





'''simple docstring'''

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   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__							):
 @register_to_config
 def __init__(							self :List[str]		,			lowercase_ :int = 7_68		,			)->				str:
     super().__init__()

     A__							=     nn.Parameter(torch.zeros(1		,			lowercase_				)				)
     A__							=     nn.Parameter(torch.ones(1		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :Optional[Union[str, torch.device]] = None		,			lowercase_ :Optional[torch.dtype] = None		,			)->				Tuple:
     A__							=     nn.Parameter(self.mean.to(lowercase_				).to(lowercase_				)				)
     A__							=     nn.Parameter(self.std.to(lowercase_				).to(lowercase_				)				)
     return self
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :str				)->				Union[str, Any]:
     A__							=     (embeds - self.mean) * 1.0 / self.std
     return embeds



 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :List[Any]				)->				Tuple:
     A__							=     (embeds * self.std) + self.mean
     return embeds










'''simple docstring'''

from __future__ import annotations

import math





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   bool		,			_lowerCamelCase   :   list[int]		,			_lowerCamelCase   :   float			):

    if depth < 0:
        raise ValueError("Depth cannot be less than 0"			)

    if len(_lowerCamelCase			) == 0:
        raise ValueError("Scores cannot be empty"			)

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1		,			node_index * 2		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			minimax(depth + 1		,			node_index * 2 + 1		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			)

    return min(
        minimax(depth + 1		,			node_index * 2		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			minimax(depth + 1		,			node_index * 2 + 1		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			)





def 		UpperCamelCase							(				):
    A__							=     [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
    A__							=     math.log(len(_lowerCamelCase			)		,			2			)
    print("Optimal value : "		,			end=""			)
    print(minimax(0		,			0		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



