



'''simple docstring'''




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def 				lowerCAmelCase_   (	_lowerCamelCase:   Union[str, Any]       ,      _lowerCamelCase:   List[str]       ,      _lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		RemBertConfig.from_json_file(A__					)
		print("""Building PyTorch model from configuration: {}""".format(str(A__					)					)					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		RemBertModel(A__					)

		# Load weights from tf checkpoint
		load_tf_weights_in_rembert(A__       ,      A__       ,      A__					)

		# Save pytorch-model
		print("""Save PyTorch model to {}""".format(A__					)					)
		torch.save(model.state_dict()       ,      A__					)


if __name__ == "__main__":
						UpperCamelCase__       :	str    = 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(
						    '''--rembert_config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained RemBERT 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__       :	List[str]    = parser.parse_args()
						convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   str							,    A__					:   list[str]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Dict			=   """"""
  for word_or_phrase in separated:
    if not isinstance(A__							,    A__					):
      raise Exception("""join() accepts only strings to be joined"""					)
    joined += word_or_phrase + separator
  return joined.strip(A__					)


if __name__ == "__main__":
  from doctest import testmod

  testmod()



