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 			__a  (	__lowerCamelCase,	__lowerCamelCase						):
     # Initialize PyTorch model
     UpperCAmelCase_      :      Dict      =					CanineConfig()
     UpperCAmelCase_      :      Optional[int]      =					CanineModel(__lowerCamelCase						)
     model.eval()
  
     print(f"""Building PyTorch model from configuration: {config}"""						)
  
     # Load weights from tf checkpoint
     load_tf_weights_in_canine(__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						)
  
     # Save pytorch-model (weights and configuration)
     print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
     model.save_pretrained(__lowerCamelCase						)
  
     # Save tokenizer files
     UpperCAmelCase_      :      List[str]      =					CanineTokenizer()
     print(f"""Save tokenizer files to {pytorch_dump_path}"""						)
     tokenizer.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. 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	 =							parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  def 			__a  (	__lowerCamelCase = 1000						):
     UpperCAmelCase_      :      List[Any]      =					0
     UpperCAmelCase_      :      List[str]      =					(n - 1) // 3
     total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
     UpperCAmelCase_      :      Any      =					(n - 1) // 5
     total += ((terms) * (10 + (terms - 1) * 5)) // 2
     UpperCAmelCase_      :      Optional[int]      =					(n - 1) // 15
     total -= ((terms) * (30 + (terms - 1) * 15)) // 2
     return total
  
  
  if __name__ == "__main__":
      print(f"""{solution() = }""")
  
  
  