
"""simple docstring"""




from timeit import timeit



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if number < 0:
          raise ValueError("the value of input must not be negative"		)
     UpperCAmelCase__							: Tuple 							=						0
     while number:
          number &= number - 1
          result += 1
     return result



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if number < 0:
          raise ValueError("the value of input must not be negative"		)
     UpperCAmelCase__							: Union[str, Any] 							=						0
     while number:
          if number % 2 == 1:
               result += 1
          number >>= 1
     return result



def 							a__ (				):






     '''simple docstring'''

     def do_benchmark(lowerCAmelCase	:       int		) -> None:
          UpperCAmelCase__							: Dict 							=						"import __main__ as z"
          print(F"Benchmark when {number = }:"		)
          print(F"{get_set_bits_count_using_modulo_operator(lowerCAmelCase		) = }"		)
          UpperCAmelCase__							: Tuple 							=						timeit("z.get_set_bits_count_using_modulo_operator(25)"       ,    setup=lowerCAmelCase		)
          print(F"timeit() runs in {timing} seconds"		)
          print(F"{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase		) = }"		)
          UpperCAmelCase__							: Any 							=						timeit(
              "z.get_set_bits_count_using_brian_kernighans_algorithm(25)"       ,    setup=lowerCAmelCase       ,    )
          print(F"timeit() runs in {timing} seconds"		)

     for number in (25, 37, 58, 0):
          do_benchmark(lowerCAmelCase		)
          print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()





"""simple docstring"""




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


A__      :	Optional[int]          =    ["""small""", """medium""", """large"""]

A__      :	Optional[int]          =    """lm_head.decoder.weight"""
A__      :	Dict          =    """lm_head.weight"""



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						torch.load(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						d.pop(lowerCAmelCase		)
     os.makedirs(lowerCAmelCase       ,    exist_ok=lowerCAmelCase		)
     torch.save(lowerCAmelCase       ,    os.path.join(lowerCAmelCase       ,    lowerCAmelCase		)		)


if __name__ == "__main__":
    A__      :	List[Any]          =    argparse.ArgumentParser()
    parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
    A__      :	Tuple          =    parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        A__      :	Tuple          =    os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
        A__      :	str          =    f"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )



