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_       )      ->Tuple:
  	# Initialize PyTorch model
  	snake_case__  					=      CanineConfig()
  	snake_case__  					=      CanineModel(lowercase__       )
  	model.eval()
  
  	print(f'''Building PyTorch model from configuration: {config}'''       )
  
  	# Load weights from tf checkpoint
  	load_tf_weights_in_canine(lowercase__				,							lowercase__				,							lowercase__       )
  
  	# Save pytorch-model (weights and configuration)
  	print(f'''Save PyTorch model to {pytorch_dump_path}'''       )
  	model.save_pretrained(lowercase__       )
  
  	# Save tokenizer files
  	snake_case__  					=      CanineTokenizer()
  	print(f'''Save tokenizer files to {pytorch_dump_path}'''       )
  	tokenizer.save_pretrained(lowercase__       )
  
  
  if __name__ == "__main__":
  			a__				: Tuple									=   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.''',
  			)
  			a__				: List[str]									=   parser.parse_args()
  			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from typing import Callable
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->str:
  
  	def wrapper(UpperCAmelCase_       ):
  		snake_case__  					=      function_with_docstring.__doc__
  		return target_function
  
  	return wrapper
  
  
  
  