

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def 			UpperCAmelCase__				(			_A      : str	,      _A      : List[str]	,      _A      : List[Any]							):





  '''simple docstring'''







  a__  =BertConfig.from_json_file(_A							)
  print(F"""Building PyTorch model from configuration: {config}"""							)
  a__  =BertForPreTraining(_A							)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_A	,      _A	,      _A							)

  # Save pytorch-model
  print(F"""Save PyTorch model to {pytorch_dump_path}"""							)
  torch.save(model.state_dict()	,      _A							)


if __name__ == "__main__":
       lowerCamelCase           =					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
       )
       parser.add_argument(
           '''--bert_config_file''',
           default=None,
           type=str,
           required=True,
           help=(
               '''The config json file corresponding to the pre-trained BERT model. \n'''
               '''This specifies the model architecture.'''
           ),
       )
       parser.add_argument(
           '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       lowerCamelCase           =					parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
