





"""simple docstring"""



import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def 		__lowercase					(       snake_case_  :  List[Any]    ,snake_case_  :  Optional[Any]    ,snake_case_  :  Optional[Any]						)       ->List[Any]:



    '''simple docstring'''







    if gpta_config_file == "":
        __A   :						Optional[int]									=					GPTaConfig()
    else:
        __A   :						Optional[int]									=					GPTaConfig.from_json_file(snake_case_						)
    __A   :						List[str]									=					GPTaModel(snake_case_						)

    # Load weights from numpy
    load_tf_weights_in_gpta(snake_case_    ,snake_case_    ,snake_case_						)

    # Save pytorch-model
    __A   :						str									=					pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    __A   :						List[str]									=					pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""						)
    torch.save(model.state_dict()    ,snake_case_						)
    print(F"""Save configuration file to {pytorch_config_dump_path}"""						)
    with open(snake_case_    ,'''w'''    ,encoding='''utf-8'''						) as f:
        f.write(config.to_json_string()						)


if __name__ == "__main__":
       a_			=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--gpt2_config_file""",
           default="""""",
           type=str,
           help=(
               """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
               """This specifies the model architecture."""
           ),
       )
       a_			=      parser.parse_args()
       convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)











"""simple docstring"""



def 		__lowercase					(       snake_case_  :  int						)       ->Optional[Any]:



    '''simple docstring'''







    stooge(snake_case_    ,0    ,len(snake_case_						) - 1						)
    return arr







def 		__lowercase					(       snake_case_  :  Optional[Any]    ,snake_case_  :  Union[str, Any]    ,snake_case_  :  Any						)       ->Tuple:



    '''simple docstring'''







    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        __A			,	__A   :						Optional[int]									=					arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        __A   :						Any									=					(int)((h - i + 1) / 3						)

        # Recursively sort first 2/3 elements
        stooge(snake_case_    ,snake_case_    ,(h - t)						)

        # Recursively sort last 2/3 elements
        stooge(snake_case_    ,i + t    ,(snake_case_)						)

        # Recursively sort first 2/3 elements
        stooge(snake_case_    ,snake_case_    ,(h - t)						)


if __name__ == "__main__":
       a_			=      input("""Enter numbers separated by a comma:\n""").strip()
       a_			=      [int(item) for item in user_input.split(""",""")]
       print(stooge_sort(unsorted))




