code: 
  """simple docstring"""
  import argparse
  
  from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						CanineConfig()
  	_UpperCAmelCase =						CanineModel(UpperCamelCase__      )
  	model.eval()
  
  	print(f"Building PyTorch model from configuration: {config}"      )
  
  	# Load weights from tf checkpoint
  	load_tf_weights_in_canine(UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      )
  
  	# Save pytorch-model (weights and configuration)
  	print(f"Save PyTorch model to {pytorch_dump_path}"      )
  	model.save_pretrained(UpperCamelCase__      )
  
  	# Save tokenizer files
  	_UpperCAmelCase =						CanineTokenizer()
  	print(f"Save tokenizer files to {pytorch_dump_path}"      )
  	tokenizer.save_pretrained(UpperCamelCase__      )
  
  
  if __name__ == "__main__":
  			__magic_name__		       =				argparse.ArgumentParser()
  			# Required parameters
  			parser.add_argument(
  			    '''--tf_checkpoint_path''',
  			    default=None,
  			    type=str,
  			    required=True,
  			    help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
  			)
  			parser.add_argument(
  			    '''--pytorch_dump_path''',
  			    default=None,
  			    type=str,
  			    required=True,
  			    help='''Path to a folder where the PyTorch model will be placed.''',
  			)
  			__magic_name__		       =				parser.parse_args()
  			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
  
  
  
  
  
style_context: 
  """simple docstring"""
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	if not isinstance(UpperCamelCase__				,     UpperCamelCase__      ):
  		raise TypeError("recursive_lucas_number accepts only integer arguments."      )
  	if n_th_number == 0:
  		return 2
  	if n_th_number == 1:
  		return 1
  
  	return recursive_lucas_number(n_th_number - 1      ) + recursive_lucas_number(
  	    n_th_number - 2      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	if not isinstance(UpperCamelCase__				,     UpperCamelCase__      ):
  		raise TypeError("dynamic_lucas_number accepts only integer arguments."      )
  	_UpperCAmelCase ,      _UpperCAmelCase =						2, 1
  	for _ in range(UpperCamelCase__      ):
  		_UpperCAmelCase ,      _UpperCAmelCase =						b, a + b
  	return a
  
  
  if __name__ == "__main__":
  			from doctest import testmod
  
  			testmod()
  			__magic_name__		       =				int(input('''Enter the number of terms in lucas series:\n''').strip())
  			print('''Using recursive function to calculate lucas series:''')
  			print(''' '''.join(str(recursive_lucas_number(i)) for i in range(n)))
  			print('''\nUsing dynamic function to calculate lucas series:''')
  			print(''' '''.join(str(dynamic_lucas_number(i)) for i in range(n)))
  
  
  
  
  