code: # 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.
  
  __version__ = "2.6.0"
  
  # 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
  
  import logging
  
  # Benchmarking
  from .benchmark_utils import (
      Frame,
      Memory,
      MemoryState,
      MemorySummary,
      MemoryTrace,
      UsedMemoryState,
      bytes_to_human_readable,
      start_memory_tracing,
      stop_memory_tracing,
  )
  from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
  from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, AutoConfig
  from .configuration_bart import BartConfig
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
  from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
  from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
  from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
  from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
  from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
  from .configuration_mmbt import MMBTConfig
  from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
  from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
  from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
  from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
  
  # Configurations
  from .configuration_utils import PretrainedConfig
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
  from .configuration_xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
  from .data import (
      DataProcessor,
      InputExample,
      InputFeatures,
      SingleSentenceClassificationProcessor,
      SquadExample,
      SquadFeatures,
      SquadV1Processor,
      SquadV2Processor,
      glue_convert_examples_to_features,
      glue_output_modes,
      glue_processors,
      glue_tasks_num_labels,
      is_sklearn_available,
      squad_convert_examples_to_features,
      xnli_output_modes,
      xnli_processors,
      xnli_tasks_num_labels,
  )
  
  # Files and general utilities
  from .file_utils import (
      CONFIG_NAME,
      MODEL_CARD_NAME,
      PYTORCH_PRETRAINED_BERT_CACHE,
      PYTORCH_TRANSFORMERS_CACHE,
      TF2_WEIGHTS_NAME,
      TF_WEIGHTS_NAME,
      TRANSFORMERS_CACHE,
      WEIGHTS_NAME,
      add_end_docstrings,
      add_start_docstrings,
      cached_path,
      is_tf_available,
      is_torch_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,
  )
  
  # Pipelines
  from .pipelines import (
      CsvPipelineDataFormat,
      FeatureExtractionPipeline,
      FillMaskPipeline,
      JsonPipelineDataFormat,
      NerPipeline,
      PipedPipelineDataFormat,
      Pipeline,
      PipelineDataFormat,
      QuestionAnsweringPipeline,
      SummarizationPipeline,
      TextClassificationPipeline,
      TokenClassificationPipeline,
      TranslationPipeline,
      pipeline,
  )
  from .tokenization_albert import AlbertTokenizer
  from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
  from .tokenization_bart import BartTokenizer
  from .tokenization_bert import BasicTokenizer, BertTokenizer, BertTokenizerFast, WordpieceTokenizer
  from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
  from .tokenization_camembert import CamembertTokenizer
  from .tokenization_ctrl import CTRLTokenizer
  from .tokenization_distilbert import DistilBertTokenizer, DistilBertTokenizerFast
  from .tokenization_flaubert import FlaubertTokenizer
  from .tokenization_gpt2 import GPT2Tokenizer, GPT2TokenizerFast
  from .tokenization_openai import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
  from .tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
  from .tokenization_t5 import T5Tokenizer
  from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer, TransfoXLTokenizerFast
  
  # Tokenizers
  from .tokenization_utils import PreTrainedTokenizer
  from .tokenization_xlm import XLMTokenizer
  from .tokenization_xlm_roberta import XLMRobertaTokenizer
  from .tokenization_xlnet import SPIECE_UNDERLINE, XLNetTokenizer
  
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  
  if is_sklearn_available():
      from .data import glue_compute_metrics, xnli_compute_metrics
  
  
  # Modeling
  if is_torch_available():
      from .modeling_utils import PreTrainedModel, prune_layer, Conv1D, top_k_top_p_filtering
      from .modeling_auto import (
          AutoModel,
          AutoModelForPreTraining,
          AutoModelForSequenceClassification,
          AutoModelForQuestionAnswering,
          AutoModelWithLMHead,
          AutoModelForTokenClassification,
          ALL_PRETRAINED_MODEL_ARCHIVE_MAP,
          MODEL_MAPPING,
          MODEL_FOR_PRETRAINING_MAPPING,
          MODEL_WITH_LM_HEAD_MAPPING,
          MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      )
  
      from .modeling_bert import (
          BertPreTrainedModel,
          BertModel,
          BertForPreTraining,
          BertForMaskedLM,
          BertForNextSentencePrediction,
          BertForSequenceClassification,
          BertForMultipleChoice,
          BertForTokenClassification,
          BertForQuestionAnswering,
          load_tf_weights_in_bert,
          BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_openai import (
          OpenAIGPTPreTrainedModel,
          OpenAIGPTModel,
          OpenAIGPTLMHeadModel,
          OpenAIGPTDoubleHeadsModel,
          load_tf_weights_in_openai_gpt,
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_transfo_xl import (
          TransfoXLPreTrainedModel,
          TransfoXLModel,
          TransfoXLLMHeadModel,
          AdaptiveEmbedding,
          load_tf_weights_in_transfo_xl,
          TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_gpt2 import (
          GPT2PreTrainedModel,
          GPT2Model,
          GPT2LMHeadModel,
          GPT2DoubleHeadsModel,
          load_tf_weights_in_gpt2,
          GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_ctrl import CTRLPreTrainedModel, CTRLModel, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
      from .modeling_xlnet import (
          XLNetPreTrainedModel,
          XLNetModel,
          XLNetLMHeadModel,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetForMultipleChoice,
          XLNetForQuestionAnsweringSimple,
          XLNetForQuestionAnswering,
          load_tf_weights_in_xlnet,
          XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_xlm import (
          XLMPreTrainedModel,
          XLMModel,
          XLMWithLMHeadModel,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_bart import (
          BartForSequenceClassification,
          BartModel,
          BartForConditionalGeneration,
          BART_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_roberta import (
          RobertaForMaskedLM,
          RobertaModel,
          RobertaForSequenceClassification,
          RobertaForMultipleChoice,
          RobertaForTokenClassification,
          RobertaForQuestionAnswering,
          ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_distilbert import (
          DistilBertPreTrainedModel,
          DistilBertForMaskedLM,
          DistilBertModel,
          DistilBertForSequenceClassification,
          DistilBertForQuestionAnswering,
          DistilBertForTokenClassification,
          DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_camembert import (
          CamembertForMaskedLM,
          CamembertModel,
          CamembertForSequenceClassification,
          CamembertForMultipleChoice,
          CamembertForTokenClassification,
          CamembertForQuestionAnswering,
          CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_encoder_decoder import PreTrainedEncoderDecoder
      from .modeling_t5 import (
          T5PreTrainedModel,
          T5Model,
          T5ForConditionalGeneration,
          load_tf_weights_in_t5,
          T5_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_albert import (
          AlbertPreTrainedModel,
          AlbertModel,
          AlbertForMaskedLM,
          AlbertForSequenceClassification,
          AlbertForQuestionAnswering,
          AlbertForTokenClassification,
          load_tf_weights_in_albert,
          ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_xlm_roberta import (
          XLMRobertaForMaskedLM,
          XLMRobertaModel,
          XLMRobertaForMultipleChoice,
          XLMRobertaForSequenceClassification,
          XLMRobertaForTokenClassification,
          XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
      from .modeling_mmbt import ModalEmbeddings, MMBTModel, MMBTForClassification
  
