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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Any              =					{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
       _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					MODELS[model_size]  # set model hyperparameters
       _UpperCAmelCase    :			Tuple              =					ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE__		,						n_layer=SCREAMING_SNAKE_CASE__		,						n_head=SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			int              =					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
       _UpperCAmelCase    :			Union[str, Any]              =					pytorch_dump_folder_path + "/" + WEIGHTS_NAME
       _UpperCAmelCase    :			int              =					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__":
   _lowerCAmelCase       :      List[str]							=							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."
   )
   _lowerCAmelCase       :      Optional[int]							=							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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:
  
  
  
  
  
       '''simple docstring'''
  
       if right <= left:
            return
       _UpperCAmelCase    :			Any              =					left
       _UpperCAmelCase    :			Tuple              =					right
       _UpperCAmelCase    :			List[Any]              =					sorting[left]
       while i <= b:
            if sorting[i] < pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					sorting[i], sorting[a]
                 a += 1
                 i += 1
            elif sorting[i] > pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					sorting[i], sorting[b]
                 b -= 1
            else:
                 i += 1
       quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						a - 1       )
       quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						b + 1		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:
  
  
  
  
  
       '''simple docstring'''
  
       if left < right:
            _UpperCAmelCase    :			Optional[int]              =					lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						pivot_index - 1       )
            quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						pivot_index + 1		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					sorting[right]
       _UpperCAmelCase    :			Any              =					left
       for i in range(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
            if sorting[i] < pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					sorting[i], sorting[store_index]
                 store_index += 1
       _UpperCAmelCase       , _UpperCAmelCase    :			List[str]              =					sorting[store_index], sorting[right]
       return store_index
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     list:
  
  
  
  
  
       '''simple docstring'''
  
       if len(SCREAMING_SNAKE_CASE__       ) <= 1:
            return sorting
       return (
           three_way_radix_quicksort([i for i in sorting if i < sorting[0]]       )
           + [i for i in sorting if i == sorting[0]]
           + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]       )
       )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod(verbose=True)
  
   _lowerCAmelCase       :      Union[str, Any]							=							input("Enter numbers separated by a comma:\n").strip()
   _lowerCAmelCase       :      List[str]							=							[int(item) for item in user_input.split(",")]
   quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
   print(unsorted)
  