code: 
  
  
  
  import argparse
  
  from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  def 		__a  (					lowerCAmelCase_     :      str    ,lowerCAmelCase_     :      List[str]    ,lowerCAmelCase_     :      Union[str, Any]       )   ->				Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     ConvBertConfig.from_json_file(lowerCAmelCase_       )
  						UpperCAmelCase_=     ConvBertModel(lowerCAmelCase_       )
  
  						UpperCAmelCase_=     load_tf_weights_in_convbert(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_       )
  						model.save_pretrained(lowerCAmelCase_       )
  
  						UpperCAmelCase_=     TFConvBertModel.from_pretrained(lowerCAmelCase_    ,from_pt=lowerCAmelCase_       )
  						tf_model.save_pretrained(lowerCAmelCase_       )
  
  
  if __name__ == "__main__":
  	__A       					=		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(
  	    '''--convbert_config_file''',
  	    default=None,
  	    type=str,
  	    required=True,
  	    help=(
  	        '''The config json file corresponding to the pre-trained ConvBERT 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.'''
  	)
  	__A       					=		parser.parse_args()
  	convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
  
  
  
  
  
style_context: 
  
  
  
  from __future__ import annotations
  
  
  
  def  __a  (					lowerCAmelCase_     :      int       )   ->				int | None:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						for r in range(1    ,lowerCAmelCase_       ):
  												UpperCAmelCase_=     []
  												for x in range(n - 1       ):
  																		UpperCAmelCase_=     pow(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_       )
  																		if val in li:
  																								break
  																		li.append(lowerCAmelCase_       )
  												else:
  																		return r
  						return None
  
  
  if __name__ == "__main__":
  	__A       					=		int(input('''Enter a prime number q: '''))
  	__A       					=		find_primitive(q)
  	if a is None:
  		print(f'Cannot find the primitive for the value: {a!r}')
  	else:
  		__A       					=		int(input('''Enter private key of A: '''))
  		__A       					=		pow(a, a_private, q)
  		__A       					=		int(input('''Enter private key of B: '''))
  		__A       					=		pow(a, b_private, q)
  
  		__A       					=		pow(b_public, a_private, q)
  		__A       					=		pow(a_public, b_private, q)
  
  		print('''The key value generated by A is: ''', a_secret)
  		print('''The key value generated by B is: ''', b_secret)
  
  
  
  
  