      from .modeling_flaubert import (
          FlaubertModel,
          FlaubertWithLMHeadModel,
          FlaubertForSequenceClassification,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      # Optimization
      from .optimization import (
          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,
      )
  
  
  # TensorFlow
  if is_tf_available():
      from .modeling_tf_utils import (
          TFPreTrainedModel,
          TFSharedEmbeddings,
          TFSequenceSummary,
          shape_list,
          tf_top_k_top_p_filtering,
      )
      from .modeling_tf_auto import (
          TFAutoModel,
          TFAutoModelForPreTraining,
          TFAutoModelForSequenceClassification,
          TFAutoModelForQuestionAnswering,
          TFAutoModelWithLMHead,
          TFAutoModelForTokenClassification,
          TF_ALL_PRETRAINED_MODEL_ARCHIVE_MAP,
          TF_MODEL_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_WITH_LM_HEAD_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      )
  
      from .modeling_tf_bert import (
          TFBertPreTrainedModel,
          TFBertMainLayer,
          TFBertEmbeddings,
          TFBertModel,
          TFBertForPreTraining,
          TFBertForMaskedLM,
          TFBertForNextSentencePrediction,
          TFBertForSequenceClassification,
          TFBertForMultipleChoice,
          TFBertForTokenClassification,
          TFBertForQuestionAnswering,
          TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_gpt2 import (
          TFGPT2PreTrainedModel,
          TFGPT2MainLayer,
          TFGPT2Model,
          TFGPT2LMHeadModel,
          TFGPT2DoubleHeadsModel,
          TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_openai import (
          TFOpenAIGPTPreTrainedModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTDoubleHeadsModel,
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_transfo_xl import (
          TFTransfoXLPreTrainedModel,
          TFTransfoXLMainLayer,
          TFTransfoXLModel,
          TFTransfoXLLMHeadModel,
          TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
          TFAdaptiveEmbedding,
      )
  
      from .modeling_tf_xlnet import (
          TFXLNetPreTrainedModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetLMHeadModel,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetForQuestionAnsweringSimple,
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_xlm import (
          TFXLMPreTrainedModel,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMWithLMHeadModel,
          TFXLMForSequenceClassification,
          TFXLMForQuestionAnsweringSimple,
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_xlm_roberta import (
          TFXLMRobertaForMaskedLM,
          TFXLMRobertaModel,
          TFXLMRobertaForSequenceClassification,
          TFXLMRobertaForTokenClassification,
          TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_roberta import (
          TFRobertaPreTrainedModel,
          TFRobertaMainLayer,
          TFRobertaModel,
          TFRobertaForMaskedLM,
          TFRobertaForSequenceClassification,
          TFRobertaForTokenClassification,
          TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_camembert import (
          TFCamembertModel,
          TFCamembertForMaskedLM,
          TFCamembertForSequenceClassification,
          TFCamembertForTokenClassification,
          TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_flaubert import (
          TFFlaubertModel,
          TFFlaubertWithLMHeadModel,
          TFFlaubertForSequenceClassification,
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_distilbert import (
          TFDistilBertPreTrainedModel,
          TFDistilBertMainLayer,
          TFDistilBertModel,
          TFDistilBertForMaskedLM,
          TFDistilBertForSequenceClassification,
          TFDistilBertForTokenClassification,
          TFDistilBertForQuestionAnswering,
          TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_ctrl import (
          TFCTRLPreTrainedModel,
          TFCTRLModel,
          TFCTRLLMHeadModel,
          TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_albert import (
          TFAlbertPreTrainedModel,
          TFAlbertMainLayer,
          TFAlbertModel,
          TFAlbertForMaskedLM,
          TFAlbertForSequenceClassification,
          TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      from .modeling_tf_t5 import (
          TFT5PreTrainedModel,
          TFT5Model,
          TFT5ForConditionalGeneration,
          TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP,
      )
  
      # Optimization
      from .optimization_tf import WarmUp, create_optimizer, AdamWeightDecay, GradientAccumulator
  
  
  if not is_tf_available() and not is_torch_available():
      logger.warning(
          "Neither PyTorch nor TensorFlow >= 2.0 have been found."
          "Models won't be available and only tokenizers, configuration"
          "and file/data utilities can be used."
      )
  