


from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
	import accelerate






def  UpperCamelCase					(	__magic_name__     :							List[str]     )   ->  Dict:
		"""simple docstring"""




		if not is_accelerate_available():
				return method
		lowercase__     			=		version.parse(accelerate.__version__     ).base_version
		if version.parse(_lowerCamelCase     ) < version.parse("""0.17.0"""     ):
				return method

		def wrapper(self     :							Optional[int]    ,    *__magic_name__     :							str    ,    **__magic_name__     :							Optional[Any]     ):
				if hasattr(self    ,    """_hf_hook"""     ) and hasattr(self._hf_hook    ,    """pre_forward"""     ):
						self._hf_hook.pre_forward(self     )
				return method(self    ,    *_lowerCamelCase    ,    **_lowerCamelCase     )

		return wrapper


import argparse

import torch

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


logging.set_verbosity_info()

def 			lowercase_				(			_lowerCamelCase							:       str		,	_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       Dict):
    # Initialise PyTorch model
    lowercase__ :   List[str]    					=				BertConfig.from_json_file(_lowerCamelCase)
    print(f'''Building PyTorch model from configuration: {config}''')
    lowercase__ :   Optional[Any]    					=				BertForPreTraining(_lowerCamelCase)

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

    # Save pytorch-model
    print(f'''Save PyTorch model to {pytorch_dump_path}''')
    torch.save(model.state_dict()		,	_lowerCamelCase)


if __name__ == "__main__":
      UpperCamelCase  							=						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.'''
      )
      UpperCamelCase  							=						parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



