




from __future__ import annotations

import random




class    snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Tuple										= 6





				def __init__(				self    :					List[str]							) ->					Dict:
							a   =      list(range(1 ,				Dice.NUM_SIDES + 1							)							)





				def 							__UpperCAmelCase							(				self    :					str							) ->					Optional[Any]:
							return random.choice(self.sides							)




def       __magic_name__    (			A		: int,  A		: int = 2       ):






			'''simple docstring'''







			a   =      [Dice() for i in range(A       )]
			a   =      [0] * (len(A       ) * Dice.NUM_SIDES + 1)
			for _ in range(A       ):
						count_of_sum[sum(dice.roll() for dice in dices       )] += 1
			a   =      [round((count * 100) / num_throws,  2       ) for count in count_of_sum]
			return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
		import doctest

		doctest.testmod()












from ..utils import DummyObject, requires_backends




class    snake_case__							(metaclass=_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= ["""torch""", """transformers""", """k_diffusion"""]





				def __init__(				self    :					Optional[int] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					List[Any]							) ->					Optional[Any]:
							requires_backends(self ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					str ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Tuple							) ->					List[Any]:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					Optional[Any] ,				*__lowerCamelCase    :					List[str] ,				**__lowerCamelCase    :					int							) ->					Dict:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)






