




"""simple docstring"""




def     a__ (				snake_case__ = 60_08_51_47_51_43				)						->					int:






			try:
						lowerCamelCase			      =					int(SCREAMING_SNAKE_CASE__				)
			except (TypeError, ValueError):
						raise TypeError("""Parameter n must be int or castable to int."""				)
			if n <= 0:
						raise ValueError("""Parameter n must be greater than or equal to one."""				)
			lowerCamelCase			      =					2
			lowerCamelCase			      =					0
			if n == 2:
						return 2
			while n > 2:
						while n % i != 0:
									i += 1
						lowerCamelCase			      =					i
						while n % i == 0:
									lowerCamelCase			      =					n // i
						i += 1
			return int(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
					print(F"""{solution() = }""")






from typing import Union

import fire
import torch
from tqdm import tqdm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str = "cpu"    ,			SCREAMING_SNAKE_CASE__			:	Union[str, None] = None      )							->      None:





    '''simple docstring'''






    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location=SCREAMING_SNAKE_CASE__      )
    for k, v in tqdm(state_dict.items()      ):
        if not isinstance(SCREAMING_SNAKE_CASE__    ,			torch.Tensor      ):
            raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'      )
        A__          =   v.half()
    if save_path is None:  # overwrite src_path
        A__          =   src_path
    torch.save(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     fire.Fire(convert)

