



import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
   import numpy as np
   import torch

   from . import (
       AlbertForPreTraining,
       BartForConditionalGeneration,
       BertForPreTraining,
       BertForQuestionAnswering,
       BertForSequenceClassification,
       CamembertForMaskedLM,
       CTRLLMHeadModel,
       DistilBertForMaskedLM,
       DistilBertForQuestionAnswering,
       DPRContextEncoder,
       DPRQuestionEncoder,
       DPRReader,
       ElectraForPreTraining,
       FlaubertWithLMHeadModel,
       GPTaLMHeadModel,
       LayoutLMForMaskedLM,
       LxmertForPreTraining,
       LxmertVisualFeatureEncoder,
       OpenAIGPTLMHeadModel,
       RobertaForMaskedLM,
       RobertaForSequenceClassification,
       TaForConditionalGeneration,
       TransfoXLLMHeadModel,
       XLMRobertaForMaskedLM,
       XLMWithLMHeadModel,
       XLNetLMHeadModel,
   )


logging.set_verbosity_info()

__lowerCamelCase         =			{
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case=False					,  __snake_case=True    )     ->     Tuple:






      '''simple docstring'''







      if model_type not in MODEL_CLASSES:
            raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()    )}."""    )

      UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	List[str]				      =		MODEL_CLASSES[model_type]

      # Initialise TF model
      if config_file in aws_config_map:
            UpperCAmelCase_  :	Optional[int]				      =		cached_file(__snake_case					,  __snake_case					,  force_download=not use_cached_models    )
      UpperCAmelCase_  :	Tuple				      =		config_class.from_json_file(__snake_case    )
      UpperCAmelCase_  :	str				      =		True
      UpperCAmelCase_  :	Union[str, Any]				      =		True
      print(F"""Building TensorFlow model from configuration: {config}"""    )
      UpperCAmelCase_  :	int				      =		model_class(__snake_case    )

      # Load weights from tf checkpoint
      if pytorch_checkpoint_path in aws_config_map.keys():
            UpperCAmelCase_  :	List[Any]				      =		cached_file(
                __snake_case					,  __snake_case					,  force_download=not use_cached_models    )
      # Load PyTorch checkpoint in tf2 model:
      UpperCAmelCase_  :	List[Any]				      =		load_pytorch_checkpoint_in_tfa_model(__snake_case					,  __snake_case    )

      if compare_with_pt_model:
            UpperCAmelCase_  :	List[Any]				      =		tf_model(tf_model.dummy_inputs					,  training=__snake_case    )  # build the network

            UpperCAmelCase_  :	Union[str, Any]				      =		torch.load(__snake_case					,  map_location="cpu"    )
            UpperCAmelCase_  :	int				      =		pt_model_class.from_pretrained(
                pretrained_model_name_or_path=__snake_case					,  config=__snake_case					,  state_dict=__snake_case    )

            with torch.no_grad():
                  UpperCAmelCase_  :	Dict				      =		pt_model(**pt_model.dummy_inputs    )

            UpperCAmelCase_  :	Dict				      =		pto[0].numpy()
            UpperCAmelCase_  :	Dict				      =		tfo[0].numpy()
            UpperCAmelCase_  :	str				      =		np.amax(np.abs(np_pt - np_tf    )    )
            print(F"""Max absolute difference between models outputs {diff}"""    )
            assert diff <= 2E-2, F"""Error, model absolute difference is >2e-2: {diff}"""

      # Save pytorch-model
      print(F"""Save TensorFlow model to {tf_dump_path}"""    )
      tf_model.save_weights(__snake_case					,  save_format="h5"    )







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case=None					,  __snake_case=None					,  __snake_case=False					,  __snake_case=False					,  __snake_case=False					,  __snake_case=False					,  )     ->     Dict:






      '''simple docstring'''







      if args_model_type is None:
            UpperCAmelCase_  :	Optional[int]				      =		list(MODEL_CLASSES.keys()    )
      else:
            UpperCAmelCase_  :	str				      =		[args_model_type]

      for j, model_type in enumerate(__snake_case					,  start=1    ):
            print("=" * 1_0_0    )
            print(F""" Converting model type {j}/{len(__snake_case    )}: {model_type}"""    )
            print("=" * 1_0_0    )
            if model_type not in MODEL_CLASSES:
                  raise ValueError(F"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()    )}."""    )

            UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[int]				      =		MODEL_CLASSES[model_type]

            if model_shortcut_names_or_path is None:
                  UpperCAmelCase_  :	Optional[int]				      =		list(aws_model_maps.keys()    )
            if config_shortcut_names_or_path is None:
                  UpperCAmelCase_  :	List[str]				      =		model_shortcut_names_or_path

            for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                zip(__snake_case					,  __snake_case    )					,  start=1    ):
                  print("-" * 1_0_0    )
                  if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                        if not only_convert_finetuned_models:
                              print(F"""    Skipping finetuned checkpoint {model_shortcut_name}"""    )
                              continue
                        UpperCAmelCase_  :	Union[str, Any]				      =		model_shortcut_name
                  elif only_convert_finetuned_models:
                        print(F"""    Skipping not finetuned checkpoint {model_shortcut_name}"""    )
                        continue
                  print(
                      F"""    Converting checkpoint {i}/{len(__snake_case    )}: {model_shortcut_name} - model_type {model_type}"""    )
                  print("-" * 1_0_0    )

                  if config_shortcut_name in aws_config_map:
                        UpperCAmelCase_  :	int				      =		cached_file(__snake_case					,  __snake_case					,  force_download=not use_cached_models    )
                  else:
                        UpperCAmelCase_  :	Any				      =		config_shortcut_name

                  if model_shortcut_name in aws_model_maps:
                        UpperCAmelCase_  :	Tuple				      =		cached_file(__snake_case					,  __snake_case					,  force_download=not use_cached_models    )
                  else:
                        UpperCAmelCase_  :	str				      =		model_shortcut_name

                  if os.path.isfile(__snake_case    ):
                        UpperCAmelCase_  :	int				      =		"converted_model"

                  convert_pt_checkpoint_to_tf(
                      model_type=__snake_case					,  pytorch_checkpoint_path=__snake_case					,  config_file=__snake_case					,  tf_dump_path=os.path.join(__snake_case					,  model_shortcut_name + "-tf_model.h5"    )					,  compare_with_pt_model=__snake_case					,  )
                  if remove_cached_files:
                        os.remove(__snake_case    )
                        os.remove(__snake_case    )


if __name__ == "__main__":
   __lowerCamelCase         =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
   )
   parser.add_argument(
       '''--model_type''',
       default=None,
       type=str,
       help=(
           F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
           '''convert all the models from AWS.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_checkpoint_path''',
       default=None,
       type=str,
       help=(
           '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
           '''If not given, will download and convert all the checkpoints from AWS.'''
       ),
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       help=(
           '''The config json file corresponding to the pre-trained model. \n'''
           '''This specifies the model architecture. If not given and '''
           '''--pytorch_checkpoint_path is not given or is a shortcut name '''
           '''use the configuration associated to the shortcut name on the AWS'''
       ),
   )
   parser.add_argument(
       '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
   )
   parser.add_argument(
       '''--use_cached_models''',
       action='''store_true''',
       help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
   )
   parser.add_argument(
       '''--remove_cached_files''',
       action='''store_true''',
       help='''Remove pytorch models after conversion (save memory when converting in batches).''',
   )
   parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
   __lowerCamelCase         =			parser.parse_args()

   # if args.pytorch_checkpoint_path is not None:
   #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
   #                                 args.pytorch_checkpoint_path,
   #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
   #                                 args.tf_dump_path,
   #                                 compare_with_pt_model=args.compare_with_pt_model,
   #                                 use_cached_models=args.use_cached_models)
   # else:
   convert_all_pt_checkpoints_to_tf(
       args.model_type.lower() if args.model_type is not None else None,
       args.tf_dump_path,
       model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
       if args.pytorch_checkpoint_path is not None
       else None,
       config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
       compare_with_pt_model=args.compare_with_pt_model,
       use_cached_models=args.use_cached_models,
       remove_cached_files=args.remove_cached_files,
       only_convert_finetuned_models=args.only_convert_finetuned_models,
   )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase         =			{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			['''RoFormerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoFormerForCausalLM''',
       '''RoFormerForMaskedLM''',
       '''RoFormerForMultipleChoice''',
       '''RoFormerForQuestionAnswering''',
       '''RoFormerForSequenceClassification''',
       '''RoFormerForTokenClassification''',
       '''RoFormerLayer''',
       '''RoFormerModel''',
       '''RoFormerPreTrainedModel''',
       '''load_tf_weights_in_roformer''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRoFormerForCausalLM''',
       '''TFRoFormerForMaskedLM''',
       '''TFRoFormerForMultipleChoice''',
       '''TFRoFormerForQuestionAnswering''',
       '''TFRoFormerForSequenceClassification''',
       '''TFRoFormerForTokenClassification''',
       '''TFRoFormerLayer''',
       '''TFRoFormerModel''',
       '''TFRoFormerPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''FlaxRoFormerForMaskedLM''',
       '''FlaxRoFormerForMultipleChoice''',
       '''FlaxRoFormerForQuestionAnswering''',
       '''FlaxRoFormerForSequenceClassification''',
       '''FlaxRoFormerForTokenClassification''',
       '''FlaxRoFormerModel''',
       '''FlaxRoFormerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
   from .tokenization_roformer import RoFormerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roformer_fast import RoFormerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roformer import (
          ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoFormerForCausalLM,
          RoFormerForMaskedLM,
          RoFormerForMultipleChoice,
          RoFormerForQuestionAnswering,
          RoFormerForSequenceClassification,
          RoFormerForTokenClassification,
          RoFormerLayer,
          RoFormerModel,
          RoFormerPreTrainedModel,
          load_tf_weights_in_roformer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roformer import (
          TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerLayer,
          TFRoFormerModel,
          TFRoFormerPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roformer import (
          FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
          FlaxRoFormerPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase         =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
