"""simple docstring"""



import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize





def    SCREAMING_SNAKE_CASE       (     snake_case):
     return np.dot(snake_case,      snake_case)



class      _A	:




    """simple docstring"""




    def __init__(		self							:     Any	,      *,
        A_							:     float = np.inf	,      A_							:     str = "linear"	,      A_							:     float = 0.0	,      )    ->       None:
         __snake_case				=							regularization
         __snake_case				=							gamma
         if kernel == "linear":
              __snake_case				=							self.__linear
         elif kernel == "rbf":
              if self.gamma == 0:
                   raise ValueError('''rbf kernel requires gamma'''    )
              if not isinstance(self.gamma	,      (float, int)    ):
                   raise ValueError('''gamma must be float or int'''    )
              if not self.gamma > 0:
                   raise ValueError('''gamma must be > 0'''    )
              __snake_case				=							self.__rbf
              # in the future, there could be a default value like in sklearn
              # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
              # previously it was 1/(n_features)
         else:
              __snake_case				=							f"Unknown kernel: {kernel}"
              raise ValueError(A_    )
    def  lowercase					(		self							:     Tuple	,      A_							:     ndarray	,      A_							:     ndarray    )    ->       float:
         return np.dot(A_	,      A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     ndarray	,      A_							:     ndarray    )    ->       float:
         return np.exp(-(self.gamma * norm_squared(vectora - vectora    ))    )
    def  lowercase					(		self							:     Dict	,      A_							:     list[ndarray]	,      A_							:     ndarray    )    ->       None:
         __snake_case				=							observations
         __snake_case				=							classes

         # using Wolfe's Dual to calculate w.
         # Primal problem: minimize 1/2*norm_squared(w)
         #   constraint: yn(w . xn + b) >= 1
         #
         # With l a vector
         # Dual problem: maximize sum_n(ln) -
         #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
         #   constraint: self.C >= ln >= 0
         #           and sum_n(ln*yn) = 0
         # Then we get w using w = sum_n(ln*yn*xn)
         # At the end we can get b ~= mean(yn - w . xn)
         #
         # Since we use kernels, we only need l_star to calculate b
         # and to classify observations

         ((__snake_case	)		,		)				=							np.shape(A_    )

         def to_minimize(A_							:     ndarray    ) -> float:
              __snake_case				=							0
              ((__snake_case	)		,		)				=							np.shape(A_    )
              for i in range(A_    ):
                   for j in range(A_    ):
                        s += (
                            candidate[i]
                            * candidate[j]
                            * classes[i]
                            * classes[j]
                            * self.kernel(observations[i]	,      observations[j]    )
                        )
              return 1 / 2 * s - sum(A_    )

         __snake_case				=							LinearConstraint(A_	,      0	,      0    )
         __snake_case				=							Bounds(0	,      self.regularization    )

         __snake_case				=							minimize(
             A_	,      np.ones(A_    )	,      bounds=A_	,      constraints=[ly_contraint]    ).x
         __snake_case				=							l_star

         # calculating mean offset of separation plane to points
         __snake_case				=							0
         for i in range(A_    ):
              for j in range(A_    ):
                   s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                       observations[i]	,      observations[j]    )
         __snake_case				=							s / n







    def  lowercase					(		self							:     str	,      A_							:     ndarray    )    ->       int:
         __snake_case				=							sum(
             self.optimum[n]
             * self.classes[n]
             * self.kernel(self.observations[n]	,      A_    )
             for n in range(len(self.classes    )    )    )
         return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()
"""simple docstring"""



import re





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''')
     if match := re.search(snake_case,      snake_case):
          return match.string == phone
     return False


if __name__ == "__main__":
       print(indian_phone_validator("+918827897895"))