





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


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     Union[str, Any]:
  return np.dot(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
class        snake_case_		:







  def __init__(		self			,     *,
      __lowercase = np.inf			,     __lowercase = "linear"			,     __lowercase = 0.0			,     )				->   None:
    lowerCamelCase   : Any								=regularization
    lowerCamelCase   : List[str]								=gamma
    if kernel == "linear":
      lowerCamelCase   : List[Any]								=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'''   )
      lowerCamelCase   : int								=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:
      lowerCamelCase   : Union[str, Any]								=F"Unknown kernel: {kernel}"
      raise ValueError(A_   )







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase   )				->   float:
    return np.dot(A_			,     A_   )







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase   )				->   float:
    return np.exp(-(self.gamma * norm_squared(vectora - vectora   ))   )







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase   )				->   None:
    lowerCamelCase   : Any								=observations
    lowerCamelCase   : Tuple								=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

    ((lowerCamelCase	)		,   	)   : List[Any]								=np.shape(A_   )

    def to_minimize(__lowercase   ) -> float:
      lowerCamelCase   : Any								=0
      ((lowerCamelCase	)		,   	)   : int								=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_   )

    lowerCamelCase   : List[Any]								=LinearConstraint(A_			,     0			,     0   )
    lowerCamelCase   : Union[str, Any]								=Bounds(0			,     self.regularization   )

    lowerCamelCase   : Union[str, Any]								=minimize(
        A_			,     np.ones(A_   )			,     bounds=A_			,     constraints=[ly_contraint]   ).x
    lowerCamelCase   : str								=l_star

    # calculating mean offset of separation plane to points
    lowerCamelCase   : Optional[int]								=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]   )
    lowerCamelCase   : int								=s / n







  def 			__lowercase   (		self			,     __lowercase   )				->   int:
    lowerCamelCase   : str								=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()








from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_							=			logging.get_logger(__name__)

snake_case_							=			{
    '''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}
class        snake_case_		(			_A):
		lowerCamelCase    :Union[str, Any]						=     "timesformer"







		def __init__(		self			,     __lowercase=2_2_4			,     __lowercase=1_6			,     __lowercase=3			,     __lowercase=8			,     __lowercase=7_6_8			,     __lowercase=1_2			,     __lowercase=1_2			,     __lowercase=3_0_7_2			,     __lowercase="gelu"			,     __lowercase=0.0			,     __lowercase=0.0			,     __lowercase=0.0_2			,     __lowercase=1e-6			,     __lowercase=True			,     __lowercase="divided_space_time"			,     __lowercase=0			,     **__lowercase			,     )				->   List[Any]:
				super().__init__(**__lowercase   )

				lowerCamelCase   : int								=image_size
				lowerCamelCase   : List[str]								=patch_size
				lowerCamelCase   : Union[str, Any]								=num_channels
				lowerCamelCase   : str								=num_frames

				lowerCamelCase   : Dict								=hidden_size
				lowerCamelCase   : int								=num_hidden_layers
				lowerCamelCase   : Dict								=num_attention_heads
				lowerCamelCase   : Dict								=intermediate_size
				lowerCamelCase   : Union[str, Any]								=hidden_act
				lowerCamelCase   : str								=hidden_dropout_prob
				lowerCamelCase   : Union[str, Any]								=attention_probs_dropout_prob
				lowerCamelCase   : List[Any]								=initializer_range
				lowerCamelCase   : List[Any]								=layer_norm_eps
				lowerCamelCase   : List[str]								=qkv_bias

				lowerCamelCase   : Tuple								=attention_type
				lowerCamelCase   : List[Any]								=drop_path_rate

