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






def    lowerCamelCase__				(				__lowerCAmelCase  :  ndarray				):




			"""simple docstring"""


			return np.dot(__lowerCAmelCase    ,							__lowerCAmelCase				)





class 	_lowerCAmelCase					:




				def __init__(					self				,				*,
				    _UpperCamelCase = np.inf				,				_UpperCamelCase = "linear"				,				_UpperCamelCase = 0.0				,				)				->      None:
							lowerCAmelCase_						=	regularization
							lowerCAmelCase_						=	gamma
							if kernel == "linear":
										lowerCAmelCase_						=	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_						=	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_						=	f"""Unknown kernel: {kernel}"""
										raise ValueError(_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      float:
							return np.dot(_UpperCamelCase				,				_UpperCamelCase     )




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




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      None:
							lowerCAmelCase_						=	observations
							lowerCAmelCase_						=	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_)	,  )						=	np.shape(_UpperCamelCase     )

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

							lowerCAmelCase_						=	LinearConstraint(_UpperCamelCase				,				0				,				0     )
							lowerCAmelCase_						=	Bounds(0				,				self.regularization     )

							lowerCAmelCase_						=	minimize(
							    _UpperCamelCase				,				np.ones(_UpperCamelCase     )				,				bounds=_UpperCamelCase				,				constraints=[ly_contraint]     ).x
							lowerCAmelCase_						=	l_star

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




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


if __name__ == "__main__":
			import doctest

			doctest.testmod()





from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands






def    lowerCamelCase__				(				):




			"""simple docstring"""


			lowerCAmelCase_						=	ArgumentParser("Transformers CLI tool"    ,							usage="transformers-cli <command> [<args>]"				)
			lowerCAmelCase_						=	parser.add_subparsers(help="transformers-cli command helpers"				)

			# Register commands
			ConvertCommand.register_subcommand(__lowerCAmelCase				)
			DownloadCommand.register_subcommand(__lowerCAmelCase				)
			EnvironmentCommand.register_subcommand(__lowerCAmelCase				)
			RunCommand.register_subcommand(__lowerCAmelCase				)
			ServeCommand.register_subcommand(__lowerCAmelCase				)
			UserCommands.register_subcommand(__lowerCAmelCase				)
			AddNewModelCommand.register_subcommand(__lowerCAmelCase				)
			AddNewModelLikeCommand.register_subcommand(__lowerCAmelCase				)
			LfsCommands.register_subcommand(__lowerCAmelCase				)
			PTtoTFCommand.register_subcommand(__lowerCAmelCase				)

			# Let's go
			lowerCAmelCase_						=	parser.parse_args()

			if not hasattr(__lowerCAmelCase    ,							"func"				):
						parser.print_help()
						exit(1				)

			# Run
			lowerCAmelCase_						=	args.func(__lowerCAmelCase				)
			service.run()


if __name__ == "__main__":
			main()



