





def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Optional[Any]:
		lowerCamelCase   : Optional[Any]								=[1]
		for i in range(2							,       SCREAMING_SNAKE_CASE_		):
				factorials.append(factorials[-1] * i		)
		assert 0 <= k < factorials[-1] * n, "k out of bounds"

		lowerCamelCase   : Tuple								=[]
		lowerCamelCase   : Optional[int]								=list(range(SCREAMING_SNAKE_CASE_		)		)

		# Find permutation
		while factorials:
				lowerCamelCase   : Optional[Any]								=factorials.pop()
				lowerCamelCase   : int								=divmod(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
				permutation.append(elements[number]		)
				elements.remove(elements[number]		)
		permutation.append(elements[0]		)

		return permutation


if __name__ == "__main__":
				import doctest

				doctest.testmod()








from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class        snake_case_		(			_A						,					_A):







		@register_to_config
		def __init__(		self			,     __lowercase = 7_6_8			,     )				->   List[Any]:
				super().__init__()

				lowerCamelCase   : str								=nn.Parameter(torch.zeros(1			,     __lowercase   )   )
				lowerCamelCase   : Union[str, Any]								=nn.Parameter(torch.ones(1			,     __lowercase   )   )







		def 			__lowercase   (		self			,     __lowercase = None			,     __lowercase = None			,     )				->   List[str]:
				lowerCamelCase   : Tuple								=nn.Parameter(self.mean.to(__lowercase   ).to(__lowercase   )   )
				lowerCamelCase   : Dict								=nn.Parameter(self.std.to(__lowercase   ).to(__lowercase   )   )
				return self







		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[Any]:
				lowerCamelCase   : Any								=(embeds - self.mean) * 1.0 / self.std
				return embeds







		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[int]:
				lowerCamelCase   : Any								=(embeds * self.std) + self.mean
				return embeds

