code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  
  from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
  from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
  					# Construct configuration depending on size
  					__a    : int      					=						{'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
  					__a					,       __a					,       __a    : Tuple      					=						MODELS[model_size]  # set model hyperparameters
  					__a    : List[str]      					=						ImageGPTConfig(n_embd=_SCREAMING_SNAKE_CASE		,   n_layer=_SCREAMING_SNAKE_CASE		,   n_head=_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[Any]      					=						ImageGPTForCausalLM(_SCREAMING_SNAKE_CASE     )
  
  					# Load weights from numpy
  					load_tf_weights_in_imagegpt(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
  					# Save pytorch-model
  					__a    : Dict      					=						pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  					__a    : Optional[Any]      					=						pytorch_dump_folder_path + '/' + CONFIG_NAME
  					print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""     )
  					torch.save(model.state_dict()		,   _SCREAMING_SNAKE_CASE     )
  					print(F"""Save configuration file to {pytorch_config_dump_path}"""     )
  					with open(_SCREAMING_SNAKE_CASE		,   'w'		,   encoding='utf-8'     ) as f:
  										f.write(config.to_json_string()     )
  
  
  if __name__ == "__main__":
  	__lowercase			:		Dict =			argparse.ArgumentParser()
  	# Required parameters
  	parser.add_argument(
  	    '--imagegpt_checkpoint_path',
  	    default=None,
  	    type=str,
  	    required=True,
  	    help='Path to the TensorFlow checkpoint path.',
  	)
  	parser.add_argument(
  	    '--model_size',
  	    default=None,
  	    type=str,
  	    required=True,
  	    help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
  	)
  	parser.add_argument(
  	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  	)
  	__lowercase			:		Any =			parser.parse_args()
  	convert_imagegpt_checkpoint_to_pytorch(
  	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  	)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 30     ):
  					__a    : Dict      					=						exponent + 2
  					__a    : List[Any]      					=						(1 + 5**0.5) / 2
  					__a    : Any      					=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5
  
  					return int(_SCREAMING_SNAKE_CASE     )
  
  
  if __name__ == "__main__":
  	print(f'''{solution() = }''')
  
  
  
  