






from __future__ import annotations

import math
import random






def     lowerCamelCase__				(   __lowerCamelCase      :					int  ,		__lowerCamelCase      :					int  ,		__lowerCamelCase      :					int		):
  __UpperCAmelCase							:     str									=  d * e - 1
  __UpperCAmelCase							:     Any									=  0
  __UpperCAmelCase							:     str									=  0
  while p == 0:
    __UpperCAmelCase							:     Optional[int]									=  random.randint(2  ,		n - 1		)
    __UpperCAmelCase							:     Dict									=  k
    while True:
      if t % 2 == 0:
        __UpperCAmelCase							:     List[str]									=  t // 2
        __UpperCAmelCase							:     Optional[int]									=  (g**t) % n
        __UpperCAmelCase							:     Any									=  math.gcd(x - 1  ,		__lowerCamelCase		)
        if x > 1 and y > 1:
          __UpperCAmelCase							:     Optional[Any]									=  y
          __UpperCAmelCase							:     Optional[int]									=  n // y
          break  # find the correct factors
      else:
        break  # t is not divisible by 2, break and choose another g
  return sorted([p, q]		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()










from sklearn.neural_network import MLPClassifier

a		:	int      					=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
a		:	List[str]      					=			[0, 1, 0, 0]


a		:	str      					=			MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


a		:	Union[str, Any]      					=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
a		:	Optional[int]      					=			clf.predict(test)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  return list(__lowerCamelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


