code: 
  
  
  '''simple docstring'''
  
  import argparse
  
  import torch
  
  from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Union[str, Any]  			=  RoFormerConfig.from_json_file(lowerCAmelCase	)
  	print(f'Building PyTorch model from configuration: {config}'	)
  	__magic_name__ :					Any  			=  RoFormerForMaskedLM(lowerCAmelCase	)
  
  	# Load weights from tf checkpoint
  	load_tf_weights_in_roformer(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)
  
  	# Save pytorch-model
  	print(f'Save PyTorch model to {pytorch_dump_path}'	)
  	torch.save(model.state_dict()	,	lowerCAmelCase	,	_use_new_zipfile_serialization=lowerCAmelCase	)
  
  
  if __name__ == "__main__":
  							lowerCAmelCase   :Any								=    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   :Optional[Any]								=    parser.parse_args()
  							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
style_context: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			list[int | float]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if len(lowerCAmelCase	) == 0:
  		raise ValueError('find_min() arg is an empty sequence'	)
  	if (
  	    left >= len(lowerCAmelCase	)
  	    or left < -len(lowerCAmelCase	)
  	    or right >= len(lowerCAmelCase	)
  	    or right < -len(lowerCAmelCase	)
  	):
  		raise IndexError('list index out of range'	)
  	if left == right:
  		return nums[left]
  	__magic_name__ :					Tuple  			=  (left + right) >> 1  # the middle
  	__magic_name__ :					str  			=  find_min(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)  # find min in range[left, mid]
  	__magic_name__ :					Optional[Any]  			=  find_min(lowerCAmelCase	,	mid + 1	,	lowerCAmelCase	)  # find min in range[mid + 1, right]
  
  	return left_min if left_min <= right_min else right_min
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod(verbose=True)