code: 
  
  
  
  import argparse
  
  from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__     )       ->				int:
  							# Initialize PyTorch model
  							__SCREAMING_SNAKE_CASE								=      CanineConfig()
  							__SCREAMING_SNAKE_CASE								=      CanineModel(snake_case__     )
  							model.eval()
  
  							print(f"""Building PyTorch model from configuration: {config}"""     )
  
  							# Load weights from tf checkpoint
  							load_tf_weights_in_canine(snake_case__							,     snake_case__							,     snake_case__     )
  
  							# Save pytorch-model (weights and configuration)
  							print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
  							model.save_pretrained(snake_case__     )
  
  							# Save tokenizer files
  							__SCREAMING_SNAKE_CASE								=      CanineTokenizer()
  							print(f"""Save tokenizer files to {pytorch_dump_path}"""     )
  							tokenizer.save_pretrained(snake_case__     )
  
  
  if __name__ == "__main__":
  			lowerCAmelCase__	:  int 		=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.''',
  			)
  			lowerCAmelCase__	:  Dict 		=parser.parse_args()
  			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
style_context: 
  
  
  
  def 			__lowercase     (				a__     )       ->				int:
  							return int(a__     ) if x - int(a__     ) <= 0 else int(a__     ) + 1
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  
  
  