, ImageFeatureExtractionPipeline, ImageSegmentationPipeline, ImageTextToTextPipeline, ImageToImagePipeline, ImageToTextPipeline, JsonPipelineDataFormat, MaskGenerationPipeline, NerPipeline, ObjectDetectionPipeline, PipedPipelineDataFormat, Pipeline, PipelineDataFormat, QuestionAnsweringPipeline, SummarizationPipeline, TableQuestionAnsweringPipeline, Text2TextGenerationPipeline, TextClassificationPipeline, TextGenerationPipeline, TextToAudioPipeline, TokenClassificationPipeline, TranslationPipeline, VideoClassificationPipeline, VisualQuestionAnsweringPipeline, ZeroShotAudioClassificationPipeline, ZeroShotClassificationPipeline, ZeroShotImageClassificationPipeline, ZeroShotObjectDetectionPipeline, pipeline, ) from .processing_utils import ProcessorMixin # 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, enable_full_determinism, set_seed, ) from .training_args import TrainingArguments from .training_args_seq2seq import Seq2SeqTrainingArguments from .training_args_tf import TFTrainingArguments # Files and general utilities from .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, is_apex_available, is_av_available, is_bitsandbytes_available, is_datasets_available, is_faiss_available, is_flax_available, is_keras_nlp_available, is_phonemizer_available, is_psutil_available, is_py3nvml_available, is_pyctcdecode_available, is_sacremoses_available, is_safetensors_available, is_scipy_available, is_sentencepiece_available, is_sklearn_available, is_speech_available, is_tensorflow_text_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torch_hpu_available, is_torch_mlu_available, is_torch_musa_available, is_torch_neuroncore_available, is_torch_npu_available, is_torch_xla_available, is_torch_xpu_available, is_torchvision_available, is_vision_available, logging, ) # bitsandbytes config from .utils.quantization_config import ( AqlmConfig, AutoRoundConfig, AwqConfig, BitNetQuantConfig, BitsAndBytesConfig, CompressedTensorsConfig, EetqConfig, FbgemmFp8Config, FineGrainedFP8Config, GPTQConfig, HiggsConfig, HqqConfig, QuantoConfig, QuarkConfig, SpQRConfig, TorchAoConfig, VptqConfig, ) try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_tokenizers_objects import * else: from .tokenization_utils_fast import PreTrainedTokenizerFast try: if not (is_sentencepiece_available() and is_tokenizers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummies_sentencepiece_and_tokenizers_objects import * else: from .convert_slow_tokenizer import ( SLOW_TO_FAST_CONVERTERS, convert_slow_tokenizer, ) try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_vision_objects import * else: from .image_processing_base import ImageProcessingMixin from .image_processing_utils import BaseImageProcessor from .image_utils import ImageFeatureExtractionMixin try: if not is_torchvision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torchvision_objects import * else: from .image_processing_utils_fast import BaseImageProcessorFast from .video_processing_utils import BaseVideoProcessor try: if not (is_torchvision_available() and is_timm_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_timm_and_torchvision_objects import * else: from .models.timm_wrapper import TimmWrapperImageProcessor # Modeling try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_pt_objects import * else: # Debugging from .cache_utils import ( Cache, CacheConfig, DynamicCache, EncoderDecoderCache, HQQQuantizedCache, HybridCache, MambaCache, OffloadedCache, OffloadedStaticCache, QuantizedCache, QuantizedCacheConfig, QuantoQuantizedCache, SinkCache, SlidingWindowCache, StaticCache, ) from .data.datasets import ( GlueDataset, GlueDataTrainingArguments, LineByLineTextDataset, LineByLineWithRefDataset, LineByLineWithSOPTextDataset, SquadDataset, SquadDataTrainingArguments, TextDataset, TextDatasetForNextSentencePrediction, ) from .generation import ( AlternatingCodebooksLogitsProcessor, BayesianDetectorConfig, BayesianDetectorModel, BeamScorer, BeamSearchScorer, ClassifierFreeGuidanceLogitsProcessor, ConstrainedBeamSearchScorer, Constraint, ConstraintListState, DisjunctiveConstraint, EncoderNoRepeatNGramLogitsProcessor, EncoderRepetitionPenaltyLogitsProcessor, EosTokenCriteria, EpsilonLogitsWarper, EtaLogitsWarper, ExponentialDecayLengthPenalty, ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, GenerationMixin, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitNormalization, LogitsProcessor, LogitsProcessorList, MaxLengthCriteria, MaxTimeCriteria, MinLengthLogitsProcessor, MinNewTokensLengthLogitsProcessor, MinPLogitsWarper, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, PhrasalConstraint, PrefixConstrainedLogitsProcessor, RepetitionPenaltyLogitsProcessor, SequenceBiasLogitsProcessor, StoppingCriteria, StoppingCriteriaList, StopStringCriteria, SuppressTokensAtBeginLogitsProcessor, SuppressTokensLogitsProcessor, SynthIDTextWatermarkDetector, SynthIDTextWatermarkingConfig, SynthIDTextWatermarkLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper, UnbatchedClassifierFreeGuidanceLogitsProcessor, WatermarkDetector, WatermarkLogitsProcessor, WhisperTimeStampLogitsProcessor, ) from .integrations.executorch import ( TorchExportableModuleWithStaticCache, convert_and_export_with_cache, ) from .model_debugging_utils import ( model_addition_debugger_context, ) from .modeling_layers import GradientCheckpointingLayer from .modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from .modeling_utils import AttentionInterface, PreTrainedModel # Optimization from .optimization import ( Adafactor, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_inverse_sqrt_schedule, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup, get_scheduler, get_wsd_schedule, ) from .pytorch_utils import Conv1D, apply_chunking_to_forward, prune_layer # Trainer from .trainer import Trainer from .trainer_pt_utils import torch_distributed_zero_first from .trainer_seq2seq import Seq2SeqTrainer # TensorFlow try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: # 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 * else: from .generation import ( TFForcedBOSTokenLogitsProcessor, TFForcedEOSTokenLogitsProcessor, TFForceTokensLogitsProcessor, TFGenerationMixin, TFLogitsProcessor, TFLogitsProcessorList, TFLogitsWarper, TFMinLengthLogitsProcessor, TFNoBadWordsLogitsProcessor, TFNoRepeatNGramLogitsProcessor, TFRepetitionPenaltyLogitsProcessor, TFSuppressTokensAtBeginLogitsProcessor, TFSuppressTokensLogitsProcessor, TFTemperatureLogitsWarper, TFTopKLogitsWarper, TFTopPLogitsWarper, ) from .keras_callbacks import KerasMetricCallback, PushToHubCallback from .modeling_tf_utils import ( TFPreTrainedModel, TFSequenceSummary, TFSharedEmbeddings, shape_list, ) # Optimization from .optimization_tf import ( AdamWeightDecay, GradientAccumulator, WarmUp, create_optimizer, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: # 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: from .generation import ( FlaxForcedBOSTokenLogitsProcessor, FlaxForcedEOSTokenLogitsProcessor, FlaxForceTokensLogitsProcessor, FlaxGenerationMixin, FlaxLogitsProcessor, FlaxLogitsProcessorList, FlaxLogitsWarper, FlaxMinLengthLogitsProcessor, FlaxSuppressTokensAtBeginLogitsProcessor, FlaxSuppressTokensLogitsProcessor, FlaxTemperatureLogitsWarper, FlaxTopKLogitsWarper, FlaxTopPLogitsWarper, FlaxWhisperTimeStampLogitsProcessor, ) from .modeling_flax_utils import FlaxPreTrainedModel else: import sys _import_structure = {k: set(v) for k, v in _import_structure.items()} import_structure = define_import_structure(Path(__file__).parent / "models", prefix="models") import_structure[frozenset({})].update(_import_structure) sys.modules[__name__] = _LazyModule( __name__, globals()["__file__"], import_structure, module_spec=__spec__, extra_objects={"__version__": __version__}, ) if not is_tf_available() and not is_torch_available() and not is_flax_available(): logger.warning_advice( "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." )