
from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowercase__							:							List[str]									=			1.054_571_817e-34  # unit of ℏ : J * s

lowercase__							:							List[Any]									=			3e8  # unit of c : m * s^-1




def 	A_ (							snake_case							:   float ,      snake_case							:   float ,      snake_case							:   float )      ->    dict[str, float]:



						'''simple docstring'''

						if (force, area, distance).count(0 ) != 1:
												raise ValueError('''One and only one argument must be 0''' )
						if force < 0:
												raise ValueError('''Magnitude of force can not be negative''' )
						if distance < 0:
												raise ValueError('''Distance can not be negative''' )
						if area < 0:
												raise ValueError('''Area can not be negative''' )
						if force == 0:
												__UpperCamelCase       =			(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
												    240 * (distance) ** 4
												)
												return {"force": force}
						elif area == 0:
												__UpperCamelCase       =			(240 * force * (distance) ** 4) / (
												    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
												)
												return {"area": area}
						elif distance == 0:
												__UpperCamelCase       =			(
												    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
												) ** (1 / 4)
												return {"distance": distance}
						raise ValueError('''One and only one argument must be 0''' )


# Run doctest
if __name__ == "__main__":
				import doctest

				doctest.testmod()






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      **snake_case							:   List[str] )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			AutoConfig.from_pretrained(snake_case ,      **snake_case )
						__UpperCamelCase       =			AutoModelForSeqaSeqLM.from_config(snake_case )
						model.save_pretrained(snake_case )
						AutoTokenizer.from_pretrained(snake_case ).save_pretrained(snake_case )
						return model


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




