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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str       ) ->       float:

				"""simple docstring"""







				return np.dot(__UpperCamelCase							,						__UpperCamelCase       )







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self				,       *,
	    UpperCamelCase__ = np.inf				,       UpperCamelCase__ = "linear"				,       UpperCamelCase__ = 0.0				,       ):
					A__  :	Optional[int]         =							regularization
					A__  :	List[str]         =							gamma
					if kernel == "linear":
									A__  :	Optional[int]         =							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'''		)
									A__  :	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:
									A__  :	List[Any]         =							F"Unknown kernel: {kernel}"
									raise ValueError(_SCREAMING_SNAKE_CASE		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					return np.dot(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	str         =							observations
					A__  :	Optional[int]         =							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

					((A__)       ,							)  :	Any         =							np.shape(_SCREAMING_SNAKE_CASE		)

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

					A__  :	Union[str, Any]         =							LinearConstraint(_SCREAMING_SNAKE_CASE				,       0				,       0		)
					A__  :	int         =							Bounds(0				,       self.regularization		)

					A__  :	Optional[Any]         =							minimize(
					    _SCREAMING_SNAKE_CASE				,       np.ones(_SCREAMING_SNAKE_CASE		)				,       bounds=_SCREAMING_SNAKE_CASE				,       constraints=[ly_contraint]		).x
					A__  :	Dict         =							l_star

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





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


if __name__ == "__main__":
					import doctest

					doctest.testmod()

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
					from .pipeline_kandinsky import KandinskyPipeline
					from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
					from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
					from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
					from .text_encoder import MultilingualCLIP